*1.1. Motivation*

In the future, power systems will operate with a high proportion of renewable energy, which needs more flexible operational resources to compensate for power imbalances that are currently scarce. Significant intermittence in renewable resources for power systems causes great changes in real-time price, and so requires energy storage to balance power. Distributed, shared energy storage technology has high application value and will be an important and widely used resource in future power systems. At present, large-scale, pumped-storage power stations are the main energy storage resource in power systems, with costs lower than those of battery energy storage systems. High cost causes a scarcity applied battery energy storage technology in power grids. To reduce the cost of energy storage services, cloud energy storage (CES) technology, presented in [1,2], is one strategy for centralizing all distributed energy storage devices from consumers into a cloud service center, as virtual energy storage capacity, instead of real devices.

Depending on the interactive value of the consumer and power grid, a CES can be an opportunity to promote a shared business model, setting the precedent for consumers

**Citation:** Li, J.; Xing, Y.; Zhang, D. Planning Method and Principles of the Cloud Energy Storage Applied in the Power Grid Based on Charging and Discharging Load Model for Distributed Energy Storage Devices. *Processes* **2022**, *10*, 194. https:// doi.org/10.3390/pr10020194

Academic Editors: Alon Kuperman and Alessandro Lampasi

Received: 15 December 2021 Accepted: 31 December 2021 Published: 20 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 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 (https:// creativecommons.org/licenses/by/ 4.0/).

as energy resource suppliers. Such a shared business model can provide users more opportunity to be involved in the power market and to make energy storage more flexible. The advantages thereof are as follows: (1) having no limits to capacity from time, location, or demand; (2) having lower costs than traditional energy storage technology, only paying for leasing; (3) large-scale, flexible, shared, and distributed energy storage devices from loadside consumers and concentrated energy storage equipment from professional providers, working together in the CES environment, to huge social benefit and improved spare energy usage; (4) the consumer who has distributed energy storage participating in their business can reduce their total electricity expenses paid to the grid.

The major consumers are residents and small businesses, who are sensitive to electricity prices. Additionally, some substations within a power grid need distributed energy storage devices for emergency service. At present, to multiple energy systems (MES) a CES can be a flexible resource for end-user demand responsiveness.

Technical support for such a system includes power-load forecasting, planning optimization, communication technology, data and economic analysis, and so on. Buyers send information, such as their needed storage capacities and payments, to sellers, transferred based on the power market, communications companies, and banks. Buyers who need energy storage capacity can purchase the right of use for a given period. After obtaining the right to its use, these consumers can charge and discharge the cloud battery, according to their demand circumstances. Consumers can buy virtual energy storage capacity in a cloud network instead of building physical energy storage to reduce cost.

A CES is used as a distribution network, wherein users benefit from each other. When its battery needs charging, CES suppliers pay for the power. When its batteries discharge, CES suppliers collect fees from the consumers and the distributed power grid. Cloud energy storage suppliers need to make optimization decisions, considering cost and profit under the constraints of consumers' demand for charging and discharging the cloud battery. Then, energy storage device suppliers control real-world equipment through the building, operating, and maintenance to provide good service.

In summary, the research goal of this paper is as follows.

#### *1.2. Paper Innovation*

This paper is to solve two problems: (1) how to plan and design an orderly, controlled CES system in a realistic power system; (2) how to manage and operate the CES system. To solve the first problem, this paper presents a detailed planning drawing. To solve the second problem, this paper analyzes the load curve of five types of distributed energy storage system using Monte Carlo simulation (MCS). A planning method and principles of the CES applied in the power grid are presented. In this paper, the main innovations are as follows.

(1) The traditional research on summarizing the main factors and probabilistic models of load distribution of different types of distributed energy storage in one paper are seldom. We establish the timely and spatial distribution of charge and discharge load model considering multi-variable series of factors such as electricity price and user demand, after summarizing the impact factors from the aspects of energy storage type, battery capacity, state of charge (SOC), charging mode and user behavior.

### *1.3. Structure of the Article*

The structure of this article is as follows: Section 2 summarizes our literature review. Section 3 presents the planning method and establishes the charging and discharging load model for distributed energy storage behaviors. Section 4 analyzes the impact of the orderly or disorderly charging and discharging of different energy storage behaviors on power grid capacity, load characteristics, and safety margin, in order to summarize the application fields of CES in supporting large power grids. Section 5 concludes the paper.

#### **2. Literature Review**

Over the last ten years, multi-type energy storage technologies for micro grids [3,4] have been the focus of a large number of studies. With the development of large-scale renewable energy resources that easily integrate distributed energy resources into power systems with high power quality and operational control, the building of new transfer lines needs significant investment to meet end-user demand response; to this end, installing suitable scales of energy storage devices is being adopted as a better planning solution. In such a solution, the operation department adopts an active control strategy, such as energy storage systems, to compensate the renewable energy sources' volatility and smooth peak–valley load differences. For producing maximum comprehensive economic benefit, virtual power plants (VPP) [5,6] aggregate scheduled and non-scheduled units, including renewable and non-renewable energy resources, storage devices, and flexible loads, to operate as a single entity participating in the power market. Much literature has considered frameworks for and the modelling of components and operational systems [7]. To reduce the imbalance between the power generation and consumption due to intermittently generating units, scientifically adopting a bidding strategy for a VPP to elicit maximum benefit is a decidedly primary issue.

Electric vehicles (EV) represent mobile charging loads, as either plug-in electric vehicles or plug-in hybrid electric vehicles. Due to EVs' ability to stabilize the grid and provide significant battery storage capacity without upfront capital cost to grid infrastructure, research on the integration of vehicle-to-grid (V2G) energy storage units and cooperation with intermittent wind/PV in a VPP is a future social focus [8,9], but it's presently unrealistic. However, research on vehicle-to-grid aggregators for frequency regulation [10] and operational modeling of electric vehicle charging stations [11] provides novel ideas about distributed energy storage consumption.

The cloud energy storage (CES) systems presented in [1,2] in 2017 centralize all distributed energy storage devices from consumers into the cloud service center as a virtual energy storage capacity, belonging to the energy storage units of such VPP. The framework of the CES system for power grids is presented in [12] and built by the consumer, the CES operator, the energy storage supplier, and the distribution grid. With the information and cash flow calculated through distributed computing, the CES operator, as a centralized agency, is an intermediary service provider in contact with the consumers, the storage

supplier, and the grid. For the centralized system, considering the hierarchical architecture of the power grid, cloud–edge intelligence [13–15] for wide-area load frequency control, substation simulation and protection control, and load modeling and management, is suitable for application in CES systems. In recent years, distributed storage coordination control strategies [16] and application–research scenarios on large-scale battery energy storage systems [17] are two common fields of energy storage system research.

As for renewable energy resources and EVs, the power grid cannot afford a large volume of electric vehicles (EVs) charging at peak load time because they greatly influence the load curve [18]. To deal with the peak load regulation and demand response for a VPP supporting EVs, three solutions are: optimization dispatch [19,20], control strategies [21], and trade mechanisms [22]. Distributed computing is the main solution for solving the distributed EVs integrated into the power market, e.g., multi-agent intelligent computing [23] and distributed online algorithm [24]. Blockchain represents realistic market and dispatch mechanisms for EVs [25–29] by its technical characteristics e.g., transparent, untampered with, privacy protection, and smart contract, allowing the tracing of EV charging and the extension of its leasing activity.

In recent three years, blockchain technology has led to a complete set of distributed energy trading and supply systems by connecting energy producers and consumers directly, greatly reducing the transactional cost of electricity and improving transactional efficiency. This enables power producers, transmission grid operators, distribution grid operators, and retail energy service providers to trade at different levels, simplifying the complex multilevel structure of current power systems. Blockchain technology can also deploy its tamper-proof characteristics in identifying the certification of carbon power and renewable energy power, directly recording renewable power, and providing the convenience of credible transactions of renewable energy. Building a power-trading platform with blockchain technology will be a technological upgrade to the current power-trading market.

Blockchain, a distributed database technology, has changed some major application fields of the internet of things (IoT) network over the last five years [30,31]. Introduced by Satoshi Nakamoto's Bitcoin in 2008 [32] as a peer-to-peer (P2P) system for distributed computing and decentralized data sharing, blockchain is composed of a distributed series of blocks, linked together by their hash values, that has the characteristics of decentralization, time traceability, autonomy, openness, and tamper-proof information by using time stamps, asymmetric cryptography, distributed consensus, and flexible programming technique. The application domains of blockchain technologies in IoT, e.g., the internet of vehicles, the internet of energy, the internet of cloud, fog computing, etc., are surveyed in [33]. Blockchain techniques, applied in the Chinese energy internet, is surveyed in [34,35]. The architecture and functionality of the blockchain groups in the intelligent distributed electrical energy systems presented in [36,37] is a realizable model for future implementation. At present, realistic engineering samples are so few that most research focuses on the blockchain's framework design concerning power system planning [37–41] and market transaction infrastructure [42–48].

### **3. Planning Method and Modelling**

This section presents the architecture design for a CES and then builds a load model of the charging and discharging of distributed energy storage.

### *3.1. Planning Method*

#### 3.1.1. Architecture Design for CES

Figure 1 shows the architecture design for the CES market trade for local and crossregion power grid. Figure 2 is the unified model architecture for trade participators, a flowchart, and trade codes. The business participators are as follows:

(1) Consumers: The consumers include the users with wind or PV resources, small commercial users (e.g., load or electric vehicles), distributed generation (e.g., wind farms or PV stations), and the power plant. Distributed generation represents a certain scale

of "wind farm /PV station and battery storage (BS)". Small commercial users include the common industrial/commercial loads or batteries. Fossil-fuel power plants output power to charge energy storage devices through the distribution grid. They send fixed addresses—an ID number—and administrator information as block data.

Local consumption of renewable energy is better than long-distance transmission to the power grid. The cost of centralized labor management of operation and maintenance is so high that the smart distributed management system needs user-end intelligence.

(**b**)

**Figure 1.** The architecture of a market trade business: (**a**) energy path system; (**b**) market trade business model architecture.

**Figure 2.** Unified model architecture for trade participators, flowchart and trade code.

3.1.2. Planning of the CES System in a Power Grid

To build the CES system in a realistic power system, this paper presents the planning ideas as follows:

The cloud energy storage operator only needs to build a software platform that has a basic distribution grid with energy storage units. During the planning process, the most important concern is the power/energy flow, information flow, and cost flow. Due to the separation of current dispatching and transaction platforms, the cost flow can be in the market transaction platform. The realistic terminal devices that use these three kinds of flows are the consumers' electrical appliances and all kinds of energy storage device, which are connected by the distribution grid. There are two kinds of energy storage devices, namely the fixed energy storage stations and distributed mobile energy storage devices. The fixed energy storage stations, because of their large capacities and high cost, need to be built by the power grid or by some significant entrepreneurial investment. Mobile batteries feature location flexibility, without a demand to be located, as they can be anywhere. Thus, no realistic building construction cost in the CES system that only the mobile battery vehicles and the charge/discharge interface stations need to be invested instead.

The CES operator is responsible for the following tasks: building an information cloud detailing all service providers and energy storage suppliers, receiving consumers' charge/discharge capacities and energy demands, receiving electric vehicles' charging demands and locations and navigating them to the nearest charging stations, optimizing dispatch suggestions, load shifting from the peak to the valley, smoothing output power, tracking planned output power, ancillary services, primary frequency control in wind/PV and storage systems, meter monitoring, solving the problem of abandoning wind and PV power in order to build a friendly power supply, and improving system operational flexibility. These tasks can be set as functional modules in the desktop platform to be chosen by consumers.

The aim is to operate economically, reliably, with high quality, and efficiently. Costs needs to consider initial investment in the platform and the operation of charging stations based on users' reported installation of system capacity and energy demands. Then the cost is converted into the service price (currency/kWh). The platform needs to manage payment of all functional modules.


*4.3. Field Study*

The aim of the field study dataset was the evaluation of the estimation of the overall distance of a run in an outside and real-world scenario. The *Trajectory* algorithm also worked best for this dataset. With an MAE of 94.0 m, it is comparable to the results of GPS, which also produced an MAE of 82.1 m, and is used in state-of-the-art running platforms tracking athlete performances. Besides, the IMU technology has the advantage that it allows velocity and distance computations indoors or in scenarios where no satellite connection for GPS is available. Based on the presented results, we argue that although the *Trajectory* algorithm has high standard deviations in the lab study for the stride length calculation, these have no major impact on the computation for longer distances based on stride length. We believe this is due to errors canceling out over time. As the subjects' average velocity was 3.48 m/s during the data acquisition, the high velocity range of 5–6 m/s was not reached for the amateur runners that participated in this study. We expect the results to be worse for the high velocity range, which can be reached by professional athletes.

The *Stride time* algorithm showed the worst performance for the field study dataset (MAE of 599.7 m). Despite its best energy efficiency, our results indicate that its accuracy is too low to use for tracking velocity and distance. The *Deep Learning* approach (MAE 194.5 m) performs better than the *Acceleration* approach (MAE 333.1 m). Due to the fact that the the neural network also needs the 6D-IMU data as an input, it has no benefit compared to the *Trajectory* approach, which performs better. The *Acceleration* approach only requires the sampling of the triaxial accelerometer, which makes it more energy efficient. Despite its decreased accuracy, we propose to use this algorithm in use cases where very strict energy limitations occur.


3.1.12. An Gouaches

Darker (T1) and lighter (T2) tan gouaches were used in print P10 and P11, respectively. Based on the p-XRF analysis, both gouaches contained a small amount of a natural iron oxide pigment rich in aluminosilicates.


*4.2. Planar Waveguide Immunosensor Design*

The PW structures were devised by standard microelectronic processes as a thin (200 nm) layer of Si3N4 sandwiched between much thicker (3 µm) layers of SiO2. The 200 nm core layer thickness was required to accommodate a single mode electromagnetic wave propagating along the waveguide [25,26]. Due to the large difference in RIs between the Si3N4 core (*n* = 2.01) and the SiO2 cladding (*n* = 1.46), the light propagates at an angle of 47◦ (corresponding to an angle of total internal reflection) and with a consequent 3000 reflections/mm approximately. The use of a slant edge of the waveguide cut at 47◦ was an optimal light coupling solution, which provided sufficient light intensity propagating through the waveguide.

In the experimental PI set-up, 650 nm light from a laser diode was focused with a lens to a narrow (less than a millimetre) spot on a slant edge of the waveguide and collected on the other side with a CCD array. A polarising element in front of a CCD camera allows the visualisation of a phase shift between p- and s-components of polarised light. The reaction cell equipped with the inlet and outlet tubes is sealed against a sensing window, which was etched in the top SiO2 layer. The surface of Si3N4 in the sensing window could be coated with a biosensing layer. Any changes in RI and/or thickness of this sensing layer affect mostly the p-component of polarised light (while the s-component acts as a reference), thus resulting in a multi-periodic output signal.


**3. Results**

#### *3.1. Flowering Period, Plant Growth, and Soil Nitrate Content*

Experiments showed that maize flowering started between 60 and 70 days after sowing (DAS) and lasted for a short period between nine (FAK 2018) and 21 days (ET 2018) (Table 3). Beside of yellow sweet clover (MC), all IFP provided an additional period with flowers. Alfalfa (MA) was buried during the mechanical weeding process in FAK. Common vetch (MV) had the earliest flowering initiation of all IFP at all locations and years. On average, 40 DAS flowering started. However, flowering did not last for a long time in 2018 in ET and TH. After nine and 28 days of flowering, the common vetch was infested by *Erysiphaceae* (TUL. & C. TUL.) spp. and the plants died. Since the flowers of summer squashes I–III (MS1, MS2, MS3) and nasturtium (MN) were below the cutting height of the crop chopper, these IFP build up flowers even after maize harvest. In case of the common beans (MB1, MB2), it was observed that the first flowers appeared very late, more than 60 DAS. They even flower after maize anthesis and sometimes shortly before harvest.

**Table 3.** Days after sowing (DAS) until begin of flowering (SBF) and end of flowering (SEF), and the whole flowering time of maize and the different intercropped flowering partner (IFP) treatments (Δ = flowering period in days) for Ettlingen (ET), Tachenhausen (TH), and Forchheim am Kaiserstuhl (FAK) in 2018 and TH and FAK in 2019. The flowering periods of maize in the intercropping treatments corresponded to those of the maize in the control. For reasons of simplicity, the intercropped treatments only show the flowering periods of the IFP.

Common bean II (MB2) not available in 2019. M Maize (Control), MA Maize + Alfalfa, MC Maize + Yellow sweet clover, MV Maize + Vetch, MN Maize + Nasturtium, MS1 Maize + Summer Squash I, MS2 Maize + Summer Squash II, MB1 Maize + Common Bean I, MB2 Maize + Common Bean II, MS3 Maize + Summer Squash III, MM1 Maize + Mixture I, MM2 Maize + Mixture II. ¶ In mixtures the first flowers in the crop stand were counted as begin flowering, regardless which of the mixture partners flowered, and for end of flowering, vice versa ‡ IFP still flowers after harvest. If common bean flowered until harvest, harvest date was set as end of flowering due to the fact that all flowers are in the harvested material.

The combined cultivation of maize and IFP did not affected the final height of maize in ET 2018 (Table 4). At ET no significant influence of N-level (*p* = 0.055) or intercropped seed placement (*p* = 0.326) on final maize plant height were observed in 2018 (Table 4). The IFP also did not show differences on plant height (*p* = 0.817). At TH (*p* = 0.943) and FAK (*p* = 0.225), there were also no significant changes in plant height in 2018 (Tables 5 and 6). In 2019, TH (*p* < 0.001) and FAK (*p* < 0.001) had significant differences in plant height. At TH, the highest values for maize plant height were found in the control, without any intercropping partner (M) with 341 cm, while the height of the maize in the intercropping plots with nasturtium (MN), summer squash I and II (MS1, MS2), and mixture I (MM1) was not significantly different from the control (M). In FAK, only common vetch (MV) and mixture II (MM2) were significantly different from the control (M), with 253 cm and 259 cm compared to 291 cm (Table 6).

In 2018, the DMY was significantly influenced by N fertilization in ET (*p* = 0.004) (Table 4). Full fertilizer application yielded the highest DMY, while there was no difference whether 100% of the required N is applied or 50% (Table 4). In 2018, no differences were measured whether the IFP was sown in the rows or between. The strongest influence on DMY was caused by the IFP used (*p* < 0.001). Significant lower yields compared to the control (M) were found by intercropping with alfalfa (MA), yellow sweet clover (MC) and common vetch (MV). Intercropping with nasturtium (MN) or common beans (MB1, MB2) showed no significant difference compared to 15.6 Mg ha−1 DMY in the control. At TH significant IFP influences on DMY in both years were determined (*p* = 0.003 and <0.001) (Table 5). In 2018, common vetch (MV) showed with 11.5 Mg ha−1 the lowest DMY, which were not different from the control (M) in 2018, while in 2019 common vetch (MV) and mixture II (MM2) had significant lower DMY compared to the control (M), with 17.9 Mg ha−1. Significant differences in the DMY were only found in 2019, not in 2018 (Table 6). Significantly lower DMY were found for common vetch (MV), summer squash III (MS3), and mixture II (MM2) in 2019.

The fractioning of the plant biomass in ET 2018 showed no significant influence of the N-Level on the share of maize, IFP and weed (*p* = 0.504, *p* = 0.067 and *p* = 0.198) (Table 4). The use of an IFP did

not reduce the share of maize (*p* = 0.341). The IFP with the highest share in the harvested biomass were the common beans (MB1, MB2). They had a share of 3.45 and 4.28%, while nasturtium (MN) with 0.61% had a low share of the harvested biomass. With a share of more than 87.0% maize was the main yield component. At TH in 2018 a significant influence by IFP on the share of maize biomass was observed (*p* < 0.001) (Table 5). The smallest proportions of maize were found with 94.5% when intercropped with common vetch (MV). In 2019 there was also a significant influence of the IFP on the maize proportion (*p* < 0.001). Smallest maize proportion was found under intercropping with both mixtures (MM1, MM2) and with common bean I (MB1). FAK in 2018 showed no significant influence on maize proportion by IFP (*p* = 0.221) (Table 6). In this year also there was a weed share between 1.55% (MS2) and 6.67% (MB2). The highest proportion of IFP in the harvested biomass was found in common vetch (MV) in 2018 and mixture II (MM2) in 2019 at 5.78% and 3.96%, respectively. Both years and all locations showed that intercropping maize leads to maize proportions of >81.8% in the harvested biomass.

**Table 4.** Growth parameters plant height (cm), dry matter yield (DMY, Mg ha−1), dry matter content (DMC, %), and the share of maize, intercropped flowering partners (IFP), and weed of the DMY from section harvest (%); for the three factor levels N-level, seed placement of IFP and the different IFP treatments at Ettlingen (ET) in 2018.

Values with the same letter within one parameter indicate non-significant differences within the three factor levels (N-Level, Seed placement and IFP) (HSD-test, α = 5%). \*\*\* *p* ≤ 0.001, \*\* *p* ≤ 0.01, \* *p* ≤ 0.05. M Maize (Control), MA Maize + Alfalfa, MC Maize + Yellow sweet clover, MV Maize + Vetch, MN Maize + Nasturtium, MS1 Maize + Summer Squash I, MS2 Maize + Summer Squash II, MB1 Maize + Common Bean I, MB2 Maize + Common Bean II, MS3 Maize + Summer Squash III, MM1 Maize + Mixture I, MM2 Maize + Mixture II.

**Table 5.** Growth parameters plant height (cm), dry matter yield (DMY, Mg ha−1), dry matter content (DMC, %), and the share of maize and intercropped flowering partners (IFP) of the DMY from section harvest (%); for the different IFP treatments at Tachenhausen (TH) 2018 and 2019.

Values with the same letter within one parameter indicate non-significant differences within the IFP (HSD-test, α = 5%). \*\*\* *p* ≤ 0.001, \*\* *p* ≤ 0.01, \* *p* ≤ 0.05. Common bean II (MB2) not available in 2019. M Maize (Control), MA Maize + Alfalfa, MC Maize Yellow sweet clover, MV Maize + Vetch, MN Maize + Nasturtium, MS1 Maize + Summer Squash I, MS2 Maize + Summer Squash II, MB1 Maize + Common Bean I, MB2 Maize + Common Bean II, MS3 Maize + Summer Squash III, MM1 Maize + Mixture I, MM2 Maize + Mixture II.

In 2018, in ET, the soil NO3-N content was mainly influenced by the N-level (*p* < 0.001), in 0–30 cm and 30–60 cm depth after harvest, and at end of vegetation period, respectively. IFP showed in ET no influence after harvest (Figure 1). The seed placement also did not show an influence at any layer and both dates.

At the end of the vegetation period, the layers from 0–30 cm and 30–60 cm showed significant differences by the IFP used (*p* = 0.044 and *p* = 0.014) (Figure 1). Common bean II (MB2) had the significant highest amount with 80.4 kg ha−1 NO3-N in the upper layer, while in the middle layer both beans (MB1, MB2) did not differ significantly from the control (M). In TH, no significant influence by IFP on soil NO3-N content was found in any soil layers, for the two dates and in 2018. In 2019, mixture II (MM2) had significant lower NO3-N contents with 3.14 kg ha−1 compared to 8.41 kg ha−1 NO3-N in the control (M) at the end of the vegetation period in 30–60 cm. Also, at the 60–90 cm layer common vetch (MV) had higher NO3-N contents than control (M), 3.25 compared to 1.49 kg ha−1 NO3-N.

**Table 6.** Growth parameters plant height (cm), dry matter yield (DMY, Mg ha−1), dry matter content (DMC, %) and the proportion of maize, intercropped flowering partners (IFP) and weed of the DMY from section harvest (%); for the different IFP treatments at Forchheim am Kaiserstuhl (FAK) 2018 and 2019.

Values with the same letter within one parameter indicate non-significant differences within the IFP (HSD-test, α = 5%). \*\*\* *p* ≤ 0.001, \*\* *p* ≤ 0.01, \* *p* ≤ 0.05. Common bean II (MB2) not available in 2019. M Maize (Control), MA Maize + Alfalfa, MC Maize + Yellow sweet clover, MV Maize + Vetch, MN Maize + Nasturtium, MS1 Maize + Summer Squash I, MS2 Maize + Summer Squash II, MB1 Maize + Common Bean I, MB2 Maize + Common Bean II, MS3 Maize + Summer Squash III, MM1 Maize + Mixture I, MM2 Maize + Mixture II.

Differences at FAK after harvest in 2018 were only detectable in the deepest layer. Common vetch (MV) and common bean II (MB2) showed increased contents of 103% and 125% compared to the control (M), respectively. At the end of the vegetation period, intercropping with yellow sweet clover (MC) showed NO3-N contents of 17.1 kg ha−1, while the control had 9.48 kg ha−1 in the upper layer.

## *3.2. Quality Parameters*

In 2018, the DMC was neither significantly influenced by N-level (*p* = 0.536), nor by seed placement of the IFP (*p* = 0.294) (Table 4). Only the IFP itself had a significant influence on the DMC (*p* = 0.012). With 34.0% nasturtium intercropping (MN) showed the highest DMC, which did not differ from the control. All DMC were in a range were ensiling could take place. At FAK (Table 6), no differences were found in 2018 (*p* = 0.096), but the DMC for some IFP were over the recommended ensiling maximum of 40% DMC due to the hot and dry weather, which accelerated the ripening. For TH in 2018, significant differences were observed (*p* = 0.003), DMC of yellow sweet clover (MC) intercropped plots were significantly higher from the control (M). In 2019, no significant differences were found between the IFP.

**Figure 1.** NO3-N (kg ha−1) of soil samplings for the different intercropped flowering partner (IFP) treatments (M Maize (Control), MA Maize + Alfalfa, MC Maize + Yellow sweet clover, MV Maize + Vetch, MN Maize + Nasturtium, MS1 Maize + Summer Squash I, MS2 Maize + Summer Squash II, MB1 Maize + Common Bean I, MB2 Maize + Common Bean II, MS3 Maize + Summer Squash III, MM1 Maize + Mixture I, MM2 Maize + Mixture II) in Ettlingen (ET) (2018 **A1**), Tachenhausen (TH) (2018 **A2**, 2019 **B1**) and Forchheim am Kaiserstuhl (FAK) (2018 **A3**, 2019 **B2**) after harvest and at the end of the vegetation period (End.Veg.Ped.; =end of growing season, when the weather causes a plant growth stop) for the three depths 0–30 cm (green bars), 30–60 cm (white bars), and 60–90 cm (gray bars). Bars with the same letter within one depth and one sampling date indicate non-significant differences (HSD-test, α = 5%).

The N-level had a significant influence on the content of CP in the biomass at harvest time (*p* < 0.001) (Appendix A Table A1). With increasing N-level there was a significant increase in CP. The highest content was achieved at 100% N-level, with 8.91%. The placement of the IFP seeds did not have an influence on CP (*p* = 0.236), while the influence of IFP was significant (*p* < 0.001). The only statistical influence between the IFP could be found between common vetch (MV), with 9.97% and all the other IFP (Figure 2). Control (M) achieved 7.08%. In 2018 at TH, nasturtium (MN) and both summer squashes (MS1, MS2) had lower CP contents than the control. In the second season, nasturtium (MN) and summer squash I and III (MS1, MS3) showed once again significantly reduced CP contents compared to the control (6.09%), while mixture II (MM2) showed an increase.

**Figure 2.** CP (crude protein), CF (crude fat), CX (crude fiber), CA (crude ash), and NfE (nitrogen-free extracts) content (% of DM) for the different intercropped flowering partner (IFP) treatments (M Maize (Control), MA Maize + Alfalfa, MC Maize + Yellow sweet clover, MV Maize + Vetch, MN Maize + Nasturtium, MS1 Maize + Summer Squash I, MS2 Maize + Summer Squash II, MB1 Maize + Common Bean I, MB2 Maize + Common Bean II, MS3 Maize + Summer Squash III, MM1 Maize + Mixture I, MM2 Maize + Mixture II) in Ettlingen (ET) (2018 **A1**), Tachenhausen (TH) (2018 **A2**, 2019 **B1**), and Forchheim am Kaiserstuhl (FAK) (2018 **A3**, 2019 **B2**). Bars with the same letter within one parameter indicate non-significant differences (HSD-test, α = 5%).

For FAK in both years, CP differences were measured (Figure 2), while in 2018, the summer squash I (MS1) had lower CP contents than the control. In 2019, common vetch (MV) had higher CP contents. No other intercropping treatment showed significant changes.

Neither the N-level (*p* = 0.407, *p* = 0.694, and *p* = 0.324) nor the seed placement (*p* = 0.854, *p* = 0.725, and *p* = 0.870) had significant influences on CF, CX, and CA content in ET 2018 (Table A1). The IFP also did not have an influence on the CF content (*p* = 0.178) (Figure 2). Common vetch intercropping (MV) showed higher CX contents than the control (M), while nasturtium (MN) and both beans (MB1, MB2) had comparable CA contents with the control. The NfE content was influenced by the N-level (*p* = 0.003) but not by the seed placement of the IFP (*p* = 0.959) (Appendix A Table A1). A high fertilization leads to lower NfE contents. The NfE behaved in the opposite way to the CP. Common vetch (MV) showed the significant lowest NfE contents in ET (Figure 2). In TH there were no significant differences between the IFP in 2018 for CF, CX, CA, and NfE, while in 2019, significant differences were observed for all parameters. For CF, none of the IFP differs from the control, while for CX and CA common bean I (MB1) had significantly higher amounts. For the NfE, all IFPs, except alfalfa intercropping (MA), had lower contents than the control. In FAK, only CF (*p* = 0.008) and CA (*p* = 0.007) showed significant differences in 2018. For CF, common bean I (MB1) showed higher contents than the control (M), while for CA, alfalfa (MA) had higher contents. In the second year, CX, CA, and NfE (*p* = 0.018, *p* = 0.031, and *p* = 0.004) showed significant differences, but none of the IFP was different from the control, except the summer squash III (MS3), which had a lower amount of NfE.

For use as feedstock in biogas plants, besides the DMY, the amount of produced biogas and methane are important factors. Only the N-level had a significant influence on the biogas yield (*p* < 0.001) (Appendix A Table A1). Significant decreases in biogas yields were found for 100% N fertilization compared to 0 and 50%, respectively, while the N-Level had no influence on methane yield (*p* = 0.714). Placement of IFP seed showed no influence on biogas and methane yield (*p* = 0.546 and *p* = 0.733) (Appendix A Table A1). The IFP had an influence on these two parameters in ET 2018 (*p* = 0.025 and *p* = 0.016) (Figure 3). Common vetch (MV) showed significantly lower biogas and methane yields, compared to the control (M). While the control achieved yields of 556 L kg−1 oDMC (organic DMC) for biogas and 300 L kg−1 oDMC for methane, intercropping with common vetch (MV) reduced these values to 539 L kg−1 and 294 L kg−1 oDMC, respectively. For TH, the response of the IFP on biogas and methane yield depended on the experimental year, while in 2018, biogas and methane yield were significantly increased by intercropping with common bean I (MB1), and in 2019, both yields were significantly decreased by intercropping with common bean I (MS1). In FAK, there were no significant differences on the biogas or methane yield for any of the IFP used, neither in 2018 nor in 2019.

In dairy cattle feeding, GE, ME, and NEL are important factors for evaluating the quality of the silage. At ET in 2018, there was only a significant influence of the N-level on the content of GE (*p* = 0.021), ME and NEL were not influenced by the level of N fertilization. The seed placement of the IFP had no influence on the feeding quality parameters (Appendix A Table A1). All three parameters showed that intercropping with common vetch (MV) significantly decreased the contents in ET.

In TH in 2018, ME and NEL showed significant differences, but no differences were found between the control (M) and any of the other IFP. The only significant difference was found between summer squash I intercropping (MS1) and common vetch intercropping (MV) (Figure 4). In 2019, except for the alfalfa intercropping (MA), all intercropping treatments had significant lower GE contents than the control (M). For ME and NEL, only intercropping with common bean I (MB1) showed significantly decreased contents. In FAK, no differences were observed in 2018 for any parameters. In 2019, only the GE showed significant changes. While intercropping with alfalfa (MA), yellow sweet clover (MC), common vetch (MV), and nasturtium (MN) did not differ significantly from the control (M), the use of the other IFPs significantly reduced the GE.

**Figure 3.** Biogas and Methane yields (L kg−1 oDMC) for the different intercropped flowering partner (IFP) treatments (M Maize (Control), MA Maize + Alfalfa, MC Maize + Yellow sweet clover, MV Maize + Vetch, MN Maize + Nasturtium, MS1 Maize + Summer Squash I, MS2 Maize + Summer Squash II, MB1 Maize + Common Bean I, MB2 Maize + Common Bean II, MS3 Maize + Summer Squash III, MM1 Maize + Mixture I, MM2 Maize + Mixture II) in Ettlingen (ET) (2018 **A1**), Tachenhausen (TH) (2018 **A2**, 2019 **B1**), and Forchheim am Kaiserstuhl (FAK) (2018 **A3**, 2019 **B2**). The upper end of each colored section in the stacked bars shows the respective value for the parameter's biogas and methane. Bars with the same letter within one parameter indicate non-significant differences (HSD-test, α = 5%).

**Figure 4.** GE (gross energy), ME (metabolizable energy), and NEL (net energy for lactation) (MJ kg−1 DM) for the different intercropped flowering partner (IFP) treatments (M Maize (Control), MA Maize + Alfalfa, MC Maize + Yellow sweet clover, MV Maize + Vetch, MN Maize + Nasturtium, MS1 Maize + Summer Squash I, MS2 Maize + Summer Squash II, MB1 Maize + Common Bean I, MB2 Maize + Common Bean II, MS3 Maize + Summer Squash III, MM1 Maize + Mixture I, MM2 Maize + Mixture II) in Ettlingen (ET) (2018 **A1**), Tachenhausen (TH) (2018 **A2**, 2019 **B1**) and Forchheim am Kaiserstuhl (FAK) (2018 **A3**, 2019 **B2**). The upper end of each colored section in the stacked bars shows the respective value for the parameter's GE, ME, and NEL. Bars with the same letter within one parameter indicate non-significant differences (HSD-test, α = 5%).


**Author details**

Daniel Sur1,2\*, Alecsandra Gorzo2 and Claudia Burz2,3

1 Department of Medical Oncology, University of Medicine and Pharmacy "Iuliu Hatieganu", Cluj-Napoca, Romania

2 Department of Medical Oncology, The Oncology Institute "Prof.dr.Ion Chiricuta", Cluj-Napoca, Romania

3 Department of Allergology and Immunology, The Oncology Institute "Prof.dr.Ion Chiricuta", Cluj-Napoca, Romania

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


*5.3. Full Project Extrapolation Results*

The trade-off between maximizing short-term profits and stretching the life of the energy storage system is a crucial factor to consider. The NPV is a useful metric that takes into account future cash flows and discount rates to determine a project's overall profitability.

In the table, the NPV of each case is calculated, discounting the daily cash flow and the purchase cost of the BESS. The results show that the best NPV is achieved when the BESS is reserved for SE and capacity firming. When only participating in the day-ahead market, the NPV is not very different, but participating in the intraday market significantly lowers the NPV, indicating lower project profitability.

The results of each simulation scenario are extrapolated for the entire project life. The average daily cash flow is determined from the average daily results of 2018, and daily degradation is similarly calculated to estimate project completion. The project ends when the accumulated degradation of the BESS reaches 20%, a commonly used value in the relevant literature (e.g., [22,23]). The average values and estimated project lifetimes for each case are presented in Table 4.

**Table 4.** Project extrapolation under each case.

It is worth noting that the BESS is not operated in the ideal scenario. In this case, the BESS is only utilized for allocating generated energy when prices are the highest. However, due to the degradation costs and the BESS efficiency, this operation is not profitable.

The SE has lower daily profits but a higher NPV compared to the case with CF. The NPV of each project is calculated using a 7.5% discount rate and is presented in Table 5. The results show that using the BESS for the SE service and capacity firming provides the highest NPV.

**Table 5.** NPV under each case.

#### **6. Conclusions**

A three-level EMS for a HF to participate in the Iberian electricity market has been presented. The day-ahead and intraday market offering strategies are formulated as linear programming problems, with generation and price forecasts at the start of each market session as inputs. The model has incorporated adjustments based on Iberian market regulations to account for committed power correction impacts. It also includes a real-time degradation model of the BESS to assess capacity and power losses.

A simulation framework has been proposed to assess various BESS market participation strategies. The results indicate that participating in both day-ahead and intraday wholesale markets generates the highest expected profits, but actual profits are lower compared to other strategies. The proposed optimization algorithm and results of the study can be applied to any electrical system that operates with concurrent day-ahead and intraday spot markets, which is a common practice in many countries. This can be used as a guide for agents trying to optimize their plants.

The results show that using the BESS in all markets does not necessarily result in a higher income, challenging the common belief that utilizing the BESS in all markets leads to the best outcome. The proposed SE service enables participation in intraday markets without the drawback of market overlap, enhancing the BESS utilization efficiency and increasing the NPV of the project. Moreover, it highlights the crucial role of adjustment mechanisms on actual income and the importance of modeling them thoroughly. Lastly, it is worth remarking that the plant proposed in this work is considered a price-taker; the effect of a plant or a number of plants large enough to impact electricity market prices is proposed as a future work.

**Author Contributions:** The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

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

**Data Availability Statement:** Data is available by request.

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


**2.1 Cancer cervical and human papilloma virus (HPV)**

 Current information noticed that almost 100 serotypes of HPV exist. But, 2 of these, 16 and 18 serotypes, are related to the development of cervical cancer in Latin women [14, 17]. During cervical carcinogenesis, a viral protein E6 sequesters to p53 protein. Also, another viral protein called E7 participates in the same process, sequestering Rb protein. In consequence, the arrest of both proteins p53 and Rb induces deregulation in the cell cycle. However, possibly the HPV is not an exclusive aetiology agent that produces this disease. Further information noticed that sex steroid hormone participates in the early stages of cervical carcinogenesis [14]. Previous report evidenced the possible relationship between E7 viral and E2 (17β, also spelled oestradiol), but this is poorly understood [14]. Perhaps, our knowledge about the risk factors that derive from the developed cancer will be increasing but also is necessary to improve our awareness of the prevention and treatment of disease.

*Pharmacology Evaluation of Bioactive Compounds that Regulate Cervical Cancer Cells DOI: http://dx.doi.org/10.5772/intechopen.82258*

### **2.2 Current therapies against cervical cancer**

 In this context, in Mexico 15% of women are detected with precancerous lesions of cervical cancer, and they could be potentially prevented. Currently, chemotherapy, radiotherapy, hormonal therapy, biological therapy and surgery are the treatments majority employed in the early stages of CC [10, 20, 21]. But, the treatments above mentioned often are associated with certain disadvantages, such as toxicity of chemical agent or drug resistance [22]. For example, *cisplatin* in combination with *bevacizumab* can increase the expectations of survival, but they are scarce to the population. In another case, *paclitaxel* is a chemotherapeutic agent used against ovarian carcinoma, but there is evidence of the resistance for this agent [11]. Also, the use of chemotherapy represents other drawbacks, the undesirable effects, the high cost of treatment and recurrence among patients who had improved [10, 20]. All of these adverse effects are due to the lack of a specific target cancer cell, because chemotherapeutics do not have a cancer-specific receptor, protein or DNA [20]. Another limit, in Mexico, there is one radiotherapy machine available for 2 million Mexican. But, reducing the doses of the chemical agent or employing natural compounds can overcome this significant barrier that exists to eliminate CC [11]. Natural or bioactive compounds are used against multiple illnesses including rheumatic, anthelmintic, diuretic, hypoglycaemia and cancer [23].

#### **2.3 Relationship between ROS and cervical cancer**

 The diseases above mentioned, including CC, could produce free radicals that induce damage to the cells, tissues and organs [12]. However, the proper function of cells depends on the mitochondria's ability to regulate metabolic processes and produce molecules, including free radicals as reactive oxygen species (ROS) [11, 24, 25]. ROS controls both physiological and pathological process related to cell proliferation, invasion cell, and tumour hypoxia and drug resistance [11, 12, 26, 27]. Also, in the cell, a defence system against ROS includes several enzymes such as superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase and glutathione S-transferase [28]. However, when the cellular antioxidant systems are damaged, antioxidants are insufficient to neutralise ROS, and then oxidative stress occurs [27, 29]. Moreover, in a pathological process, ROS are responsible for damaging proteins, lipids and nucleic acids [29, 30]. Nonetheless, bioactive compounds are strongly linked to the radical scavenging capacity and protect the cell against oxidative DNA damage [12, 25, 31].

#### **2.4 Alternative therapy among cervical cancer**

The plants, fruits and vegetable contain large amounts of bioactive compounds that act as antioxidant, and they can be used with therapeutic purpose [27, 32, 33]. Among antioxidants, different subclasses are described, (a) flavonoids, (b) phenolic acids and polyphenols, (c) stilbenoids, (d) catechins and (e) tannins, and they are abundant and available in natural products previously mentioned [20, 31, 34]. Other compounds related with their oxidant capacity and ROS productions are listed in **Table 1**. The antioxidants that are most well-known, curcumin, resveratrol and gallic acid, have activity against cancer cell line in vitro [15, 24, 28, 35]. For example, galangin is a flavonoid with various biological effects in different cancer cells [25]. Also, the green tea has cancer-preventive effects due to containing catechins known as EGCG (−)-epicatechin-3-gallate, (−)-epigallocatechin and (−)-epicatechin. Also, CTS extract possesses chlorogenic acid, (+)-catechin, caffeic acid, phloretic acid, veratric acid, hesperidin, quercetin and naringenin. Additionally, fucoidan is a major bioactive

#### **Table 1.**

*List of phenolic compounds. Antioxidant properties can be risen with phenolic compounds described here. However, possible signalling pathways, apoptotic or inflammation, can be affected by this kind of compounds.*

 compound in *Sargassum polycystum* and demonstrated anti-proliferation, antitumor and anticancer properties [13]. Moreover, pterostilbene can be found in berries and grapes and showed therapeutic effects in a variety of cancer types [36]. Other biocompounds such as triptolide, celastrol and tripchlorolide were isolated from *Tripterygium wilfordii*; these compounds are immunosuppressive and anticancer [37]. Gallic acid is a natural phenolic compound with the potential to act against different cancers or viruses [38]. Further information, extracts from *Annona muricata* leaves were shown to have the capacity to induce apoptosis in HeLa cells, suggesting that the extracts have the potential to be used as a treatment against virus-induced cancer cells [39]. Also, this compound can be used in combination with a chemotherapeutic agent to increase the efficiency of chemotherapy [11].

Also, the genus *Annona* has been shown to have promising compounds, called Annonaceous acetogenins (AGEs), that can be utilised in the treatment of cancer because they induce cell cytotoxicity by inhibiting the mitochondrial complex I, and their capacity of acetogenins to inhibit NADH oxidase was also shown to be important for their antitumour function [40, 41]. However, the biological activities of the compounds obtained from *Annona* species are diverse. For example, the crude extract of *A. crassiflora* significantly alters cell viability of cervical cancer cell lines as well as proliferation and migration and induces cell death [42]. Many studies have described, or continuous screening the anticancer bioactive compounds, and explained the potential of plants against illness [25]. Though,

the success of the prevention or treatment depends on the quality and quantity of bioactive compounds. But, the biological effects and mechanisms of action of flavonoids, phenolic acids, stilbenoids and tannins have been studied lightly.