Perspectives for Implementing Distributed Generation in Developing

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Perspectives for Implementing Distributed Generation in Developing Countries through Modeling Techniques Luis Fabian Fuentes-Cortes, and José María Ponce-Ortega ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b03359 • Publication Date (Web): 16 Nov 2017 Downloaded from http://pubs.acs.org on November 21, 2017

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ACS Sustainable Chemistry & Engineering

Perspectives for Implementing Distributed Generation in Developing Countries through Modeling Techniques Luis Fabián Fuentes-Cortés a, José María Ponce-Ortega b*

a

Tecnológico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., 64849, México. b

Chemical Engineering Department, Universidad Michoacana de San Nicolás de Hidalgo, Francisco J. Mujica S/N, Edificio V1, Ciudad Universitaria, Morelia, Michoacán, 58060, México.

*Author for correspondence: J. M. Ponce-Ortega Email: [email protected] Tel. +52 443 3223500 ext. 1277

ACS Paragon Plus Environment


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Abstract This paper addresses the status, problems and development of distributed energy resources in developing countries. We have focused on the projects in Latin America, Asia, Eastern Europe and North Africa. The problems for developing new energy technologies are linked to the particular conditions as regulated markets, energy policies, urban planning and development, low-income communities, environmental impact and economic instability. We are presenting a perspective based on the literature review of the different proposals for solving the particular problems associated to the implementation of energy projects under the modeling perspective, highlighting the residential environment and domestic applications. Five different fields used on modeling approaches are included in the review: technologic development, economic performance, environmental impact, social context and modeling proposals. We have identified the addressed problems, the applied modeling techniques, the trends showed by the results presented and the expectations for future works in the field of distributed generation. Keywords: Distributed Energy Resources, Modeling, Combined Heat and Power, Renewable Energy, Housing Complexes.


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Introduction Distributed energy generation (DG) is defined as the allocation of small conversion units close to the consumers instead of the large power plants which are allocated far from the consumption cores (see Figure 1).1 This strategy allows mitigating the energy losses due to the transmission,2 flexibility in the use of renewable energy resources,3 energy autonomy and self-supporting of the end users (see Figure 2),4 increasing the efficiency in the use of fossil fuels5 and, in consequence, reducing the environmental impact associated to electricity generation (see Figure 3).6 In developed countries, especially in Europe, the dissemination of projects based on distributed energy resources (DER) is growing and it has allowed the mitigation of CO2 emissions, reduction in the use of primary energy sources (PES) and increasing the use of renewable technologies.7 This development has been possible due to the economic conditions, which bring on the investment in new technology,8 flexibility on energy policies, which include subsides for more efficient and cleaner technologies,9 and the social interest on reducing environmental impacts10 and increasing the quality on energy supply.11 However, developing countries are characterized by low industrial development and low Human Development Indexes (HDI). These conditions are the result of low-income, economic and policy volatility, historical difficulties on transferring technology, high costs in technological innovation and lack of proper environmental and technological policies.12,13 Nevertheless, the industrial growth and educative advantages in the last decades have stimulated changes in economic sectors, as implementation of new regulations and policies on energy markets.14 These transitions have motivated the implementation of technologies that can reduce the environmental impact and stimulate the social and economic development in their communities.15 As consequence, the use of mathematical modeling techniques for identifying trends, obstacles, challenges, opportunities and advantages in the starting up of distributed energy resources have been presented taking into account the particularities of developing countries.16 The changes in energy and environmental policies have generated specific subside programs for stimulating distributed generation (DG) projects oriented to agriculture, industry and services.17,18 Due to the importance of the productive sectors, they have become in the core of the energy development as well as research projects. Besides, the



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residential user, usually, is addressed considering the energy behavior and the consumption efficiency leaving aside the possible incorporation of the communities and individual residential producers equipped with DG technologies into the energy market.19-23 Mathematical modeling techniques consist, basically, in the analogical, abstract, simplified and systematic representation and description of selected features of specific phenomena, considering the criteria and interest of the researcher using mathematical expressions. In engineering, they are used for predicting and anticipating problems, obstacles and opportunities in new technological developments without the direct and physical observation of the system. It avoids substantial costs on empirical and physical developments.5,16,22 The main modeling tools used in engineering for solving the problems in energy systems are control, simulation and optimization. These techniques are not exclusive between them. Control models are used for determining, governing and adjusting the response and dynamic of the system to the presence of variations of operating conditions. Simulation models allow describing the effect of the use of different parameters in the results of the operation of the system using an empiric approach. Optimization models are used for defining the best conditions of operation and design according to a specific objective function that can be minimized or maximized. The optimization models present the following general structure: f min ( x, y )

(1)

Subject to: g ( x, y ) ≤ 0

(1a)

h ( x, y ) = 0

(1b)

The behavior of the objective function (f, usually associated to economic or environmental criteria) depends on the behavior of the decision variables x and y. The system operation and design are delimited using inequality (g) and equality (h) constraints. Even to the advantages of applying modeling techniques in the analysis of energy systems, there are a set of limitations commonly associated to the complexity and time scale of the modeling implementation. It leads to use a reduction in variables, expressions and parameters as well as the time horizon (how long must be the time period considered)


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and the time resolution (how long time must be considered for capturing the changes in the different variables). Computational costs and limitations result in to deprecate aspects that, according with the criteria of the researcher, are not determinant in the performance of the model. In this work, we present a review of the recent literature relative to modeling the design, control and implementation of distributed generation systems in developing countries with a special emphasis in residential users and optimization perspectives. Despite that Russia and China are part of the biggest world economies and have developed extensive research on renewable sources and distributed systems, they are frequently included on the lists of developing countries due to the levels of HDI. However, we have excluded the studies produced in these countries from this study to make visible and prioritize countries that are not normally included in the reviews for developing countries. The document is organized according with five particular areas for solving problems in the operation of energy technologies. •

The first considered area addresses the technological issues, taking into account aspects as technological performance, adaptation, efficiency, supply quality, primary energy sources and selected technologies according with the local technological limitations.

•

The second area includes the economic and energy market regulations, subsidies, prices, costs, tariffs as well as changes and transitions in the economic and energy policies that impact in the implementation of DG.

•

The third area addresses the conflicts originated by the particularities of the urban planning as sizing, allocation, grid availability and safety.

•

The subsequent area is the analysis of the urban development and social issues associated to the operation of DG.

•

Finally, there are presented the modeling limitations and advantages by themselves, considering the multi-objective approaches and techniques used for addressing the particular conditions on developing countries.

•

In the last part of the work, we present the expectative of future works and a valuation of the actual developed modeling framework for the implementation of DG in developing countries.



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Technological framework The classification for the technologies addressed on this section has been prepared according with the primary energy sources as: solar, wind, hydraulic, biofuels, fossil fuels and hybrid systems (see Table 1). From a technological point of view, the problems addressed on developing countries present no significant difference compared with the ones generated in developed countries. The reviewed works present significant changes in the operational conditions for improving the efficiency of the equipment, optimal coupling of different technologies

24, 25, 29

24, 31, 43, 44, 45, 47, 55

the

and assessing the global energy

performance of the system. 33, 38, 42, 46, 50, 59 As most of the research produced in developing countries, the efficiency of the equipment and system is linked to the reduction of costs as well as the greenhouse gas emissions (GHGE) associated to fuel consumption. In the case of renewable sources, it implies to increase the energy production. The main considered approaches, from a technical point of view, are the size, efficiency, selection, supply quality and interaction of different technologies. Determining the size involves identifying properly the installed capacity of production of energy to meet the demands of the end user. The selection of technologies considers defining the type of unit, energy source and configuration of the system. In developing countries, the economic condition is the main issue for increasing the participation of renewable resources in the energy production.63 Therefore, the use of more efficient technologies is a way for stimulating the energy transition from the use of centralized systems to implement distributed energy systems. It is expected that it results in lower costs for the end users and reducing the capital cost.64 On the other hand, the efficiency assessment is used as a strategy for replacing unsuitable or obsolescent equipment using technologies according with local context and economic capacities.65 The interaction with the local grid is addressed considering two issues. The first one is the interconnection to the local grid for improving the quality of the supply considering the weak of the local supply or just for reducing phenomena such as the flicker, shut downs or low voltage. These conditions are remarkable in old facilities and represent a problem for remote and non-priority users connected to the grid or during peak consumption hours.66 The main researches have focused on forecasting,26 or controlling the effects of the interconnection of new technologies to the grid

37, 49

as well anticipating the benefits of


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including new contributors to the energy supply.26,33,35,36,37 The second one is the interaction on the micro-grid context. It implies the synchronization with different small producers which use DE for interchanging energy utilities. The developing of micro-grids is on an early stage. Therefore, control problems are common on emerging systems.67 For facing this problem, optimal operation policies 56 and optimal control approaches 34 are the main contributions for solving the quality supply problems. In some cases, where multiple users and producers are allocated in remote areas and where the utility grid is not available, it is possible to establish a micro-grid scheme for interconnecting the present systems and units. However, it increases the level of complexity adding the particular conditions of offgrid systems. Off-grid systems need to be addressed considering particular conditions for ensuring the quality supply. Some remarkable issues are the definition of operational policies for synchronizing the energy supply and the demands, especially during peak hours. The main strategies focus on adjusting the operation of the generation technologies

33, 58, 61, 62

and the

use of the storage for reducing gaps.62, 60 Off-grid systems are particularly interesting for developing countries where are concentrated most of the communities with lack of energy utilities.67 In the section of social and environmental impact, this condition will be addressed again. Finally, the design of distributed systems considers, at least, two aspects: the size and the configuration, i.e. the selection and coupling of different technologies, of the system. It is defined by multiple factors as the demand behavior, costs and availability of the equipment, availability of resources and limitations on the energy conversion based on the local policies.68 Different criteria have been developed for sizing the distributed energy systems in developing countries. As it can be anticipated, the main conditions are the capital and operational cost, local grid

39

25, 27, 28, 41, 48

but other criteria such as the integration with the

and demographic conditions

41

also affect. Integral approaches include the

simultaneous optimization of the operational policy coupled to sizing the system considering the energy behavior of the end user and multi-objective approaches that take into account environmental objectives.49, 51, 57, 61, 62 The multi-objective approaches and the economic and environmental impacts will be addressed in different sections of this work.



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It is possible to identify trends according with the local energy market, ambient conditions and availability of resources. The interest in innovative technologies, considering the implementation of new equipment, has led to use modeling techniques for adapting the detailed design to ambient local conditions for modifying the operation, physical conditions and configuration of the equipment.24, 31, 55 Countries where fossil fuels are the predominant resources (as Iran), technologies as combined heat and power (CHP) based on internal combustion engine (ICE) and microturbine (MT) have become on the core of the distributed energy research, due to the local costs. Also, the independence of the potential or availability of renewable resources represents an advantage over the solar or wind technologies. Despite the use of fossil fuels, it is compensated with the efficiency of the equipment.70 Implementation of CHP units has allowed the costs reduction on the use of biofuels, stimulating the development of local bioenergy markets, which can contribute to the development the energy conversion on rural areas where the available biomass can feed distributed energy systems. Special cases for developing these conditions and technologies are Brazil and Mexico.71, 72 On the other hand, as it was mentioned above, the use of renewable energy, considering wind, solar, hydro and geothermal sources, depends of the potential and availability. In countries with a large territory and solar potential, centralized facilities have been implemented with different levels of success. However, the conditions for distributed technologies are different. They depend of the land and available space for installing the technologies.73 As a result, solar units based on photovoltaic technologies are predominant on the distributed systems. However, the use of Organic Rankine and Kalina cycles has been emerged as a promissory technology for using solar collectors and low enthalpy geothermal resources.74 India and Brazil, considering the territorial extension, availability of resources and policies for stimulating the development of renewable sources, are the countries with predominant research on renewable technologies.75, 76 The modeling of photovoltaic, wind and hydro units includes, as a remarkable issue, the quality of the supply. The technologies are dependable of ambient conditions (solar irradiation, wind speed and water flow). As consequence, the main objectives addressed on the works are increasing energy generation ensuring to meet the supply 25, 28, 27, 32, 39, using storage technologies, commonly batteries, for reducing the gaps between generation and


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consumption, 30, 34, 58 reducing the flicker effect carriers.

56

26, 33, 35, 36, 37

or interacting with external

The use of biofuels considers the efficiency of generation technologies for

reaching a trade-off between the cost of treatment and production of the fuel and the environmental benefits and accessibility of resources.42,

43, 44, 46, 47

The primary

considerations for using fossil fuels, as was mentioned above, are the costs and availability of the resources. However, it is possible to improve the configuration of the system, selecting properly technologies,57, 53 for operating more efficiently the units.50, 52

Economic and energy market framework Capital and operating costs are presented commonly as objective functions from an economic perspective. Despite it is a desired objective, on developing countries, it is a priority the design and minimizing cost is a common objective function, the economic approach should considerer the effects of economies in transition on the energy market.77 Aspects as the price variations, levels of liberation on local markets and the policies for energy purchase, sale and interchange are frequently assumed as direct costs and incomes. However, it is not a trivial issue. Modeling market conditions, especially in countries under transition on the energy policies, requires different levels of complexity according with local conditions and the interest of the researchers.78 Besides, the local policies as economic incentives for migrating from conventional technologies to renewable resources and the use of taxes for mitigating the environmental impact must be considered on the actual modeling approaches on developing countries.79 Table 2 shows the main issues presented on the reviewed literature. The political framework, including the geopolitical determinants, ideological discourse as well as internal conflicts and equilibrium between groups with different political positions, such as socialists, liberals, conservatives, progressives or socialdemocrats, define the conditions of the energy market.102 These positions have led to the implementation of unsuitable energy reforms, regulations and policies, which consider private participation with different levels of regulation,103, 104, 105 event that most of the countries considered on this review have a tendency to establish a liberalized energy market.106 This transition, from non-market and regulated markets, to a relative liberalized market, besides the economic instability, volatility conditions have motivated the research, looking for a stable development of new technologies with participation of the state,107



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transition conditions 108 and anticipating the liberalization conditions 78 and considering the technological migration.109 The modeling of energy market effects on technologies present important issues because of the scale of the addressed system. Two approaches have been presented to address this point: the ones focused on specific technologies and the ones that involve general conditions of the total of the energy mix and facilities. The first ones represent technical perspectives of the economic conditions, i.e. the analysis of coupling or introducing energy technologies in a specific economic environment. 30, 40, 41, 80, 81, 83, 84, 85, 86, 93, 98, 99, 100, 101

The second ones represent economic perspectives for introducing or coupling

energy technologies to the actual infrastructure and economic environment.

82, 87, 88, 89, 90, 91,

92, 94, 96, 97

When there are considered the economic and politic contexts, modeling approaches have addressed the condition on prices of local energy markets considering the uncertainty on prices linked to inflation,84 and volatility 81, 83, 86 or forecasting future price behavior 41 for designing and implementing decentralized energy technologies. A second remarkable approach is the effect of price regulations, which has been presented for designing DG systems,

80, 82, 89

operation of energy supply

interconnected to the grid.93,

94, 95, 96

87, 96

and integration of new producers

All of them present general strategies for the

implementation of DG in the current context, which could be adapted to other countries considering the local background. On the other hand, there are approaches directed to attracting private investment considering possible revenues,30 competitive costs,85 open market scenarios 90, 94 and local incentives and policies.88, 91, 97, 101 The last point leads to the analysis of the incentives and taxes used for stimulating the technological migration on the energy sector. The current lack of concrete carbon policies has led to use as reference the ones implemented on developed countries presenting the trends and perspectives of implementation for DG.44, 100 The incentives for implementing energy projects have been addressed considering the public 88, 97, 101 and mixed founding.98 Closeness and integration of European developing countries to the EU have stimulated the approaches for migration on the economic energy conditions.110 This fact is reflected, especially on the modeling research from Turkey 95 and Poland.87, 92 A similar condition is presented on the Malaysian scenario.94 On the opposite side, Latin America,


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Africa and Mid East present approaches based on regulations and strategies considering the energy access to energy on grid-off in low income communities. This fact leads to the next part of the review, which addresses the social impact of the DG penetration on developing countries.

Urban planning and social approaches We have included both approaches, social and urban planning, considering that the urban planning is a social product defined by the politic background, territorial considerations and cultural preferences. 111, 112 The democratic development has stimulated the participation of the communities on the urban planning, installation of new energy facilities.

115, 116

113, 114

which includes the

On the other hand, we are including the social

impact of developing DG projects, which is crucial on off-grid rural and remote areas with low-income.117 Under these perspectives, we have classified the works on the current literature on urban and rural areas and the ones related to social specific issues and the ones related to the planning of new facilities considering urban distributions (see Table 3). As it has been mentioned before, the economic performance of distributed systems are used as natural and main objective function disregarding the social effects of the system. It is assumed that the implementation of DG in low-income off-grid communities presents, by itself, a social advantage.126 However, the integral assessments have demonstrated that implementing adequate social metrics can improve the design and operation of the energy systems as well as anticipating conflicts between different stakeholders.127, 128 On the other hand, ignoring the social effects of implementing energy systems can lead to the failed implementation of new projects.128 The political and ideological environments on developing countries contribute to the emergence of social movements associated to territorial defense, which affect directly the installation of new facilities.130 As consequence, recent modeling approaches contribute to the social discussion around DG using specific metrics for trading-off the differences between the economic interest and social issues. The information provided by modeling techniques is useful for the different social stakeholders that have variable priorities over the system. However, due to the nature of the social phenomena, the social metrics consist most of the time of assumptions or parameters associated to the size or allocation of the system.131 The



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allocation is the sitting of the system and is affected by conditions as available room, regulations building design and urban configurations. The heterogeneity of the political and social interest has resulted on a diversity of issues, according with the local conditions and the available and quantifiable data. The ones associated to the urban planning are linked to the allocation and sizing of the system for reducing the negative impacts, economic or environmental, of the system.92,

96, 119

The

qualitative risk assessment (QRA) has been used anticipating accidents associated to the fuel storage and operation of CHP technologies near to residential areas.119, 132 The energy demand is normally considered as a technical issue in modeling approaches, ignoring that the energy behavior is a consequence of demographic, cultural and economic factors.133 Under this perspective, modeling approaches consider the energy demands linked to 84

demographic phenomena

and user behavior.118 The technological impact, as a

quantifiable variable, is presented in terms of innovation caused by the migration to new technologies..101 In rural areas, the main problem that is presented is maximizing the access of the communities to the energy utilities.25,

120, 121, 124

Also, the access considering renewable

sources and DG is presented as a tool for increasing the levels of life quality and social development of the communities and reducing the economic disparities between communities.121 The conflict resolution as a multi-stakeholder condition has been addressed for the territorial distribution of energy resources.125 African and Latin American countries represent the priority of the global programs for fighting against the poverty. It includes guaranteeing the access to energy on remote low-income communities.

134

The presented review reveals that the social approach is

considerable in these countries, especially in Africa where the primary concern of the researches is to develop a framework for reducing the communities without access to energy utilities.

Environmental impact The environmental-based approaches are presented accounting for two different criteria. The first one considers integral approaches that evaluate multiple environmental impacts using global methodologies. The second one considers approaches that take into


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account a specific environmental impact determined by the specific conditions and needs of the case study used for the developed model (see Table 4). The most common considered approach is the emissions control using different methodologies. It derives from two important facts. The first one is the one concerning about the global warming. Some of the technologies considered for distributed generation are fed with fossil fuels and, as consequence, produce significant emissions. Research groups on developing countries, despite that the industries do not have the same level of infrastructure and energy consumption as the ones of developing countries, are conscious about the global effect of the greenhouse gas emissions (GHGE).144 The second fact is associated with the local conditions of the urban development. Of the twenty biggest cities of the world, sixteen are located in developing countries.145 Cities as Delhi, 146 Mumbai, 147 São Paulo 148 and Mexico City 149 have reported sensitive effects of air pollution due to the local industry and automotive emission sources. As consequence, the installation of new energy facilities should be considered to reduce the local impacts of fossil fuel consumption.150, 151 The reduction of emissions, as the general environmental impact, has been addressed using three strategies. The first one is measuring and controlling the direct emissions associated to the fuel consumption.57, 96, 100, 121, 140, 142, 143 The second one is to monetize the environmental impact according with local policies or using the ones used on developed countries such as carbon tax or bonus.137, 139, 141 The third strategy is to use methodologies based on parameters of emissions associated to the resources and pollution generated by the implementation of the facility,120, 136, 138 as the Life Cycle Assessment (LCA). The monetization approach, despite the carbon tax has been used as a popular policy for controlling the externalities associated to energy production, has been criticized due to the arbitrary economic value used, which is defined by negotiations between policy makers and it is a current topic of discussion for the environmental policy efficiency.152, 153 The carbon policies are in the design stage in developing countries. Therefore, recent works have evaluated the performance of carbon policies and monetization according with the local energy conditions.40, 100, 141 On the other hand, LCA or LCC have been criticized by the use of standardized parameters that can be not suitable with local conditions.154, 155, 156 However, the integral



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approaches need to be evaluated according with the information required by the researchers, developers, designers, investors and policy makers. Other methodologies, as weighting on multiple environmental impacts, 100,135 have been implemented for addressing the environmental impact of DG technologies. The consumption of resources, as water, has been addressed for distributed energy, especially CHP units.57, 100 A common assumption is to consider that the CHP system reduces by itself the use of water

157

. This fact is just for

comparing with centralized plants. CHP technologies and solar collectors, technologies, whose operation is linked to the water usage, need to be evaluated according with local conditions and constraints considered on the local environmental policies. The waterenergy nexus applied on developing countries can contribute with new perspectives, methodologies and trends on DG.158 The availability and consumption of fossil fuels in Iran is reflected in the technologies that are analyzed on the presented studies.159 The operation and design of CHP technologies are the main issues on the migration from centralized plants to DG systems over other technologies. The independence of hydro, solar and wind potentials as well as the flexible operation have contributed to consider the CHP units as the core technology on the Iranian scenario.160

Modeling issues Table 5 shows the modeling details of optimization approaches for solving the problems addressed on the previous sections. As we have mentioned before, the economic objective functions for reducing the capital and operating costs of the system are the predominant approaches. It was expected since the economic situation of developing countries is the main concern for the technology transitions of the energy sector.164 Therefore, the mono-objective assessment, which is the most common modeling configuration, prioritizes to have an economically feasible and suitable design and operation configuration of the DG system. Also, the multi-objective approach is used for trading-off the social and environmental objectives with the economic performance of the system. The multi-objective approaches reflect the concern of the researchers for tradingoff the economic, environmental and social impacts of the system. Despite of the importance of the economic performance of the units, recently integral perspectives include the analysis of environmental and social assessments. Countries with a considerable urban


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development and dependence of fossil fuels, as Mexico, Iran and South Africa, show a remarkable concern to reduce the levels of emissions. Countries where the access of energy represents a social challenge, as Ghana and South Africa, the social aspect is included as part of the multi-objective analysis. The differences on the use of linear or nonlinear models obey to the scale and information required by the researcher. Macroscopic models that look for integrating new technologies over complex systems, as regional grids,30, 87, 97, 122 micro-grids

94

or regions

with dispersed communities 25, 120 prefer to use linear approaches assuming the efficiency of the equipment and other variables and conditions, which generate nonlinear and bi-lineal expressions, as constants. In this way, it is possible to obtain general behaviors of the coupling of technologies and modeling the distribution problems using a supply chain approach.121 On the other hand, the nonlinear approach is used for considering the detailed behavior of the equipment for evaluating the performance and effects of different operative policies.24, 28, 29, 40, 50, 51, 53, 56, 57, 60, 62, 80, 84, 100, 119, 140, 163 It leads to the solution method for nonlinear models. The use of heuristic tools,24, 50, 53, 60, 62, 81, 82, 140, 143 despite to the polemic around these methodologies,165,

166, 167

is the predominant trend for solving nonlinear

problems over the analytical approaches. The multi-objective strategies present two approaches. The first one is to present multiple Pareto points for exploring the behavior of the system under different conditions and the effects on the objective functions.24, 51, 62, 80, 84, 119, 121, 136, 140, 141, 143 In this approach, the researcher defines the criteria for solving the problem and presents the set of solutions to the decision makers who choose a specific Pareto point as the best solution that reaches the trade-off between the conflict objective functions according with their specific interest. The second one is to define the interest and priorities of the decision makers or the particular solution area for defining a set of specific solutions for the problem. 40, 57, 97, 100, 120, 163

In this way, it is possible to show how the priorities of the decision makers affect the

performance of the system, and to avoid computing multiple Pareto solutions which are out of the interest area.40, 57, 168, 169 Finally, the main approaches for considering the uncertainty use the multi-scenario approach

81, 83, 84, 162, 163

for analyzing the behavior and sensitivity of the system to the

variations on specific variables or fuzzy logic

49

for controlling the effects of uncertainties



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on the objective functions. The stochastic approaches have focused on economic, ambient, social and demographic phenomena as well as the resources availability that affect the energy projects. The considered economic phenomena are the volatility, policies and inflation effects on energy prices and costs.83, 84 The ambient conditions are solar irradiation and temperature.84 The considered social effects are the energy behavior of the end users. 49, 81, 83, 163

The considered demographic conditions are associated to migration, which affects

the energy demands.84 The availability of resources has been considered for biomass produced and collected for feeding trigeneration systems.162 A remarkable issue is the computational cost of solving a specific model considering the multiple nonlinearities, uncertainties and objectives involved on the design and operation of energy systems. The economic limitations for researchers in developing countries compel to find a compromise between the complexity of the model and the available computational infrastructure.170 Therefore, designing and solving a suitable model that reflects the reality and contributes with useful information is not a trivial issue.171 The models that focus on the economic effects prefer to avoid the technical performance of the units and the ones that focus on the technological performance simplify the economic environment. As consequence, the development of models is dependable of the objectives of the researcher and the technological limitations for solving the addressed problem. However, the approaches presented in this review tradeoff the limitations and to consider multiple issues simplify the problems selecting the specific information, variables and behaviors needed for a suitable solution.49, 57, 162, 163 Multi-objective approaches have become fundamental for reaching the trade-off between the economic performance of the system and the environmental impact associated to the implementation and operation of new technologies. The general approaches include to explore different Pareto optimal solutions for presenting configurations, besides the economic behavior of the system, that minimizes the emissions 40, 51, 57, 80, 84, 97, 119, 121, 136, 140, 141, 143, 163

, water consumption

57, 100, 163

as well as integral approaches, considering multiple

environmental impacts, as the ones based on life cycle assessment methodologies

120, 136

.

Also, multi-objective techniques have considered to account for social aspects for developing DG systems. At difference of the environmental approach, the social approach includes multiple and different objectives, which have been addressed considering a


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compromise respect to the economic and environmental performance of the system.119, 120, 121

Under these conditions, multi-objective approaches have generated a framework for

reducing the conflict between different criteria including different perspectives in the design.

Discussion and conclusions A review on modeling approaches for the operation and design of distributed energy systems is presented classifying the works according with the technological, economic, social and environmental contributions in developing countries. The particular conditions, as energy market transitions, policies, regulations, taxes and environmental concerns, have been addressed using control, simulation and optimization models. The models based on optimization and applied to residential cases have been the focus of the presented analysis. The main issue of the modeling approaches is to improve the economic performance of minimizing the capital and operating costs of the technologies. As result of the economic conditions on developing countries, it is important to guarantee a suitable economic performance for easing the transition from centralized plants to distributed energy systems. The trends to liberalize markets, or at least reducing regulations, allow the arrival of new actors to the scenario as private investors. As consequence, the development of new energy projects must be attractive for external investment. These conditions are addressed for analyzing the multiple effects of regulations on prices, tariffs, subsides, programs and limitations on energy policies. However, as the conditions are on a transition period, the future works can consider the evolution, new regulations and dynamics on policies, markets and government participation. Developing countries present different and heterogeneous conditions which need to be addressed considering the particular conditions. In this sense, the modeling approaches presented in this review try to do a significant analysis for reaching compensation between the local conditions and to contribute with general strategies to the energy field research since process systems engineering perspective. As the economic performance is the core of the modeling of energy systems, the social, environmental and technical issues are dependable of reaching the economic conditions. It has resulted in the monetization of the environmental and social externalities associated to the implementation of DG projects as a way to control and measure the negative and positive effects.172 As consequence, the common practice is to use the



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monetization of externalities as cost of emissions, carbon tax, life cycle cost, social cost and cost of innovation. Despite it is a common tool for parameterizing and standardizing the impacts, it is possible to use a mono-objective model instead of a multi-objective approach just adding the monetized externality to the economic objective function.

57, 83, 100, 139

Monetization is not an objective way to measure the social and environmental affectations. 173, 174

Recent works, anticipating or evaluating the environmental policies, have assessed

the effects of carbon policies for reducing the emissions under the conditions of their countries.57,

83, 100

The results show a low level of efficiency for controlling the

environmental impact, however they could be a good economic strategy for promoting energy transitions. 57 Social approaches and urban planning have a similar problem. The social impact is addressed based on economic assumptions or technical parameters which are linked on an arbitrary way to the configuration of the system.101, 120, 121 Increasing the DG units does not guarantee by itself the access of the communities to the energy utilities or the social development of the population. However, it is needed to develop more objective metrics for measuring social impacts. In contrast with the environmental effects, which have specific metrics for obtaining the direct impact, the social conditions require a more complex approach and need to be addressed on multi-disciplinary approaches and environments.175, 176

It is needed to have more suitable metrics and parameters associated to the social impact

that can be used on mathematical models for anticipating social conflicts and benefits linked to the operative changes and implementation of DG facilities. 177 Since the main economic activity on developing countries is agriculture, the biomass derivate of rural productive processes is a potential alternative as primary energy source. Improving the technologies and developing subsidies and bonus for the use of biofuels can reduce the energy costs, mitigate the environmental impact and stimulate the development of local bio-energy markets. Significant approaches have been developed emphasizing the technological and economic considerations for creating productive chains and environments for the creation of DG projects on rural areas under this conception and showing opportunities for new energy producers.178 Improving modeling mathematical techniques for defining the features and trends is not a field closed for the specialists of energy systems. The complexity and heterogeneity


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of developing countries demand the assistance of professionals from different areas which can contribute to generate integral approaches that surpass the current limited metrics, parameters and conflicts and allow having more realistic approaches based on mathematical language. The approaches presented in this review show a strong preference of renewable sources used as core of energy systems, especially in off-grid systems, based on novel criteria and the relative low dependence and operative with respect to the technologies that uses fossil fuels and biofuels. Developing countries have increased the participation of renewable sources, India has increased 20% the generation using renewable technologies. Brazil has developed significant economic mechanisms for stimulating the technological transition in the energy sector using renewable sources.179 However, the dependence of ambient conditions leads to include additional technologies, as storage units, to increase the size of the system and to use conventional technologies as back up. As consequence, the capital costs increase and generate a dependence of subsides and external approaches. According with data of the World Bank, around 15 % of the global population lacks of energy utilities. Most of this population is concentrated in low-income rural areas of developing countries.180 Therefore, it is important to include multiple technological alternatives for defining the design and configuration of the system instead of the consideration of a unique energy source and considering the availability of local energy resources. On this perspective, hybrid systems, despite of the complexity inherent to the modeling and operation of the units, are suitable alternatives for improving the quality of energy and economic performance of the systems. On the other hand, an important problem is to define tariffs and prices. Despite the possible duality of consumer-producer involved in the development of DG projects, in the current conditions, external carriers, government and private industries are the main participants in the implementation of DG systems. Therefore, establishing suitable policies of tariffs and prices for local production considering the conditions of the end user is a pending issue for emerging technologies.179 Also, this analysis must include a special issue, which is the economic advantage for the end users. This problem increases the associated complexity considering the presence of multiple participants interconnected and the establishing of micro-grids.



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Considering the presented framework, we can list the challenges for future modeling approaches: •

The evolution of generation and storage technologies, including multiple configurations and equipment.

•

The economic performance of the projects, considering multiple regulations and market liberalizations.

•

Determining a suitable program for subsides to stimulate small scale generation projects.

•

Dynamic prices and tariffs according with the local availability of resources, external carriers, generation scheduling, end user considerations and operation policies.

•

Environmental impact assuming the local concerns as emissions, water consumption and land usage. Water-energy-food nexus is a potential analysis for obtaining desirable solutions considering an integral context.

•

Including the social aspects focusing in the importance of the energy access for the human development of the communities.

Author information Corresponding Author *Ponce-Ortega José M. Tel. +52 443 3223500. Ext. 1277. Fax. +52 443 3273584. E-mail: [email protected] Notes The authors declare no competing financial interest.

Acknowledgement The authors acknowledge the financial support from the Mexican Council for Science and Technology (CONACyT).

Acronyms and abbreviations Acronym ACS AE AP

Description Annual cost of the system Aqua Electrolyzer Annualized profit


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C CC CE CF CB CInnov CS DER DG DMOO EH EMR EP EPR ESCA EU ExEf FC FL FW GA GHGE HDI HL HP HT ICE LCA LCC LCOE LP MILP MINL MOEA MT NB NBI NL NLP NPV NPW NSGA ORC PLER PSP PSO PV REL RES RRI SC SCES SE SocRisk

Capacitor Capital Cost Cost of Energy Cost of Fuel Conventional Boiler Cost of innovation Compromise Solution Distributed Energy Resources Distributed Generation Dynamic Multi-objective Optimization Electric Heaters Electricity Match Rate Economic Potential Energy Price Risk Electric System Cascade Analysis European Union Exergetic Efficiency Fuel Cell Fuzzy Logic Flywheel Genetic Algorithm Greenhouse Gas Emission Human Development Index Heat Losses Heat Pump Hydro Turbine Internal Combustion Engine Life Cycle Assessment Life Cycle Cost Levelized Cost of Electricity Linear Programming Mixed Integer Linear Programming Mixed Integer Non-linear Multi-objective Evolutionary Algorithm Microturbine Net Benefit Normal Boundary Intersection Non-linear Non-linear Programming Net Present Value Net Present Worth Non-dominant Sorting Genetic Algorithm (MATLAB) Organic Rankine Cycle Project Lifetime Economic Return Power System Production Particle Swarm Optimization Photovoltaic Reliability Renewable Energy Sources Return per-risk Index Solar Collector Superconducting Energy Storage Stirling Engine Social Risk



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SW TPP TST WEHC WT ∆f Abbreviation Bat. Desal. Det. Econ. Environ. Feat. Mult. Obj. Res. Strat. Tec. Tech. Uncert. Var.

Water consumption Thermal Power Plant Thermal Storage Tank Wave Energy Hyperbaric Converter Wind Turbine Difference between the generation and demand loads Description Battery Desalination Deterministic Economic Environmental Features Multiple Objective Residential / Domestic Strategy Technical issue Generation Technologies Uncertainty Variables

References (1)

Alanne, K.; Saari, A. Distributed energy generation and sustainable development. Renew. Sust. Energy Rev. 2006, 10(6), 539-558.

(2)

Bayod-Rújula, A. A. Future development of the electricity systems with distributed generation. Energy. 2009, 34(3), 377-383.

(3)

Mohammed, Y. S.; Mustafa, M.; Bashir, N.; Mokhtar, A. S. Renewable energy resources for distributed power generation in Nigeria: a review of the potential. Renew. Sust. Energy Rev. 2013, 22, 257-268.

(4)

Walker, G. What are the barriers and incentives for community-owned means of energy production and use? Energy Pol. 2008, 36(12), 4401-4405.

(5)

Chicco, G.; Mancarella, P. Distributed multi-generation: a comprehensive view. Renew. Sust. Energy Rev. 2009, 13(3), 535-551.

(6)

Akorede, M. F.; Hizam, H.; Pouresmaeil, E. Distributed energy resources and benefits to the environment. Renew. Sust. Energy Rev. 2010, 14(2), 724-734.

(7)

Ferreira, H. L.; Costescu, A.; L'Abbate, A.; Minnebo, P.; Fulli, G. Distributed generation and distribution market diversity in Europe. Energy Pol. 2011, 39(9), 5561-5571.


Page 22 of 50

Page 23 of 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(8)

Lopes, J. P.; Hatziargyriou, N.; Mutale, J.; Djapic, P.; Jenkins, N. Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities. Electr. Power Syst. Res. 2007, 77(9), 1189-1203.

(9)

Anaya, K. L.; Pollitt, M. G. Integrating distributed generation: Regulation and trends in three leading countries. Energy Pol. 2015, 85, 475-486.

(10) Hoffman, S. M.; High-Pippert, A. Community energy: a social architecture for an alternative energy future. Bull. Sci. Technol. Soc., 2005, 25(5), 387-401. (11) Pepermans, G.; Driesen, J.; Haeseldonckx, D.; Belmans, R.; D’haeseleer, W. Distributed generation: definition, benefits and issues. Energy Pol. 2005, 33(6), 787798. (12) Bell, M.; Pavitt K. Technological accumulation and industrial growth: contrasts between developed and developing countries. In Technology, globalization and economic performance, Archibugi, D., Michie, J., Eds.; Cambridge University Press, 1997, 83-137. (13) Fatas, A.; Mihov, I. Policy volatility, institutions, and economic growth. Rev. Econ. Stat. 2013, 95(2), 362-376. (14) Newman, C.; Rand, J.; Tarp, F. Industry switching in developing countries. World Bank Econ. Rev. 2012, 27(2), 357-388. (15) Hussein, Z.; Hertel, T.; Golub, A. Climate change mitigation policies and poverty in developing countries. Environ. Res. Lett. 2013, 8(3), 035009. (16) Pandey, R. Energy policy modelling: agenda for developing countries. Energy Pol. 2002, 30(2), 97-106. (17) Kolk, A.; van den Buuse, D. In search of viable business models for development: sustainable energy in developing countries. Corp. Govern. 2012, 12(4), 551-567. (18) Sovacool, B. K. The political economy of energy poverty: A review of key challenges. Energy Sust. Dev. 2012, 16(3), 272-282. (19) Martos, A.; Pacheco-Torres, R.; Ordóñez, J.; Jadraque-Gago, E. Towards successful environmental performance of sustainable cities: Intervening sectors. A review. Renew. Sust. Energy Rev. 2016, 57, 479-495. (20) Thiam, D. R. An energy pricing scheme for the diffusion of decentralized renewable technology investment in developing countries. Energy Pol., 2011, 39(7), 4284-4297.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 50

(21) Bhattacharyya, S. C. Energy access programmes and sustainable development: A critical review and analysis. Energy Sust. Dev. 2012, 16(3), 260-271. (22) Manfren, M.; Caputo, P.; Costa, G. Paradigm shift in urban energy systems through distributed generation: Methods and models. Appl. Energy. 2011, 88(4), 1032-1048. (23) Mandelli, S.; Barbieri, J.; Mereu, R.; Colombo, E. Off-grid systems for rural electrification

in

developing

countries:

Definitions,

classification

and

a

comprehensive literature review. Renew. Sust. Energy Rev. 2016, 58, 1621-1646. (24) Caballero, G. E. C.; Mendoza, L. S.; Martinez, A. M.; Silva, E. E.; Melian, V. R.; Venturini, O. J.; del Olmo, O. A. Optimization of a Dish Stirling system working with DIR-type receiver using multi-objective techniques. Appl. Energy. 2017, 204, 271-286. (25) Zeyringer, M.; Pachauri, S.; Schmid, E.; Schmidt, J.; Worrell, E.; Morawetz, U. B. Analyzing grid extension and stand-alone photovoltaic systems for the cost-effective electrification of Kenya. Energy Sust. Dev. 2015, 25, 75-86. (26) Molina, M. G.; Espejo, E. J. Modeling and simulation of grid-connected photovoltaic energy conversion systems. Int. J. Hydrogen Energy, 2014, 39(16), 8702-8707. (27) Xavier, G. A.; Martins, J. H.; Monteiro, P. M. D. B.; Diniz, A. S. A. C. Simulation of distributed generation with photovoltaic microgrids—Case study in Brazil. Energies. 2015, 8(5), 4003-4023. (28) Bandyopadhyay, S. Design and optimization of isolated energy systems through pinch analysis. Asia-Pac. J. Chem. Eng. 2011, 6(3), 518-526. (29) Stoppato, A.; Cavazzini, G.; Ardizzon, G.; Rossetti, A. A PSO (particle swarm optimization)-based model for the optimal management of a small PV (Photovoltaic)pump hydro energy storage in a rural dry area. Energy. 2014, 76, 168-174. (30) Mayr, D.; Schmid, E.; Trollip, H.; Zeyringer, M.; Schmidt, J. The impact of residential photovoltaic power on electricity sales revenues in Cape Town, South Africa. Util. Policy. 2015, 36, 10-23. (31) Carranza, O.; Figueres, E.; Garcerá, G.; Gonzalez-Medina, R. Analysis of the control structure of wind energy generation systems based on a permanent magnet synchronous generator. Appl. Energy. 2013, 103, 522-538.


Page 25 of 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(32) Hernandez-Escobedo, Q. Wind Energy Assessment for small urban communities in the Baja California peninsula, Mexico. Energies. 2016, 9(10), 805. (33) Rodrigues, R. V.; Rossi, L. A. Performance of small wind turbines: simulation of electricity supply to loads connected to the public or isolated grid. Eng. Agric. 2016, 36(2), 281-290. (34) Molina, M. G.; Mercado, P. E. Stabilization and control of tie-line power flow of microgrid including wind generation by distributed energy storage. Int. J. Hydrogen Energy. 2010, 35(11), 5827-5833. (35) Cendoya, M. G.; Toccaceli, G. M.; Battaiotto, P. E. Wind generation applied to water desalination and H2 production in remote areas with weak networks. Int. J. Hydrogen Energy. 2014, 39(16), 8827-8832. (36) Arbaoui, A.; Asbik, M.; Loudiyi, K.; Benhamou, K. Added value of power control in improving the integration of wind turbines in weak grid conditions. Energy Power Eng. 2010, 2(04), 230. (37) Laghari, J. A.; Mokhlis, H.; Karimi, M.; Bakar, A. H. A.; Mohamad, H. A new technique for islanding operation of distribution network connected with mini hydro. Front. Inf. Technol. Electron. Eng. 2015, 16(5), 418-427. (38) Vilanova, M. R. N.; Balestieri, J. A. P. Hydropower recovery in water supply systems: Models and case study. Energy Convers. Manag. 2014, 84, 414-426. (39) Martínez, M.; Molina, M. G.; Machado, I. R.; Mercado, P. E.; Watanabe, E. H. Modelling and simulation of wave energy hyperbaric converter (WEHC) for applications in distributed generation. Int. J. Hydrogen Energy. 2012, 37(19), 1494514950. (40) Fuentes-Cortés, L. F.; Serna-González, M.; Ponce-Ortega, J. M. Analysis of Carbon Policies in the Optimal Design of Domestic Cogeneration Systems Involving Biogas Consumption. ACS Sust. Chem. Eng. 2017, 5(5), 4429-4442 (41) Silva, T. R.; Barros, R. M.; Tiago Filho, G. L.; dos Santos, I. F. S. Methodology for the determination of optimum power of a Thermal Power Plant (TPP) by biogas from sanitary landfill. Waste Manag. 2017, 65, 75-91. (42) Ray, A.; Jana, K.; De, S. Polygeneration for an off-grid Indian village: Optimization by economic and reliability analysis. Appl. Therm. Eng. 2017, 116, 182-196.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(43) Kalina, J. Integrated biomass gasification combined cycle distributed generation plant with reciprocating gas engine and ORC. Appl. Therm. Eng. 2011, 31(14), 2829-2840. (44) Sami, S.; Marin, E. A Numerical model for predicting dynamic performance of biomass-integrated Organic Rankine Cycle, ORC, system for electricity generation. Am. J. Energy Eng. 2016, 4(3), 26. (45) Villa, A. A. O.; Campos, R. J. A.; Dutra, J. C. C.; Recarte, J.; Guerrero, H. Numerical analysis of energetic, exergetic and ecological efficiency by using natural gas and biogas in cogeneration system. Int. J. Mech. Eng. Autom. 2014, 1(1), 31-40. (46) Stanek, W., Gazda, W., & Kostowski, W. (2015). Thermo-ecological assessment of CCHP (combined cold-heat-and-power) plant supported with renewable energy. Energy. 2017, 92, 279-289. (47) Pérez, N. P.; Machin, E. B.; Pedroso, D. T.; Roberts, J. J.; Antunes, J. S.; Silveira, J. L. Biomass gasification for combined heat and power generation in the Cuban context: Energetic and economic analysis. Appl. Therm. Eng. 2015, 90, 1-12. (48) Ünal, A. N.; Ersöz, Đ.; Kayakutlu, G. Operational optimization in simple trigeneration systems. Appl. Therm. Eng. 2016, 107, 175-183. (49) Moradi, M. H.; Hajinazari, M.; Jamasb, S.; Paripour, M. An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming. Energy. 2013, 49, 86101. (50) Pirkandi, J.; Jokar, M. A.; Sameti, M.; Kasaeian, A.; Kasaeian, F. Simulation and multi-objective optimization of a combined heat and power (CHP) system integrated with low-energy buildings. J. Build. Eng. 2016, 5, 13-23. (51) Fuentes-Cortés, L. F.; Ponce-Ortega, J. M.; Napoles-Rivera, F.; Serna-González, M.; El-Halwagi, M. M. Optimal design of integrated CHP systems for housing complexes. Energy Convers. Manag. 2015, 99, 252-263. (52) Basrawi, F.; Yamada, T.; Obara, S. Y. Theoretical analysis of performance of a micro gas turbine co/trigeneration system for residential buildings in a tropical region. Energy Buildings. 2013, 67, 108-117.


Page 26 of 50

Page 27 of 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(53) Sanaye, S.; Khakpaay, N. Simultaneous use of MRM (maximum rectangle method) and optimization methods in determining nominal capacity of gas engines in CCHP (combined cooling, heating and power) systems. Energy. 2014, 72, 145-158. (54) Abbasi, M.; Deymi–Dashtebayaz, M.; Farzaneh–Gord, M.; Abbasi, S. Assessment of a CHP system based on economical, fuel consumption and environmental considerations. Int. J. Global Warm. 2015, 7(2), 256-269. (55) Rodríguez, C. E. C.; Palacio, J. C. E.; Venturini, O. J.; Lora, E. E. S.; Cobas, V. M.; dos Santos, D. M.; Gialluca, V. Exergetic and economic comparison of ORC and Kalina cycle for low temperature enhanced geothermal system in Brazil. Appl. Therm. Eng. 2013, 52(1), 109-119. (56) Mohammadi, M.; Hosseinian, S. H.; Gharehpetian, G. B. Optimization of hybrid solar energy sources/wind turbine systems integrated to utility grids as microgrid (MG) under pool/bilateral/hybrid electricity market using PSO. Sol. Energy. 2012, 86(1), 112-125. (57) Fuentes-Cortés, L. F.; Dowling, A. W.; Rubio-Maya, C.; Zavala, V. M.; PonceOrtega, J. M. Integrated design and control of multigeneration systems for building complexes. Energy. 2016, 116, 1403-1416 (58) Ajayi, O. O.; Ohijeagbon, O. D.; Mercy, O.; Ameh, A. Potential and econometrics analysis of standalone RE facility for rural community utilization and embedded generation in North-East, Nigeria. Sust. Cities Soc. 2016, 21, 66-77. (59) Gazda, W.; Stanek, W. Energy and environmental assessment of integrated biogas trigeneration and photovoltaic plant as more sustainable industrial system. Appl. Energy. 2016, 169, 138-149. (60) Das, D. C.; Roy, A. K.; Sinha, N. GA based frequency controller for solar thermal– diesel–wind hybrid energy generation/energy storage system. Int. J. Elec. Power. 2012, 43(1), 262-279. (61) Yazdanpanah, M. A.; Barakati, S. M.; Farahat, S. An efficient sizing method with suitable energy management strategy for hybrid renewable energy systems. Int. T. Electr. Energy. 2014, 24(10), 1473-1492. (62) Yazdanpanah, M. A. Modeling and sizing optimization of hybrid photovoltaic/wind power generation system. J. Ind. Eng. Int. 2014, 10(1), 49.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(63) Kammen, D. M.; Kirubi, C. Poverty, energy, and resource use in developing countries. Ann. N. Y. Acad. Sci. 2008, 1136(1), 348-357. (64) Ren, H.; Zhou, W.; Gao, W.; Wu, Q. Promotion of energy conservation in developing countries through the combination of ESCO and CDM: A case study of introducing distributed energy resources into Chinese urban areas. Energy Pol. 2011, 39(12), 8125-8136. (65) Ockwell, D. G. Energy and economic growth: Grounding our understanding in physical reality. Energy Pol. 2008, 36(12), 4600-4604. (66) Heffner, G.; Maurer, L.; Sarkar, A.; Wang, X. Minding the gap: World Bank's assistance to power shortage mitigation in the developing world. Energy. 2010, 35(4), 1584-1591. (67) Bhattacharyya, S. C. Financing energy access and off-grid electrification: A review of status, options and challenges. Renew. Sust. Energy Rev. 2013, 20, 462-472. (68) Zhou, Z.; Liu, P.; Li, Z.; Ni, W. An engineering approach to the optimal design of distributed energy systems in China. Appl. Therm. Eng. 2013, 53(2), 387-396. (69) Zangeneh, A.; Jadid, S.; Rahimi-Kian, A. A hierarchical decision making model for the prioritization of distributed generation technologies: A case study for Iran. Energy Pol. 2009, 37(12), 5752-5763. (70) Maghanki, M. M.; Ghobadian, B.; Najafi, G.; Galogah, R. J. Micro combined heat and power (MCHP) technologies and applications. Renew. Sust. Energy Rev. 2013, 28, 510-524. (71) Coimbra-Araújo, C. H.; Mariane, L.; Júnior, C. B.; Frigo, E. P.; Frigo, M. S.; Araújo, I. R. C.; Alves, H. J. Brazilian case study for biogas energy: production of electric power, heat and automotive energy in condominiums of agroenergy. Renew. Sust. Energy Rev. 2014, 40, 826-839. (72) Islas, J.; Manzini, F.; Masera, O. A prospective study of bioenergy use in Mexico. Energy. 2007, 32(12), 2306-2320. (73) Devabhaktuni, V.; Alam, M.; Depuru, S. S. S. R.; Green, R. C.; Nims, D.; Near, C. Solar energy: Trends and enabling technologies. Renew. Sust. Energy Rev. 2013, 19, 555-564.


Page 28 of 50

Page 29 of 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(74) Rahbar, K.; Mahmoud, S.; Al-Dadah, R. K.; Moazami, N.; Mirhadizadeh, S. A. Review of organic Rankine cycle for small-scale applications. Energy Convers. Manag. 2017, 134, 135-155. (75) Chandel, S. S.; Shrivastva, R.; Sharma, V.; Ramasamy, P. Overview of the initiatives in renewable energy sector under the national action plan on climate change in India. Renew. Sust. Energy Rev. 54, 2016, 866-873. (76) Pereira, M. G.; Camacho, C. F.; Freitas, M. A. V.; Da Silva, N. F. The renewable energy market in Brazil: Current status and potential. Renew. Sust. Energy Rev. 2012, 16(6), 3786-3802. (77) Sheng, Y.; Shi, X. Energy market integration and equitable growth across countries. Appl. Energy. 2013, 104, 319-325. (78) Pollitt, M. G. The role of policy in energy transitions: Lessons from the energy liberalisation era. Energy Pol. 2012, 50, 128-137. (79) Halsnæs, K.; Shukla, P. Sustainable development as a framework for developing country participation in international climate change policies. Mitig. Adapt. Strat. Gl. 2008, 13(2), 105-130. (80) Fuentes-Cortés, L. F.; Ávila-Hernández, A.; Serna-González, M.; Ponce-Ortega, J. M. Optimal design of CHP systems for housing complexes involving weather and electric market variations. Appl. Therm. Eng. 2015, 90, 895-906. (81) Samper, M. E.; Vargas, A. Investment decisions in distribution networks under uncertainty with distributed generation—Part II: Implementation and results. IEEE T. Power Syst. 2013, 28(3), 2341-2351. (82) Prenc, R.; Škrlec, D.; Đurović, M. Ž. The implementation of capital budgeting analysis for distributed generation allocation problems. Electr. Eng. Springer. 2015, 97(3), 225-238. (83) Akbari, K.; Nasiri, M. M.; Jolai, F.; Ghaderi, S. F. Optimal investment and unit sizing of distributed energy systems under uncertainty: A robust optimization approach. Energy Buildings. 2014, 85, 275-286. (84) Fuentes-Cortés, L. F.; Santibañez-Aguilar, J. E.; Ponce-Ortega, J. M. Optimal design of residential cogeneration systems under uncertainty. Comput. Chem. Eng. 2016, 88, 86-102.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(85) Blum, N. U.; Wakeling, R. S.; Schmidt, T. S. Rural electrification through village grids—Assessing the cost competitiveness of isolated renewable energy technologies in Indonesia. Renew. Sust. Energy Rev. 2013, 22, 482-496. (86) Mirkhani, S.; Saboohi, Y. Stochastic modeling of the energy supply system with uncertain fuel price–A case of emerging technologies for distributed power generation. Appl. Energy. 2012, 93, 668-674. (87) Wierzbowski, M.; Lyzwa, W.; Musial, I. MILP model for long-term energy mix planning with consideration of power system reserves. Appl. Energy. 2016, 169, 93111. (88) Heidari, A.; Aslani, A.; Hajinezhad, A. Scenario planning of electricity supply system: case of Iran. J. Sci. Technol. Pol. Manag. 2017, 8(3), 299-330. (89) Žarković, M.; Škokljev, I. (2013). Case study–Serbia: Regulated and market based power system production capacity planning. INFOTEH-JAHORINA. 2013, 12, 136 – 141. (90) Banshwar, A.; Sharma, N. K.; Sood, Y. R.; Shrivastava, R. Market based procurement of energy and ancillary services from Renewable Energy Sources in deregulated environment. Renew. Energy. 2017, 101, 1390-1400. (91) Thiam, D. R.; Benders, R. M.; Moll, H. C. Modeling the transition towards a sustainable energy production in developing nations. Appl. Energy. 2012, 94, 98-108. (92) Juroszek, Z.; Kudelko, M. A model of optimization for local energy infrastructure development. Energy. 2016, 96, 625-643. (93) Ramamurthi, P. V.; Fernandes, M. C.; Nielsen, P. S.; Nunes, C. P. Utilisation of rice residues for decentralised electricity generation in Ghana: An economic analysis. Energy. 2016, 111, 620-629. (94) Andiappan, V.; Ng, D. K. Synthesis of tri-generation systems: Technology selection, sizing and redundancy allocation based on operational strategy. Comput. Chem. Eng. 2016, 91, 380-391. (95) Bas, E. The integrated framework for analysis of electricity supply chain using an integrated SWOT-fuzzy TOPSIS methodology combined with AHP: The case of Turkey. Int. J. Elec. Power. 2013, 44(1), 897-907.


Page 30 of 50

Page 31 of 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(96) Sampaio, H. C.; Dias, R. A.; Balestieri, J. A. P. Sustainable urban energy planning: The case study of a tropical city. Appl. Energy. 2013, 104, 924-935. (97) Gitizadeh, M.; Kaji, M.; Aghaei, J. Risk based multiobjective generation expansion planning considering renewable energy sources. Energy, 2013, 50, 74-82. (98) Contreras, J.; Rodríguez, Y. E. Incentives for wind power investment in Colombia. Renew. Energy. 2016, 87, 279-288. (99) Khadivi, S.; Moradi, M. A. Cost‐benefit analysis of energy saving strategies adopted by the commercial sector in Tehran. Environ. Prog. Sust. Energy. 2015, 34(3), 850857. (100) Fuentes-Cortés, L. F.; Ma, Y.; Ponce-Ortega, J. M.; Ruiz-Mercado, G.; Zavala, V. M. Valuation of water and emissions in energy systems. Appl. Energy. 2016. DOI: http://dx.doi.org/10.1016/j.apenergy.2016.09.030. (101) Shafiei, E.; Saboohi, Y.; Ghofrani, M. B. Optimal policy of energy innovation in developing countries: Development of solar PV in Iran. Energy Pol. 2009, 37(3), 1116-1127. (102) Recalde, M. Y. The different paths for renewable energies in Latin American Countries: the relevance of the enabling frameworks and the design of instruments. Wiley Interdiscip. Rev.: Energy Environ. 2016, 5(3), 305-326. (103) Jamasb, T.; Nepal, R.; Timilsina, G. R. A quarter century effort yet to come of age: a survey of electricity sector reform in developing countries. Energy J., 2017, 38(3). (104) Ibarra-Yunez, A. Energy reform in Mexico: Imperfect unbundling in the electricity sector. Util. Policy. 2015, 35, 19-27. (105) Yaqoot, M.; Diwan, P.; Kandpal, T. C. Review of barriers to the dissemination of decentralized renewable energy systems. Renew. Sust. Energy Rev. 2016, 58, 477490. (106) Nagayama, H. Electric power sector reform liberalization models and electric power prices in developing countries: An empirical analysis using international panel data. Energy Econ. 2009, 31(3), 463-472. (107) Inoue, C. F.; Lazzarini, S. G.; Musacchio, A. Leviathan as a minority shareholder: Firm-level implications of state equity purchases. Acad. Manag. J. 2013, 56(6), 17751801.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 50

(108) Baker, L.; Newell, P.; Phillips, J. The political economy of energy transitions: The case of South Africa. New Political Econ. 2014, 19(6), 791-818. (109) Imaz, M.; Imaz, M.; Sheinbaum, C.; Sheinbaum, C. Science and technology in the framework of the sustainable development goals. World J. Sci. Technol. Sustain. 2017, 14(1), 2-17. (110) Lienert, M.; Lochner, S. The importance of market interdependencies in modeling energy systems–the case of the European electricity generation market. Int. J. Elec. Power. 2012, 34(1), 99-113. (111) Basiago, A. D. Economic, social, and environmental sustainability in development theory and urban planning practice. Environmentalist. 1998, 19(2), 145-161. (112) Pacione, M. Urban environmental quality and human wellbeing—a social geographical perspective. Landsc. Urban Plan. 2003, 65(1), 19-30. (113) De Souza, M. L. Social movements as ‘critical urban planning’ agents. City. 2006, 10(3), 327-342. (114) de la Barrera, F.; Reyes-Paecke, S.; Harris, J.; Bascuñán, D.; Farías, J. M. People’s perception influences on the use of green spaces in socio-economically differentiated neighborhoods. Urban For. Urban Gree. 2016, 20, 254-264. (115) Fernández, I.; Ruiz, M. C. Descriptive model and evaluation system to locate sustainable industrial areas. J. Cleaner Prod. 2009, 17(1), 87-100. (116) Huang, Z.; Yu, H.; Peng, Z.; Zhao, M. Methods and tools for community energy planning: A review. Renew. Sust. Energy Rev. 115, 42, 1335-1348. (117) Mandelli, S.; Barbieri, J.; Mereu, R.; Colombo, E. Off-grid systems for rural electrification

in

developing

countries:

Definitions,

classification

and

a

comprehensive literature review. Renew. Sust. Energy Rev. 2016, 58, 1621-1646. (118) Pavičević, M.; Novosel, T.; Pukšec, T.; Duić, N. (2017). Hourly optimization and sizing of district heating systems considering building refurbishment–Case study for the city of Zagreb. Energy. 2017, 137, 1264-1276. (119) Fuentes-Cortés, L. F.; Martinez-Gomez, J.; Ponce-Ortega, J. M. Optimal design of inherently safer domestic combined heat and power systems. ACS Sust. Chem. Eng. 2015, 4(1), 188-201.


Page 33 of 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(120) Pérez-Fortes, M.; Laínez-Aguirre, J. M.; Arranz-Piera, P.; Velo, E.; Puigjaner, L. Design of regional and sustainable bio-based networks for electricity generation using a multi-objective MILP approach. Energy. 2012, 44(1), 79-95. (121) Beck, J.; Kempener, R.; Cohen, B.; Petrie, J. A complex systems approach to planning, optimization and decision making for energy networks. Energy Pol. 2008, 36(8), 2795-2805. (122) Herran, D. S.; Nakata, T. Design of decentralized energy systems for rural electrification in developing countries considering regional disparity. Appl. Energy. 2012, 91(1), 130-145. (123) Ho, W. S.; Khor, C. S.; Hashim, H.; Lim, J. S.; Ashina, S.; Herran, D. S. Optimal operation of a distributed energy generation system for a sustainable palm oil-based eco-community. Clean Technol. Environ. Policy. 2015, 17(6), 1597-1617. (124) Campana, P. E.; Holmberg, A.; Pettersson, O.; Klintenberg, P.; Hangula, A.; Araoz, F. B.; Yan, J. An open-source optimization tool for solar home systems: A case study in Namibia. Energy Convers. Manag. 2016, 130, 106-118. (125) Rojas-Zerpa, J. C.; Yusta, J. M. Application of multicriteria decision methods for electric supply planning in rural and remote areas. Renew. Sust. Energy Rev. 2015, 52, 557-571. (126) Brass, J. N.; Carley, S.; MacLean, L. M.; Baldwin, E. Power for development: A review of distributed generation projects in the developing world. Annu. Rev. Environ. Resour. 2012, 37, 107-136. (127) Bhattacharyya, S. C. Review of alternative methodologies for analysing off-grid electricity supply. Renew. Sust. Energy Rev. 2012, 16(1), 677-694. (128) González-Bravo, R.; Fuentes-Cortés, L. F.; Ponce-Ortega, J. M. Defining priorities in the design of power and water distribution networks. Energy. 2017, 137, 1026-1040. (129) Miller, C. A.; Richter, J.; O’Leary, J. Socio-energy systems design: a policy framework for energy transitions. Energy Res. Soc. Sci. 2015, 6, 29-40. (130) King, B. G.; Pearce, N. A. The contentiousness of markets: Politics, social movements, and institutional change in markets. Annu. Rev. Sociol. 2010, 36, 249267.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(131) Adil, A. M.; Ko, Y. Socio-technical evolution of decentralized energy systems: A critical review and implications for urban planning and policy. Renew. Sust. Energy Rev. 2016, 57, 1025-1037. (132) Jonkman, S. N.; Van Gelder, P. H. A. J. M.; Vrijling, J. K. An overview of quantitative risk measures for loss of life and economic damage. J. Hazard. Mater. 2003, 99(1), 1-30. (133) Shove, E.; Walker, G. What is energy for? Social practice and energy demand. Theor. Cult. Soc. 2014, 31(5), 41-58. (134) González-Eguino, M. Energy poverty: An overview. Renew. Sust. Energy Rev. 2015, 47, 377-385. (135) Tasri, A.; Susilawati, A. Selection among renewable energy alternatives based on a fuzzy analytic hierarchy process in Indonesia. Sust. Energy Technol. Assess. 2014, 7, 34-44. (136) Morales-Durán, V.; Fuentes-Cortes, L. F.; González-Brambila, M.; El-Halwagi, M. M.; Ponce-Ortega, J. M. Involving Environmental Assessment in the Optimal Design of Domestic Cogeneration Systems. Process Integr. Optim. Sust. 2017, 1(1), 15-32. (137) Basrawi, F.; Ibrahim, T. K.; Habib, K.; Yamada, T. Effect of operation strategies on the economic and environmental performance of a micro gas turbine trigeneration system in a tropical region. Energy. 2016, 97, 262-272. (138) Akinyele, D. O.; Rayudu, R. K. Techno-economic and life cycle environmental performance analyses of a solar photovoltaic microgrid system for developing countries. Energy. 2016, 109, 160-179. (139) Ehyaei, M. A.; Ahmadi, P.; Atabi, F.; Heibati, M. R.; Khorshidvand, M. Feasibility study of applying internal combustion engines in residential buildings by exergy, economic and environmental analysis. Energy Buildings. 2012, 55, 405-413. (140) Abdollahi, G.; Meratizaman, M. Multi-objective approach in thermoenvironomic optimization of a small-scale distributed CCHP system with risk analysis. Energy Buildings. 2011, 43(11), 3144-3153. (141) Nojavan, S.; Majidi, M.; Najafi-Ghalelou, A.; Ghahramani, M.; Zare, K. A costemission model for fuel cell/PV/battery hybrid energy system in the presence of


Page 34 of 50

Page 35 of 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


demand response program: ε-constraint method and fuzzy satisfying approach. Energy Convers. Manag. 2017, 138, 383-392. (142) Basrawi, F.; Yamada, T.; Obara, S. Y. Economic and environmental based operation strategies of a hybrid photovoltaic–microgas turbine trigeneration system. Appl. Energy. 2014, 121, 174-183. (143) Zangeneh, A.; Jadid, S.; Rahimi-Kian, A. Promotion strategy of clean technologies in distributed generation expansion planning. Renew. Energy. 2009, 34(12), 2765-2773. (144) Wei, T.; Yang, S.; Moore, J. C.; Shi, P.; Cui, X.; Duan, Q.; Yang, Z. Developed and developing world responsibilities for historical climate change and CO2 mitigation. Proc. Natl. Acad. Sci. U. S. A. 2012, 109(32), 12911-12915. (145) The world’s cities in 2016. United Nations, Department of Economic and Social Affairs,

Population

Division.

2016.

http://www.un.org/en/development/desa/population/publications/pdf/urbanization/the _worlds_cities_in_2016_data_booklet.pdf (Accessed Sep 6, 2017) (146) Kumar, P.; Gurjar, B. R.; Nagpure, A. S.; Harrison, R. M. Preliminary estimates of nanoparticle number emissions from road vehicles in megacity Delhi and associated health impacts. Environ. Sci. Technol. 2011, 45(13), 5514-5521. (147) Kumar, P.; Jain, S.; Gurjar, B. R.; Sharma, P.; Khare, M.; Morawska, L.; Britter, R. New Directions: Can a “blue sky” return to Indian megacities?. Atmos. Environ. 2013, 71, 198-201. (148) Orlando, J. P.; Alvim, D. S.; Yamazaki, A.; Corrêa, S. M.; Gatti, L. V. Ozone precursors for the São Paulo metropolitan area. Sci. Total Environ. 2010, 408(7), 1612-1620. (149) Arceo, E.; Hanna, R.; Oliva, P. Does the effect of pollution on infant mortality differ between developing and developed countries? Evidence from Mexico City. Econ. J. 2016, 126(591), 257-280. (150) Martínez-Zarzoso, I.; Maruotti, A. The impact of urbanization on CO2 emissions: evidence from developing countries. Ecol. Econ. 2011, 70(7), 1344-1353. (151) Bilgen, S. Structure and environmental impact of global energy consumption. Renew. Sust. Energ. Rev. 2014, 38, 890-902.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(152) Oreskes, N. Metaphors of warfare and the lessons of history: time to revisit a carbon tax?. Clim. Change. 2011, 104(2), 223-230. (153) Kallis, G.; Gómez-Baggethun, E.; Zografos, C. To value or not to value? That is not the question. Ecol. Econ. 2013, 94, 97-105. (154) Zhang, Y. I.; Singh, S.; Bakshi, B. R. Accounting for ecosystem services in life cycle assessment, Part I: a critical review. Environ. Sci. Technol., 2010, 44(7), 2232-2242. (155) Hertwich, E. G. Life cycle approaches to sustainable consumption: a critical review. Environ. Sci. Technol. 2005, 39(13), 4673-4684. (156) Reap, J.; Roman, F.; Duncan, S.; Bras, B. A survey of unresolved problems in life cycle assessment. Int. J. Life Cycle Assess. 2008, 13(5), 374. (157) Makropoulos, C. K.; Butler, D. Distributed water infrastructure for sustainable communities. Water Resour. Manag. 2010, 24(11), 2795-2816. (158) Siddiqi, A.; Anadon, L. D. The water–energy nexus in Middle East and North Africa. Energy Pol. 2011, 39(8), 4529-4540. (159) Lotfalipour, M. R.; Falahi, M. A.; Ashena, M. Economic growth, CO2 emissions, and fossil fuels consumption in Iran. Energy. 2010, 35(12), 5115-5120. (160) TeymouriHamzehkolaei, F.; Sattari, S. Technical and economic feasibility study of using Micro CHP in the different climate zones of Iran. Energy. 2011, 36(8), 47904798. (161) Hosseini, S. E.; Andwari, A. M.; Wahid, M. A.; Bagheri, G. A review on green energy potentials in Iran. Renew. Sust. Energy Rev. 2013, 27, 533-545. (162) Andiappan, V.; Ng, D. K.; Bandyopadhyay, S. Synthesis of biomass-based trigeneration systems with uncertainties. Ind. Eng. Chem. Res. 2014, 53(46), 1801618028. (163) Cao, Y.; Fuentes-Cortes, L. F.; Chen, S.; Zavala, V. M. Scalable modeling and solution of stochastic multiobjective optimization problems. Comput. Chem. Eng. 2017, 99, 185-197. (164) Allan, G.; Eromenko, I.; Gilmartin, M.; Kockar, I.; McGregor, P. The economics of distributed energy generation: A literature review. Renew. Sust. Energy Rev. 2015, 42, 543-556.


Page 36 of 50

Page 37 of 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(165) Coello, C. A. C. Evolutionary multi-objective optimization: A critical review. In Evolutionary optimization, Springer, United States, 2003, 117-146. (166) Bhoskar, M. T.; Kulkarni, M. O. K.; Kulkarni, M. N. K.; Patekar, M. S. L.; Kakandikar, G. M.; Nandedkar, V. M. Genetic algorithm and its applications to mechanical engineering: A review. Mater. Today Proc. 2015, 2(4-5), 2624-2630. (167) Richter, H.; Yang, S. Dynamic optimization using analytic and evolutionary approaches: A comparative review. In Handbook of Optimization, Springer, Berlin Heidelberg, 2013, 1-28. (168) Kurka, T.; Blackwood, D. Selection of MCA methods to support decision making for renewable energy developments. Renew. Sust. Energy Rev. 2013, 27, 225-233. (169) Dowling, A. W.; Ruiz-Mercado, G.; Zavala, V. M. A framework for multistakeholder decision-making and conflict resolution. Comput. Chem. Eng. 2016, 90, 136-150. (170) Altbach, P. G. Advancing the national and global knowledge economy: the role of research universities in developing countries. Stud. High. Educ. 2013, 38(3), 316-330. (171) Alexandrov, N. M.; Lewis, R. M. An overview of first-order model management for engineering optimization. Optim. Eng. 2001, 2(4), 413-430. (172) Fritsche, U. R. Modeling externalities: Cost-effectiveness of reducing environmental impacts. In Integrated Electricity Resource Planning, Springer Netherlands, 1994, 67-82. (173) Shabman, L.; Stephenson, K. Environmental valuation and its economic critics. J. Water Resour. Plan. Manag. 2000, 126(6), 382-388. (174) Savenije, H. H. Why water is not an ordinary economic good, or why the girl is special. Physics and Chemistry of the Earth, Parts A/B/C. 2002, 27(11), 741-744. (175) Alhoori, H.; Furuta, R. Do altmetrics follow the crowd or does the crowd follow altmetrics?. In Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries, IEEE Press. 2014, September, 375-378. (176) Costa, E.; Pesci, C. Social impact measurement: why do stakeholders matter?. Sust. Account. Manag. Pol. J. 2016, 7(1), 99-124. (177) Ludu, A. Boundaries in Social Systems. In Boundaries of a Complex World. Springer Berlin Heidelberg, 2016, 79-105.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 50

(178) Sharma, D. C. Transforming rural lives through decentralized green power. Futures, 2007, 39(5), 583-596. (179) Jairaj, B.; Martin, S.; Ryor, J.; Dixit, S.; Gambhir, A.; Chunekar, A.; Bharvirkar, R.; Jannuzzi, G.; Sukenaliev, S.; Wang, T. The Future Electricity Grid. World Resources Institute,

2016.

https://www.wri.org/sites/default/files/The_Future_Electricity_Grid.pdf

(accessed

Nov 11, 2017) (180) World

development

indicators

2016,

The

World

Bank,

USA

2016.

http://databank.worldbank.org/data/download/site-content (accessed Jul 10, 2017)


Page 39 of 50

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Tables Table 1. Relevant aspects of DG technologies research on developing countries. Primary Energy sources

Solar

Tech.

Storage

Utilities

User

SC / SE

--

Power

--

--

Efficiency

PV

--

Power

Res.

On

Sizing

-Bat.

Power Power

Grid Res.

On On

Supply quality Sizing

PV

Bat.

Power

Res.

Off

Sizing

PV

Bat., pumped water

Power Water

Res.

Off

PV

Bat.

Power

Res.

On

WT

--

Power

--

WT

--

Power

WT

Bat.

WT

SCES

WT

C

Zeyringer et al. 25 26

Molina et al. Xavier 27 Bandyopadhyay

Brazil Kenya Argentina Brazil

Nigeria

Mayr et al. 30

--

Storage synchronization Efficiency

South Africa Mexico

Res.

On

Sizing

Power

Res.

On / Off

Power

Grid

On

Efficiency, supply quality Storage synchronization

Desal.

On

--

Power

HT

--

Biomass /CHP Natural gas / ICE / MT Natural gas / ICE /

24

Stoppato et al. 29

HT

Biogas / ICE / PV

Caballero et al.

Efficiency

--

C

Country

India

WT

WEHC Biogas / FC / ICE / MT / SE Biogas / TPP Biomass / ethanol / ICE Biomass / ICE / ORC Biomass / ICE / ORC Biogas / MT

Author

28

Power, water Power

Hydraulic

Fossil fuels

Approach

PV PV

Wind

Biofuels

Grid

Power, water Power

Grid

On

Mult.

On

Res.

On

Supply quality Supply quality Supply quality, islanding operation. Efficiency

Carranza et al.31 HernándezEscobedo 32 Rodrigues et al.33

Mexico Brazil

Molina et al.34

Argentina

Cendoya et al.35

Argentina

36

Morocco

Laghari et al. 37

Malaysia

Arbaoui et al.

Vilanova et al.

Brazil

38

Grid

On

Sizing

Martinez et al.

39

Brazil

TST

Power, heat

Res.

On

Sizing, selection

Fuentes-Cortés et al. 40

Mexico

--

Power

Mult.

On

Sizing

Silva et al. 41

Brazil

--

Power, cooling

Res.

Off

Efficiency

Ray et al. 42

India

--

Power

--

--

Efficiency

Kalina 43

Poland

--

Power, heat

--

--

Efficiency

Sami et al. 44

Ecuador

--

--

Efficiency

Villa et al. 45

Brazil

Res.

On

Efficiency

Stanek et al. 46

Poland

Res.

Off

Efficiency

Pérez et al. 47

Cuba

Res. / Ind.

On

Sizing

Ünal et al. 48

Turkey

Hosp.

On

Supply quality

Moradi et al. 49

Iran

-TST ----

Power, heat Power, heat, cooling Power, heat Power, heat, cooling Power, heat



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

MT Natural gas / MT Natural gas / ICE / MT / SE / FC

Geothermal sources

Hybrid systems

Page 40 of 50

--

Power, heat

Res.

--

Efficiency

Pirkandi et al. 50

Iran

TST

Power, heat

Res.

On

Sizing, selection

Fuentes-Cortes et al. 51

Mexico

Res.

On

Efficiency

Basrawi et al. 52

Malaysia

Res.

On

Sizing

Sanaye et al. 53

Iran

Edu.

On

Sizing

Abassi et al.54

Iran

Power, heat, cooling Power, heat, cooling Power, heat, cooling

Natural gas / MT

--

Natural gas / ICE

TST

Natural gas / ICE

--

ORC / Kalina

--

Power

--

--

PV / WT

Bat.

Power

Res.

On

ICE / FC / MT / SE / SC

TST

Power Heat Cooling

Res.

On

PV / WT

Bat

Power

Res.

Biogas / ICE / PV

TST

Power, heat, cooling

SC / WT / FC /PV

Bat, FW, C, AE

PV / WT PV / WT

Efficiency, selection Micro-grid interaction

Rodriguez et al. 55

Brazil

Mohammadi et al. 56

Iran

Selection, sizing


Mexico

Off

Standalone operation

Ajayi et al. 58

Nigeria

Res.

On

Efficiency

Gazda et al. 59

Poland

Power

Res.

Off

Supply quality

Das, et al. 60

India

Bat

Power

Res.

Off

Yazdanpanah et al. 61

Iran

Bat

Power

Res.

Off

Size, supply quality Size, supply quality

Yazdanpanah 62

Iran


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Table 2. Economic issues addressed for developing countries. Issue

Prices and tariffs

Tech.

Country

Res.


Mexico

Natural Gas / Diesel / ICE / MT

Effects of uncertainty on demand taking in account the financial risk.

Mult.

Samper et al.81

Argentina

RES – Mult.

Analysis of relation between feed-in tariffs and allocation of DG.

Mult.

Prenc et al.82

Croatia

Uncertainty on electric tariffs and prices.

Hosp.

Akbari et al.83

Iran

Res.

Fuentes-Cortés et al.84

Mexico

Res.

Blum et al.85

Indonesia

PV / CHP / SC ICE / FC / MT / SE

Sensitivity to the changes on the energy price. Determining competitive costs (LCOE) in operation of DG and attracting private investment.

WT / PV

Uncertainty on fuel prices.

Res.

PV

Revenues on non-liberalized markets. Influence of power systems reserves in the integration of DG to the power mix. Regulations and government participation Size of facilities on regulated market Energy investments on liberalized markets. Implementation of sustainable technologies on transition markets. Influence of internal and external costs in the integration of DG on municipalities. Costs projection of integration of biogas from sanitary landfill and urban energy production including demographic conditions.

Res.

Mult. Mult. Mult. Mult. Mult.

Biogas / TPP

Mult. Mult. Mult. Mult.

WT

ICE / SC / PV Biogas / ICE PV

Effects of fuel prices and different incentive systems. Effects of financial support based on Public Private Partnership for implementing DG on isolated communities. Influence of environmental taxes for investing on DG for commercial buildings. Valuation of environmental externalities (Water consumption and GHGE) Impacts of incentives for innovation in energy technologies.

India

Poland

Res.

Silva et al. 41

Brazil

Res.

RES – Mult.

Iran Serbia

Juroszek et al.92

Allocation of trigeneration plants.

Effects of carbon tax and bonus.

Poland

Res.

Biomas / MT

Biogas / ICE / FC / MT / SE

South Africa

Senegal and South Africa

Res.

Mult.

Iran

Thiam et al.91

Costs of biofuels and supply chain.

Electricity supply chain. Coupling of DG to the electric sector. Energy dispatch based on costs, allocation and urban integration.

Mirkhani et al.86 Mayr et al. 30 Wierzbowski et al.87 Heidari et al. 88 Žarković 89 Banshwar et al.90

Mult.

Biomass / TPP

Mult.

Taxes and incentives

Author

Effects of hourly tariffs of energy from external carriers.

Mult.

Interaction of energy producers

End user

ICE

Diesel / PV / HT

Market conditions

Economic perspective

Ramamurthi et al.93 Andiappan et al. 94

Ghana Malaysia

Mult.

Bas 95

Turkey

Mult.

Sampaio et al.96

Brazil

Res.

Fuentes-Cortes et al. 40

Mexico

Mult.

Gitizadeh et al.97

Iran

Res.

Contreras et al.98

Colombia

Com.

Khadivi et al. 99

Iran

Res.

Fuentes-Cortes et al.100

Mexico

Mult.

Shafiei et al.101

Iran



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Table 3. Urban and social approaches on DG projects. Technology

Grid

System approach

On

Allocation

On

System operation of district heating.

Reducing external and internal costs of the system. Minimizing operational cost considering energy demand.

On

Expansion of the electric utilities.

Planning and integration of DG facilities.

PV

On

Integration of PV facilities

ICE / FC / MT / SE

On

Allocation, selection and size.

On

Selection and size.

Biofuels / ICE

Off

Allocation and size.

Biomass / TPP

Off

Allocation

Biomass / Mult.

Off / On

Allocation and technologies selection

Biomass / Mult.

Off

Sizing and operation of the system.

PV

On

Sizing and allocation

PV

Off

Sizing and selection.

Mult.

On / Off

Sizing and selection

Mult.

CB / SC / EH / HP

Mult. Urban areas

ICE / FC / MT / SE

Rural areas

Issue addressed

Improving costs of implementing energy technologies Reducing social risk minimizing the fatalities associated to explosions. Minimizing costs and emissions. Maximizing the installed units on different communities. Access of electric utilities in rural areas. Integration of DG to the electric system. Integration of the palm – oil to the energy production Contributions of PV to an extended grid. Use of solar technologies on low-income households. Open source application. Planning new energy facilities

Methodology or Metric

End user

Reference

Social cost

Res.

Juroszek et al. 92

Poland

Changes on the energy behavior

Res.

Pavičević et al. 118

Croatia

Economic and environmental conditions desire for urban development

Mult.

Sampaio et al.96

Brazil

Innovation on energy technologies.

Mult.

Shafiei et al.101

Iran

Quantitative Risk Assessment (QRA)

Res.

FuentesCortes et al.119

Mexico

Uncertainty on demand linked to demographic changes.

Res.

FuentesCotés et al.84

Mexico

Units installed

Res.

PérezFortes et al. 120

Ghana

Communities with access to electricity. Economic disparities and energy integrations.

121

South Africa

Res.

Herran et al.122

Colombia

Development of ecocommunities

Res.

123

Malaysia

Access to electricity on remote areas.

Res.

Zeyringer et al. 25

Kenya

Satisfaction of the end user.

Res.

Campana et al. 124

Namibia

Conflict solution

Mult.

RojasZerpa et al. 125

Venezuela


Res.

Beck et al.

Country

Ho et al.

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Table 4. Environmental approaches used for DG. Technology

Methodology or Metric.

Issue addressed

End user

Mult.

Hierarchy process using weights.

Selecting technologies.

Mult.

ICE / FC / MT / SE

Life Cycle Assessment – Eco-indicator 99

Sizing and selecting technologies.

Res.

Biofuels / ICE

Life Cycle Assessment

Determining generation units and allocation.

Mult.

PV / MT

Life Cycle Cost

PV

Life Cycle Assessment

Integral

Tech.

Specific

Environ. Impact

ICE

Emissions

Biomass / TPP

Emissions

Mult.

Emissions

ICE / FC / MT / SE /SC

Emissions, water.

ICE

Emissions, water

PV / CHP / SC

Emissions

MT

Emissions

PV / FC

Emissions

PV / MT

Emissions

PV / WT / MT / FC

Emissions

Methodology Minimizing external costs of air pollution. Maximizing avoided emissions Constraint on emissions Minimizing the direct emissions and water consumption. Minimizing direct emissions and water consumption. Minimizing costs of emissions Minimizing direct emissions Minimizing direct emissions Minimizing the Emissions Reduction Index Minimizing direct emissions.

Determining operation strategies. Operation on micro-grid environment Issue addressed

Res. Res. End user

Reference Tasri et al.135 MoralesDurán et al.136 PérezForte at al. 120 Basrawi et al.137 Akinyele et al.138 Author

Country Indonesia Mexico

Ghana Malaysia Nigeria Country

Determining the units needed for meeting the energy demand

Res.

Ehyaei et al. 139

Iran

Allocation of energy facilities in rural areas

Res.

Beck et al.121

South Africa

Planning DG facilities.

Mult.

Sampaio et al. 96

Brazil

Selecting and sizing energy facilities.

Res.

FuentesCortés et al. 57

Mexico

Valuating the externalities on GHGE and water consumption in energy systems.

Res.

FuentesCortés et al.100

Mexico

Optimal size of technologies.

Hosp.

Akbari et al. 83

Iran

Optimal operation of CCHP system

Building

Abdollahi et al.140

Iran

Analysis of cost emissions performance.

Res.

Nojavan et al. 141

Iran

Size and storage operation.

Res.

Basrawi et al. 142

Malaysia

Expansion planning.

Res.

Zangeneh et al. 143

Iran



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Table 5. Description of optimization modeling approaches. Technology SC / SE PV

Mode feat. NL GA MILP

Econ.

Objectives Environ. Soc.

Tec. HL, PSP

TC

Multi-obj. strat.

Uncert. Var.

NSGA-II

Det.

Mono-obj.

Det.

PV

NLP

COE

PV / ICE

NL PSO

CC, CF

Mono-obj.

Det.

PV

MILP

TC

Mono-obj.

MINLP

TC

NLP

Biogas / ICE / FC / MT / SE Biogas / TPP Biomass / ICE / PV ICE / MT ICE / MT MT ICE / FC / MT / SE ICE PV / WT ICE / FC / MT / SE / SC SC / WT / FC / PV PV / WT ICE ICE / MT RES – Mult.

Mono-obj.

Det.

Reference

Country

Caballero et al.24 Zeyringer et al. 25

Bandyopadhyay 28

Stoppato et al.

Brazil Kenya India

29

Nigeria

Det.

Mayr et al. 30

South Africa

CS

Det.


Mexico

NB

Mono-obj.

Det.

Silva et al. 41

Brazil

LP

AP

Mono-obj.

Det.

Ray et al. 42

India

LP LP-FLPSO

TC

Mono-obj.

Det.

Ünal et al.48

NPV

MINLPSO NL GA

Det.

Pirkandi et al. 50

Det. Det.

CS

Det.


Mexico

Mono-obj.

Det.

Das et al. 60

India

TC

ExEf GHGE

GHGE, SW ∆f

ACS

Iran

Det.


Mexico

RRI

Mono-obj.

Energy Demand

Samper et al. 81

Argentina

NPV

Mono-obj.

Det.

Prenc et al. 82

Croatia

Akbari et al. 83

Iran


Mexico

Energy demands, costs and prices. Energy demand, costs, prices, ambient temperature.

MINLP

TC

Mult.

MILP

TC

Mult. Biomass / MT RES – Mult.

NLP

TC

Mono-obj.

Det.

MILP

TC

Mono-obj.

Det.

MILP

PLER, EPR

NBI

Det.

TC

Mono-obj.

εConstraint

GHGE

Mono-obj.

GHGE GHGE, SW CInnov

MILP

TC

MINLP

TC

MILP

Iran

εConstraint

GHGE

ICE / FC / MT / SE

NLP

Iran

Yazdanpanah

TC

PV CB / SC / EH / HP ICE / FC / MT / SE Biofuels / ICE

62

Mexico

Det.

MILP

NLP

Fuentes-Cortes et al. 51 Sanaye et al. 53 Mohammadi et al. 56

Iran

NSGA-II

TC

EMR

Det.

PV / CHP / SC

ICE

Iran

Mono-obj.

TC

NLP

Moradi et al.

TC

MINLP

NL GA NL GA

Load Profiles

NPW

TC

MINLP

Mono-obj.

Turkey 49

Constraint multiple conditions εConstraint Mono-obj.

NL-GA

NL-GA NLPSO

GHGE

NPV

GHGE Impact (LCA)

SocRisk 2002

SocC

Det.

CS

Det.

Mono-obj.

Det.

Mono-obj.

Det.

εConstraint εConstraint

Det. Det.


Wierzbowski et al. 87 Juroszek et al. 92 Andiappan et al. 94

Gitizadeh et al. 97

Fuentes-Cortés et al. 100 Shafiei et al. 101 Pavičević et al. 118

Fuentes-Cortés et al. 119 Pérez-Fortes et al. 120

Poland Poland Malaysia Iran Mexico Iran Croatia Mexico Ghana

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Biomass / TPP Biomass / Mult. Biomass / Mult.

DMOO

Det.

Beck et al. 121

South Africa

NPV

Mono-obj.

Det.

Herran et al. 122

Colombia

MILP

TC

Mono-obj.

Det.

Ho et al.123

Malaysia

ICE / FC / MT / SE

MINLP

TC

GHGE, Ecoindicator – 99 LCA

εConstraint

Det.

Morales-Durán et al. 136

Mexico

MT

NL GA

TC

GHGE

MOEA

Det.

MILP

NPV

LP

PV / FC

MILP

PV / WT / MT / FC Biomass / MT / ICE

NL GA

TC TC

MILP

EP

ICE

NLP

TC

GHGE

GHGE GHGE

Units installed

ExEf

εConstraint NSGA-II Mono-obj.

GHGE, SW

CS

Det. Det. Biomass availability Energy demand


Abdollahi et al. 140

Nojavan et al. 141

Zangeneh et al. 143

Andiappan et al.

Iran Iran Iran

162

Malaysia

Cao et al. 163

Mexico


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Caption for Figures: Figure 1. Transition from centralized systems to DG systems. Figure 2. Basic configuration of DG systems. Figure 3. Efficiency comparison between DG and centralized systems.


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Figure 1. Transition from centralized systems to DG systems.



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Figure 2. Basic configuration of DG systems.


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Figure 3. Efficiency comparison between DG and centralized systems.



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FOR TABLE OF CONTENTS USE ONLY

Synopsis: This manuscript presents a review and discussion of the proposed methods based on modeling for implemented integrated approaches for distributed generation in developing countries.


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Perspectives for Implementing Distributed Generation in Developing

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