# **Sustainable Energy Systems Efficiency and Optimization**

Edited by Alan Brent and Toshihiko Nakata Printed Edition of the Special Issue Published in *Energies*

www.mdpi.com/journal/energies

## **Sustainable Energy Systems: Efficiency and Optimization**

## **Sustainable Energy Systems: Efficiency and Optimization**

Editors

**Alan Brent Toshihiko Nakata**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Alan Brent Sustainable Energy Systems Victoria University of Wellington Wellington New Zealand Toshihiko Nakata Graduate School of Engineering Tohoku University Sendai Japan

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Energies* (ISSN 1996-1073) (available at: www.mdpi.com/journal/energies/special issues/ Sustainable Energy Systems Efficiency and Optimization).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

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## **Contents**

## **About the Editors**

#### **Alan Brent**

Alan Brent is the inaugural Chair in Sustainable Energy Systems at Victoria University of Wellington, New Zealand. The Chair supports the transition to sustainable energy in New Zealand as well as in the regional economy and society. This is aligned with the strategic sustainability focus at Victoria University of Wellington, to meet current and future challenges, by taking an inter-disciplinary approach, and engaging with partners across society in trans-disciplinary ways.

#### **Toshihiko Nakata**

Toshihiko Nakata heads the Energy Systems Analysis Laboratory in the Graduate School of Engineering of Tohoku University, Japan. His laboratory focuses on the analysis and integrated design of sustainable energy systems.

## **Preface to "Sustainable Energy Systems: Efficiency and Optimization"**

New, alternative energy technologies are rapidly becoming affordable, and it is expected that these will be immensely disruptive to our traditional mode of centralised energy generation, transmission, and distribution. Additionally, the severe climate (and other) impacts of many traditional energy generation and utilisation techniques are widely accepted. As stated in the World Energy Outlook of the International Energy Agency (IEA), "the global energy scene is in a state of flux, thrown off balance by falling costs for a range of technologies, led by wind and solar. . . ". Furthermore, the IEA states that it ". . . expects renewable electricity generation to increase by a further two-fifths by 2021. However, renewable heat and transport are lagging behind, despite good potential. . . ". For these changes or transitions to be just and sustainable, systemic analyses are required, with an emphasis on optimizing the energy sector to be better integrated with the other sectors of the economy, thereby ensuring the efficiency of future energy value chains. Such systemic analyses utilise concepts, methods and tools such as system dynamics, urban metabolism, industrial ecology, and life cycle analyses, to inform policy- and decision-making.

This book is a collection of papers from a Special Issue in the journal *Energies*, with contributions from researchers across the globe. The book provides insights to scholars on how the concepts, methods and tools of systemic analyses have been utilised in various contexts—from the household and community level, to industrial and utility-scale levels—to enable a transition to sustainable energy systems, with an emphasis on the efficiency and optimisation of future energy value chains.

> **Alan Brent, Toshihiko Nakata** *Editors*

### *Article* **Conceptualizing Household Energy Metabolism: A Methodological Contribution**

#### **Adél Strydom 1 , Josephine Kaviti Musango 1,\* and Paul K. Currie 1,2**

Received: 3 June 2019; Accepted: 22 August 2019; Published: 29 October 2019

**Abstract:** Urban metabolism assessments enable the quantification of resource flows, which is useful for finding intervention points for sustainability. At a household level, energy metabolism assessments can reveal intervention points to reshape household energy consumption and inform decision-makers about a more sustainable urban energy system. However, a gap in the current urban metabolism research reveals that existing household energy consumption studies focus on outflows in the form of greenhouse gas emissions, and have been mostly undertaken at the city or national level. To address this gap, this study developed a method to assess household energy metabolism focusing on direct energy inflows in the form of carriers, and through-flows in the form of services, to identify intervention points for sustainability. Then, this method was applied to assess the energy metabolism of different households in Cape Town, South Africa, as categorized by income groups. The study argued that the developed method is useful for undertaking bottom–up household energy metabolic assessments in both formal and informal city settings in which more than one energy carrier is used. In cities where only national or city-level data exists, it provides a method for understanding how different households consume different energy carriers differently.

**Keywords:** urban metabolism; household energy metabolism; household energy metabolism assessment methods; household energy consumption

#### **1. Introduction**

Energy is an integral part of human well-being, and is one of the basic services that humans require in order to thrive. It provides the means to cook, to heat, to cool, to light up homes, to charge mobile phones, and to operate other electronic devices. The modern energy system causes significant environmental impacts. Most (60–80%) of the global final energy consumption is in urban areas, and is responsible for between 71–86% of greenhouse gas emissions [1,2], making it a large contributor to climate change. Given the central role that cities play as energy consumers, it is crucial to understand the different energy consumption patterns therein so as to identify intervention points for reshaping energy flows toward a more sustainable energy system.

Cities can be examined at different levels, of which the household is the smallest structural level [3]. As a sector, the household is crucial driver of energy consumption in cities [4–7]. Despite technical innovation in the form of energy efficiency and renewable energy technologies, household energy consumption continues to rise [8,9]. Therefore, households are a critical point of intervention.

Many households in the Global South lack access to high-quality energy services [10]. In particular, inadequate electricity access results in these households consuming alternative energy carriers that are often inefficient or physically harmful. These households are expected to experience an increase in energy consumption, which means increased greenhouse gas emissions [11]. Hence, the focus in the

Global South should be on improving access in the most efficient way while addressing high levels of consumption in other parts of the city [12], ultimately reducing greenhouse gas emissions while increasing access.

Urban metabolism uses the concept of flows to understand how resources move through a city [12], making it possible to understand flows that shape or have the potential to reshape urban areas to become sustainable. Thomson and Newman [13] highlighted that a city's metabolism consists of many metabolisms. Therefore, it is vital to take a deeper exploration into cities to explore metabolisms of different resource types or metabolisms at different levels. For instance, it can be scaled to address specific resources (e.g., energy) at specific levels (e.g., household).

Studying energy at the household level provides the closest look at how human activity contributes to energy consumption. It follows that researchers persistently recommend a household focus for metabolism studies [14–16]. Although various urban metabolism tools have been developed, including Integrated Land Use, Transportation, Environment (ILUTE) (ILUTE "simulates the evolution of an integrated urban system over an extended period of time "[17]) [17], Integrated Urban Metabolism Analysis Tool (IUMAT) [18], and URBANISM ("*UrbanSim* is an integrated transportation land-use model" [19]) [19], few practical assessments have been done on energy metabolism at the household level.

It is essential to understand how household characteristics and activities shape energy flows. This includes addressing not only electricity, but also alternative energy carriers servicing many households in the Global South, such as charcoal and paraffin. While some households may consume large amounts of energy and have the potential for reducing this consumption, others still require sufficient access to energy carriers that do not threaten their health. Existing household energy metabolism assessments tend to use disaggregated city-level data, resulting in coarse estimates. In light of this, bottom–up assessments may offer robust insights on how different household types use energy. Further, both direct and indirect household energy consumption are overwhelmingly examined in the literature. However, this paper focuses only on the direct energy consumption, because (i) it is specifically focused on energy access, which is a concept that refers only to direct energy, and (ii) indirect energy is the subject of further research, particularly around implications of transportation as well as water and food consumption.

After presenting an overview of the literature related to urban energy metabolism and household energy consumption, the objective of this paper is therefore to present a method developed for undertaking a differential household energy metabolism assessment, focusing specifically on inflows in the form of energy carriers and through-flows in the form of energy services. Further, the method is applicable to both formal and informal settings within the urban environment, where formal dwellings include brick and mortar houses and apartments in high-income areas, and where informal dwellings are often built from scrap building materials and located in low-income areas. The method was applied to the city of Cape Town, South Africa, as it is a city where bottom–up household energy data are severely lacking. The city is also home to a broad range of income groups and dwelling types, making it a useful location for developing and applying the method.

#### **2. Urban Energy Metabolism for Sustainability**

The origin of the term 'urban metabolism' is highly contested. In 1883, Marx first imagined the notion that a society as a whole has a resource metabolism, in which nature is transformed as needed to provide society with the necessary commodities [11,20,21]. Some argue the first explicit mention of the term 'urban metabolism' was made in 1965 by Wolman, who presented the metabolism of a hypothetical American city to demonstrate the metabolic needs of a city, such as the materials the city's inhabitants need to sustain their home, work and leisure lives [8,16,20,22]. Meanwhile, others believe that Theodor Weyl pioneered the term in 1894, in his discussion of food consumption, comparing nutrient discharge with the food intake into the city of Berlin [23].

The most frequently cited definition of urban metabolism is that of Kennedy et al. (2007, p. 44), which defines it as "the sum total of the technical and socioeconomic processes that occur in cities, resulting in growth, production of energy, and elimination of waste". The authors originate from the Industrial Ecology discipline and, as the definition suggests, are particularly focused on the quantification of resource flows. The definition is criticized for being too restrictive in its implied methods and practical application of an urban metabolism assessment [3,11]. The main criticism is the bias toward quantification and consequent disregard for the emergent properties that are possible through resource exchange [11]. Similarly, Barrera et al. [3] argued that the definition should also include the social and political aspects of a city, such as how resources are distributed. Currie and Musango [14] and Musango et al. [11] call for the inclusion of people and information flows, while acknowledging the role of built and natural systems in conveying flows.

Currie and Musango [14] thus define urban metabolism as the "collection of complex sociotechnical and socioecological processes by which flows of material, energy, people, and information shape the city, service the needs of its populace, and impact the surrounding hinterland". This definition includes a significantly broader scope, namely a shift from a purely accounting view to one that accounts for complexity and an explicit consideration for the needs of the residents in a city, thereby addressing not only efficiency, but also equity. However, this paper aims to understand a very specific aspect of resource flows in the city: that of energy consumption within households. For this specific application, the broad definition provides a foundation of accounting for various technical flows into and out of the city. This foundation aids in contextualizing flows of specific resources on a specific scale of the city, which, in this, case is household energy.

While the theoretical approach is developed enough for practical applications, urban metabolism assessments of cities are still limited, particularly in the Global South [8,11,21]. Possible reasons are that: (i) urban metabolism assessments lack standardized methods [11,16,24] and (ii) are simpler to conduct where rich data for resource flows already exist [25]. Another argument is that cities have different contexts, which implies that a standard method might not be as viable to large-scale practical applications of urban metabolism as the literature suggests.

Urban metabolism can highlight intervention points for lightening resource dependence in cities. Its inclusion of all types of resources may hinder its ability to make practical and spatially explicit recommendations for cities. This paper is of the similar view with Carréon and Worrell [20] that in order to progress toward a sustainable energy system, an important starting point is to understand the flows of energy through the city. Approaching a sustainable energy system also means understanding how people access and use energy differently in the city, to ensure access to safe, reliable, and modern energy sources to all citizens.

#### *The Urban Energy System*

Energy is a unique resource to examine, as it does not flow in the same manner as most resources. Instead, it flows through the different phases of the urban energy system. Zhang et al. [26] disaggregate the system into five phases: namely energy exploitation, energy transformation, industry, living, and recovery. Carreón and Worrell [20] indicate three phases of energy system, namely (i) energy sources, which are connected by (ii) energy carriers to meet (iii) the city's energy demand. Both studies emphasize that energy flows from one phase to the next. Therefore, understanding the energy system requires understanding urban energy flows.

A city's energy system can be conceptualized as depicted in Figure 1, which was adapted from Carreón and Worrell [20], Chen and Chen [27] and Zhang et al. [28]. Energy exploitation is the first phase of the system, which allows the identification of the source of the various energy flows. This phase includes all the mining activities for raw materials. In the second phase, energy is transformed into carriers. The physical infrastructure of grids, refineries, and power plants transform energy into the carriers of fuel, electricity, gas etc., which hold the energy.

**Figure 1.**The urban energy system. Sources: Adapted from Carreón and Worrell [20], Chen and Chen [27], and Zhang et al. [28].

The third phase is energy demand. According to Carreón and Worrell [20], this phase can be divided into energy sectors and end use, which represent two flows within the same phase. Figure 1 shows a possible further disaggregation in sectors based on Chen and Chen [27], who provided detail on the various energy sectors found in a city. Some disagreement exists around the difference between energy end use and energy services. What Carreón and Worrell [20] regarded as end use, Bristow and Kennedy [29] and Barrera et al. [3] regarded as services. Carreón and Worrell [20] regarded energy services as a further phase after energy end use. This study makes use of the views of Bristow and Kennedy [29] and Barrera et al. [3] based on Fell [30], who regarded energy services as the function performed using energy. A fourth phase needs to be added in order to account for Zhang et al.'s [28] energy recovery. The top arrow in Figure 1 indicates that the entire system represents the flow of energy from extraction to processes in the city, and finally discharge into the environment. Not all energy may flow through all phases, but it always flows from left to right, from exploitation to discharge.

#### **3. Towards Conceptualizing Household Energy Metabolism**

Various urban energy metabolism assessments have been conducted. Chen and Chen [27] translated the city's energy activities into carbon flows in order to model the carbon metabolism and associated energy-use activities. Zhang et al. [31] studied the energy metabolism of various sectors in a city as well as their associated carbon footprints. Both studies argue that carbon flows should be central to urban energy metabolism assessments, as this helps to understand the carbon profile of cities and consequently the amount of pressure that a city's energy system places on the environment and consequently the contribution of a city to climate change risk [27,31]. Therefore, the focus is mainly on the energy outflows. Weisz and Steinberger [32] similarly focus on the energy outflows in their review on the various ways in which a city can reduce both its energy and material flows. The notion to focus strongly on greenhouse gas emissions is limited. Energy assessments also require expansion beyond carbon to include the local dynamics of energy provision and use, so as to understand future energy demand, infrastructure pressures, and how to effectively plan fast-growing cities.

There is another prevailing gap in addressing the through-flows of energy within the urban system. While Chen and Chen [27] deemed the flows between sectors important, they mainly addressed the inflows in the form of extraction and the eventual outflows to the carbon sink. This correlates with the first and last phase of the energy system, and leaves a gap for addressing the flows within, namely carriers, sectors, and services. This gap is addressed by Zhang et al. [28], who argued that urban metabolism struggles to address ecological trophic levels within the energy system. They shifted the focus to analyze the relationships within this system using through-flow analysis and ecological network utility analysis, and found a total of 73 different metabolic pathways between 17 energy sectors, concluding that it is possible to make the city's energy flows more efficient by adjusting these relationships [28]. For example, to balance out a system where demand is higher than supply, energy consumption must either be lowered, or energy production must be increased. Consequently, it is possible to grasp the adequacy of supply of primary energy sources to meet the needs of the energy service phase.

Carreón and Worrell [20] identified another gap: most energy metabolism studies are overwhelmingly linear, using only accounting approaches and input–output analysis, disregarding causal relationships between elements such as climate, demographics, and infrastructure. Beyond quantification are possibilities to address the other mentioned aspects of sustainability, such as equity. The energy literature is firmly embedded in a perspective of reducing and controlling. Weisz and Steinberger [32] argued that energy access is widely overlooked in energy metabolism studies. Therefore, interventions in areas that still lack access to energy might involve increasing inflows or shifting the energy carrier within a certain flow in order to provide more reliable, affordable, or efficient energy services. The concept of a sustainable energy system should go beyond quantifying carbon emissions and reducing energy flows [33]. Brunner's [34] call to reshape flows rather than

to reduce or control once again arises, and the energy metabolism literature can benefit from this perspective, as reshaping will allow for increased flows or a change in energy carriers where necessary.

Another gap in the literature on urban energy metabolism is that most of these studies were done at the city level, taking a top–down approach using national data, thereby disregarding the dynamics of space and time within a city [20]. This study addresses this gap by focusing on household energy metabolism, given the calls in Musango et al. [11], for conducting bottom-up research in cities in order to account for spatio-temporal dynamics.

Cities exist in various structural and societal scales and levels [3,11,35]. Barrera et al. [3] provided a useful representation of the levels within the energy system, distinguishing between the macro, meso, aggregated, and micro levels. Each of these levels can then be divided into behavioral and structural categories. Table 1 provides more detail.

Source: Barrera et al. [3].

As Table 1 shows, the smallest scale of the energy system can be understood as buildings, or houses. On the behavioral level, it specifies the household as the smallest scale, which indicates a difference between a house and a household. However, the smallest scale of a city is the individual. Moll et al. [36] and Biesiot and Noorman [37] argued that while individuals perform different consumer activities, these are mostly focused within the household, and therefore the household, not the individual, is the smallest unit. In terms of energy consumption, this paper views the household as the smallest unit, as energy consumption within the household contributes to services that are shared between the individuals within. Furthermore, the household is a standardized unit in metabolic study, and the majority of energy consumption studies present their data for the household as a whole.

There exists no clear definition of a household energy metabolism. However, the literature provides definitions of household metabolism, from which a definition for energy in particular can be inferred. The most basic definition of household metabolism is "the integral patterns of natural resources flowing into and out of households" [37]. Donato et al. [38] provided further detail by defining household metabolism as the biophysical assessment of households from the point of view of raw materials, energy carriers, and water required, and emissions and wastes resulting from household consumption patterns. The inputs are further categorized in direct inputs of energy (electricity, heat, and vehicle fluids) and material, and indirect inputs of economic goods and services.

Based on the above, a household energy metabolism can be understood as the process by which energy flows are sourced and delivered through various carriers, which are conveyed through the house to service a household's direct and indirect energy requirements, and result in waste or emissions. Through-flows are equally important to inflows and outflows, justifying the reference to through-flows in the household energy metabolism definition.

A brief distinction between direct and indirect energy consumption in the household is needed, as total household energy requirements include both. Direct energy is related to the energy consumed within the household and includes energy for space heating, water heating, cooking, lighting, and electronics, while indirect energy is used for the production, transportation, and disposal of goods and services consumed by the inhabitants of the house [5,7,36]. While Benders et al. [7] indicate that the majority of household energy consumption research includes only direct energy, the case for including indirect energy has strengthened significantly, and other scholars have considered the total household energy to include indirect consumption [3,36,38].

Existing household metabolism assessments are mostly embedded in a sustainable development approach. However, they vary considerably with regard to the resources studied. Both Moll et al. [36] and Donato et al. [38] identified intervention points for making the household more sustainable, but the former addresses total household energy requirements, while the latter reviewed a body of household metabolism research papers, including both energy and material resources. Yang et al. [39] and Biesiot and Noorman [37] focused on the environmental effects of household resource consumption with a strong focus on greenhouse gas emissions. The former analyzes emissions from energy, material, food, and waste, while the latter is interested only in total energy consumption. Frostell et al. [40] also studied total household energy requirements, but went beyond accounting to find ways of changing the energy consumption behavior. Within this variance exists a strong focus to measure the emissions impact of households, whether this pertains to the energy consumption alone or a broader study of household resource requirements [38].

In their literature review, Donato et al. [38] argued that the methods for conducting household metabolism assessments have not yet reached maturity, and only provided a review of its status. When considering the diversity in approaches to the household's resources, it is understandable that most of the studies reviewed use hybrid methods. Moll et al. [36] and Biesiot and Noorman [37] used a combination of input–output analysis and process analysis to account for the complex nature of quantifying indirect energy consumption. Biesiot and Noorman [37] outlined that the direct energy requirements can be determined by: (i) considering the money spent on energy, (ii) dividing this into energy activities, (iii) accounting for the energy requirements of these activities, and (iv) converting this energy into CO2 emissions. The framework is useful, as it provides a method to quantify various household activities in energy terms. The framework can be applied to different scenarios, thereby projected future energy consumption.

Top–down approaches dominate in household metabolism assessments. Biesiot and Noorman [37] and Yang et al. [39] acquired their data sets from national data and disaggregated it to the household level. Yang et al. [39] acquired supplementary data from household surveys. Both Moll et al. [36] and Donato et al. [38] argued for the need to undertake a bottom–up approach to assessing household metabolism. Moll et al. [36] found significant variances in consumption patterns between the countries they studied, and concluded that it is crucial to study different types of households before identifying intervention points. Biesiot and Noorman [37] shared a similar view that different households display different lifestyles, and therefore different consumption patterns. Therefore, a differential household energy metabolism approach is crucial in order to account of variances in consumption patterns, lifestyles, and countries or areas within specific cities.

In order to conduct a differential household energy metabolism assessment, reliable and accurate data is crucial [36]. Biesiot and Noorman [37] recommended collecting the following data sets before attempting a household energy metabolism assessment: (1) energy production and consumption data, (2) economic input–output matrices, (3) household budget surveys, and (4) goods and services price information.

The studies focusing specifically on direct household energy consumption fall mostly outside of the urban metabolism field, but prove useful in identifying methods for collecting bottom–up data. Smart meters in households are proving to be a very effective and reliable way of collecting quantitative energy consumption data, as they account for exact consumption [41–43]. Studies that do not utilize smart meters often measure electricity consumption using utility bills [15,44]. Neither one of these approaches are appropriate for a study on direct energy consumption across a range of income groups and energy carriers, as an approach is needed that accounts for the different energy sources that a household may access, which is not depicted in smart meter data. While smart meters are excellent for tracking direct electricity consumption, they do not indicate exactly how this electricity is used in the house—for example, which appliances it services. A holistic understanding of household energy consumption means examining how multiple carriers feed into a variety of services accessed.

Existing household energy metabolism assessments account for the total energy requirements and total emissions. Limited studies have explored how energy flows into and through the household [37,45]. To study energy through-flows, the concept of energy services is useful. Both Sovacool [45] and Barrera et al. [3] advocated for household energy consumption studies to address services. Barrera et al. [3] indicate that the usefulness of viewing energy flows as services is due to the simplicity in translating the energy activities performed in a household, thereby making explicit what the individuals in a household choose to consume. It relates energy consumption to activities, which is easier for consumers to comprehend than referring to the amounts of Joules, kilowatt-hours, or liters of carriers consumed. In this way, intervention points become more tangible or accessible to the individual.

The main energy services within the household across different regions are similar. Based on work in the United States of America (USA) and Western Europe, Abrahamse et al. [4] developed an energy service hierarchy and suggested that space heating is the highest energy consuming service, followed by water heating, refrigeration, lighting, cooking, and finally space cooling. Sovacool [45] examined middle-income households from a broad range of countries and suggested that the primary energy services (in order) are space heating, water heating, cooking, appliances, and lighting. Kwak et al. [46] studies North Korea, and found that space heating and space cooling were major contributors to energy consumption due to the country's four distinct seasons. In Finland, which experiences colder weather than most countries, space heating is the primary energy service [47].

In contrast to hierarchies developed by country, studies that compare energy services between low-income and high-income households have observed differing energy hierarchies. Sovacool [45] found that the energy in low-income households predominantly service lighting and cooking, while other surveys also include hot water, television, and radios. A study on energy services in rural African regions listed cooking, lighting, and water heating as primary energy services [10]. Offering an interesting contrast is the types of additional energy services found in high-income households: swimming in heated swimming pools or cooking with the television on are common findings [45]. This once again stresses the need for a differential household energy metabolism assessment in order to understand whether households in the same city may appear strikingly different when their energy inflows and through-flows are analyzed.

Sovacool [45] stressed that services also make it possible to identify the level of access of the household by a proposing an energy ladder. This energy ladder differs from the traditional energy ladder, which focuses only on carriers, and suggests that households transition to more efficient fuel types as their economic situation improves [48]. Sovacool's [45] energy ladder includes three drivers to be applied to the various steps of the ladder: satisfying subsistence needs; convenience, comfort and cleanliness; and conspicuous consumption [45].

The basic energy carriers for a large number of households around the world are fairly consistent. Sovacool [45] identified electricity, natural gas, coal, liquefied petroleum gas, kerosene, and fuel oil, with electricity being the most dominant energy carrier. However, as with differences in service between low-income and high-income households, subsistence households may demonstrate the widest range of energy carriers. Figure 2 conceptualizes the energy ladder and household energy services of various income groups according to Sovacool [45]. Households driven by subsistence only can be regarded as not having sufficient access to energy.

**Figure 2.** Household energy ladder, adapted. Source: Adapted from Sovacool [45].

A useful way to look at energy access is to consider the concepts of 'energy poverty', 'fuel poverty', and 'energy vulnerability'. All of these are conceptualizations that aim at identifying the group lowest on the energy ladder, but they differ quite significantly. Energy poverty is a term that is typically used to refer to inadequate energy access in the Global South, and links to the wider relationship between energy and development [49]. Fuel poverty and energy vulnerability refer to people typically in the Global North who have access to energy, but cannot afford to purchase sufficient amounts [49–51]. The difference between the two is that energy poverty is a state of being, while energy vulnerability can change according to external factors such as the dwelling quality, energy costs, and stability of household income [51]. Bouzarovski and Petrova [49] categorize all three terms under the umbrella term of 'domestic energy deprivation'.

It is essential to consider energy in terms of drivers rather than a state of being, as it provides a clear pathway for improving energy security, which is important especially in the Global South, where energy access is a concern. Rather than containing the household in a negative state of being, the energy ladder creates a conceptualization of being able to climb up to a position in which energy consumption ceases being driven by subsistence and starts being driven by comfort, cleanliness, and convenience.

To achieve a more sustainable energy system may in some cases mean changing the energy carrier. Camara et al. [52] explicitly stated the importance of addressing the forms in which low-income households access energy, as a change in energy carrier could result in higher energy efficiency. Therefore, these households could climb to the second level of the energy ladder without experiencing an increase in energy spending. Examples of key intervention points in the Global South are improved cook stoves and cleaner fuel, such as a transition away from solid fuels and paraffin toward gas or

electricity and improved wood burning stoves with, for example, chimneys [10,53–57]. This shows that improved access to modern energy carriers (such as electricity) is not the only possible intervention, but that in some cases, changing the fuel type from solid fuel to liquid petroleum gas or kerosene (widely considered to be modern fuel types) can also be beneficial [54]. Improved cook stoves and cleaner fuel is mostly discussed within the context of improving health; however, Williams et al. [57] and Maes and Verbist [55] discussed it in conjunction with air pollution, and Williams et al. [57] included hardships experienced by the women collecting solid fuels on foot. Given that a sustainable energy system must consider both social and environmental factors, the quality of cooking fuel or technology must necessarily reduce pollutants, as well as negative health impacts.

This paper proposes a method for examining the relationship between carriers, services, and drivers of household energy consumption. It forms part of a larger research project, which includes household consumption of food and water, and production of wastes, and a number of methods were performed together with co-researchers. However, this paper retains focus on the methods that relate only to household energy metabolism.

#### **4. Materials and Methods**

In order to develop a method for conducting a differential household energy metabolism assessment, this paper created two key conceptualizations. The first is that of Cape Town's household energy flows in terms of carriers, services, and drivers, and the second illustrates the relationship between carriers and services in Cape Town households. In order to achieve this, various methods were used. Figure 3 illustrates the process and methods used to reach these conceptualizations.

**Figure 3.** Method conceptualization. Note: CT is Cape Town; HH is household.

Defining components of the energy system was essential in conceptualizing Cape Town's household energy flows in terms of carriers, services, and drivers. However, since this energy system was created based on studies from the Global North, it was essential to adapt the carriers and services to correspond to the carriers and services accessed by the residents of Cape Town. This conceptualization is depicted in Figure 4. Based on the iterative process between the literature and the data collected, the paper made several changes to the services found in the literature:

**Figure 4.** Cape Town household energy carriers and services.

The services of computing and entertainment were grouped together as Entertainment, since many residents watch television or on-demand streaming services on their computers.

Following Sovacool [45], it was important to identify the drivers that are specific to the study. Since this paper's focus was in households in Cape Town that still require sufficient access to energy, it was interested in the two drivers lowest on the energy ladder: how a household could move from 'satisfying subsistence needs' (not having sufficient energy access) to 'convenience, comfort, and cleanliness' (having sufficient access). For this reason, the top driver, namely 'conspicuous consumption', was omitted, as this focuses on the next level of the energy ladder (mostly associated with high-income households), and requires an in-depth study of its own.

Once the two energy drivers were selected, it was possible to add this information to the conceptualization of Cape Town's household energy flows in order to create a conceptualization of Cape Town's household energy flows—carriers, services, and drivers. This conceptualization is depicted in Figure 5. The services are reorganized according to the energy ladder in the literature, as well as the energy services and carriers applicable to Cape Town. The energy ladder specifies that low-income households are driven by satisfying subsistence needs, while middle-income households are driven by convenience, comfort, and cleanliness.

In order to understand the relationship between energy carriers and services, and thereby create the second conceptualization for Cape Town's household energy flows, a household energy survey with questions about the use of 44 key household appliances was designed and distributed in Cape Town. The appliances were categorized twice: first by energy carrier, and second by the energy services detailed in Figure 5. After answering basic questions around household size, household

income, and dwelling type, respondents were asked to explain the frequency with which they use the 44 appliances. The answers were presented in brackets: for example, 2–3 h per day or 4–5 times per week. This allowed respondents to answer the surveys without having the exact amounts and times on hand. While this is not as accurate as a metering exercise, it allows the opportunity to understand how energy is consumed in households that use more energy carriers than electricity (for which metering exercises are ideal). This survey was also designed in such a way to be easily understandable to all residents and to gain a broad idea of how many households consume energy, rather than a specific idea of a small sample of households.

**Figure 5.** Conceptualization of Cape Town's household energy flows.

An online version of the survey was aimed at reaching middle-income and high-income households. A group of enumerators visited low-income suburbs of Cape Town with hard copies of the surveys. The income groups were determined using the local income groups, as reflected in the South Africa Statistics.

The total number of households surveyed was 676, with only 391 surveys that were completed in full. Only 360 surveys that were useful for Cape Town were utilized in this paper. Responses came in from 56 of Cape Town's 190 suburbs, with the most responses (66) received from the suburb of Khayelitsha. The enumerators surveyed a larger group of respondents than the online survey. However, some of their respondents earn middle or high incomes. This indicates that within suburbs such as Khayelitsha, Mfuleni, and Mitchell's Plain reside many middle-income and high-income residents, and that enumerators visited these households, too.

#### **5. Results**

Figure 6 represents energy consumption based on activity for the household brackets using the average consumption per bracket. This consumption is divided into energy carriers (the left-hand side of each diagram) and energy services (the right-hand side of each diagram). Energy carriers indicate the total amount of activity for each carrier for each income bracket, while energy services indicate the end-use of energy for each bracket. This diagram provides a basic understanding of how the relationship between carriers and services changes with income. Figure 6a provides insight on the household level, and Figure 6b shows energy consumption per capita, which highlights the effect on average consumption as household size decreases and income increases.

**Figure 6.**Household energy consumption categorized into carriers and services.

Figure 6 provided a basic understanding of how the consumption of carriers and services changes with monthly income, indicating that the method applied can provide this overview for a city, especially where a huge discrepancy exists in income levels. The monthly income levels display the income brackets that were presented to the respondents when answering the survey. However, it was also important to also understand more closely the relationship between carriers, services, and drivers for each income group in order to make more specific interventions for sustainability and to create the second pivotal conceptualization, namely diagrams that connect the carriers, services, and drivers for various income groups. These results and intervention points are discussed in detail in Strydom et al. (58). This section discusses how these results were reached.

In order to understand the inflows and through-flows of energy for various income groups, the first step was to create pie charts for each income group depicting the average carriers and services for that income group. Figure 7 depicts these pie charts for a low-income household in Cape Town.

**Figure 7.** Average carriers and services for low-income households in Cape Town Source: Strydom et al. [58].

While Figure 7 provides insight into the size of various carriers and services, and could do so for other cities where this method is applied, it does not yet clarify the relationship between carriers and services. In order to understand carriers and services in terms of inflows and through-flows, Sankey diagrams were useful. Figure 8 is a Sankey diagram connecting carriers and services for a low-income household in Cape Town.

**Figure 8.** Average energy flows for low-income households in Cape Town. Source: Strydom et al. [58].

Figure 8 makes it possible to understand the relationship between carriers and services of the studied city, thereby making apparent intervention points for sustainability. In the case of Cape Town, it becomes clear that paraffin contributes greatly to lighting in low-income households. However, as paraffin is a very inefficient carrier, one requires much more paraffin to light a house than one would should electricity be the main lighting source. This means that replacing paraffin with electric light bulbs in low-income households will provide the same amount of lighting, if not more, while using less energy. Further insights and intervention points for Cape Town are discussed in full in Strydom et al. [58]. This diagram illustrates the potential insights into the household energy consumption of a specific income group that can be gained when applying the discussed method to a city.

In order to create the final conceptualization for energy consumption, it is important to connect energy carriers and services with energy drivers. Figure 9 provides this conceptualization for low-income households in Cape Town.

Figure 9 was created by totaling the energy services Sovacool [45] defined as satisfying subsistence needs and totaling the services defined as comfort, convenience, and cleanliness in another Sankey diagram. Creating such a diagram for each of the four income groups in Cape Town provided the opportunity for comparison and for understanding what drives energy consumption across income groups. The full results are discussed in Strydom et al. [58].

By viewing the two types of Sankey diagrams together, it is possible to arrive at a conceptualization of the relationship between carriers, services, and drivers for various income groups in cities across the world. Especially in the Global South, where low-income households consume a variety of energy carriers, it is possible to understand the importance of different energy carriers to fulfill different households' needs. Once such an understanding of household energy consumption is reached, it is possible to identify different intervention points for different income groups based on these energy flows. It is also possible to understand the core driver for accessing energy. In low-income households in Cape Town, it is clear that residents simply aim to satisfy subsistence needs, but that certain services associated with comfort, convenience, and cleanliness are important to them. By comparing these findings across income groups in other cities, it is possible to know what drives energy consumption and consequently which energy services a city's residents view as essential. With this knowledge, locally applicable intervention points for sustainability can be identified.

**Figure 9.** Energy drivers for low-income households. Source: Strydom et al. [58].

#### **6. Conclusions**

This paper reviewed the literature on urban metabolism, urban energy metabolism, and household energy metabolism to identify a conceptual framework with which to examine the inflows and through-flows of direct energy at the household level. The framework was further expanded to include drivers of energy consumption as they correlate with the services accessed in order to understand the level of access experienced in the household. It further examined the relationship between energy carriers and energy services based on income in Cape Town. The results show that both pie charts and Sankey diagrams provide the means to identify intervention points for sustainability.

This can in the future be applied to other cities where data is scarce and where residents access a range of energy carriers, not just electricity, as current studies assessing household energy consumption focus predominantly on electricity use and take national or city-level data to speak for household level energy consumption. It is flexible in that the carriers and services can be adapted depending the city studied. Since this study was conducted in a city that has both formal and informal housing, it also shows that the method can be used for both settings and for comparing results across formal and informal settlements. By using this method, it is possible to gain insight into the energy inflows and through-flows of households across income groups and dwelling types, and therefore provides a means to collect bottom–up data at the city level for a high-resolution image of the current state of energy consumption in our cities.

**Author Contributions:** Writing—original draft, conceptualization A.S.; methodology and data curation, A.S. and P.K.C.; supervision, writing, review and editing, J.K.M. and P.K.C.

**Funding:** This research was funded by the Centre for Renewable and Sustainable Energy Studies (CRSES), Stellenbosch, South Africa; and the National Research Foundation, South Africa, grant number 99103.

**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*

## **Community Resilience-Oriented Optimal Micro-Grid Capacity Expansion Planning: The Case of Totarabank Eco-Village, New Zealand**

#### **Soheil Mohseni 1,\* , Alan C. Brent 1,2 and Daniel Burmester 1**

Received: 1 June 2020; Accepted: 18 July 2020; Published: 2 August 2020

**Abstract:** In the grid-tied micro-grid context, energy resilience can be defined as the time period that a local energy system can supply the critical loads during an unplanned upstream grid outage. While the role of renewable-based micro-grids in enhancing communities' energy resilience is well-appreciated, the academic literature on the techno-economic optimisation of community-scale micro-grids lacks a quantitative decision support analysis concerning the inclusion of a minimum resilience constraint in the optimisation process. Utilising a specifically-developed, time-based resilience capacity characterisation method to quantify the sustainability of micro-grids in the face of different levels of extended grid power outages, this paper facilitates stakeholder decision-making on the trade-off between the whole-life cost of a community micro-grid system and its degree of resilience. Furthermore, this paper focuses on energy infrastructure expansion planning, aiming to analyse the importance of micro-grid reinforcement to meet new sources of electricity demand—particularly, transport electrification—in addition to the business-as-usual demand growth. Using quantitative case study evidence from the Totarabank Subdivision in New Zealand, the paper concludes that at the current feed-in-tariff rate (NZ\$0.08/kWh), the life cycle profitability of resilience-oriented community micro-grid capacity reinforcement is guaranteed within a New Zealand context, though constrained by capital requirements.

**Keywords:** renewable energy systems; microgrids; optimal expansion planning; energy resilience; resilient energy systems; critical loads; electric vehicles; techno-economic analysis; HOMER Pro; New Zealand

#### **1. Introduction**

The past two decades have witnessed a remarkable evolution of micro-grids (MGs) from a nascent concept to a pivotal player in the transition to 100% renewable energy [1–3]. The power industry has accordingly seen an ever-increasing penetration of distributed energy resources into utility grids. The key drivers behind the stakeholders' willingness towards additional, often non-trivial, capital investments for resilience capacity are [4–6]: (1) the constantly falling costs of renewable energy technologies, which are transforming the economics of green energy, most notably solar photovoltaic (PV) panels and battery energy storage systems (BESSs), (2) the growing awareness about the inadequacy of current approaches to energy resilience at community-scale, specifically the use of diesel generators to provide backup power during utility grid outages, and (3) the growth of engaged prosumers, who place great value on their energy self-sufficiency for sustainable living.

In the context of on-grid MGs, energy resilience is generally defined as "the amount of time that an MG can sustain critical loads during a grid outage", and often serves as a synonym for outage survivability [7]. A recent, growing strand of the literature has documented and emphasised the increasingly important role of planning for resilience within the context of grid-tied MGs, which, in turn, improves the resilience of a nation's entire power grid. For instance, Eskandarpour et al. [8] have formulated a mixed-integer linear programming problem for the optimal sizing and siting of MGs within power systems by considering the cost of unserved energy during utility grid outages as the objective function, while adhering to a limited budget for resilience improvements. In another instance, Barnes et al. [9] have revealed the potentially significant benefits of including energy resilience constraints in the long-term investment planning problem of networked MGs in terms of the prolonged outage survivability and overall cost-efficiency. Furthermore, Anderson et al. [7] have shown the economic and resilience benefits delivered by renewable energy technologies in a hybrid MG. More specifically, they have demonstrated that adding a 845-kW PV system together with a 172-kWh BESS to an existing 305 kW of backup diesel generators extends the time period the MG can sustain crucial loads by 1.8 days, while generating savings of US\$104,000 in energy costs over the reinforced MG life-cycle. Table 1 provides an overview of recent studies centred on the optimal capacity expansion planning of grid-tied renewable energy systems. Moreover, Table 2 presents a summary of the previous studies that focused on the long-term, integrated resource and resilience planning in the context of renewable and sustainable energy systems (in addition to the studies reviewed above). The reader is referred to [10] for a more detailed review of the methods and research trends in the MG resilience improvement literature.

As Tables 1 and 2 indicate, no previous study, as far as can be ascertained, has introduced a systematic method to quantify the cost of insuring grid-tied renewable energy systems against sustained grid outages in the optimal equipment capacity expansion planning processes—to meet some desired minimum level of resilience. Accordingly, this paper bridges the gap between these two streams of literature by proposing a modelling framework to estimate the additional costs incurred to procure energy resilience in the optimal capacity expansion planning processes of grid-connected community MGs. The paper additionally presents a sensitivity analysis to highlight the impact of varying minimum degrees of energy resilience required by stakeholders on the optimal life-cycle costs associated with the capacity reinforcement of grid-connected MGs.

The remainder of this paper is organised as follows. Section 2 describes the overall structure of the test-case, the fundamental assumptions underlying the study, and data requirements for the case study. Section 3 provides the modelling approach, while Section 4 presents the results and examines the robustness of optimal MG capacity configurations. Finally, conclusions are made and potential areas for future work are discussed in Section 5.

**Table 1.** Summary of the recent previous work dealing with the cost-optimal capacity expansion planning of renewable energy systems.

**Table 1.** *Cont.*

*Energies***2020**, *13*, 3970

**Table 2.** Summary of the previous work focused on the integration of resilience constraints into the long-term investment planning processes of renewable energy systems.

**Table 2.** *Cont.*

**Table 2.** *Cont.*

1*N-k*contingency criterion guarantees that *N*critical components within a power network can continue normal operation in cases any*k*components simultaneously suffer a failure.

#### **2. Test-Case System: Totarabank, New Zealand**

The Totarabank Subdivision consists of eight freehold residential lots (ranging in size from 1200 m2 to 2100 m2 ) and a communal building (on the ninth, common lot) with a large area of around 6 ha held in common ownership [23]. Located in central Wairarapa, New Zealand (GPS coordinates: 41◦1 ′4" S 175 ◦40 ′0" E), the subdivision adheres to sustainability design criteria to suggest ways for resilience integration. Figure 1 shows the geographical location and description of the case study subdivision Totarabank.

**Figure 1.** Satellite photographs of the case study area: (**a**) location of the subdivision on a New Zealand map; (**b**) layout of the subdivision showing the lots (image courtesy of Google Earth).

 site's energy resilience, self As of May 2020, Totarabank has 14 inhabitants, and an existing installed grid-integrated power generation capacity of 11.4 kW, 100% from solar PV modules, which have an average remaining life span of 19.25 years. Accordingly, the load power demand is met through electricity importing from the main grid at night when the onsite PV system is not delivering, while any surplus electricity is sold back to the grid during the day. However, the primary issue of the current power dispatching strategy is its failure to serve the crucial loads at night if the upstream network fails or requires maintenance. This, together with the anticipated growth in demand (led by the electrification of transport), reveals the necessity for the reinforcement of the current energy system—to improve the site's energy resilience, self-sufficiency, and security of supply.

#### *2.1. Candidate Technologies*

The considered site is richly endowed with renewable energy resources, particularly solar and wind. The candidate technologies included in the model for optimisation are (1) PV modules, (2) wind turbines (WTs), and (3) a BESS. Furthermore, an unreported preliminary techno-economic study suggested that a BESS is the most cost-effective technology for onsite energy storage among feasible options for Totarabank, namely hydrogen storage system (electrolyser, hydrogen reservoir, and fuel cell), batteries, flywheels, and super-capacitors. More specifically, a Li-ion battery system was chosen, as it offers the best combination of energy density, low self-discharge, and manufacturing cost, compared with other mature battery technologies [24]. Accordingly, the selected components were assembled in an AC-coupled configuration to form the grid-connected MG shown in Figure 2.

**Figure 2.** Schematic of the conceptualised grid-tied, AC-coupled MG system.

#### 2.1.1. PV Modules

 of solar irradiance and the module's temperature, which is estimated from Equation (2) The power output from each PV module in time *t* is obtained from Equation (1) as a function of solar irradiance and the module's temperature, which is estimated from Equation (2) [25]. of solar irradiance and the module's temperature, which is estimated from Equation (2)

$$P\_{PV}^t = P\_{PV,r} \times \eta\_{PV} \times f\_{PV} \times \left(\frac{G\_T^t}{G\_{T,STC}}\right) \times \left(1 - \left(\frac{k\_p}{100} \times \left(T\_{PV}^t - T\_{STC}\right)\right)\right) \tag{1}$$

$$T\_{PV}^t = T\_a^t + G\_T^t \times \left(\frac{\text{NOCT} - 20}{0.8}\right) \tag{2}$$

, denote the module's rated power, efficiency, derating factor, , where *PPV*,*r* , η*PV*, *fPV*, *kp*, and *NOCT* denote the module's rated power, efficiency, derating factor, temperature coefficient, and nominal operating cell temperature, respectively; *G t T* is the global horizontal irradiance in time *t*; *GT*,*STC* and *TSTC* represent the solar irradiance and temperature at the standard test conditions, respectively; and *T t a* is the ambient temperature. , denote the module's rated power, efficiency, derating factor, ,

#### 2.1.2. Wind Turbines

The power output from each WT is obtained from the manufacturer-provided power curve, shown in Figure 3 [26]. Additionally, the wind speed data is hub height normalised using the power-law, which is expressed in Equation (3) [27].

**Figure 3.** Power curve of the selected wind turbine. Data source: [26].

$$V\_{lubb}^t = V\_{ref}^t \left(\frac{l\_{lubb}}{l\_{lref}}\right)^\vee. \tag{3}$$

ℎ

where *Vre f* represents wind speed measured at the height of *hre f* in time *t*, and γ is the wind shear exponent.

#### 2.1.3. Battery Arrays

The energy content of the battery bank, comprising of battery packs connected in parallel, in time *t* can be calculated from Equation (4) [28].

$$E\_B^t = E\_B^{t - \Delta t} + \left(P\_B^t \times \Delta t\right) \tag{4}$$

where ∆*t* denotes the operating time increment and *P t B* is the charging/discharging power of the battery bank in time *t* that can be calculated based on the system-wide supply-demand balance constraint from Equation (5) [29].

$$P\_B^t = P\_{PV}^t + P\_{WT}^t \mp P\_{\mathcal{g}}^t - P\_{L'}^t \tag{5}$$

where *P t WT* is the power output from WTs in time *t*, *P t g* is the exchanged power with the utility grid, a positive (negative) value of which represents an imported (exported) power, and *P t L* is the total residential load power demand. A positive value of *P t B* represents the charging state, whereas negative values signify the discharging state.

Moreover, the energy stored in each battery pack is constrained to lie within the feasible range of values, [*EB*,*min*, *EB*,*max* ].

#### 2.1.4. Hybrid Inverter

A grid-interactive inverter (also called multi-mode battery inverter/charger, or bidirectional dual-mode hybrid inverter) is used in an AC-coupled configuration, which is capable of managing inputs from multiple sources in both on- and off-grid operating modes. The hybrid inverter is modelled by its overall efficiency.

#### 2.1.5. Utility Grid

The cost of importing/exporting electricity from/to the utility grid in time *t* is obtained from Equation (6) [30].

$$\text{cost}\_{\mathcal{S}}^{t} = \begin{cases} \text{ } \pi\_{\text{ex}}^{t} \times \text{P}\_{\mathcal{S}}^{t} \times \Delta t & \text{if } \text{P}\_{\mathcal{S}}^{t} > 0, \\\text{ } \text{Fit} \times \text{P}\_{\mathcal{S}}^{t} \times \Delta t & \text{if } \text{P}\_{\mathcal{S}}^{t} < 0, \end{cases} \tag{6}$$

where π *t ex* is the wholesale power price in time *t* and *FiT* represents a fixed feed-in-tariff.

#### 2.1.6. Electric Vehicle Charging Station

AC level 2 charging, which is the most common home charging solution, is considered in this project [31]. Accordingly, a level 2 electric vehicle supply equipment (EVSE) is employed, which is modelled by its efficiency. It is connected directly to the MG's hybrid inverter via a dedicated circuit and provides charging through a 240 V AC plug.

#### *2.2. Key Assumptions*

The following simplifying assumptions were made:

solar inverters will require more sophisticated market designs (such as peer-to-peer markets) for the intra-community electricity exchanges to establish a fair playing field—which are currently cleared under a pure flat-rate tariff structure. Note that the rationale behind making this assumption stems from the difference in the service life of PV panels and solar inverters.

#### *2.3. Data*

The techno-economic specifications of the selected components and the associated sources are summarised in Table 3.

**Table 3.** Techno-economic specifications of the components.

1 Photovoltaic (PV) module costs include the cost associated with the solar inverter. 2 WT costs include the cost of the required guyed tower. 3 The replacement and O&M costs were adjusted in accordance with the capital-to-replacement and capital-to-O&M cost ratios used in [44–46]. 4 The approximate average operational service life of small and micro WTs is 120,000 h. However, since they do not operate at wind speeds below cut-in, their service life is typically considered as 20 years [47]. 5 N/A: Not applicable, since the WT's performance is modelled by its characteristic power-speed curve, in the same way as in [48]. 6 The battery bank's efficiency represents its round-trip efficiency.

Moreover, in view of the study objectives and simplifying assumptions outlined above, the costs associated with the EVSE were exogenously treated. More specifically, they were takeninto account through cost premiums imposed on the optimised MG whole-life cost solutions. Specifically, the Delta AC Mini Plus EVSE, was chosen in this analysis to serve the level 2 charging. It has the following techno-economic specifications [49,50]: capital and replacement costs = \$2564/unit, O&M cost = \$17/unit/year, service life = 25 years, efficiency = 99%, maximum output power = 7.36 kW, and input rating = 230 V AC. Accordingly, the EV charging load was classified as deferrable, the monthly average value of which was

) × ,

assumed to be constant throughout the year, with an average value of 48 kWh/d, and a peak value of (*n* × 7.36) kW, in compliance with the maximum power output from *n* EV chargers.

Table 4 presents the data and sources for the parameter settings of the conceptualised MG system.

**Table 4.** Conceptualised MG system's scalars and sources. Conceptualised MG system's scalars and sources.

1*EB*,*max* denotes the maximum usable capacity of each module. 2 By definition, *EB*,*min* = (1 − *DOD*) × *EB*,*max* , where *DOD* is the depth of discharge [%] [35]. , , , = (1 − , , , = (1 − ) × ,

Figure 4 shows the average wholesale power price for each hour across the five-year period of 2015 to 2019 [57], which were considered as forecasts of utility power price.

**Figure 4.** Forecasted profile for the wholesale electricity price at hourly resolution. The numerical values belonging to this figure are listed in Table S1 in Supplementary Material.

GREEN Grid study's sample of dwellings to the Totarabank's household population according to the Figure 5 depicts the forecasted residential load power demand profile. The time-series data were synthesised from the GREEN Grid household electricity demand data [58] through downscaling the GREEN Grid study's sample of dwellings to the Totarabank's household population according to the proportionate scaling method described in [59]. GREEN Grid study's sample of dwellings to the Totarabank's household population according to the

**Figure 5.** Forecasted profile for residential loads at hourly resolution. The numerical values belonging to this figure are listed in Table S2 in Supplementary Material.

Forecasts of wind and solar resources were determined using the NASA Surface Meteorology and Solar Energy database [52]. Accordingly, the monthly-averaged wind speed values over a 10-year historical period, and monthly-averaged insolation and air temperature values over a 22-year historical period were retrieved from the database. Monthly mean forecasts of wind speed and solar radiation are shown in Figure 6, while Figure 7 displays monthly mean temperature [52]. In general, an annual average wind speed value of 5 m/s, and an annual insolation value of 4 kWh/m2 /d are commonly considered acceptable thresholds for the commercial viability of wind and solar projects, while seasonal variations of resources can also affect the optimal resource combination of a PV/WT renewable energy system [60]. As can be seen from Figure 6, not only is wind energy a more reliable source of power than solar in the considered area, but it also shows a more constant trend than solar energy.

**Figure 6.** Forecasted monthly average insolation and wind speed data.

**Figure 7.** Forecasted monthly average ambient temperature data.

#### **3. Methodology**

This section presents the methodology developed for the resilience-constrained capacity expansion planning of MGs.

#### *3.1. Modelling Approach*

The U.S. National Renewable Energy Lab's Hybrid Optimization of Multiple Energy Resources was used to minimise the project's life free optimisation algorithm, called "HOMER Optimizer", which frees the user from the need to define the search space. Additionally, the conceptualised MG's life The U.S. National Renewable Energy Lab's Hybrid Optimization of Multiple Energy Resources was used to minimise the project's life free optimisation algorithm, called "HOMER Optimizer", which frees the user from the need to define the search space. Additionally, the conceptualised MG's life The U.S. National Renewable Energy Lab's Hybrid Optimization of Multiple Energy Resources (HOMER) Pro software (version 3.13.8) [61] was used to minimise the project's life-cycle cost subject to techno-economic design constraints for different levels of energy resilience. The software features two algorithms to search for the cost-optimal design configuration: (1) an original grid search algorithm that simulates all of the feasible resource combinations that meet the loads, and (2) a new proprietary derivative-free optimisation algorithm, called "HOMER Optimizer", which frees the user from the need to define the search space. Additionally, the conceptualised MG's life-cycle cost reflects all of the cost components associated with supplying energy to the considered site in present value, namely, the capital, replacement, and O&M costs of new energy infrastructure, as well as the net

cost of energy exchange with the utility grid. At each time-step of the MG operation, an energy dispatch analysis is carried out, whereby the load demand is served by the least-cost combination of dispatchable and non-dispatchable distributed energy generation units, released energy from storage devices, traded energy with the upstream grid, and load shifting. Additionally, deferrable loads are placed in second priority, and supplied using the surplus power (to the needs of primary loads) from non-dispatchable micro-generation units—solar PV and WT technologies—ahead of charging the storage devices. The energy scheduling problem is solved for each time-step of the baseline year and is repeated for the remaining years of the planning horizon, while discounting future cash flows to adjust the results for inflation. HOMER Pro is also equipped with the "Multi-Year" module, which allows for modelling the dynamic characteristics of the economic MG planning problem by running a simulation for each ensuing year of the project lifetime. However, since the HOMER Optimizer does not support multi-year planning, first, the optimal capacity configuration was determined using the HOMER Optimizer without considering the dynamic characteristics of the problem, and then the search space was defined based on the preliminary results obtained, with adequate margins, in size steps of 1 unit. Finally, the model was re-run using the HOMER's original grid search algorithm—which simulates all of the feasible equipment capacity combinations with respect to the defined design space. This enabled avoiding sub-optimal solutions, while retaining computational tractability. The conceptualised MG system was simulated under both the cycle-charging and load-following energy dispatch strategies in each simulation case, and the strategy that resulted in a lower levelised cost of energy (LCOE) was selected as the optimum solution.

In addition, HOMER employs specifically-developed algorithms to synthesise one-year of hourly time-series insolation, air temperature, and wind speed data from the corresponding monthly average values to reflect the characteristics of real data (of the same level of granularity) in terms of seasonal, daily, and hourly patterns. Accordingly, hourly-basis, year-round profiles for weather data were derived based on the corresponding monthly average data, shown in Figures 6 and 7.

#### *3.2. Characterisation of Energy Resilience*

To quantify the resilience capacity of the MG system to prolonged utility grid outages, the simulation was executed multiple times with varying values of grid reliability. Three built-in parameters in HOMER Pro were used to characterise the grid reliability, namely, (1) mean outage frequency, (2) mean repair time, and (3) repair time variability. Each random outage was injected into the model at a pseudo-random time-step throughout the year-long operational period, while the associated failure duration was determined by independent sampling from a normal distribution defined by mean repair time and repair time variability. Accordingly, the mean outage frequency was varied from 1 to 20 in steps of 1, the mean repair time was varied from 1 h to 168 h (2 weeks) in steps of 4 h, while the repair time variability was fixed at 1 h—which adds a stochastic dimension to the model. That is, 840 ((168 h/4 h) × 20 frequencies) independent outage time-series were produced for the optimisation model. Moreover, to improve the cost-efficiency of the optimal MG capacity configuration solutions, the model was designed to sustain only the critical loads during extended utility grid outages.

In this light, the total load on the MG system was partitioned into critical and non-critical loads, and non-critical loads were turned off when the grid was off. To this end, first, the residential load duration curve was derived by sorting the hourly-basis, one-year residential load curve, as illustrated in Figure 8. Accordingly, the average value of intermediate loads was determined and set as the threshold criterion for residential load partitioning. More specifically, the residential load profile was clipped at this point to form the critical residential load profile. The amounts of residual residential demand that was left from the peak being clipped established the profile of time-stamped non-critical residential load demand, which was only active under the normal, grid-connected operation regime. Additionally, in the grid outage events, the deferrable load was limited to as much as the energy

needed to fully charge one EV (Nissan Leaf, battery capacity = 30 kWh [34])—to meet the emergency management needs at the site.

**Figure 8.** Illustration of the proposed method to determine the load partitioning threshold on a representative load duration curve presented in [62]. (The load curve was taken from the cited reference, and then adapted to describe the proposed load partitioning process.)

#### *3.3. Model Assumptions and Design Standards*

Table 5 lists further input data for the model parameters and their respective sources.

MG capacity expansion planning model's parameters. **Table 5.** MG capacity expansion planning model's parameters.

1 The time-based autarky ratio indicates the self-sufficiency degree of the MG and can be determined by dividing the yearly number of time-steps where no electricity is purchased from the grid (to meet the full demand) by the total number of time-steps considered for the representative year of the optimisation [66].

Figure 9 shows a flowchart of the research methodology to include energy resilience criteria in the long-term MG optimisation model implemented in HOMER, which is illustrated by means of the considered case study example. As Figure 9 shows, first, the HOMER model is run for each of the above-described 840 outage time-series for the test-case under investigation and the solutions are recorded. Then, the model results of the numerical case example at each simulation run are aggregated to obtain the sensitivity of the total discounted system costs, energy exchange levels with the national grid, and the optimal MG configuration to the variations of the key grid outage parameters.

**Figure 9.** Flowchart of the research methodology.

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

This section presents and discusses the numerical simulation results obtained for the test-case MG laid out in Section 2 by applying the method described in Section 3.

#### *4.1. Feasibility and Optimal Capacity Configuration*

The optimum equipment capacity configuration was determined first with the grid reliability level set at 100%. Table 6 presents the optimisation results for this baseline scenario. (It should be noted that all the simulation results are based on the manufacturer-provided equipment specifications and technical data, which represent the performance of the equipment under standard test conditions.) The table demonstrates that a WT is a more economically viable power generation technology than solar PV for generation expansion at the considered site, as it could be expected from the historical meteorological data (see Figure 6). However, given the complementary daily and seasonal cycles of wind speed and insolation, the existing installed capacity of solar PV is still expanded in the cost-optimal solution, albeit by a small margin. This not only validates the viability of the existing PV generation system, but also indicates its sizeable impact on the diversification of the feasible future generation mix for the site.

**Table 6.** Optimal capacity additions and corresponding costs for a 100% reliable grid.

1 The optimal equipment capacity was determined as multiples of the selected components' nameplate capacities. 2 The optimal PV capacity represents the newly added capacity of the site's PV plant. 3 The electric vehicle supply equipment (EVSE) did not take part in the optimisation procedure. The optimal size of the EVSE was determined outside the model by finding the total minimum EV charging capacity that meets the peak EV charging demand of the site under cooperative EV charging conditions. The obtained total minimum capacity was then divided by the maximum charging capacity of each charger and rounded up to the nearest integer to obtain the optimal number of chargers. 4 The optimal size of the EVSE is defined based on the maximum charging capacity of each charger. 5 TNPC represents the conceptualised MG's "total net present cost". The reader is referred to [67–69] for a detailed description of the net present value method and levelised cost of energy (LCOE) calculations. TNPC represents the conceptualised MG's "total net present cost". The reader is referred to –

The total net present cost (TNPC) of the system is broken down into the main cost components in Figure 10. Three key observations emerge collectively from Table 6 and Figure 10, which can be generalised to any MG capacity reinforcement problem: (1) the system's low O&M costs helped offset its relatively high capital costs, which were mainly generated by new WT and BESS capacity additions, (2) a comparison of the obtained LCOE with the current average price of domestic electricity in the Wairarapa region, New Zealand, where the site is located (\$0.34/kWh [70]) indicates that an optimally planned community-based renewable power generation system even surpasses retail grid parity, and (3) the negative value obtained for the net energy purchased from the utility grid suggests that not only did energy exchange with the utility grid provide power quality benefits and help avoid energy spillage (which was often carried out through a dedicated dump load to balance supply and demand), but it also contributed to the profitability of the project, albeit slightly. generalised to any MG capacity reinforcement problem: (1) the system's low O&M costs helped offset

Breakdown of the MG's total net present cost (\$). **Figure 10.** Breakdown of the MG's total net present cost (\$).

— To understand in more detail the active power flow pattern between the MG and the main grid, the year-round profiles for energy purchased from the grid and energy sold to the grid, obtained for the cost-minimal solution under normal, grid-connected conditions, are plotted in Figures 11 and 12, respectively. The trends visible when comparing the figures are revealing. The battery bank in the optimal equipment combination enabled the MG to engage in energy arbitrage, by charging when utility rates were low and discharging during more remunerative times of day, (The buy-back rate is fixed at NZ\$0.08/kWh. However, given the availability of long-term data forecasts, the optimisation protocol incorporates some degree of forward-looking behaviour into the energy exchange decision-making process with respect to the load on the MG in the medium to longer term—identifying the more profitable time-steps for feeding power back into the grid. That is, the profitability is measured independently of the single rate feed-in tariff.) and/or by shifting the excess power from non-dispatchable renewables for sale at more valuable times of day. This, additionally, helped reduce peak demand charges.

**Figure 11.** Year-round profile for energy purchased from the grid (kWh). The numerical values belonging to this figure are listed in Table S3 in Supplementary Material.

**Figure 12.** Year-round profile for energy sold to the grid (kWh). The numerical values belonging to this figure are listed in Table S4 in Supplementary Material.

#### *4.2. The Cost of Energy Resilience*

station's capacity family community's energy demand for mobility. station's capacity family community's energy demand for mobility. Using the methodology described in Section 3 for the planning for energy resilience, the optimal MG capacity expansion problem was solved independently to global optimality for a series of different scenarios defined by mean grid outage frequency and mean grid repair time. As an example, Figure 13 shows the one-year outage database (with hourly intervals) built stochastically for the middle-case scenario, with a grid outage frequency of 10 per year and a mean repair time of 84 h. Accordingly, Figures 14 and 15 respectively depict the resulting TNPC and the corresponding LCOE of the MG expansion under varying degrees of grid reliability characterised by the above two parameters. Moreover, the total annual amount of electricity sold to the grid [kWh] and the total annual amount of electricity purchased from the grid (kWh) at different grid reliability levels are displayed in Figures 16 and 17, respectively. Note that all of the results presented in this sub-section are adjusted for the EVSE costs, which were determined outside the model by matching the charging station's capacity to the peak EV charging demand of the site subject to the cooperative use of the charging infrastructure. Such cooperative behaviour within the community is expected to uphold the installation of only two EV chargers at the site (as indicated in Table 6) to fit for the considered multi-family community's energy demand for mobility.

**Figure 13.** Micro-grid system state (normal/outage) for the middle-case outage scenario.

Sensitivity of the MG's TNPC to variations in grid reliability parameters. Sensitivity of the MG's TNPC to variations in grid reliability parameters. **Figure 14.** Sensitivity of the MG's TNPC to variations in grid reliability parameters. The numerical values belonging to this figure are listed in Table S5 in Supplementary Material. Sensitivity of the MG's TNPC to variations in grid reliability parameters.

Sensitivity of the MG's LCOE to variations in grid reliability parameters. Sensitivity of the MG's LCOE to variations in grid reliability parameters. **Figure 15.** Sensitivity of the MG's LCOE to variations in grid reliability parameters. The numerical Sensitivity of the MG's LCOE to variations in grid reliability parameters. values belonging to this figure are listed in Table S6 in Supplementary Material.

•

**Figure 16.** Sensitivity of the total annual energy exports to variations in grid reliability parameters. The numerical values belonging to this figure are listed in Table S7 in Supplementary Material.

**Figure 17.** Sensitivity of the total annual energy imports to variations in grid reliability parameters. The numerical values belonging to this figure are listed in Table S8 in Supplementary Material.

– – The two-way sensitivity analyses, shown in Figures 14–17, are revealing in the following ways:

• The values of total annual energy imports and exports indicate that the MG's net purchased trading with the grid, as the MG's resilience to grid outages improved, only partially off for the target community's energy resilience— — • • The values of total annual energy imports and exports indicate that the MG's net purchased trading with the grid, as the MG's resilience to grid outages improved, only partially off for the target community's energy resilience— — • The values of total annual energy imports and exports indicate that the MG's net purchased electricity from the grid was approximately a monotonically decreasing function of the grid unreliability. That is, as the failure frequency of the grid and/or its mean repair time increased, the total power sold back to (purchased from) the grid increased (decreases) or remained constant. The underlying reason responsible for this model behaviour is the increase in the excess non-dispatchable power generation capacity of the MG during normal, grid-connected operations, as the grid reliability decreases. However, the increase in revenues generated from trading with the grid, as the MG's resilience to grid outages improved, only partially offset the additional costs incurred. This emphasises the necessity to include a minimum acceptable limit for the target community's energy resilience—to be derived from specifically-developed surveys to estimate the value of lost load—in the resilience-oriented MG capacity expansion planning processes to improve the accuracy of results.

#### 4.2.1. Optimal MG System Type

Figure 18 provides the resulting optimal system architecture for different levels of the failure frequency of the grid and the associated mean repair time. As this figure shows, the MG design space is divided approximately equally across two grid-connected system structures, namely, (1) existing PV/added PV/added WT/added BESS, and (2) existing PV/added WT/added BESS. Notably, the figure indicates that it is cost-optimal to add new PV generation capacity for a system resilient to relatively low-frequency, long-duration outages, or high-frequency, short-duration outages, or any combination in between. However, for grid outage scenarios that lie inside the region enclosed by the upper pseudo-triangle shown in Figure 18, the optimally reinforced MG structure excludes additional PV panels. This can be explained by the increase in the probability of occurrence of sustained outages during the night-time hours, where solar panels produce no electricity. Thus, it is more economical to only add WT and BESS capacity, in view of the lower diurnal variations of wind speed and dispatchability of the BESS. It is also worth noting that while the considered bounds for the two-dimensional sensitivity analyses might far exceed the local utility grid's reliability (especially the scenarios that lie in the region enclosed by the upper pseudo-trainable in Figure 18), the results of such sensitivity analyses are important in evaluating the robustness of the baseline system costs and architecture. Accordingly, Figure 18 demonstrates that the obtained grid-connected MG configuration of existing PV/added PV/added WT/added battery in the baseline scenario (100% reliable grid) is highly robust to variations in grid reliability. dimensional sensitivity analyses might far exceed the local utility grid's reliability (especially

**Figure 18.** Optimal MG configuration at different grid reliability levels. The numerical values belonging to this figure are listed in Table S9 in Supplementary Material.

#### 4.2.2. Indicative Resilient System Optimisation Analysis

According to the results presented in Figure 14, addressing energy resilience is possible at comparably small total discounted cost increases of about 16% for a sufficiently resilient system (within a New Zealand context), tolerable of two sustained outages per year with each duration of the outage lasting up to four days (or, nearly equally, tolerable of four outages per year each with a maximum outage duration of up to two days), while optimally planning the capacity additions for the site under study. To indicate the effect of planning for energy resilience on the added MG equipment capacity, and in turn, the MG's life-cycle cost, this sub-section details the results obtained for the MG system resilient to two extended outages per year each with a maximum outage duration of up to four days. Moreover, all the analyses presented hereafter are based on this scenario. Table 7 details the capacity expansion planning results obtained for the case considered in this paper under the grid unreliability scenario outlined above. The table demonstrates that the only change in the optimal capacity configuration of the MG with respect to the baseline case (refer to Table 6) is the addition of one more battery module to cater for the prolonged grid outages, which will need to be replaced in year 15 of the project.

**Table 7.** Optimal capacity additions for the system resilient to 2 extended outages per year each with a maximum duration of up to 4 h.

#### *4.3. Capital Budgeting Metrics*

This section measures the profitability of the investigated MG capacity expansion project based on three key investment appraisal metrics, namely the return on investment (ROI), internal rate of return (IRR), and discounted payback period (DPP). HOMER calculates theses economic sustainability metrics with reference to a base case, which is normally the system with the lowest capital cost that can meet the load demand on the MG. Accordingly, given the presence of the utility grid at the considered site, the base case in this analysis is the existing PV/grid architecture, which incurs no capital costs in view of the presence of a 50 kVA transformer at the site.

#### 4.3.1. Return on Investment

The ROI is defined as the yearly cost savings an investment project generates for the injection of the financial capital, which can be calculated from Equation (7) [71–73]:

$$ROI = \frac{\sum\_{i=0}^{PL} \left( \text{CC}\_{ref}(i) - \text{CC}(i) \right)}{\text{PL} \times \left( \text{CC} - \text{CC}\_{ref} \right)},\tag{7}$$

where *CC* and *CCre f* represent the capital cost of the suggested system and the reference system, respectively; *CC*(*i*) and *CCre f*(*i*) denote the nominal cash flow for the proposed system and the reference system in year *i*; and *PL* represents the project lifetime.

#### 4.3.2. Internal Rate of Return

Considering the base case defined above, the IRR is defined as the discount rate at which the net present value of all cash flows from the proposed investment equals to that of the base case. Accordingly, the IRR can be calculated as follows [71,72]:

$$IRR \ = \sum\_{i=0}^{PL} \frac{\left(PV(\text{NCI}\_{ref}(i) - \text{NCI}(i))\right)}{\left(1 + dr\right)^{i}} - \left(\text{CC} - \text{CC}\_{ref}\right). \tag{8}$$

where *dr* is the real discount rate, and the term (*PV*(*NCIre f*(*i*) − *NCI*(*i*)) indicates the present value of the difference of the two net cash inflow sequences (for the proposed project and the base case) during the period *i*, while the net cash inflow represents the system's income from selling electricity to the grid and assets' salvage value minus the components' capital, replacement, and O&M costs, and the costs associated with the purchase of electricity from the grid.

#### 4.3.3. Discounted Payback Period

The DPP refers to the time required to recover the difference in the discounted cash flow difference between the proposed system and the base case system, which can be expressed mathematically as [71,72,74]

$$DPP = \frac{1 - \frac{\ln\left(\frac{1}{\left(\text{CC} - \text{CC}\_{ref}\right) \times dr}\right)}{\left(PV(N\text{CI}\_{ref}(i) - N\text{CI}(i))\right)}}{\ln(1 + dr)}}{\ln(1 + dr)},\tag{9}$$

where ln represents the natural logarithm.

#### 4.3.4. Resulting Cash Flow Metrics

Table 8 lists the obtained cash flow metrics for the proposed MG capacity expansion programme with reference to the case where any shortfalls in meeting the growing residential load power demand and EV charging loads—arising from the shortage in the existing PV generation capacity—is drawn from the utility grid.

**Table 8.** Calculated capital budgeting metrics for the proposed resilience-oriented, community-scale renewable energy development project.

Table 8 demonstrates that the project yields relatively high ROI and IRR values through cost savings realised by building an at least 80% energy self-reliant community energy system, which is able to exploit the differences between the feed-in-tariff rate and wholesale power prices during off-peak periods of energy-use—by making effective use of a battery bank. The table also shows that the conceptualised resilient community MG for critical services would pay for itself in as little as 4.74 years, which provides further support for the financial sustainability of the proposed development plan. Finally, the results of this cost-benefit analysis collectively indicate that not only is the proposed capital project economically feasible without any subsidies delivered as tax incentives (e.g., renewable energy investment tax credits or production tax credits), but it also represents an attractive investment opportunity, which is capable of generating moderate to substantial savings in the community's energy bills in the medium to long run.

#### **5. Conclusions**

Radial electricity distribution networks have historically been designed to reduce unplanned, equipment failure-induced outages. However, the combination of multi-day outages as a result of many of the recent extreme weather events, and the improved cost-efficiency of distributed energy resources—including renewable energy generation and storage technologies—have stimulated considerable interest in deploying community-led sustainable energy systems that are capable of meeting critical loads if the upstream network fails. This study developed the first grid-connected community MG equipment capacity expansion planning method that integrates energy resilience constraints without creating redundant capacity. The proposed method yields robust decision-making

support by evaluating the trade-off between life-cycle costs and the resilience of energy supply. Specifically, to quantify the resilience of the proposed MG, a time-series of random outages throughout the year were generated for a range of outage frequencies of certain durations and incorporated into the HOMER Pro model.

The numerical simulation results obtained from the application of the model to the case example of Totarabank eco-village, New Zealand, have provided several novel insights:

Although this paper has focused particularly on the capacity expansion planning of grid-tied community MGs, the proposed framework of planning for resilience can be generalised to handle various types of sustainable energy systems of different scales. Moreover, the area of application of the proposed framework is not restricted to the expansion planning problems. It can be easily extended for application to the general renewable energy system capacity planning problem of greenfield sites having access to the national grid.

The study has a number of limitations, namely:

#### *Future Work*

To address the limitations outlined above, future work needs to develop a more detailed resilient-oriented MG capacity expansion planning modelling framework that: (1) takes into account the local extreme weather impact on the MG's own onsite renewable energy generation equipment and expands the proposed resilience quantification method to include the hours of the autonomy of the storage bank; (2) integrates scheduled outages into the resilience-oriented MG capacity planning processes by combining them with those of the random unplanned outages to generate an integrated outage time-series to be fed into the optimisation problem; (3) considers the true value of lost load perceived by end-consumers that can be derived from specifically-developed surveys, whilst additionally reflecting the seasonal differences in the elasticity of non-critical loads; and (4) harnesses the potential of EV batteries in facilitating the integration of renewable energy sources, which may provide the opportunity to reduce the total discounted system costs and improve the cost-efficiency of planning for resilience, security, and autonomy of community energy systems.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1996-1073/13/15/3970/s1, Table S1: numerical values underlying Figure 4; Table S2: numerical values underlying Figure 5; Table S3: numerical values underlying Figure 11; Table S4: numerical values underlying Figure 12; Table S5: numerical values underlying Figure 14; Table S6: numerical values underlying Figure 15; Table S7: numerical values underlying Figure 16; Table S8: numerical values underlying Figure 17; and Table S9: numerical values underlying Figure 18.

**Author Contributions:** Conceptualization, S.M.; methodology, S.M.; software, S.M.; validation, S.M., A.C.B., and D.B.; formal analysis, S.M., A.C.B., and D.B.; investigation, S.M.; resources, S.M., A.C.B., and D.B.; data curation, S.M.; writing—original draft preparation, S.M.; writing—review and editing, A.C.B. and D.B.; visualization, S.M.; supervision, A.C.B. and D.B.; and project administration, A.C.B. All authors have read and agreed to the published version of the manuscript.

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

**Acknowledgments:** The authors would like to gratefully acknowledge suggestions and contextual input provided by the Totarabank Subdivision developer Andy Duncan.

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

#### **Nomenclature**

#### **References**

© 2020 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* **Study on the Suitability of Passive Energy in Public Institutions in China**

#### **Shui Yu \*, He Liu, Lu Bai and Fuhong Han**

School of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang 110168, China<EMAIL_ADDRESS>(H.L<EMAIL_ADDRESS>(L.B<EMAIL_ADDRESS>(F.H.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +86-188-0248-3528

Received: 23 May 2019; Accepted: 19 June 2019; Published: 25 June 2019

**Abstract:** To solve the problem of the low utilization ratio of clean renewable energy in public institutions, the basic information of energy utilization in public institutions was investigated. The suitability of passive energy use in public institutions was studied. According to the basic information and evaluation index of passive energy utilization in public institutions, the suitability of different types of passive energy (solar and geothermal energy) was studied by combining the resource conditions in different climate zones and the characteristics of energy utilization in typical public institutions, and the suitability distribution map was formed. In terms of research methods, the CRITIC (Criteria Importance Though Intercrieria Correlation) method based on the characteristics of objective data, and the natural breakpoint method based on the structure of objective data, were selected. Based on the climatic zoning of the buildings, this study conducted a suitability zoning. Each climatic region of buildings was divided into three sub-regions, which were the passive energy suitability regions of public institutions in the climatic region of the buildings. Finally, all of the regions with the same suitability were partitioned in order to obtain the final results. The distribution map of suitability for the different types of public institution buildings in the different regions is creatively established, which provides the basis for the selection of passive energy technology application schemes for public institution buildings in different regions, and provides macro guidance for energy planners and scheme designers.

**Keywords:** public institution; passive energy; CRITIC method

#### **1. Background**

After more than ten years of development, with the gradual attention of the state to the energy conservation work of public institutions, public institutions in various places have carried out many explorations in their own energy management and have achieved certain results [1–7] The relevant systems research and construction have been carried out throughout the country [8]. Some provinces and cities have introduced local approaches and supporting policies. In Beijing, solar hot water systems are mandatory in new homes. In Fujian, measures have been taken to strengthen the promotion, application, and management of renewable energy in civil buildings. Guangdong has incorporated green building management requirements into legislation. Some provinces and cities have carried out energy consumption data statistics, energy audits, or publicity for some public institutions. At the same time, however, there are also some problems that restrict the continuous promotion of the establishment of energy-saving institutions, and have not yet played a role in social demonstration.

In 2017, China accounted for 23.2% of the global energy consumption and 33.6% of the global energy consumption growth. China has led the world in energy growth for 17 consecutive years. In 2017, China's natural gas consumption increased by 15%, accounting for 32.6% of the net increase in global natural gas consumption. Coal accounted for 60.4% of China's energy mix in 2017, down from

the 73.6% of a decade earlier and the 62.0% of 2016 [9]. The five-year plan is an important part of China's national economic plan, as well as its long-term plan. It mainly anticipates major national construction projects, the distribution of productive forces, and a significant proportion of the national economy, and sets goals and directions for the long-term development of the national economy. The outline of the 12th Five-Year Plan for the National Economic and Social Development of the People's Republic of China (2011–2015) is referred to as the 12th Five-Year Plan. The energy consumption structure of public institutions in the 12th Five-Year Plan is as follows: electricity accounts for 45.37%, raw coal 30.86%, and other 23.77%. Compared with 2010, the proportion of electricity increased by 11.07 percentage points, while that of raw coal decreased by 17.16 percentage points. Compared with 2012, the per capita energy consumption of the national public institutions decreased by 15.21% in 2017, and the energy consumption per unit of building area decreased by 12.39%. The average energy consumption decreased by 12.85% in 2016, and the energy consumption of construction area decreased by 10.17%. Compared with 2008, the energy consumption per unit building area of public institutions decreased by 22.9% in 2017, and the per capita energy consumption decreased by 29.4%.

However, as of the 12th Five-Year Plan, China's total building energy consumption was 857 million tons of standard coal, accounting for 20% of the country's total energy consumption. The total energy consumption of public institutions is 183 million tons of standard coal, accounting for 21.35% of the total energy consumption of buildings in China, and 4.26% of the total energy consumption of the whole society. The per capita comprehensive energy consumption is 370.73 kg of standard coal per person, and the energy consumption per unit building area is 20.55 kg of standard coal per square meter. The energy consumption per unit area of urban residential buildings is 12.90 kg of standard coal per square meter, and that of the rural residential buildings is 7.82 kg of standard coal per square meter. The energy consumption per unit floor area of public institutions is 1.60 times that of urban residential buildings, and 2.63 times that of rural residential buildings [10].

Donglin Zhang, of Chongqing University, points out that the government's focus on energy efficiency in office buildings is to reduce the use of electricity and increase the use of renewable energy [11]. The energy intensity of office buildings of state organs in China is high, which is an abnormal situation. Meanwhile, with the development of residential buildings to high-rise and super-high-rise buildings in recent years, the energy consumption of power facilities has increased significantly, while the utilization rate of solar energy has decreased significantly, leading to the increase of energy intensity of such buildings [12]. Moreover, the utilization rate of clean renewable energy in universities and hospitals is low [13].

To sum up, public institutions are not only an important subject of energy consumption, but also an important field of energy conservation. This study explores and studies the current status, existing problems, and causes of passive energy applications, and promotes the use of passive energy sources in public institutions in China. In view of the current low utilization rate of clean renewable energy in public institutions, the basic information and research about the energy utilization of public institutions was carried out in order to study the suitability of passive energy utilization in public institutions, and to creatively establish the buildings suitable for different regions and different types of public institutions. The suitability map provides the basis for the selection of passive energy technology application schemes for public buildings in situations in different regions, providing macro guidance for energy planning and solution designers.

#### **2. Content**

Passive energy: For buildings, renewable energy that can be used in large quantities is a passive energy source. Renewable energy is inexhaustible, but not all renewable energy can be used for buildings. So, renewable energy includes passive energy, and solar energy, geothermal energy, and wind energy are all passive energy sources. The passive energy sources in the study are solar energy and geothermal energy. Active energy: For buildings, non-renewable energy, secondary energy, and

renewable energy that cannot be used in large quantities are active energy sources, such as natural gas (non-renewable energy) and electric energy (secondary energy).

China is located in the eastern part of Eurasia in the Northern Hemisphere, mainly in the temperate zone and subtropical zone, with relatively abundant solar energy resources. According to the long-term observation data accumulated by more than 700 meteorological stations nationwide, the annual total solar radiation in China is roughly between 3.35 × 103 MJ/m2 and 8.40 × 103 MJ/m2 , with an average value of about 5.86 × 103 MJ/m2 . China is rich in geothermal resources [14]. More than 2700 geothermal outcrops have been discovered. Most provinces (districts) in China have different geothermal outcrops, and there are many hotspots in Yunnan—345 places in Tibet, 342 places in Tibet, 320 places in Hebei, 295 places in Sichuan, and 229 in Guangdong, and so on. Most of the geothermal resources in China are in the low temperatures and have hot water, and there are only 600 hot spots above 80 ◦C. From the perspective of geothermal distribution in China, there is a trend of increasing geothermal quantity and increasing water temperature from the central to the eastern continental margin and the southwest. According to the survey and evaluation results of the China Geological Survey of the Ministry of Land and Resources, the amount of shallow geothermal energy resources in the cities above the prefecture level is equivalent to 95 × 108 t of standard coal, and the annual recoverable resources are equivalent to 7 × 108 t of standard coal. China has many types of geothermal resources, a wide distribution, large reserves, and great potential for development and utilization.

In view of the lack of basic data for passive energy applications in public institutions and the underutilization of passive energy sources, the scope and objectives of the research are first determined. The utilization of solar energy in public institutions mainly includes photothermal conversion, photovoltaic power generation, and solar lighting, and the utilization of geothermal energy in public institutions mainly includes underground water source heat pumps and soil source heat pumps. The main types of public institutions are government agencies (mainly office buildings) and educational buildings (for teaching in universities). In the three aspects of building and health-related buildings (mainly in outpatient and inpatient buildings), passive energy mainly considers solar energy and shallow geothermal energy. The utilization of solar energy is photothermal conversion, and the utilization of geothermal energy is shallow geothermal energy.

Public institutions were referred to as "government agencies" in early comprehensive policy documents. Among the ten key energy-saving projects in the "11th Five-Year Plan", the "Government Institutions Energy Conservation Project" defines government agencies as government agencies, institutions, and social organizations at all levels, including the military, armed police, education, public services, and so on. Government agencies include party organs, people's organs, administrative organs, Chinese People's Political Consultative Conference organs, judicial organs, procuratorial organs, and other public institutions, such as those directly under the state organs, and receiving all or part of financial funds for education, science and technology, culture, health, sports, and other related public welfare industries. Business unit organizations include social groups and related organizations such as workers, youth, and women who use financial funds in whole or in part. In the Regulations on Energy Conservation of Public Institutions promulgated in October 2008, public institutions are defined as state organs, institutions, and organizations that use financial funds in total or in part [15].

#### *2.1. Government Buildings*

Government buildings are public buildings constructed by central or local governments at all levels through financial allocation or administrative financing, and they are exclusively used by government departments at all levels. Government office buildings are mainly used for public management affairs and public services. The social functions they carry are necessary for social and economic development. The distribution of building energy consumption aims to meet the reasonable-use functions of buildings [11]. (1) Functional features of government office buildings: (a) Office space, including the leadership office and the general staff office. (b) Public service rooms, including conference rooms, reception rooms, printing rooms, archives, information centers, mailrooms, guard rooms, toilets, and so on. (c) Equipment room, including refrigeration machine room, variable pressure pump room, elevator machine room, power distribution room, and so on. (d) Auxiliary rooms, including canteens, fire rooms, garages, and so on. (2) Government building features: Government buildings are mostly multi-story buildings with sloping roofs or high-rise buildings with flat roofs. Limited by decoration and energy-saving standards, few government office buildings are equipped with glass curtain walls. The exterior is solemn and there is no balcony [16].

The factors affecting the energy consumption of government office buildings are as follows: (1) Construction scale. Buildings larger than 20,000 m2 are classified as large government buildings, otherwise, they are classified as non-large government buildings. According to the survey and analysis, in non-large government office buildings, the energy consumption generally decreases with the increase in the building scale, but it shows a smooth trend in a certain interval. In general, the energy consumption per unit area of large government office buildings is much lower than that of non-large government office buildings, as shown in Figures 1 and 2.

**Figure 1.** Relationship between the scale and energy consumption of non-large government office buildings.

**Figure 2.** Energy consumption map of different building areas of government office buildings.

(2) Air conditioning form. The survey found that the energy consumption of government office buildings is easily affected by personnel behavior and load rate [17]. Therefore, although the central air conditioning system is superior to the non-central air conditioner in the performance of a single unit, the application process is not for a central air conditioning system to save energy and the central air conditioning system [11], as shown in Figure 3.

**Figure 3.** Energy consumption per unit area of different air conditioning forms in government office buildings.

#### *2.2. University Building*

A university building is not a building, but a building group that contains many types of buildings. The functions of various buildings are not identical. The main types of college buildings are as follows: (a) Laboratory building. Most of the equipment used in the experimental building is mostly used during the day, and its energy consumption is closely related to various professions. Therefore, the energy consumption of the experimental building is significantly related to the type of colleges and universities. (b) Administrative building. The administrative building is mainly an office space. The density of the energy consumption is related to the working hours. The energy consumption is related to its scale. The site has high requirements for environmental comfort. The air conditioning energy consumption is large in summer, and the energy consumption of indoor office equipment is relatively fixed. The frequency of use of power equipment, such as elevators, is high during commuting hours. (c) Teaching building. The teaching building is a place for students to study and attend classes. As a result of the concentration of personnel, the main energy-consuming equipment in winter is certain lighting fixtures and multimedia. In summer, the air-conditioning energy consumption is too large, and the utilization rate of night classrooms affects the total energy consumption of such buildings.

From what has been discussed above, the sum of the energy consumption of different functional buildings in colleges and universities constitutes the total energy consumption of campus buildings. The energy consumption of public buildings in colleges and universities is related to many factors, such as the climate zone in which universities are located, as well as the types of colleges and universities. However, each university building group includes seven categories, namely: laboratory buildings, administrative buildings, teaching buildings, gymnasiums, libraries, dormitories, and restaurants. Common public buildings (because dormitories and canteens are gradually contracted by enterprises, so they are not in the scope of public institutions): the energy consumption of these buildings accounts for a large proportion of the total energy consumption of the campus [18], and the research is mainly based on teaching buildings.

#### *2.3. Hospital Building*

The outpatient department is the first place to contact the patient. The patient and family members must come to the hospital first to register in the clinic, and then will distribute to the various parts of the hospital. Therefore, the outpatient building is the place with the highest population density. Hospital composition: According to the functional department, it can be divided into various outpatient departments, public departments, and medical technology departments. The outpatient department includes the departments of internal medicine, surgery, pediatrics, office, treatment room, and so on. The public department includes the registration office, the toll collection office, the checkout office, and the medicine taking office, and the medical technology department includes the laboratory and the X-ray inspection room. Characteristics: (1) The flow density is large, and the stagnation time is extremely long. (2) The service target is a special group, so the air environment quality in the ward is very high. (3) The air conditioning system is a seasonal air conditioning system. (4) There is a certain pressure difference between the inside of the clinic and the corridor, so as to maintain the positive pressure in the room [19]. (5) Seasonal characteristics: The survey found that the seasonal characteristics of hospital energy consumption are significant. The hospital's electricity consumption is different in different months. Electricity peaks occur in July–September, while other seasons use less electricity, as shown in Figure 4. (6) Regional characteristics: Because the number of hospitals and climate characteristics are different in different regions, the energy consumption varies greatly, as shown in Figure 5 [20].

*Energies* **2019**, *12*, 2446

**Figure 4.** Monthly power consumption-change chart of a large hospital.

**Figure 5.** Comparison of the average energy consumption and average energy consumption of the average unit building area in each city.

#### **3. Methods**

Currently, the main method of suitability research is an indicator using the weight method, and currently, the method of determining the weight can be divided into the subjective weight method, the objective weight method, and the main objective weight method. The subjective weighting method is a kind of method in which researchers assign weights to each index according to their subjective value judgment. This kind of method is divided into the expert evaluation method, analytic hierarchy process, and so on. The weight of each index depends on the knowledge structure and personal preference of each expert. Although this is a good reflection of the subjective will, it lacks scientific stability. Considering its obvious defects, it is generally only applicable to the evaluation of data collection difficulties and information that cannot be accurately quantified. The objective weighting method is used to determine the weight according to the relationship between the original data, using a certain mathematical method. The judgment result does not depend on the subjective judgment of the person and has a strong mathematical theoretical basis [21]. Combination weighting method: Combining the results of subjective and objective weighting methods.

#### *3.1. Method Selection*

In the empowerment of the indicator system, the existing method of empowerment is both subjective and objective. Because this research is relatively pioneering, there are few domestic related researches. Therefore, the subjective empowerment method seems very weak. At this stage, the most widely used method of objective weighting is the entropy method [21–23].

Entropy was originally a thermodynamic concept. It was introduced by Claude Elwood Shannon to information theory, and it has been widely used in engineering, social, and economic fields [24–26]. In general, if the entropy of an index is smaller, the greater the degree of variation in the value of the target, the greater the amount of information, and the greater the role of the overall evaluation, the greater the amount of weight it has in the overall evaluation. On the contrary, the greater the entropy (*Ej* ) of certain indicators shows the smaller the degree of variation of its index value, the smaller the amount of information provided, the smaller the comprehensive evaluation of the role, and its weight should be smaller. The information entropy calculation formula of the indicator *j* is as follows:

$$E\_{\vec{j}} = -\left(\ln m\right)^{-1} \sum\_{j=1}^{m} p\_{\vec{i}\vec{j}} \cdot \ln p\_{\vec{i}\vec{j}} \tag{1}$$

where, *m* is the number of objects to be evaluated, and *pij* = *dij* P*m j*=1 *dij* if *pij* = 0, we define the following:

$$\lim\_{p\_{ij}\to 0} p\_{ij} \cdot \ln p\_{ij} = 0 \tag{2}$$

Scope of application: (1) can be used to determine the weight of the indicators in any evaluation question, and (2) can be used to eliminate the indicators that contribute little to the evaluation results in the indicator system [22]. Advantages: Objectivity, relative to those subjective valuation methods, the accuracy is higher and the objectivity is stronger, which can better explain the results obtained. Adaptability can be used for any process that needs to determine the weight, and can also be used in combination with some methods. Disadvantages: only the influence of the index variation on the weight is considered, and the conflict between the indexes is not considered [23]. To sum up, in order to make up for the shortcomings of the entropy method, we have chosen the CRITIC (Criteria Importance Though Intercrieria Correlation) method as the method of empowerment in this study. Its basic idea is to determine the objective weight of the indicators based on two basic concepts. First, the contrast intensity, which represents the value difference between the different evaluation schemes of the same index, is expressed in the form of standard deviation, that is, the standard deviation difference indicates the value difference between the different schemes within the same index. The larger the standard deviation is, the greater the value difference between different schemes will be. The second is the conflict between the evaluation indicators. The conflict between the indicators is based on the correlation between indicators. For example, there is a strong positive correlation between two indicators, indicating that the conflict between two indicators is low. The first *j* indicators and other indicators of the conflicting quantitative indicators are for P*n i*=1 (1 − *rtj*), *rtj* is the correlation coefficient between evaluation index t and evaluation index *j*, and σ*j* is the standard deviation of the index *j*. The determination of the objective weight of each index is a comprehensive measure of the comparative strength and conflict. If *n* is the number of evaluation indicators, let *Cj* denote the amount of information contained in the indicator *j*, then *Cj* can be expressed as follows:

$$\mathcal{C}\_{j} = \sigma\_{j} \sum\_{i=1}^{n} (1 - r\_{tj}) \tag{3}$$

where, σ*j* is the standard deviation of the *j*th index. The larger the *Cj* is, the more information the indicator *j* contains, and the greater the relative importance of the index is, so the objective weight *Wj* of indicator *j* should be as follows [27]:

$$\mathcal{W}\_{\bar{j}} = \frac{\mathbf{C}\_{\bar{j}}}{\sum\_{j=1}^{n} \mathbf{C}\_{j}} \tag{4}$$

#### *3.2. Selection of Indicators*

The evaluation index of passive energy utilization of public institutions is determined. When the selection of the index is taken into consideration, it considers both the supply side and the demand side and covers the characteristics of public institutions and the characteristics of energy [28]. According to the basic information and evaluation indexes of the passive energy utilization of public institutions, combined with the resource conditions in different climate zones and the characteristics of the energy use of typical public institutions, the research on the suitability of different types of passive energy is conducted so as to form the suitability distribution map.

Considering the characteristics of public institutions and passive energy, from the perspective of the supply-side, the energy distribution in various regions is extremely important. The amount of energy in a region can reflect how much energy the region can provide to public institutions, which plays a major role in the suitability research index system. From a macro point of view, the application of technology is not considered. Therefore, technical indicators related to passive energy, such as average sunshine duration and hydrogeological factors, are not considered in this study. In addition, economic factors should also be considered. This study selects regional fixed asset investment. Because public institutions are funded by the government to complete construction projects, the selection of fixed asset investment can reflect the size of the government's investment in the construction of the area.

From the demand-side perspective, it is necessary to understand the building's energy use characteristics. From the perspective of the nature of building energy consumption characteristics, it can be seen from the existing building climate zoning map that the energy consumption characteristics of all kinds of buildings are the same in each partition. Therefore, in the research on the passive energy suitability of public institutions, this research is carried out based on the climate zoning map. From the perspective of building energy consumption characteristics, the energy consumption index of public institutions is basically the same, so the number of public institutions can be used to reflect the size of the energy consumption.