Regular testing is required to ensure the leak rate limits are not exceeded. There are three types of tests in the containment leakage rate testing program: Type A, B and C. Type A test is actually the containment integrated leak rate test (CILRT) intended to measure the reactor containment overall integrated leakage rate under conditions representing design basis loss of coolant accident (LOCA) peak pressure (410 kPa). Type B tests are intended to pneumatically detect and measure local leakage across each pressure retaining or leakage limiting boundary for containment mechanical and electrical penetrations. Type C tests are intended to pneumatically measure containment isolation valve leakage rates. The purpose of all tests is to ensure that leakage through the containment or systems and components penetrating the containment does not exceed allowable leakage rates specified in the NEK Technical Specifications. Integrity of the containment structure is maintained during its service life.

NPP Krško CILRT test was performed in October 2016 during the regular outage. Based on temperature, pressure and relative humidity (RH) measurements, the leakage rate was calculated in accordance with standard ANSI/ANS-56.8-2002 [3]. In order to independently verify test data, the existing GOTHIC plant model [4] was adapted to comply with the test alignment and conditions, and was used to simulate containment behavior during the CILRT. Both the measured and calculated results are presented and evaluated in paper.

#### **2. Containment CILRT Test**

Pressurizing a large structure, such as the containment with a free volume of about 40,000 m3, is not an easy task. Careful planning, setting up containment systems and equipment in required positions, venting and draining connecting lines, preparing the testing equipment according to standards [3], etc. are prerequisites for this kind of work.

The test is divided in five phases:

Before the test it was necessary to prepare the containment in a way to determine the local leakage rate using local leakage rate tests of types B and C. The pressure, temperature, relative humidity, dew temperature and volume flow were measured during the test.

During the pressurization phase, which lasted 18 h, the test pressure 408 kPa (absolute pressure) was achieved. The average pressurization rate was 17 kPa/h. Five mobile compressors connected to the CILRT test containment penetrations were used in that process. During the stabilization phase, which lasted 4 h, containment conditions were controlled in parallel with preparations for the testing phase. The intention of that phase was to obtain stable containment initial conditions (pressure, temperature) in order to obtain correct mass leakage rate measurements in the subsequent testing phase. The reactor containment fan cooler (RCFC) Train A was available during pressurization and stabilization phases.

The minimal time interval for the stabilization phase is 4 h. After that time interval, all criteria according to ANSI/ANS-56.8-2002 standard were obtained.

The testing phase lasted 8 h. The aim of this phase was to evaluate containment inventory mass decrease. Based on temperature and pressure measurements, the air mass was calculated using the ideal gas law:

$$m = \frac{p \cdot V}{R \cdot T}.\tag{1}$$

The processing of measured data was carried out by the plant, according to ANSI/ANS-56.8-2002 standard, using three standard procedures (mass point method, total time method and point-to-point method) for calculating containment leakage rate. The procedures were implemented in the Excel spreadsheet. There were 24 temperature measurements sensors installed throughout the containment and two pressure sensors, in addition to six relative humidity measurements and two verification flow measurements. The average containment temperature was calculated using measured temperatures and the weighting factors depending on the free volume of the area where the sensor is installed. The maximum allowable leakage by the ANS (American Nuclear Society) standard is 0.75 *La*, which is equal to 0.15% of air weight loss per day.

Two criterion types are used to evaluate the testing phase: The as-found and as-left leakage rate requirements.

The as-found leakage rate is the leakage rate prior to any repairs or adjustments to the containment barrier being tested. The criterion is defined as:

$$
\Delta LCL + \Delta L + TLS < L\_{a\_1} \tag{2}
$$

where *UCL* is the statistical upper confidence limit of the measured data, Δ*L* is the leak penalties for penetrations not prepared for CILRT due to a number of reasons, such as to keep the plant in safe condition during the test, to provide cooling in the case of an accident, etc. and *TLS* is the total leakage savings for penetrations with initial high leakage that were repaired after the test. The sum of these three terms was about 20% of maximum leakage *La*.

The as-left criterion prescribes the total leakage made of the upper confidence limit and leak penalties to be less than 75% of the maximum allowable leaking rate *La*:

$$
\Delta LCL + \Delta L < 0.75 \cdot L\_a. \tag{3}
$$

This limit does not include leakage savings since the impaired penetrations are repaired after the test. The as-left criterion was measured to be 25% of the prescribed leakage of 0.75 *La*.

In the testing phase final hour (the testing phase lasted 8 h), some non-physical behavior was noticed (increase of air mass in the containment in the situation when the containment is at higher pressure than any surrounding building or environment). It is expected that some combination of air redistribution within the containment and influence of the heat transfer to plant systems that were in the operation during the test can cause such behavior. That result should be further studied in more detail, but, in overall, the testing phase proved that the containment is airtight and no significant radioactive releases could be expected in a case of a DBA or severe accident causing increased containment pressure.

The following verification phase, which lasted 4.5 h, was conducted in order to check the accuracy of the testing phase results. The superimposed leakage rate method was used to verify the containment air mass decrease calculation. A valve connecting the containment to the outside atmosphere was opened and the leakage was set to 3.083 × 10−3 m3/s. According to specifications [3] that superimposed leakage rate needs to fit between 0.75 *La* and 1.25 *La*. The flow rate of 3.083 × 10−3 m3/s corresponds to 0.82 *La*. The same method as in the previous phase was used to calculate the mass inventory. The results confirmed the accuracy of the mass calculations. The calculated mass in the containment decreased with the same rate as was the leakage through the valve. The acceptability criterion for the total verification phase leakage rate *Lc* is:

$$L\_0 + L\_{tc} - 0.25L\_a < L\_c < L\_0 + L\_{tc} + 0.25L\_a. \tag{4}$$

The flow rate *L0* corresponds to the superimposed verification flow rate and *Ltc* to calculate the leak rate in the testing phase. Thus, the lower limit leak rate calculated in such way is 0.73 *La* and the upper limit 1.23 *La*. The calculated leak rate in the verification phase varied slightly with the mean value 0.8 *La*. The acceptability criterion was satisfied.

The final depressurization phase lasted 15 h. A controlled air release, the release rate was carefully monitored in order not to damage sensitive containment equipment, was performed to depressurize the containment to standard atmospheric conditions.

#### **3. GOTHIC Model of the NPP Krško**

The computer code GOTHIC [5,6] was used for calculation of containment thermal hydraulic behavior. The code solves the conservation of mass, momentum and energy equations for multiphase (vapor phase, continuous liquid phase and droplet phase) multicomponent (water, air, hydrogen and noble gases) compressible fluid flow. In order to predict the interaction between phases for non-homogenous, non-equilibrium flow, the constitutive relations were used. One dimensional finite difference discretization was used to model heated or unheated structures. Hydraulic volumes can be lumped or subdivided in 1, 2 or 3 dimensions. Special models were used to simulate the operation of engineered safety equipment: Pumps, fans, valves, doors, vacuum breakers, spray nozzles, heat exchangers, heaters and coolers. Each component can be controlled by trip logic based on time, pressure, vapor temperature, liquid level or conductor temperature. The version of the code used in calculations was 7.2b. The newer version (version 8.3) was available, but for this type of the problem both versions are expected to give the same results.

NPP Krško containment outline is shown in Figure 1.

**Figure 1.** Nuclear power plant (NPP) Krško containment outline.

The GOTHIC multi compartment model of the containment was developed based on the plant's SAR (safety analysis report) Chapter 6 licensing model [7] and verified against the vendor's DBA LOCA analysis [4]. Figure 2 is taken directly from the GOTHIC code and shows the basic layout of used volumes and complexity of used internal flow paths and heat structures. In order to perform this calculation, the existing model is slightly modified. The initial conditions were adjusted to take into account real conditions in the plant in the beginning of the test. The flow path and the flow boundary condition were added to simulate the pressurization of the containment. However, the problem was that the plant personnel had not implemented a flow measurement for air added by the compressors. There was neither a measurement of the temperature nor RH of added air. The initial mass flow rate and thermodynamic parameters of the compressed air during pressurization were adjusted to obtain the measured rate of containment pressure and temperature increase. An iterative approach was used to reproduce initial phase of the test (during the pressurization) and to determine leakage flow path cross section (in the testing phase). A leak path cross section was selected to get a measured pressure behavior during the testing phase (A = 10–6 m2, De = 1.128 × 10–3 m, which is much less than the value usually used to model the containment design leak rate). Calculation of the verification phase was

based on the cross section of the pipe and the measured discharge flow rate (real valve position was not known). The conditions during the last part of the test, the depressurization phase, were approximate. The plant personnel wanted to perform fast depressurization within allowable pressure decrease limits. Similarly, the calculation of that phase was just an attempt to follow a similar approach.

**Figure 2.** GOTHIC containment nodalization.

The containment was modeled with 10 hydraulic volumes representing the following compartments:

There are 74 heat structures in the model. Steel liner, shield building wall, internal concrete walls and floors were explicitly modeled. Other internal heat structures (mostly carbon and stainless steel) were based on the SAR Chapter 6 containment design data. The heat structures were divided between control volumes depending on their approximate location. The internal heat structures were mainly heat capacities in the model so it was decided to preserve their mass and area exposed to the compartment. For bounding heat structures (both to the annulus and between compartments) the first priority was to determine the proper thickness and heat transfer area of the structures. The heat transfer coefficients were calculated internally by the code. Different combinations of Uchida condensation coefficients and natural heat transfer coefficients were used depending on the orientation of heat structures.

The flow paths in the model were based on real openings and communications between the compartments. More than one opening was used between the same volumes if they were located at different elevations to promote internal thermal mixing flow. That can be important for long-term containment transients. When required, e.g., in case of numerical difficulties, it is possible to join more flow paths in one equivalent flow path. The leakage (initially based on the design leakage) flow paths exist between the containment dome and annulus and between the annulus and the environment. In case of CILRT, the annulus was open to the environment and we were simply interested in the equivalent leak of the containment liner and that was the reason why we used a direct connection of the containment and environment (instead of the usual two step connection). Another reason for using a direct connection was that during containment depressurization a dedicated line was used to decrease the containment pressure by discharging containment air to the environment. In the model we used two parallel flow paths of different cross section areas (the discharge path had a valve and the leakage path was without it). The leak path is a connection of a very small cross section and it represents the whole containment leakage. It is possible, in a real situation, to have distributed the leak, but it is usual to address it with a concentrated leak path. GOTHIC has a separate model to address distributed leakage, however it was not used here because we wanted to keep connection with other codes (e.g., MAAP—Modular Accident Analysis Program) where the leak is expressed in terms of simple cross section area. All internal ventilation ducts can be taken into account (not used in this calculation), what will result in an increase of used control volumes beyond the number used for main compartments. In addition, the nodalization can be further subdivided axially using floor elevations and laterally in two halves taking into account existence of two RCFC trains and giving us opportunity to study the influence of a single train RCFC operation on the achieved initial conditions before the test. The additional subdivision of the containment was not used in this calculation, but it is planned for next analyses.

#### **4. Results and Discussion**

#### *4.1. GOTHIC Calculation and Comparison With the Test Data*

Containment pressure and temperatures are shown in Figures 3–5. Calculated values were in good agreement with the measured data. Pressurization, measurement and depressurization phases were clearly visible. Pressure scale during testing and verification phases was magnified to highlight the pressure decrease caused by leakage and the testing procedure (Figure 4). During the verification phase, an additional flow path between the containment and the environment was opened to calculate and verify the testing phase results. The calculated temperature decrease (label tv1\_calc, other labels are used for representative locations measured temperature) in the depressurization phase was much faster than the measured data (Figure 5), due to the already mentioned approximate nature of the calculation in that phase (uncertainty of the boundary conditions—unknown depressurization rate).

**Figure 3.** Measured and calculated containment pressures during the test.

**Figure 4.** Decrease of the containment pressure due to leakage in the testing phase.

**Figure 5.** Measured and calculated containment temperatures during the test.

Measured (six positions) and calculated (RH1 calc, for RH in containment dome) relative humidity values are shown in Figure 6.

**Figure 6.** Measured and calculated containment relative humidity.

#### *4.2. Sensitivity Calculations*

Toward the end of the measuring phase, the increase of the air mass in the containment was calculated using measured data and the prescribed mass point method (Figure 7). The mass increase was characterized as an unphysical behavior because the high pressure in the containment was precluding any air inflow from the surrounding buildings or environment. The temperature transient alone can affect measurement results but cannot change amount of air available within the containment. In order to check reasons for such unphysical behavior, an additional run was performed by the GOTHIC code. A heat source with the power of 1 MW was introduced in the containment dome at 86400 s for the period of 100 s to determine sensitivity of the containment pressure and temperature (affecting measuring procedure) values on localized heat imbalance. In reality it was more likely that additional heat loss (instead of heat source) was present in the containment due to cold water in some systems.

**Figure 7.** Calculated containment mass (linear fit) based on the testing phase measurement.

Sensitivity of the pressure to the presence of heat sources is shown in Figure 8. The resulting change in pressure and temperature distributions was not able to affect the calculation of air mass in the containment.

**Figure 8.** Containment pressure increase due to the addition of a localized heat source.

Another possible reason for such behavior could be related to the existence of air pockets having temperatures different than temperatures measured in available points and later redistribution of that air. The additional subdivision of the containment model could help to address that issue. We are planning to perform that calculation in next step. The main problem will be to find proper initial conditions (taking into account available measurements) and it will be more of the trial and error procedure to see what kind of initial distribution can cause a noticeable type of influence. In this paper, the primary interest was in collecting available measurements and performing the initial calculation. In addition, the model that was used in this article will be used for SAR calculations with real (based on CILRT) and design leakage. That way it will be possible to see the influence of real leakage on already performed calculations with conservative assumptions.

Taking into account that average values of temperature, RH and pressure were calculated from measured values using pre-calculated weighting factors we wanted to see what could be the influence of these factors to the calculated mass. The plant test procedure used volume factors to calculate volume averaged containment temperature from the temperatures measured at different locations. The temperature was used in the ideal gas equation to calculate dry air mass. The weighting factor is the ratio between the volume whose temperature is being "measured" by the detector located in the compartment and the total containment free volume. This paper used new factors calculated using a 3D model of the containment to post-process measured data (Figure 9). Even with an accurate 3D model, it was necessary to use some kind of arbitrary decision related to the placement of the boundary between more or less open areas.

**Figure 9.** Subdivision of the upper containment part in the volumes for each instrumented location (volume weighting factors).

The old and new calculated volume weighting factors are shown for the available temperature measurement locations in Table 1 and for RH measurement locations in Table 2. The new weighting factors (even in the case when the change was rather significant), used with the measured temperature values, gave only a slightly higher average containment temperature (Figure 10) and, thus, lower containment mass, which means that the leakage rate was higher. Again, that influence could not explain the change in predicted air mass in the containment close to the end of measurement. The overall influence of new weighting factors to the calculation of leakage rates was relatively small (*La,new* = 0.0217%/day, *La,old* = 0.0207%/day), but they could be used to get more realistic leakage rates prediction in the future.

**Table 1.** Volume weighting factors for temperature measurements.

**Table 2.** Volume weighting factors for RH measurements.

**Figure 10.** Average containment temperature change due to change in weighting factors.

#### **5. Conclusions**

During October 2016 an integrated leakage rate test was performed at full design pressure in the NPP Krško. Prerequisites for the test were local leakage rate tests of types B and C. The pressure, temperature, relative humidity, dew point temperature and the volumetric flow rate were measured during the test. The processing of measured data was carried out by the plant, according to the ANSI/ANS-56.8-2002 standard, using three standard procedures for calculating the containment leakage rate. The test results met all the criteria and showed that the leakage rate of the containment paths was below the prescribed limits.

In parallel to the processing of measured data, the calculation of the containment behavior was calculated using the GOTHIC 7.2b software package. The existing GOTHIC containment model was modified to reproduce the test results. Some of the data required for setting the model were not available and an iterative approach was used to determine the leakage flow path cross section. This way all the future analysis using this model can, in addition to the DBA leakage, take into account the proper (real) leakage rate of the system. It is now possible, during any possible accident, to see the pressure behavior of the containment and to estimate radiological consequences using a realistic leakage of the containment. To perform this type of the calculation, which is completely within the scope of the code, is just a matter of performing modeling in the proper way with proper data. It is expected that this type of calculation could be performed for any PWR (Pressurized Water Reactor) plant and similar agreement with the measured data could be obtained (if the required data is available and the test is properly conducted).

Toward the end of the measuring phase, the increase of the air mass in the containment was calculated using measured data and the prescribed mass point method. In order to check the reasons for such unphysical behavior, a sensitivity run was performed with the GOTHIC code and an additional heat source in the containment. We have expected that this run, together with careful examination of change in the containment and boundary conditions could help to find/quantify possible reasons for unphysical containment air mass increase toward the end of the test. The conclusion was that the measurement procedure is not inherently sensitive to localized change of the heat rate in containment. In addition, new weighting factors of the temperature and relative humidity sensors were determined using containment 3D geometry, just to be sure that representative average temperature and RH were correctly calculated from measured values. The influence of these new weighting factors is relatively small and unable to explain a change in the predicted air mass in the late phase of the test, but they can be used to get more realistic leakage rates in the future. In the next step we will try to explore, using a more detailed containment model, the possible influence of a single RCFC train operation in the initial phase of testing (asymmetry, air pockets) to test results.

**Author Contributions:** Conceptualization, S.V. and D.G.; methodology, S.V. and S.Š.; software, D.G.; validation, S.V., T.F. and V.B.; formal analysis, S.V. and S.Š.; investigation, S.V. and S.Š.; data curation, V.B.; writing—original draft preparation, S.V. and S.Š.; writing—review and editing, D.G.; visualization, T.F. and D.G.; supervision, D.G.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors express their gratitude to the NPP Krško for providing the CILRT test data for the purpose of conducting GOTHIC simulations presented in the paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Multiple-Criteria Decision-Making for Assessing the Enhanced Geothermal Systems**

#### **Sara Raos 1,\*, Perica Ilak 1, Ivan Rajšl 1, Tena Bili´c 1 and Ghislain Trullenque 2**

Received: 1 April 2019; Accepted: 24 April 2019; Published: 26 April 2019

**Abstract:** This paper presents the main features of a multiple-criteria decision-making tool for economic and environmental assessment of enhanced geothermal systems projects. The presented holistic approach takes into account important influencing factors such as technical specifications, geological characteristics, spatial data, energy and heat prices, and social and environmental impact. The multiple-criteria decision-making approach uses a weighted decision matrix for evaluating different enhanced geothermal systems alternatives based on a set of criterions which are defined and presented in this paper. The paper, defines and quantifies new criterions for assessing enhanced geothermal systems for a particular site. For evaluation of the relative importance of each criterion in decision making, the weight is associated with each of the listed criterions. The different scenarios of end-use applications are tested in the case study. Finally, in the case study, the data and statistics are collected from real geothermal plants. The case study provides results for several scenarios and the sensitivity analysis based on which the approach is validated. The proposed method is expected to be of great interest to investors and decision makers as it enables better risk mitigation.

**Keywords:** enhanced geothermal systems; economic assessment; environmental assessment; optimization; levelized cost of energy

#### **1. Introduction**

The growing concern around rising energy costs, the dependence on fossil fuels, and the environmental impact of energy supply makes it necessary to find economical and environment-friendly energy alternatives. The largest share in newly-installed power capacities around the world is covered by wind and solar plants. Besides those two renewables, geothermal energy also represents large untapped renewable potential and low environmental impact, especially regarding greenhouse gases emissions. Despite many other advantages, like a reliable, constant baseload electricity or direct heat usage and a small land area footprint, geothermal energy is nowadays still a small contributor to the primary energy consumption. Its worldwide installed capacity is estimated at 12.9 GW [1] and share in total electricity generation of less than 1%. The main reasons are related to the risks and uncertainties of sustained fluid provision from the reservoirs and large upfront costs associated with exploration, well drilling and stimulation [2]. Furthermore, the traditional hydrothermal systems, based on mature and well-known technology, enable the exploitation of mainly high-enthalpy reservoirs, whereas a huge geothermal potential is present in low permeable, low porosity and low to medium enthalpy bedrock. In order to enhance reservoir productivity in low permeable rocks, Enhanced Geothermal Systems (EGS) technology has been developed. The EGS technique consists of creating a fracture system in the targeted geological formation through which geothermal fluid can circulate. Hydraulic and chemical stimulations are used to create these fractures. The thermal energy stored in the hot

rock mass gets extracted by circulating the fluid through the reservoir. The circulation of the fluid is obtained with different extraction technologies, among which the most prominent are so-called injector-producer doublets or borehole heat exchangers.

Aside from exploiting the geothermal heat from low permeable bedrock, additional energy can be recovered from thousands of mature or abandoned oil wells. Mature oil fields have been used for production for a long time, but their production has reached its peak and has started to decline. The typical production pattern of most oil producing wells displays an increase of water with time, from 0% initially to a point, typically above 95%, when it is not economic to produce the remaining oil. Mature oil fields account for more than 70% of world's oil and gas production, and regarding European oil fields, it is expected that wells are nowadays producing much more water than oil, with average water to a fluid ratio of 90% and with temperature up to 90 ◦C and sometimes higher. This remaining heat is currently usually wasted, as it is simply re-injected into the reservoir for pressure maintenance or sweep purpose. There are several studies concentrating on energy recovery from mature or abandoned oil fields. In Reference [3] a preliminary assessment of the potential for geothermal exploitation of the co-produced water from wells in the Villafortuna-Trecate oil field in Italy was made by comparing three different implementation scenarios for the possible use of the co-produced hot water: direct use district heating (DH), electric power generation through Organic Rankine Cycle (ORC) plant, and co-generation of heat and power. In Reference [4] energy from abandoned oil and gas reservoirs is used by oxidizing the residual oil with the injected air. In Reference [5] a simulation for the determination of geothermal power production from abandoned oil wells by injecting and retrieving a secondary fluid is performed. Technical feasibility study of acquiring geothermal energy from existing abandoned oil and gas wells is conducted in Reference [6]. Computational results indicate that the geothermal energy produced from abandoned wells depends largely on the flow rate of the fluid and the geothermal gradient. Moreover, the results also indicate that the distance of the two proposed wells should not be less than 40 m to avoid their interrelationships. A Poland case study on the usage of abandoned oil and gas wells for recovering geothermal heat is given in Reference [7]. Some important aspects of power generation using the co-produced hot oil and liquid, with temperatures around 120 ◦C, from Huabei oil field were studied in Reference [8]. Study on the geothermal power generation using abandoned oil wells is done in Reference [9] with isobutane as working fluid. Also, some interesting insights are given regarding how to increase obtained heat. The results in Reference [10] show that it is necessary to consider the oil and gas saturation while estimating geothermal reserve in oil and gas reservoirs. In Reference [11] the Croatian case of the binary power plant installed in Velika Ciglena is described and in Reference [12] the economic feasibility of this power plant is presented. Moreover, the thermodynamic cycle optimization of Velika Ciglena power plant is conducted in Reference [13]. The explorations in Croatia for the national oil company showed a very high temperature of geothermal water in the oil negative well (about 170 ◦C). Nowadays, the power plant at Velika Ciglena is the largest ORC geothermal power plant in Europe, with design conditions that allow for a 15 MWe installed capacity. The power plant is ORC using the isopentane as a working fluid and an air-cooling system that was chosen for the condensing process. Currently, the plant is producing electricity but there is still enough potential capacity for a DH, for which studies are currently being carried out. Furthermore, in Reference [14] a global review of 18 significant EGS sites and technologies that have been applied in the EU, Japan, South Korea, Australia, and the USA was given. The results from this study show that the site characteristics are a key factor of successful EGS development. It was concluded that for sites suitable for EGS, the local geological conditions (stress field, temperature field, rock composition, the range of existing permeability, reservoir properties, etc.) mostly determining the amount of recovered geothermal heat by means of EGS techniques.

Many previous studies have focused either on economic assessment [15,16] or environmental assessment based on the life cycle environmental impact of geothermal power generation as studied in References [17,18]. A review in Reference [19] presents an analysis of existing software packages for estimating and simulating costs, conventionally used in studying EGS facilities. The focus of the review is the top European software EURONAUT and the US GEOPHIRES package. EURONAUT is implemented based on the studies conducted at the EGS plant in Soultz-sous-Forêts. The root of the program is economic estimation via discontinuous cash flows, and all other calculations are developed as separate modules that can be joined together via various interfaces [19]. GEOPHIRES, however, is a software tool that combines reservoir, wellbore, and power plant models with capital and operating costs, correlations, and financial levelized cost model to assess the technical and economic performance of EGS. This software differs from the Geothermal Energy Technology Evaluation Model (GETEM) [20] and Hot Dry Rock economic model (HDRec) [21], which are two examples of technological/economic models initially used to simulate the operation of EGS plant. The distinction is mainly due to the fact that the currently available packages do not permit the simulation of not only electricity production but also direct-use-heat production and a combined heat and power (CHP) production, which was implemented in GEOPHIRES software.

The rate of geothermal development and implementation has been conditioned not only by mentioned geological conditions, drilling, and stimulation technologies, but also by legal frameworks, and regulative and social constraints. An overview of legislative and socio-economic issues of geothermal energy is available in References [22,23]. More on public and political acceptance issues could be found in Reference [24]. Establishing functional legal frameworks remains a challenge for countries seeking to develop their first geothermal projects to this day.

EGS technology enables exploitation of geothermal energy at a wide range of temperature and on a large geographic scale. However, EGS technology is not yet mature enough to be commercially competitive with other renewable resources. Almost all the EGS pilot plants, currently operating, need to be jointly funded by governments in order to operate or/and develop. Finally, the risks, uncertainties, and costs related to EGS projects mean that it is essential to conduct exhaustive studies involving modelling and simulation of EGS geothermal reservoirs and above-ground power plant facility at any location where it is desired to develop this kind of technology. This requires a holistic approach which should consider different scenarios and various influencing factors, from choosing the right extraction technology to the analysis of the energy prices and market signals. Given the fact that a taken decision will trigger financial consequences over a long period of time, the software for estimating and simulating the costs is an essential tool in order to successfully choose and face an EGS project. This paper presents the concept of such multi-scale Decision-Making Support Tool for Optimal Usage of Geothermal Energy (DMS-TOUGE) and multiple-criteria decision-making (MCDM) matrix that will be fully developed as part of the Horizon 2020 project: Multidisciplinary and multi-context demonstration of EGS exploration and Exploitation Techniques and potentials (MEET, GA No 792037).

The main objective of this work is to present the MCDM matrix and selected weighted criteria used to evaluate and compare different EGS sites and technologies and to demonstrate this MCDM matrix as a means of conducting a rapid preliminary evaluation of the technical and economic feasibility of an EGS project with related environmental and social impact. The MCDM matrix is used in this work for assessment of four different geothermal sites, and by using real data for each of the selected sites the comparison between sites was enabled.

The contribution of this work in relation to other selection matrices is that it provides expanded and detailed criteria related to environmental and social impact giving the necessary emphasis on so far neglected important aspects for successful completion of geothermal projects. Moreover, the novelty of this work is that by combining technical, economic, environmental, and social aspects of geothermal projects, the MCDM presented in this work gives a comprehensive assessment of EGS projects. Namely, since the EGS related projects are high-insensitive investments, for a DM interested in sustainably operate an EGS-plant close to densely populated areas, it is imperative to include many different aspects in techno-economic analysis and decision-making process. It is, however, challenging to predict all possible scenarios and influencing factors. Therefore, the process of developing a DMS-TOUGE and related MCDM matrix is a multiple-stage process. At this stage of the development and modelling process, the tool is intended for an evaluation of an EGS project at early development stage, meaning that a great number of the variables should be approximatively forecasted and estimated. Using optimization, the adequate evaluation of the production for chosen site and technology can be displayed. With progress in time, the tool will be useful for upgrade or extension of an already existing chosen geothermal site. In that case, the DM can provide more detailed information about EGS power plant operating cycle since the plant has already been generating electricity or heat for direct usage.

The rest of paper is organised as follows. Methodology, background, and main components of DMS-TOUGE are explained in Section 2. In the same chapter one of the main component of DMS-TOUGE, the MCDM is presented in more detail. Section 3 describes in detail the MCDM matrix and its criterions used in this paper for the evaluation of different EGS sites. Section 4 describes the case study examples, presents the scenario results, and gives the sensitivity analysis based on these results. The obtained results are discussed. Section 5 concludes the paper and gives the main directions for future development and research.

#### **2. Background**

#### *2.1. Decision-Making Support Tool for Optimal Usage of Geothermal Energy (DMS-TOUGE)*

The DMS-TOUGE represents a holistic approach for economic and environmental assessment of EGS sites that provides the capability of simultaneous site-specific environmental and economic analysis, among others considering low-enthalpy energy from co-produced hot water during oil fields exploitation. It considers existing infrastructure with possible extension or upgrades, the possible costs of future facilities, co-use/re-use of existing mature or abandoned oil fields boreholes, and other different geological contexts. Furthermore, the DMS-TOUGE will be useful for the decision makers (DM) involved in activities associated with: applications of EGS techniques to currently unexploited reservoir types (such as Variscan orogenic belt) by means of injector-producer doublets, increasing the productivity of existing power plants by reinjection of geothermal fluids with a colder temperature combined with the generation from the small-scale ORC units, and usage of hot fluids from mature and abandoned oil fields for electricity or heat production.

The DMS-TOUGE tool will use both external data entered directly by DM and internal data from database, depending on the quantity and quality of input data entered by a DM, such as: water temperature, geothermal capacity, electricity and heat prices, injection water flow rate values, turbine technology specifications, generator type, heat exchangers, working fluid, type of extraction technology (injector-producer doublets, deep borehole heat exchanger), risk analysis (thermal effect, possible appearance of scaling, radioactive deposits, mechanical evolution of casing) or environmental data (CO2 emission schemes, security of energy supply issues). In order to better anticipate and include future events, different possible scenarios are evaluated and accounted and accordingly the tool can use forecasted data (load, prices, etc.) which can occur over the operation lifetime of EGS technology. Some of the most important environmental (external) factors that are integrated into the tool are: proximity of nearest suitable power system grid and/or nearest suitable district heating system where EGS could be integrated into, proximity and availability of water for water cooling mechanism, possibility for different usage of geothermal energy for agricultural, industrial and district heating needs, and geopolitical environment and relevant legislative framework. The DMS-TOUGE relies on optimization algorithms to value/quantify different technologies. It will be used to quantify environmental and social impacts and calculate system levelized cost of energy (sLCOE) of technology in order to find the best-suited option for a given site. Moreover, when assessing the best-suited option and technology for a given site, malicious and faulty components should be taken into account since, in reality, it is illusory to expect that all components of the geothermal system are functioning without problems. An example of addressing this issue is presented in Reference [25]. Therefore, it is mandatory to include any possible risks involved with different components of the system. As an integral part of the DMS-TOUGE, any possible risks will be analyzed, such as market (price) risks and technical issues: thermal effects, scaling effect, radioactive deposits, and mechanical effect of the casing through the conditional value-at-risk (CVaR) measure [26–28]. Technical issues and risk of escalating costs need to be managed and hedged because of the potential damages likely to take place on an operational plant. The DMS-TOUGE will be verified and validated based on the comparison between tool output and real-life expert analyses on existing operating EGS sites and also historical data related to existing EGS sites. Output data of DMS-TOUGE will be available as raw data or in a form of directives and suggestions that are suitable for decision makers and investors. The raw data will be processed by a special subprocess, a separate MCDM, into a decision. The MCDM will be further discussed in Section 3. The schematic depiction of the main features of the DMS-TOUGE is shown in Figure 1. It should be emphasized that DMS-TOUGE as a whole is still in the development phase, and some parts have been modelled, including MCDM described later.

**Figure 1.** Schematic description of the main processes in Decision-Making Support Tool for Optimal Usage of Geothermal Energy (DMS-TOUGE).

#### *2.2. Geothermal Project Development Phases*

The geothermal project development occurs in various phases, with a unique duration of each phase. There is a series of seven key development steps before the actual operation and maintenance phase commences: (i) a preliminary survey, (ii) exploration, (iii) test drilling, (iv) project review and planning,

(v) field development, (vi) construction, and (vii) start-up and commissioning. The development of a typical utility size geothermal project will usually take between 5 to 10 years [29], depending on the country's geological conditions, information available about the resource, institutional and regulatory climate, access to suitable financing, and other factors. All seven-key development steps could be compressed in the first three following phases, followed by the fourth operation and maintenance (O&M) phase:

Each phase includes activities and elements that incur different related costs, which in DMS-TOUGE can be estimated or inputted by a decision maker. These costs, along with the estimated power generation over a project's lifetime, are the basis for the sLCOE calculation for a defined scenario. The number of the activities in each phase, and consequently the capital and O&M costs of the project, depend also on the state of the project, i.e., is it a greenfield project representing a new EGS site or a brownfield project representing an existing EGS site with possible upgrade, or co-production and/or conversion of mature or abandoned oil fields. DMS-TOUGE will be able to calculate necessary costs for sLCOE estimation according to the input data regarding the above-mentioned steps and phases. Capital costs are present in the first three phases, and O&M cost in the fourth phase. All possibly occurring capital costs that will be estimated and calculated with DMS-TOUGE for defined EGS project are summarized in Figure 2. Furthermore, for brownfield project with existing infrastructure the drilling part is omitted and thereby approximately 40–60% of capital expenditure (CAPEX) is avoided, meaning that all the activities related to drilling process depicted in Figure 2. are omitted in that case.

**Figure 2.** Capital costs included in Decision-Making Support Tool for Optimal Usage of Geothermal Energy (DMS-TOUGE) determination of a system levelized cost of energy (sLCOE).

#### *2.3. Extraction Technology*

Different extraction technologies need to be compared when assessing EGS sites, such as the producer-injector doublet and deep borehole heat exchanger. The production system, which consists of a production well, a surface heat exchanger, and a reinjection well, with all produced and cooled thermal water, returned to the aquifer is the so-called doublet system (Figure 3a). The production and the reinjection well must be drilled to the same geological horizon, i.e., reservoir. Consequently, the geofluid circulates in a closed loop: from the reservoir through the production well to the heat exchanger and back to the reservoir through reinjection wells. It is worth noting that pumping the fluid back in the reservoir maintains its hydrostatic pressure. (In the reservoir, around the bottom of the injection well, the permanently inflowing cooled water forms a cold front which propagates toward the production well). Borehole heat exchanger for shallower depths consists of single (Figure 3b) or double U-tubes (Figure 3c). In deeper wellbores, the presence of a liner will entail a coaxial-type solution (Figure 3d), which better optimises thermal and hydraulic behaviour. Injection takes place through the annulus and production (or heat delivery) through a centred co-axial pipe.

**Figure 3.** Extraction technologies: (**a**) producer-injector doublet, (**b**) borehole heat exchanger single U-tube, (**c**) borehole heat exchanger double U-tube, (**d**) coaxial borehole heat exchanger.

#### *2.4. Geothermal Facility Arrangement*

Different end-user options need to be compared with the simulation and optimization of three different end-user scenarios; (i) only direct-use heat production; (ii) only electricity production, and (iii) CHP production. The geothermal heat can be delivered to a nearby DH network and/or power plant via heat exchangers. Figure 4 presents a schematic description of possible end-user options. In the case of direct use option, (Figure 4a) the thermal energy from the geothermal fluid is transferred to the working fluid, usually water or steam, in DH network via heat exchanger. In the case of electricity production, the thermal energy is transferred to the working fluid circulating in the secondary system, ORC, also via heat exchanger, as depicted in Figure 4b. For the third end-user option (CHP) two configurations of the CHP plant are proposed: series and parallel, respectively. For the series configuration (Figure 4c) the brine (geothermal water) delivers the heat to the ORC at a higher temperature and subsequently to the DH system or other direct use application at a lower temperature. The flow rate of the brine is constant and equal for both systems. In the case of the parallel configuration (Figure 4d), geothermal heat is delivered to both the DH system and the ORC at a high temperature but with lower flow rate compared to a series configuration. The selection of the configuration depends on the features of the selected geothermal site and its surrounding, as well as on heat characteristics needed in the connected DH system.

A default power plant type is based on the resource temperature [30] and since the focus of the research are low enthalpy resources, i.e., medium and low-temperature geothermal fields with temperatures of 200 ◦C and lower, the binary power plants show good performance and therefore will be modelled in DMS-TOUGE. The concept of the binary power plant is based on the ORC. In the ORC system, the fluid flow is divided into four steps. The working fluid is heated up and vaporized in a hot heat exchanger (the evaporator), and at this point, the temperature of the working fluid is the highest. This saturated steam drives the turbine, which enables electricity production by lowering the fluid pressure to its low level. The fluid is then condensed in a cold heat exchanger (condenser) and pumped again into the evaporator, increasing thereby the fluid's pressure. As for the condensers, the most commonly used heat rejection equipment at binary geothermal power plants are the air-cooled condensers. The use of air eliminates the requirement for makeup water. However, compared to water cooling towers, air-cooled condensers require more space and represent a larger parasitic power load on the plant.

**Figure 4.** Schematic description of the possible end-user options: (**a**) direct use, (**b**) only electricity production, (**c**) combined heat and power (CHP)–series configuration, (**d**) CHP–parallel configuration.

The geothermal fluid, the hot source of the heat, is circulated in another loop on the other end of the evaporator. Therefore, for the binary plants, the major equipment components estimated are turbine-generator, air-cooled condenser, geothermal heat exchangers, and working fluid pumps, as depicted in Figure 5.

**Figure 5.** Schematic diagram of a basic binary cycle geothermal power plant: (**a**) water-cooled condenser and (**b**) air-cooled condenser.

#### **3. Multiple-Criteria Decision-Making Analysis**

As a subprocess in the DMS-TOUGE, an MCDM analysis will be used and performed using the weighted decision matrix (WDM). For evaluating different EGS options, a set of criterions is defined and presented in this subsection. For evaluation of the relative importance of each criterion in decision making, the weight is associated with each of the listed criterions. Performance, *xij*, of option *i* on criterion *j* is arbitrarily defined with a numerical value from 1 to 5, whose higher value means better performance, *xij* ∈ {1, 2, 3, 4, 5}. Finally, total performance, *Xi*, of *i*th EGS option on all criteria, ∀*j*, is assessed by summing all performance values, *xij*, multiplied by its weight as defined in Equation (1).

$$X\_i = \sum\_j^I w\_j \cdot \mathbf{x}\_{ij\prime} \tag{1}$$

where *Xi* is the total performance of *i*th EGS option, *i* ∈ *I*, where *I* is a total number of EGS options. The *wj* is weight i.e., relative importance in the decision making of criterion *j*, *j* ∈ *J*, where *J* is a total number of criteria. The *xij* is the performance of option *i* on criterion *j*. To use WDM successfully while assessing EGS options for a specific geothermal site, a set of well-defined criteria is needed. The criteria on which EGS option will be evaluated is listed below in Sections 3.1–3.12. It should be noted that, although the Equation (1) shows a simple summation, the actual calculation of total performance for a selected site was performed as an average mean of *Xi* performances of each criterion. Also, in this work every criterion *j*, is assumed to have equal relative importance in the decision making. Thereby, all criteria are evaluated assuming all *wj* to be equal and valued with 1. It is worth mentioning that the actual range for *wj* has not been yet precisely defined, as it is a sensitive part of the MCDM matrix. Therefore, such a range must be chosen that will adequately and meaningfully reflect the relative importance of certain criteria in relation to others. In the future work and through a process of validation and verification of the MCDM matrix and DMS-TOUGE, the discrete sequential values for *wj* will be chosen.

#### *3.1. First Criterion: Installed Power, xi*,1

When considering energy investment, installed power (capacity) is the first and most important parameter [31]. It later determines both costs (CAPEX, OPEX-O&M) and revenues (power output). According to Reference [30], performance *xi*,1 of option *i* on 1st criterion should be determined by *P*/*Pr* ratio in p.u. (ratio of installed power, *P*, of ORC technology in *i*th option to the reference installed power, *Pr*, e.g., according to Reference [31] for ORC technology reference installed power ranges from 1 MW to 5 MW depending on site features) (Table 1).

**Table 1.** Performance values *xi*,1 for 1st criterion.

#### *3.2. Second Criterion: Fluid Heat Flow, xi*,2

Expected heat flow, *Q* (W), for the two main extraction technologies, a traditional doublet with fluid extraction and reinjection, and wellbore heat exchanger, a closed loop system, is defined with Equation (2):

$$Q = q \cdot \rho \cdot c\_{\mathcal{P}} \cdot (T\_H - T\_{\mathcal{C}}) \, \tag{2}$$

where *q* is the fluid flow rate (m3/s), ρ is fluid density (kg/m3), *cp* is specific heat capacity of fluid at constant pressure (J/kg·K). *TH* is the fluid temperature at the wellhead (◦C), *TC* is the fluid temperature at the exit of the steam turbine (◦C).

Heat flow as a criterion was first proposed in Reference [31] and is used here with modifications in means of corresponding ranges for temperature. The idea is to emphasize the importance of flow rate and temperature of the produced fluid, and the impact of selected technology on flow rate and temperature. Reference [31] proposes criterion, here referred to as the *xi*,2, whose value is based on the ratio between the fluid flow rate, *q*, and flowing temperature at the wellhead, *TH*. According to [31] best suited ranges for valuing performance *xi*,2 in heat flow criterion are defined by flow rates between 0 m3/h and 100 m3/h and when temperatures are between 80 ◦C and 160 ◦C, since those ranges correspond to operative conditions for an ORC plant. For this criterion, the temperature range is modified for the values between 60 ◦C and 160 ◦C, since one of the focus of the ongoing H2020 MEET project is to make those temperatures also exploitable (Table 2).

**Table 2.** Performance values *xi*,2 for 2nd criterion.

#### *3.3. Third Criterion: Theoretical Maximum E*ffi*ciency, xi*,3

In this paper, the ORC based power plants are proposed for exclusively electricity generation. It is mainly due to the low-to-medium temperature range of the produced geothermal fluid. The thermal efficiency evaluated at the heat exchanger of the conversion plant in such fields is usually less than 10%, and for the binary power plants, according to Reference [32] could be calculated with Equation (3).

$$
\eta\_{\text{max}} = 6.9681 \cdot \ln(T\_H) - 29.713,\tag{3}
$$

where the *TH* represents the temperature of the produced fluid at the wellhead in (◦C) and η*max* is expressed in (%).

However, in case of power plants different from ORC, meaning other types of conversion plants, the thermal exchanged cycle between the two fluids, one circulating in the primary cycle (geofluid) and other circulating in the secondary circle (working fluid), can be assessed using the Carnot's ideal efficiency. In those cases, the expected theoretical maximum efficiency of conversion, η*max* expressed in (%), is evaluated using the Equation (4) as according to Reference [33].

$$
\eta\_{\text{max}} = (1 - T\_{\text{C}}/T\_{H}) \cdot 100\% \,\tag{4}
$$

and it depends on the geological site features (the *TH* part, where *TH* in (K) is the outlet temperature from the borehole at the wellhead), technology and environment features (the *TC* part, where *TC* in (K) is the inlet temperature at the wellhead). The performance is valued as shown in Table 3. Depending on the end-user option the Equations (3) or (4) are used for assessment of the selected site.

**Table 3.** Performance values *xi*,3 for 3rd criterion.

*3.4. Fourth Criterion: Geothermal Gradient, xi*,4

When setting the starting point of the geothermal plant feasibility analysis the geological factors should be considered. The efficiency of the heat transfer through the wellbore is highly dependent on the reservoir's initial temperature, which is a function of the well depth. Also, high thermal conductivity is required, so that the heat stored in the rocks could be transferred to the wellbore fluid. According to Reference [34], these two influencing factors could be collectively combined and represented with geothermal gradient, *GT* ( ◦C/100 m). The paper also suggested a range of geothermal gradient based on measured gradients for several analyzed oil fields across the world, which was taken for evaluating the performance *xi*,4 in the geothermal gradient criterion (Table 4).

**Table 4.** Performance values *xi*,4 for 4th criterion.

#### *3.5. Fifth Criterion: The Fluid Temperature at Wellhead, xi*,5

According to Reference [31] the outlet temperature of the fluid from the wellhead is one of the main features of the geological site. It later determines installed power, technology, efficiency, revenues, and costs. Namely, this criterion has been defined to emphasize the relevance of this temperature, since it has a great impact on the conversion cycle in the power plant. The higher the fluid temperature at the wellhead, the higher the amount of the heat that can potentially be transferred to the secondary working fluid (depending of course on the heat exchanger technology). Performance *xi*,5 of option on 5th criterion increases linearly depending on fluid temperature, *TH* (Table 5). Here, the focus is on the utilization of temperatures from 60 ◦C to 160 ◦C (although upper bound for analyzed geo sites in the scope of this research is expected to be 90 ◦C) for cases of smart ORC units. This approach is adapted and modified from the work [31].

**Table 5.** Performance values *xi*,5 for 5th criterion.

#### *3.6. Sixth Criterion: Global E*ffi*ciency, xi*,6

Aside from the geological setting and wellbore conditions, the supply of heat or/and electricity is directly related to the performance of the plant energy conversion. Therefore, a global efficiency criterion should be established to evaluate the multi-stage heat (energy) loss within the energy conversion cycle and the impact of the ambient temperature on stored heat (Table 6). According to

Reference [33] the total heat loss is addressed by means of coefficients of the different stages of the conversion cycle resulting in an overall evaluation for plant conversion. Equations (5)–(7) defines those coefficients, where Equation (5) represents the heat loss due to Non-Condensable Gases (NCG), where *C* [%] is the estimates of NCG weight, because the presence of NCG can negatively impact the operation of the plant turbine. The Equation (6) represents the parasitic load heat loss, including well pumps, cooling tower, condenser, where *PTPL* is total parasitic load and *Pgross* gross thermal power. Moreover, Equation (7) defines the parasitic loss during the working fluid transport where the *Lp* is the pipe length in km.

$$
\eta\_{\rm NCG} = 1 - 0.0059 \cdot \text{C} \tag{5}
$$

$$\text{TQT} = 1 - P\text{TPL}/P\_{\text{gross}} \tag{6}$$

$$
\eta\_{\text{pipe}} = 1 - 0.003 \cdot L\_p \tag{7}
$$

Overall plant efficiency is then calculated according to the Equations (8)–(10), where Equation (8) represents evaluation of the electricity generation, Equation (9) is used to evaluate combined heat-electricity production (CHP), where the second heat exchanger is required to exploit the remaining thermal energy of geothermal water into another district heating fluid, and Equation (10) represents the evaluation of direct usage of heat only in DH systems. Moreover, to measure the operational performance of the turbine and the generator efficiency, η*t* and η*g*, respectively, were included.

$$
\eta\_{\rm G(E)} = \eta\_{\rm max} \cdot \eta\_{\rm NCCG} \cdot \eta\_{\rm l} \cdot \eta\_{\rm TPDL} \cdot \eta\_{\rm pips} \tag{8}
$$

$$
\eta\_{\text{I\!G}(\text{C\!HP})} = \eta\_{\text{max}\text{1}} \cdot \eta\_{\text{N\!C\!G}^{\text{-}}\text{\prime\!It}^{\text{-}}\text{\prime\!} \text{\prime\!T\!PL}^{\text{-}} \text{\prime\!T\!p\text{ip}} \text{\prime\!!} \text{\prime\!p\text{max}} 2 \tag{9}
$$

$$
\eta\_{\rm G(DM)} = \eta\_{\rm max} \cdot \eta\_{\rm pipe} \cdot \eta\_{\rm TPL} \tag{10}
$$

where η*max*1 in the equation related to the CHP represents the efficiency of conversion in ORC for electricity production and η*max*2 is the efficiency of conversion of the remaining heat from the geothermal fluid for direct heat usage.

**Table 6.** Performance values *xi*,6 for 6th criterion.

#### *3.7. Seventh Criterion: Corrosion and Scaling Hazard, xi*,7

The corrosive or scaling tendency of the geothermal site is evaluated with the Langelier Saturation Index (LSI) (Table 7). The LSI later determines O&M costs. The less the LSI, the better the performance of the option will be (see Reference [31]).

**Table 7.** Performance values *xi*,7 for 7th criterion.

#### *3.8. Eighth Criterion: Distance from Power*/*Heating Grid, xi*,8

Construction of power lines and substations for plant connection to the grid imposes significant costs and should be addressed accordingly. Therefore, the distance between the geothermal plant production site and the nearest power and/or district heating system connection point must be addressed. Depending on that distance, *d* (km), the investment costs and also sLCOE vary. Apart from the distance, considering that the grid connection costs are site-specific, there are many other factors that have an impact on the investment costs. Due to the complexity if all the factors were included, the main influencing factor, namely the distance, was taken for the evaluation of the performance *xi*,8. The range is shown in Table 8, finishing with the most favored onsite use, i.e., a very small distance between the plant facility and the connection point.

#### *3.9. Ninth Criterion: Load Factor, xi*,9

Generally, combined heat and electricity production increases the net efficiency of the power plant, which in turn improves power plant economics. This is even more important in the case of geothermal plants, where thermodynamic efficiencies are typically much lower compared to conventional power plants, due to the lower working fluid temperatures. Considering direct-use systems, heat is only supplied to the process, like greenhouse or district heating, when it is needed. As a result, according to Reference [34], the load factor, *fL*, can vary from 15% to 75% depending on the application. Reference [34] examined the costs of the delivered heat as a function of a load factor for U.S. climates. Knowing that industrial applications can have a load factor of 0.30 to 0.75, the space heating only application 0.15 to 0.30, aquaculture 0.50 to 0.80 and greenhouses 0.18 to 0.24, the results showed that the high load factor correlates with lower cost of delivered heat and consequently affects the project's economic feasibility and sLCOE. As for electricity production, the load factor is typically 0.8 and higher. For combined heat and electricity production, the load factor is higher than in the case of only heat production. Based on forenamed results, the range for performance *xi*,9 of alternative is determined and showed in the Table 9.

**Table 9.** Performance values *xi*,9 for 9th criterion.

#### *3.10. Tenth Criterion: sLCOE, xi*,10

The average cost of the project over the lifetime will be addressed by the sLCOE (system LCOE, depending on the end-user option) in (€C/MWh) which also accounts for the costs of integration. This sLCOE will be calculated from all the available external data provided from DM and/or internal data from the internal database of the DMS-TOUGE, and according to Equation (13). Performance *xi*,10 of option *i* on sLCOE criterion is determined by *sLCOE*/π ratio in p.u. (ratio of sLCOE of ORC technology in *i*th option to the average market price, π, in different forecasts and for different horizons) shown in Table 10.

**Table 10.** Performance values *xi*,10 for 10th criterion.

#### *3.11. Eleventh Criterion: Social Impact, xi*,11

According to Reference [22], the social acceptability is conditioned by the deviation from the regular condition in the area and utility of the affected parties from the project. As geothermal technologies are site-specific (the geology is different all over Europe and knowledge of the local geological setting is essential) and capital-intensive, the needs regarding exploration, resource development, construction, and O&M are covered by the local workforce. According to Reference [35], the costs of social acceptance

could be presented as the external costs of a geothermal project. Depending on the site, type, and size of the project the amount of those external costs range, on the average, 0.5–2% and 1.5–4% of the total construction costs, i.e., approximately 17,000–220,000 € and 265,000–7,040,000 €, for direct use and multi-purpose projects, respectively. Moreover, employment potential could be divided into direct, indirect, and induced employment effect and quantified in terms of full-time jobs/MW (FT) and person × years of construction and manufacturing employment (Table 11). Total direct, indirect, and induced employment ratio is a ratio of the installed capacity, *Pinst*., (MW) and full-time jobs calculated from the Equation (11) (shown in Figure 6). Equation (12) represents construction and manufacturing employment (C&M), where those jobs are expressed as full-time positions over one year (person × year) as a function of installed capacity, *Pinst*., (MW). However, those C&M jobs are spread over several years taking into account the development time frame for the new projects. The social impact criterion will be obtained by the average of performances of the following sub-criterions: *xi*,11,1 social acceptance costs of direct use or electricity production sub-criterion, *xi*,11,2 social acceptance costs of combined heat-electricity production sub-criterion, *xi*,11,3 FT employment sub-criterion, *xi*,11,4 C&M employment sub-criterion. Each sub-criterion will be evaluated with a weight in a range from 1 to 5. Figure 5 shows the full-time jobs function as a defined in Equation (11).

$$FT\text{ jobs} = \log\_{1.068}(P\_{\text{inst.}}) \tag{11}$$

$$\text{C\&M jobs} = \text{~22.4} \cdot \text{P}\_{\text{inst}}.\tag{12}$$

**Table 11.** Performance values *xi*,11 for 11th criterion.

**Figure 6.** Total employment – full-time jobs function depending on the installed capacity of the project

#### *3.12. Twelfth Criterion: Environmental Impact, xi*,12

According to Reference [24], the environmental impact should account for the impact on sustainability, landscape, subsidence, and potentially induced micro-seismicity, and account for the level of noise, atmospheric emissions, potential water contamination, and production of radioactive scaling. The fluid extraction could cause subsidence because of reservoir pressure decline and uplift during borehole drilling activities. This is measured in mm/year of soil decay. Moreover, the pore pressure reduction in production and increase in reinjection operations have been associated with increased induced seismicity, often microseisms of low energy (<2–3 M Richter scale) [31]. According to Reference [36] the ranges for this sub-criterion were assigned. The impact on the landscape is measured as land use intensity (LUI) for installed power in m2/kW, and the range was estimated according to Reference [37]. The noise impact during routine operation is mainly caused by cooling towers and electrical transformers, and according to Reference [38], typical acceptable levels are 71–83 dB at 900 m distance from the facility. When considering atmospheric emissions, closed cycles, such as binary plants, have no gaseous emissions or they are close to zero and so do not contribute to air pollution. Considering objects of interest in this research (Figure 3), the impact on surface waters can be excluded. Groundwater contamination may occur if the casings in reinjection wells should fail, allowing fluid to leak. According to WHO, the range of total dissolved solids (TDS) and pH values was determined for the quantification of this sub-criterion. Radioactivity is mainly caused by interaction between the geothermal fluid and certain formations containing radioactive elements. As emphasized in Reference [39], the content of radionuclides in acidic magmatic rocks is generally higher compared to sedimentary rocks. Furthermore, uranium (U) and thorium (Th) are the most common radioactive elements found in granites. As defined in Reference [39], different types of rocks can contain different radioelements. Therefore, this sub-criterion is defined with the ranges of the forenamed most common radioactive elements but also taking into account the type of the rock – granitic rocks, shales, basaltic rocks, sandstones, and carbonates - in ascending order of the weights. The environmental impact criterion will be obtained by the average of performances of the following sub-criterions shown in Table 12: *xi*,12,1 subsidence sub-criterion, *xi*,12,2 potential seismicity sub-criterion, *xi*,12,3 land use sub-criterion, *xi*,12,4 noise sub-criterion, *xi*,12,5 potential water contamination sub-criterion, and *xi*,12,6 radioactivity sub-criterion. Each sub-criterion will be evaluated with a weight in a range from 1 to 5.

**Table 12.** Performance values *xi*,12,j for 12th criterion are defined according to values available in References [31,36–39].

#### **4. Results**

In this section, the purpose of the MCDM matrix is discussed and decision-making results are presented. The matrix is used to rank four different geothermal projects for two given end uses mentioned in Section 2.4, only electricity production and direct heat use, respectively. For a successful comparison, the geothermal projects being compared should be targeting the same end use, so that the comparison considers how key technical and economic factors are being utilised. Therefore, the two main scenarios are considered separately for all the projects (four sites in Table 13). In order to be able to execute an overall comparison, all the criterions mentioned in Section 3 are assessed. As stated before, relative importance in the decision making of each criterion is considered equal, meaning each criterion is equally influential on the final decision and therefore *wj*, ∀*j* is set as 1. Final evaluation of chosen sites that includes all criteria is calculated as the average mean of 12 criterions Moreover, the extraction technology examined in this analysis is doublet producer-injector technology, described in Section 2.3. For the four geothermal sites, the input parameters used for Equations (1)–(12) are presented in Table 13.

These input parameters represent realistic geothermal sites. Some of the observed sites are currently in usage for electricity production, aquaculture, agriculture, and food industry. The observed sites are not in the same geological and environmental area so the MCDM tool could be properly used, because it evaluates and compares different geothermal sites at various geological settings for two scenarios.

**Table 13.** Input parameters for the multiple-criteria decision-making (MCDM) matrix for four selected geothermal sites.

1 Density of the geothermal water changes with the temperature according to Reference [40].

#### *4.1. Economic Parameters*

The problems encountered with the development of geothermal energy systems are mostly related to the high upfront costs and the related finances. The high upfront costs are usually caused by the costs involved with the drilling of the wells, as mentioned in Section 2.2. The drawback with financing geothermal projects relates to the substantial uncertainties in the performance of the wells. Namely, EGS technology is still in an R&D phase since only a handful of projects have been realized. With estimated CAPEX and OPEX values, the LCOE of each site is calculated (Table 14), according to Equation (13) and passed on to the MCDM matrix. For analysis in this study, 55% of the CAPEX is excluded, since all the selected geothermal sites do not require drilling phase, i.e., the geothermal injector-producer doublet exist. Moreover, CAPEX is represented with specific investment costs in €/MW and OPEX is calculated as the percentage of the CAPEX in the range between 7–20%, depending on the year of operation.

$$LCOE = \frac{\sum\_{t=1}^{T} \frac{\underline{CAPEX\_t + OPEX\_t}}{\left(1 + r\right)^t}}{\sum\_{t=1}^{T} \frac{E\_t}{\left(1 + r\right)^t}},\tag{13}$$

where *Et* represents the amount of the electricity or heat produced in the year *t*. *r* represents the discount rate. *T* is the lifetime of the project.

**Table 14.** Economic parameters used for levelized cost of energy (LCOE) calculations.

1 Base case values are used in the base case scenario, left values represent decreased and right values increased values compared to the base case scenario that is used in the sensitivity analysis; 2 The annual produced electricity or heat is calculated as a product of Equation (2) and (3) or Equation (4).

#### *4.2. First Scenario—Electricity Production Only*

This scenario includes the end user option for only electricity production. The results of running the modelled MCDM matrix on the four selected geothermal sites to rank their feasibility according to economic-environment-social assessment are illustrated in Table 15. To estimate each criterion, the parameters from Table 13 and from Table 14 the base case values are used.

**Table 15.** Ranking of the selected geothermal sites in this study for electricity production.

#### *4.3. Second Scenario—Heat Production Only*

This scenario includes the end user option for only heat production, for direct use applications. The results of running the modelled MCDM matrix on the four selected geothermal sites for this scenario to rank their feasibility according to economic-environment-social assessment are illustrated in Table 16. To estimate each criterion, the parameters from Table 13 and from Table 14 base case values are used. Load factor for specified sites is defined according to the application of the heat and as follows. It is modelled that all the sites use the obtained heat for district heating and accordingly the load factor is set to 0.3.

**Table 16.** Ranking of selected geothermal sites in this study for heat production.

#### *4.4. Sensitivity Analysis*

Sensitivity analysis was carried out to see how the change of independent variables (CAPEX, discount rate, OPEX, lifetime) affects the dependent variable, i.e., system LCOE, under certain conditions. A sensitivity analysis makes it possible to distinguish between high-leverage variables, whose values have a significant impact and low-leverage variables, whose values have minimal impact. Site 1 and scenario of only electricity production were chosen for sensitivity analysis for the system LCOE. Values of observed input parameters used in the sensitivity analysis are shown in Table 17.

**Table 17.** The sensitivity of the system levelized cost of energy (LCOE) to input parameters.

Tornado plot showing the sensitivity of calculated LCOE to changes in a selection of parameters is shown in Figure 7. The central value of the plot represents the calculated LCOE for the base case with the amount of 93 €/MWh. It could be seen that the most influence on system LCOE has CAPEX, followed by OPEX, discount rate and the lifetime of the project, respectively. Lowering the CAPEX, which consist mostly of the drilling costs, is crucial for making the EGS geothermal sites commercially available and competitive with other renewable sources.

**Figure 7.** Tornado plot showing the sensitivity of the calculated levelized cost of energy (LCOE) to changes in a selection of parameters.

#### *4.5. Discussion*

The presented MCDM as a subprocess of the DMS-TOUGE will enable a comprehensive understanding of the interaction between economic, geological, social, and technical uncertainty. The analysis conducted in this paper is a first order analysis and will benefit from additional work gained from the MEET project. It is expected that the model will receive several modifications in order to make it more accurate and impactful. As it can be seen from the final evaluation of each site in Table 15, the temperature and the flow rate are one of the most important constraints for development of an EGS project, especially in the case of electricity production only. Site 1 presents the highest temperature of the produced geothermal brine, and the highest flow rate. These conditions lead to the greater installed power at this site, compared to the others, and thereby the largest amount of electricity produced. When comparing the results obtained for two given scenarios of end-use, it is clear that the electricity generation is a better solution for the Site 1, since the final evaluation for this scenario is 3.5, compared to the scenario of heat production with a final evaluation of 3.33. The selected site is built to produce electricity instead of heat. It can be concluded that in most cases, higher reservoir temperature and hence higher temperature at the wellhead are suitable and economically viable for electricity production. Sometimes, specific end-user requirements, for example, heat load for industry regardless of the very expensive pipeline system, may determine final end user application. This can be manipulated by DM via weight factors settings. Moreover, for Site 2 in the scenario of

electricity production, the evaluation is 2.83, that is, significantly lower than for the scenario of heat production with evaluation of 3.67. The decision for this site between the two scenarios is in favour of direct use heat production since, as it can be seen from the parameters from Table 13, the inlet temperature, as well as the difference between inlet and outlet temperature, is smaller than that for Site 1 or Site 3. In addition to this, the fluid flow rate for Site 2 is notably lower compared to Site 1 and Site 3. Therefore, the results obtained with the proposed MCDM matrix demonstrate that using low-to-moderate-temperature geothermal resources in the direct-heat applications, given the right conditions, is an economically feasible business as stated in Reference [41]. As stated before, practical results can be altered according to end user needs. For Site 3, the final evaluation for the electricity generation is 3 and 3.5 for the heat production scenario. Although the inlet temperature is quite high, the lowest fluid flow rate of all selected sites affects the final evaluation, especially in the scenario of electricity production. Site 4 has the lowest evaluation of 2.75 in the scenario of electricity generation. Namely, this site has the closest difference between the temperature of the produced geothermal fluid and the injection temperature, leaving thereby a small amount of heat that can be transferred to the secondary working fluid. The smaller the difference between those temperatures, the smaller the amount of produced electricity. However, in the case of the second scenario of heat production, it is visible that this site has a better final performance indicator. Therefore, lower reservoir temperature and a smaller difference between wellhead inlet and outlet temperature are more suitable for direct heat use applications. Social and environmental impact of selected EGS sites are favorable for both scenarios and therefore have high-performance indicators for those criteria. Namely, the installed power of the selected sites imposes modest land-use intensity, considering that technical characteristics of the EGS systems atmospheric emissions are zero. Moreover, selected air-cooled systems do not require additional cooling water, leading to no surface water exploitation. The social impact is positive, since the selected projects are relatively small to medium size projects, and represent more benefits than disadvantages at the local level since the balance between power plant size and local impact and benefits in terms of FT and C&M jobs is established.

#### **5. Conclusions**

This paper presents the concept of multi-scale Decision-Making Support Tool for Optimal Usage of Geothermal Energy (DMS-TOUGE) and one of its main features, the multiple-criteria decision-making. These features are used for economic and environmental assessment of enhanced geothermal systems projects. The paper contributes with expanded and detailed criteria related to environmental and social impact giving the necessary emphasis on so far neglected important aspects for successful completion of geothermal projects. Moreover, by combining technical, economic, environmental, and social aspects of geothermal projects, the multiple-criteria decision-making presented in this work gives a comprehensive assessment of enhanced geothermal systems projects. The multiple-criteria decision-making (MCDM) matrix is tested on the case of four operating demonstration geothermal sites where it is used for assessment and comparative analysis. In the application of MCDM matrix, the choice of the weight to assign to the different criterions affects the evaluation of the project. In this work, it is considered that each criterion has equal relative importance in decision making, therefore the weights, *wj*, ∀*j*, associated with each criterion, are considered identical and are valued with unitary weight. It is, however, important to highlight that the weights assigned to each criterion can vary depending on the DM's standpoint. Therefore, the results of the final evaluation of the project will vary if a greater weight is assigned to specific criterions according to DM's assessment. Further steps in tool development are the incorporation of many other relevant decision-making input parameters, not included in the analysis made in this study, testing its capabilities and subsequent verification and validation of the tool. Its proper function is of key importance for work packages and several deliverables of H2020 project such as generation of several layers for mapping (of the main promising European sites where EGS can or should be implemented in a near future) in different resolutions: EU wide layer, layer considering different geologic features, and pilot site layer. The multi-scale

Decision-Making Support Tool for Optimal Usage of Geothermal Energy (DMS-TOUGE) will be fully developed as part of the Horizon 2020 project: Multidisciplinary and multi-context demonstration of EGS exploration and Exploitation Techniques and potentials (MEET, GA No 792037). The developed tool will be useful for the decision makers involved in enhanced geothermal system projects associated with applications to nearly unexploited reservoir types (Variscan orogenic belt). This will provide valuable information to decision makers and investors for assessment of enhanced geothermal systems projects considering site-specific environmental, techno-economic and geological features.

**Author Contributions:** S.R., P.I. and I.R. have contributed to developing the concept, formulation and validation of the methodology. S.R. and P.I. have been involved in the implementation of the problem, and analysis of the results. I.R., T.B. and G.T. have been involved in investigation, supervision and project administration. All the authors are involved in preparing the manuscript.

**Funding:** This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 792037 and support from Department of Energy and Power Systems of University of Zagreb Faculty of Electrical Engineering and Computing.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Importance of Reliability Criterion in Power System Expansion Planning**

**Goran Slipac 1,\*, Mladen Zeljko 2 and Damir Šljivac 3**

Received: 15 March 2019; Accepted: 30 April 2019; Published: 7 May 2019

**Abstract:** The self-sufficiency of a power system is no longer a relevant issue at the electricity market, since day-to-day optimization and security of supply are realized at the regional or the internal electricity market. Research connected to security of supply, i.e., having reliable power capacities to meet demand, has been conducted by transmission system operators. Some of the common parameters of security of supply are loss of load probability (LOLP) and/or loss of load expectation (LOLE), which are calculated by a special algorithm. These parameters are specific for each power system. This work presents the way of calculating LOLP as well as the optimization algorithm of LOLP, which takes into consideration the particularities of the power system. It also presents a difference in the treatment of LOLP regarding the observed power system and the necessary installed power capacity if applied to the calculated LOLP in relation to the optimized LOLP. As a conclusion, the study analyzed the parameters impact the regional electricity market—where the participants are countries with different development levels and various particularities of power systems—i.e., what it means when the same LOLP criterion is applied to them and the optimized LOLP.

**Keywords:** security of supply; LOLP optimization; VOLL; generation expansion planning; reliability

#### **1. Introduction**

The electricity market and the security of electricity supply are defined by several directives and regulations. One of the most important documents stipulating measures for secure customer security is the 2005/89/EC Directive [1], whereby the energy union concept is based on a few (eight in total) new bills, a new regulation on the preparation for risks being among them, which should replace said 2005/89/EC Directive. The analysis of the adequacy of power systems is conducted by the transmission system operators on the basis of methodological instructions by ENTSO-e, and that report [2] serves as the basis for the assessment of mid-term security of supply of the power system. The basic probability indicators for drawing up the mid-term adequacy of power systems are loss of load probability (LOLP) and loss of load expectation (LOLE). LOLP in a power system is expressed as a percentage of time, annually or monthly, in which the power system will not be able to meet demand with available generation capacities. LOLE is the index on which the basis of LOLP shows the total number of hours in a year, in which the power system will not be able to meet demand. In this paper, reliability calculations are described, followed by examples of generation expansion of a real power system under different reliability indices. As it is stated in [3], it is necessary to quantify the value of lost load (VOLL) to obtain the economic value of adequacy. In order to put a value on reliability, a commonly employed parameter is the VOLL, which measures the damage suffered by consumers when the supply is curtailed. In cases of productive activities (such as industrial processes engaged in the production of

a good), an objective measure of the cost of interruptions based on the loss of production or the linked benefit is possible.

At the moment in the European Union (EU), a new Regulation (EU) 2018/2019 of the European parliament and of the Council of risk-preparedness in the electricity sector is being prepared, and repealing Directive 2005/89/EC is supposed to be adopted at the beginning of 2020. As it is described in the new Regulation, the electricity sector in the EU is undergoing a profound transformation, characterized by more decentralized markets with more players, a higher penetration of renewable energy, and better interconnected systems. The main idea of the internal electricity market is that well-functioning markets and systems with adequate electricity interconnections are the best guarantee of security of supply.

A common approach to electricity crisis prevention and management also requires that member states use the same methods and definitions to identify risks relating to the security of supply and are in a position to effectively compare how well they and their neighbors perform in that area. This Regulation identifies two indicators for monitoring the security of electricity supply in the Union—expected energy non-served (EUE), expressed in GWh/year, and LOLE, expressed in hours per year. Those indicators are part of the European resource adequacy assessment carried out by ENTSO-E. In the Regulation, there is a description of the methodology of calculation of security of supply. To ensure the coherence of risk assessments in a manner that builds trust between member states in an electricity crisis, a common approach to identifying risk scenarios is needed. Therefore, after consulting the relevant stakeholders, ENTSO-E should develop and update a common methodology for risk identification in cooperation with the Agency for the Cooperation of Energy Regulators (ACER) and with the Electricity Coordination Group (ECG), which was set up by a Commission decision on 15 November 2012 as a forum in which to exchange information and foster cooperation among member states—in particular, in the area of security of supply—in its formation composed only of representatives of the member states. ENTSO-E should propose the methodology, and ACER should approve it. When consulting the ECG, ACER is to take utmost account of its views. ENTSO-E should update the common methodology for risk identification where significant new information becomes available.

ENTSO-e conducts studies about Scenario Outlook and Adequacy Forecast, trying to calculate a Pan-European power system adequacy. In their report [2], there is a question raised related to capacity adequacy. If the reliability index is met beyond the national legal requirement, how large is the related capacity margin surplus available for the neighboring area, or even system wide? Additionally, if the reliability index is not met, by how much would the available capacity need to increase to meet the target? Another question is about the methodology applied.

The LOLP index is used in the analyses of generation parts of power systems but also in reliability analyses of both the transmission and the distribution system. There are many more papers regarding the use of VOLL for both transmission and distribution system analysis. VOLL is an essential parameter for transmission system reliability management [4]. Theoretical analysis shows that using more detailed VOLL data allows for better-informed transmission reliability decisions. Incorporating variations and uncertainties into expansion planning produces a plan that has better economy and reliability performance during operations. Recent progress on robust optimization techniques can consider uncertain parameters in expansion optimization [5].

There are also some papers and studies regarding planning a reserve margin, which is a common metric used in generation planning to determine an electric utility's resource need above the typical annual peak load. As a proxy for system reliability, the planning reserve margin is useful in informing resource decisions between detailed reliability studies [6]. Questions about providing electricity with adequate power quality and reliability at a reasonable rate are becoming more important. Therefore, there are a number of country-specific analyses, such as German security of supply or questions about capacity market [7,8]. Of high interest in the field of reliability of power systems or the security of supply is a capacity market or remuneration mechanism. In Reference [9], the analysis begins with identifying the optimum level of investments to the 2030 horizon to ensure capacity adequacy, assuming a reference scenario of EU energy system developments, followed by a simulation of the operation of a wholesale energy-only market to estimate the ability of these investments in order to recover their capital cost. The amount of capacity remuneration required to cover for the missing revenue margin is estimated, as are the implications from hypothetical asymmetric remuneration across the member-states of the EU. Also in Reference [10], there is a question raised regarding the competitive energy market and whether it will be sufficiently addressed in the capacity market to garner adequate resources to keep the lights on. The European electricity market has been constructed as an "energy only market" without the compensation for the costs of generation adequacy. Such a market is able to operate in the presence of the capacity excess. However, gradually, the power reserve margins have been diminishing, causing a fear of power balancing in the coming years [11]. All those papers are dealing with one common question—security of supply or capacity adequacy? The basic methodology always relies on a probabilistic approach.

The consequence of capacity shortage is the reduction in electricity supply, which results in a certain VOLL or energy for customers. The reserve of a power system in all three technological parts—generation, transmission, and distribution—depend on the level of damage, i.e., the specific level of value of undelivered kWh of electricity. Both LOLP and LOLE indices are calculated via an algorithm in which the equivalent curve of load duration is formed by the method of convolution [3–6] from the load duration curve, taking into consideration the probability of outage of every individual generation facility as well as the outage cost, which is VOLL times unserved energy. The fundamental thesis in this paper states that LOLP, and thus LOLE, should not be calculated, but may be optimized regarding VOLL and the available technologies for new generation facilities. The paper further analyzes power systems with different optimum levels of LOLP, which participate in the electricity market. The main objective of this paper is to better understand reliability by the optimization of LOLP and LOLE regarding VOLL and available technologies for new generation facilities.

#### **2. LOLP Optimization Methodology Description**

Computer models for the analysis and the planning of the power system were developed on algorithms that described certain aspects of power systems; some of them were simulations, some were optimization types, and some models were a combination of the two [12–14]. One of the most famous models used to calculate the LOLP through long-term planning of a power system is the Wien automatic system planning package (WASP), which is used in this analysis as well. The basis of such analysis is load duration curve (LDC) of the power system. When taking into consideration the probability of the first unit outage, the result is the equivalent load duration curve (ELDC1). The result of the outage of two first units is ELDC2, and ELDC*n* is the result when the probability of the outage of the last unit is taken into consideration [13,15]:

$$\text{ELDC}\_{n}(\mathbf{x}) = p\_{n} \cdot \text{ELDC}\_{n-1}(\mathbf{x}) + q\_{n} \cdot \text{ELDC}\_{n-1}(\mathbf{x} - \text{MW}\_{n}), \tag{1}$$

where ELDC*n* is the equivalent load duration curve after unit *n* is on the grid; *p* is the probability that unit *n* is available; *q* is the probability that unit *n* is unavailable; MW*n* is the capacity of unit *n* and *x* is load. The expected generation for each unit is then:

$$E\_n = p\_n \cdot T \cdot \int\_{a\_n}^{b\_n} ELDC\_{n-1}(x) dx,\tag{2}$$

where *En* is the generation of unit *n*; *T* is the time period represented by the load duration curve; ELDC*n*−1 is the equivalent duration curve, which incorporates the effect of the force outages of unit *n*−1; *an* is the system capacity for units 1, 2, 3, ... , *n*−1; and bn is the system capacity for units 1, 2, 3, ... , *n*.

*Energies* **2019**, *12*, 1714

For certain installed capacities of all power plants in the power system (ICP), LOLP is the index determining the probability of the occurrence that the load is higher than the sum of the available capacities of all considered power plants. Energy not served (ENS) is electricity that will not be delivered and represents a part below the ELDCN curve limited by ICP on the left side, as it is shown in Figure 1.

**Figure 1.** Calculation of the equivalent load duration curve.

To complete the calculation of LOLP, it is necessary to calculate the total cost of the power system, as described in [13,14]. The optimization of the total cost of the power system requires the minimum of a goal function in a specific scenario of the development of the power system. The objective function represents the total cost of the power system, which consists of investment costs for new power plants, fuel costs, operation and maintenance costs (O & M), costs of VOLL, and the remaining value of new power plants after the last planning period year. The LOLP and the LOLE indices are calculated for each year.

$$B\_{\bar{j}} = \sum\_{t=1}^{t=T} \left[ I\_{\bar{j},t} - S\_{\bar{j},t} + F\_{\bar{j},t} + M\_{\bar{j},t} + VOLL\_{\bar{j},t} \right] \tag{3}$$

*B* represents the total cost of the power system of the observed scenario; *I* is the capital investment cost in new generation units; *S* is the salvage value of investment costs after the last year of the observed period; *F* represents the fuel costs; *M* represents the non-fuel operation and the maintenance costs; *VOLL* is the outage costs; *t* represents the years of the scenario; *T* is the length of the study period, i.e., the number of years in the scenario; and index *j* is the observed scenario of the generation expansion.

Three main LOLP optimization steps are described as follows:

1. In the first step, one should calculate the power capacity to meet LOLP criteria from 5% to 0.001% on a technical level without any economic consideration. Those values are taken from expert analysis as very high and low boundaries of the LOLP value. The results of calculation for each value of LOLP (starting from five and ending with 0.001%) are the number of MW of installed capacity that should be built in order to meet the criteria of LOLP. Those calculations are conducted under no economical limitations. Thus, the first step is a calculation of how many MW's have to be installed to meet the reliability criteria of LOLP values. That is why LOLP values are first calculated for various combinations of operating power plants. The key point in this stage is to consider reliability aspects only, which means that economic variables are excluded. LOLP criterion is met by introducing new power plants only when it is necessary to satisfy the reliability criteria regardless of the costs of the power system. The solution is acceptable if the calculated LOLP value is lower or equal to the proposed one for all years of the planned period, i.e.:

$$LOLP \le LOLP\_{\text{UL}\_{\bullet}} \tag{4}$$

where *LOLPUL* is the proposed value of LOLP for which the new construction of power plants in the power system is calculated. One can analyze the impact of different technologies or different characteristics of the same technologies of new generation capacity in order to conduct sensitivity analysis.

#### **3. LOLP Optimizing Case Study Analysis**

In this paper, two technologies were chosen for power plant candidates—a coal-fired thermal power plant and a natural gas-fired combined gas power plant with usual characteristics taken from literature. Analyses were conducted separately for power plant candidates, coal-fired thermal power plants (TPP), and natural gas-fired combined cycle gas turbine power plants (CCGT). The values of LOLP were simulated for this analysis ranging from 5% to 0.001%. Results are shown in Figure 2.

**Figure 2.** Necessary construction of new capacities to meet loss of load probability (LOLP).

It should be noted that new generation capacity was added in quantities of 20 MW in order to have smooth or continual curves. Characteristics of such small units are the same as regular size units in both analyzed technologies.

After calculating the necessary new constructions of power plants with the given value of LOLP for each LOLP limitation separately, the objective function was calculated for two levels of VOLL—\$2 USD/kWh and \$10 USD/kWh.

Two levels of VOLL were chosen for analysis, one of \$2 USD/kWh and the other of \$10 USD/kWh. The former correlated with the gross domestic product (GDP) of around \$10,000 USD per capita, and the latter with the GDP of around \$40,000 USD per capita [16–18]. The aim was to compare two

different levels of economic systems—a developing one and a developed one. It was estimated that there was no need to consider more levels of outage, since this analysis showed differences between higher and lower levels of outage, which corresponded to different developments of economic systems. Other data referring to specific costs of fuel and maintenance were taken from [19,20].

Optimizing the analysis of LOLP started with the calculation of necessary installed power in the observed power system under the limitation of LOLP of 0.0001% (0.5 minutes per year), 0.001% (5.5 min per year), 0.01% (52 min per year), 0.03% (2.5 h per year), 0.05% (4.5 h per year), 0.07% (6 h per year), 0.1% (9 h per year), 1% (3.6 days per year) and 5% (18 days per year) with two levels of outage of \$10 USD/kWh and \$2 USD/kWh. It should be noted that the same power system was always analyzed, and limitations of LOLP varied just as with VOLL. After obtaining the combination of existing and new power plants, i.e., the total available power for each respective year and for each observed LOLP, the costs of the power system, i.e., the goal function, was calculated as described in the previous chapter. All the values calculated in the goal function refer to the year 2018, which was used as the base in this analysis.

If LOLP was limited at 0.0001%, which is an extreme requirement (usual value amounts at 8 h or 0.1%, e.g., in Ireland), there was a visible increase of necessary capacity or investment costs related to constructing new generation capacities, which was understandable considering the high demand of reliability of the power system. Such a level of security of supply could not be considered necessary due to questionable economic profitability, since the power system disposed a very large reserve (if isolated), thus such a power system would be considered over-capacitated in a techno-economic sense. If such a power system was observed in reality, necessary construction for fulfilling a certain safety level of supply, e.g., of 0.0001%, there would be a need to ensure huge amounts of generation capacities reserve if there were a consideration of not being in the regional electricity market. Where these capacities would be placed in a mathematical sense is not of much importance, except for the fact that they would need to be available, i.e., properly electrically connected.

When the necessary construction of generation capacities of a power system with a coal-fired thermal power plant was analyzed depending on the level of LOLP, we got a curve of necessary construction, as shown in the Figure 3. This curve of necessary construction of generation facilities depending on the LOLP fit well with the theoretical curve. The probabilities (which were extremely small) for which the limitation of LOLP was very small (<0.001), necessary construction of generation capacities grew asymptotically by coming closer to the ordinate axe.

**Figure 3.** LOLP optimization for two values of lost load (VOLL) levels in the case of a coal-fired thermal power plant (TPP).

If a certain value is added to unsupplied kWh of electricity, then the outage cost becomes dominant in the goal function of the total cost. As a result, the total costs of the power system grow as the level of security of supply decreases. However, more power plant construction leads to a level of higher security of supply. In this case, as more power plant capacities demanded higher investment, the dominant member of the goal function referred to the investment into new power plant capacities, thus the total cost of the power system grew. Therefore, the goal function of the total cost regarding different values of LOLP had the form of a bathtub curve with one more or less expressed as the minimum.

In the cost structure of the power system, it is visible that investment costs, residual value, and fuel costs were the same in both observed cases for VOLL of \$2 USD/kWh and \$10 USD/kWh starting from LOLP 0.01%, which was understandable since investments for necessary new construction of power plants depended on the given security of supply, and fuel consumption depended on electricity consumption. In the case of VOLL of \$10 USD/kWh, the total outage cost as the component of the total cost of the power system increased sharply. It was expected that, in the case of a LOLP value higher than 5%, they would eventually become a dominant part of the total cost of the power system.

These costs were added and presented according to VOLL of \$2 USD/kWh and \$10 USD/kWh, and the curves of the total costs were obtained. The change of total costs depending on the security of supply in the case of the combined cycle gas turbine (CCGT) only was milder than the case with the coal-fired power plant only. The reasons for this were primarily lower investment costs of new power plants and a lower probability of power plant outages, as shown in Figure 4.

**Figure 4.** LOLP optimization for two VOLL levels in the case of combined cycle gas turbine (CCGT).

LOLP would accordingly be some kind of necessity or signal for the construction of the power system. In this example, it would particularly refer to the generation capacity, which needed to fit the economic condition of the country, i.e., the power system being analyzed. Optimum LOLP represents not only the criterion for the necessary construction of power plants (i.e., generation capacity) but also an analytic parameter that shows the level of development of the power system and the economic condition as well, i.e., the standard of living of its inhabitants, usually described as GDP per capita. The variable that describes the level of development particularly well is VOLL, and the variable is shown as the one that can connect the level of construction of the power system and the level of economic development. Of course, it is expected that the power system is better constructed, i.e., that more generation facilities are constructed if economic activity is more intensive, i.e., if an economic system can generate a level of additional value that may construct and maintain such a power system to fulfill customers' needs in critical periods. However, if the economic system is not well developed and cannot generate additional value to invest into the energy sector, the power system is then weaker, especially in terms of generation. Consequently, the transmission part of the power system is shown as less reliable and less constructed. On the other hand, electricity and reserve in the power system can be bought at the electricity market. In any case, the construction of generation facilities or the procurement of such facilities, i.e., of electricity at the electricity market, requires additional financial means. An economy that is developed and that has a big additional value is expected to be able to

ensure electricity at a considerably higher price in critical periods. The question is whether there will be enough available capacities, which presents a risk. This risk is best described as a dependence on generation facilities, whose work cannot be controlled; without those facilities, there is no safety, long-term or short-term, regarding electricity procurement, amount, dynamics, and quality.

If necessary, power plant construction is a function of LOLP, one gets curves of necessary power, as shown in Figure 5. These curves fit well with theoretical curves of necessary new construction of generation facilities depending on security of supply. In part of the curve with LOLP, from 0.0001% to 0.003%, the difference in necessary construction totals equaled hundreds of MW, as seen in Figure 2. The curve of necessary new construction for the case of the coal-fired TPP showed more value because of less reliability of such facilities in comparison to the gas-fired CCGT. Those differences became negligible for LOLP values higher than one and lower than 0.1 percent.

**Figure 5.** LOLP optimization for VOLL \$2 USD/kWh with CCGT and coal fired TPP.

The final (third step) in the determination of the optimal security of supply in comparison to the outage cost was derived by comparing the total costs of the power system for both observed levels of the outage cost. Analysis was carried out with two technologies, the coal-fired TPP and the CCGT.

Figures 5 and 6 show the total costs of the power systems for both observed technologies of the coal-fired TPP and the CCGT depending on the security of supply for the level of VOLL of \$2 USD/kWh and \$10 USD/kWh. The differences in total costs for the different technologies were expressed at higher levels of customer security of supply, i.e., at lower levels of LOLP when a much higher installed capacity of new power plants was necessary.

**Figure 6.** LOLP optimization of LOLP for VOLL \$10 USD/kWh with CCGT and coal fired TPP.

Apart from specific characteristics of the analyzed power plant, it is important to see that the VOLL changed the form of the total power system costs curve. For lower levels of VOLL (e.g., for \$2 USD/kWh) the diagram of the total cost of the power system is rather symmetrical, while in case of higher VOLL (e.g., for \$10 USD/kWh), the curve of total costs clearly expressed minimum.

In conclusion, qualitative analysis of a power system requires the knowledge of economic development levels as well as its perspective. The measure of economic development, which as a variable influences the necessary construction of the power system, is the VOLL. Therefore, we should emphasize that economic development is not the only variable influencing the development of the power system, but it is definitely one of the key variables influencing inhabitants' living standards, traffic intensity, etc. A developed economy enables the development of a power system. Therefore, this is a firm connection between energy, power, and an economic system. The better developed those systems are, i.e., the more synchronized the development of the economic and the power systems is, the narrower the area of optimum construction, i.e., the minimum of total costs, will be.

Interconnected power systems (electrically, regulatory, and economically, which is defined by the electricity market) may count on the power and the energy procured on the market, not only in the case of trading but also in day-to-day operations as a result of operational costs optimization [21–23]. As opposed to self-sufficient power systems in the case of the regional electricity market, the LOLP is not the result but is set as a criterion. Thus, in that case, we may talk about risks managing the possibilities of procuring losses and delivering surpluses. If there are no possibilities of procuring power and energy at the market, and the power system is not developed enough, the LOLP increases and goes into the right part of the diagram, as shown in Figures 5 and 6. In those cases, not only does the procurement of power and energy at the market become important, but the price becomes important as well, which grows due to increased demand in critical periods. Consequently, there is an increase in the total costs of the power systems. Which marginal price of power and electricity is economical or possible to procure power and energy at the market depends on the possibilities of purchase and sale, the economic system development, the available generation capacities, the electricity prices in the regional market, etc. Generally, the more developed and the richer an economic system is, the higher price it can take (Italy, for instance), and vice versa. According to this, the LOLP index expresses the measure of constructiveness of the observed power system, the availability of the generation facilities in the surroundings, and the price of electricity present with the risk of procurement from the market.

Furthermore, in the case of over-construction (installation) of generation facilities in the power system, we arrive to the left part of the diagram, as shown in Figures 5 and 6. These figures display the state of over-construction of the power system if only the observed power system is taken into consideration. Of course, this is just a partially correct view, since power plants (in most cases) are not built to supply one power system but to generate electricity and sell it to customers or to the regional market. The investors build power plants where the final price of electricity is higher, competitive at the market, and where they can sell electricity to customers. However, from the point of view of national energy safety, customer security of supply is higher if the power system is over-constructed, i.e., if there is enough constructed power plants, generation, and other energy facilities, regardless of equivalent working hours. The diagram shows that the minimum of the curve of total costs of the power system moved to the left. The curve's gradient of increase of total costs of the power system for the cases of lower safety levels of customer supply was higher, which means that the increase of total costs due to increase of total damage due to undelivered electricity was more intensive. In practice, this does not mean that power system that has capacity adequacy at its disposal would suffer from reductions. Namely, it is necessary to settle necessary power and energy from neighboring power systems, i.e., at the market of electricity, under the condition that it is available in the amount and the dynamics, such as the price necessary to settle the electricity consumption of the observed power system. In such circumstances, the power system is oriented towards the import of electricity.

The diagrams in Figures 3–6 show for which values of LOLP the minimum observed costs of the power system were achieved. Figure 1 shows the values of LOLP for the observed outage cost and the observed technologies of the new TPP.

#### **4. Case Study**

In this analysis, there are four scenarios, as follows:

All four scenarios presumed the same level and characteristics of electricity consumption and the same characteristics of the new power plants.

Since the field of interest for analysis is defined by values for which the total observed costs are at a minimum, this chapter analyzes the case study scenario of power system development for LOLP values in the field from 1% for the scenario of VOLL of \$2 USD/kWh to 0.01% for the scenario of VOLL of \$10 USD/kWh. In addition, scenarios of a limited value of LOLP are analyzed. The comparison of these scenarios indicates the essence of the differences between the concepts of the generation expansion planning.

The main results in every scenario presented the number of power plants or the total new capacity becoming operational in the observed period. Although putting power plants into operation was different for each observed scenario, a certain measure of available generation capacity for each scenario was presented by the total construction of the power plants at the end of the observed period necessary to meet certain LOLP requirements. The number of each thermal power plant candidate in a certain year was defined by the number of units that became operational that year.

Load factor of thermal power plants is generally a good indicator of power plant utilization in the power system. Load factors of new power plants, i.e., candidate power plants, were analyzed and aggregated according to technology. Due to the fact that exchange with neighboring power systems was not modeled, the presumption in the analysis was that electricity generation in the thermal and the hydro power plants met the demand. This, of course, meant that the electricity generation was reduced just to meet demand, but it also showed the relation between the necessary generation capacity increases to meet LOLP criteria. Obviously, there was a need for the power plant to work as long as possible to ensure return of investments.

Figure 7 shows the load factor curves of three new thermal power plants, which were analyzed in a scenario with LCP and VOLL of \$10 USD/kWh as new thermal power plants, and as such, were put into operation in various years of observed periods.

The left diagram on Figure 7 shows the load factor of the CCGT. The load factor varied regarding other power plants being put in the operation, e.g., the coal-fired TPP. In comparison with the coal-fired TPP, the CCGT had lower investment costs and significantly higher fuel costs, thus the power plant had a lower priority in engagement, and its generation depended on the existing structure of generation in the power system.

As opposed to the 400 MW CCGT, the coal-fired TPP had an even load factor over the years, primarily due to the fact that it had a strong priority in engagement to cover load diagram because of the variable cost of electricity production, e.g., fuel prices. As a case study, the CCGT of 200 MW was set to the same techno-economical characteristic as the one of 400 MW apart from its installed power and technical minimum. It was shown that the load factor for that thermal power plant was

even because it was the thermal power plant of lower capacity, thus its relative oscillations of total generation were lower than with a combined gas-fired thermal power plant of 400 MW.

**Figure 7.** Load factor of new power plants in a scenario with the least cost planning (LCP) algorithm and VOLL of \$10 USD/kWh.

This scenario was characterized by a LOLP diagram and a diagram of outage cost per years, as seen in Figure 8. Outage cost (OC) is a kind of damage suffered by consumers (industrial, service, households, etc.) when the supply is curtailed because the power system is not able to meet demand. Outage cost is the total cost, and VOLL is the specific cost, usually expressed in currency over kWh of electricity not delivered to customers. In our study, the first third of the observed period was significant, since the existing state of construction of the generation facilities was adequate to fulfill the security of supply criteria. The maximum value LOLP reached was 0.63% in the years after decommissioning the power plants that were operational in the planned period.

**Figure 8.** LOLP and outage costs (OC) in a scenario with LCP and VOLL \$10 USD/kWh.

In the scenario in which LOLP was given at the level of 0.01%, putting power plants into operation was somehow different, just as the total construction at the end of the observed period was different.

Load factors of new power plants had a decreased gradient towards the end of the observed period. This was understandable if we consider that more power plants were put into operation due to increased levels of customer security of supply along with the similar electricity consumption. This meant that, in this construction scenario, it was a priority to engage power, as seen in Figure 9.

The curve of the value of LOLP and the outage cost per year in the observed planned period showed significant variations, but remained within the framework given by limiting the value to 0.01%. Of course, oscillations depended on putting new power plants into operation and thereby a much higher necessary installed power. All the curves are shown in Figure 10.

**Figure 9.** Load factor of new power plants in scenario with LOLP = 0.01% and VOLL \$10 USD/kWh.

**Figure 10.** LOLP and OC in a scenario with LOLP = 0.01% and VOLL \$10 USD/kWh.

The next group of scenarios was analyzed with the VOLL \$2 USD/kWh for two cases, one without limiting the value of LOLP and one limiting the value of LOLP onto 1%. Load factors of power plant candidates remained almost the same on average, but the load factor of the 400 MW CCGT varied depending on the new thermal power plants put into operation, especially the coal-fired TPP. All the curves are shown in Figure 11.

**Figure 11.** Load factor of new power plants in the LCP scenario and VOLL \$2 USD/kWh.

The curves in Figure 12 convey the value of LOLP and the outage cost per year, which showed characteristic behaviors. One of those was year 2022, the fourth year when no TPPs were put into operation. The maximum achieved value of LOLP was 3.95%.

Load factors of new thermal power plants, especially the coal-fired TPP and the 200 MW CCGT, which were put into operation in the observed period, were relatively constant, whereby the load factor of the CCGT with higher capacity slightly decreased towards the end of the planned period, as seen in Figure 13.

**Figure 12.** LOLP and OC in a scenario with LOLP = 0.01% and VOLL \$2 USD/kWh.

**Figure 13.** Load factor of new power plants in a scenario with LOLP = 1% and VOLL \$2 USD/kWh.

The curves of the outage cost as well as the values of LOLP per year are presented in Figure 14. Characteristic years for this group of scenarios were 2021 and 2025 as well as the last year of the planned period.

**Figure 14.** LOLP and OC in a scenario with LOLP = 1% and VOLL \$2 USD/kWh.

#### **5. Scenarios Comparison**

The comparison of these four scenarios was possible based on three elements. The first comparison was of scenarios in the group with the same level of VOLL for two different levels of LOLP. The second comparison of scenarios was on the basis of the same level of LOLP for two levels of the VOLL. The third comparison was for different levels of VOLL and LOLP.

The comparison of scenarios with VOLL \$10 USD/kWh and for LOLP of 0.01% without limitations showed significant differences in the second half of the observed period. If the scenarios were compared according to total installed power or the necessary new installed capacity, as shown in Figure 15, it was obvious that by 2019, the installed total power was almost the same, i.e., the necessary new installed capacity. As previously stated, the scenario without limitations corresponded to the LCP scenario, whereas the second scenario with LOLP 0.01% was the scenario with the optimized level of LOLP.

**Figure 15.** Scenario with VOLL \$10 USD/kWh and LOLP = 0.01% versus the LCP scenario.

The comparison of scenarios with VOLL \$2 USD/kWh and two levels of LOLP without limitations and with a limitation of 1% is shown in Figure 16. These two scenarios differed only in the dynamics of necessary realization in two years of the observed period. This was seen in the second half of the planned period in a way that one power plant was the candidate in the scenario with LOLP of \$2 USD/kWh and was put into operation a year earlier than in the scenario without the limitation of LOLP. That meant that, in the group of scenarios with low outage costs, there was actually no difference between generation expansion planning using the method of LCP and the method of optimizing LOLP. The reason for this was that the influence or the share of costs referring to outage cost was almost negligible. These costs went from 0.3% for the scenario with LOLP 3% to 0.96% for the scenario without limitations of LOLP.

This would mean that the issue of customer security of supply for low levels of GDP was not relevant. That issue becomes relevant with the development of an economy and therefore the increase in total GDP, i.e., GDP per capita.

The comparison of two scenarios—in this case, the scenario with VOLL \$2 USD/kWh and the level of LOLP without limitations compared to the scenario with VOLL \$10 USD/kWh and the level of LOLP of 0.01%—regarding the necessary new construction of power plants is shown in Figure 16.

In this example, there were quantified differences between the necessary constructions of the generation facilities for the LCP scenario compared to the optimized level of the LOLP scenario. The conclusion was drawn that, for a developed economy measured by the VOLL \$10 USD/kWh, it was necessary to dispose of the capacity in power plants higher than 400 to 800 MW. It should be noted that additional installed generation capacity was not necessary because, in both scenarios, it was modeled in the same way. However, it was necessary for fulfilling the criteria of customer security

of supply. Namely, a high level of economic development consequently had a high level of outage cost, which then in turn necessitated a high level of customer security of supply, requiring additional necessary installed capacity for an equal level of electricity consumption. The difference between these two scenarios was critical. The first scenario corresponded to the method of LCP of the power system, whereby the second corresponded to the scenario with given criterion of LOLP, i.e., directly to the set level of customer security of supply. Apart from that, the difference was in the level of VOLL.

**Figure 16.** Scenario with VOLL \$2 USD/kWh and LOLP = 1% versus the scenario with LCP.

Figure 17 shows the comparison of the necessary new construction of generation capacities for the scenarios corresponding to the LCP scenario for VOLL \$2 USD/kWh and \$10 USD/kWh. The differences arose from the contribution of outage cost.

**Figure 17.** Scenario with VOLL \$10 USD/kWh and LOLP = 0.01% versus the scenario with VOLL = \$2 USD/kWh and LCP.

#### **6. Conclusions**

This paper presented a developed method for incorporating reliability criterion in the optimization of power system expansion planning. Analysis of security of supply was significant when power systems were poorly interconnected with other power systems, which is once again significant when considering the regional electricity market as a real business environment.