Energy Supply System Modeling Tools

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This entry is adapted from the peer-reviewed paper 10.3390/en15072668

The fulfillment of the sustainable development goals of the United Nations (UN) in remote communities undoubtedly goes through the consequent development of the energy supply system (ESS). Structuring a procedure for modeling the ESS, according to development requirements, is vital for decision making.

sustainable livelihoods energy management isolated microgrids renewable energy energy system optimization

1. Introduction

The sustainable development goals (SDGs) promote the need for greater efforts in the research of sustainable energy projects. The inclusion of affordable and clean energy for all is a clear demonstration of the correlation between access to energy and sustainable development, as it modernizes people’s lives, facilitating connectivity, improving health systems, and optimizing production, among other things.

To determine compliance with the SDGs, the United Nations Statistical Commission pertaining to the 2030 Agenda for Sustainable Development proposed an indicator framework ^[1]. The indicators proposed by United Nations (UN) do not allow establishing the relationship between energy demand coverage and the development of remote communities. Dawodu et al. ^[2] and Yang et al. ^[3] enounced the postulate that “what is not measured cannot be controlled”. Developing a system of indicators to establish this relationship, taking into account the work that UN has provided with regard to methodologies to measure development, would be a contribution to the measurement of sustainable development ^[1].

2. Sustainable Livelihoods Approach Indicators

The term “sustainable livelihoods (SLs)” was first used by Robert Chambers in the mid-1980s. These can be defined as the capabilities, assets (both material and social resources), and activities needed to live. A livelihood is sustainable when it can cope with and recover from sudden breaks and shocks and maintain its capabilities and assets both now and in the future without undermining the foundations of its natural resources. Therefore, livelihoods are affected by external effects that increase their resilience and, consequently, decrease their vulnerability according to Duffy et al., Gutiérrez-Montes et al., and Jacobs ^[4]^[5]^[6].

The sustainable livelihoods approach has been used by the DFID and Food and Agriculture Organization (FAO) to analyze how a population or community is developing its livelihoods, as well as to assess changes in these over time ^[7]. This model uses five capitals well known as natural, human, social, physical, and financial to quantify the community’s assets.

The asset pentagon is adopted to graphically represent the quantification of the five capitals. This was developed to allow information about people’s assets to be presented visually, providing important interrelationships among the various assets ^[8]. The asset pentagon shows the state of the assets, where loss implies a deformation or narrowing of the resulting figure when each of the five assets is evaluated. Figure 1 shows how community capitals interrelate to contribute to vulnerabilities and the trend of changes in vulnerabilities. This graph has been modified from the original version of FAO model, focusing on the enablers of energy policies ^[7]; it presents how energy policies, processes (including energy supply planning), and institutions can be decisive in the accumulation or loss of assets.

Figure 1. General vision of sustainable livelihoods framework (FAO) modified for energy projects in remote communities.

The incorporation of cultural and political capital, in the community capitals framework model (CCF) allows analyzing the sustainability of livelihood strategies and the impact of development initiatives in a holistic manner, as it facilitates the identification of the effects (positive and negative) of a livelihood on the remaining capitals and, therefore, on the wellbeing of households and communities according to Cherni et al., Pandey et al., and Scoones ^[9]^[10]^[11].

Additionally, recent works carried out by Jordaan et al. ^[12], Nogueira et al. ^[13], and Butler ^[14], proposed other capitals derived from the previous models as a way of focusing on the objectives pursued.

Table 1 presents a summary of the criteria and factors adopted in models that integrate endogenous variables in the decision-making process taken from the papers consulted that include indicators for the endogenous dimensions and are related to energy projects. The table shows that there is no coincidence of the criteria used in projects based on endogenous dimensions, since these criteria and factors are selected according to the intrinsic characteristics of each project.

Table 1. Criteria and factors of endogenous models for decision-making process.

Capitals	Criteria	Authors								Total
Capitals	Criteria	Akinyele et al. ^[15]	Cherni et al. ^[9]	Bhattarai and Thompson ^[16]	Karthik et al. ^[17]	de Souza Ribeiro et al. ^[18]	Ahmadi and Rezaei ^[19]	Bhattacharyya ^[20]	Zhang et al. ^[21]	Total
Economic	Initial capital and lifecycle costs	1	1	1	1		1		1	6
	Project financing	1	1		1					3
	Returns on investment	1		1	1			1		4
	O&M costs	1		1		1	1		1	5
Technological	Energy demand profiling	1				1	1	1	1	5
	Maturity of available technologies	1	1							2
	Technology selection	1				1				2
	Reliability of supply	1				1	1	1	1	5
	Future energy demand	1								1
	Types of load/appliances	1			1		1		1	4
	Technical design and feasibility evaluation	1				1			1	3
Social	Cooperativism		1							1
	Leadership		1							1
	Common goals	1	1							2
	Project objectives defined	1								1
	Community Involved Level	1								1
	Educating the potential	1								1
	Identifying suitable sites	1								1
	Characterization of the physical resources of the community: housing, aqueducts, roads, etc.							1		1
Environment	Air quality							1		1
	Land							1		1
	Water and water quality							1		1
	Environmental impact and benefits analysis	1						1		2
Political	Presence of political will or government support	1						1		2
	Fiscal incentives							1		1
	Public and political acceptance							1		1
	Regulatory framework for capacity building and job creation	1								1
Total		19	6	3	4	5	5	11	6	59

The literature analysis allowed to verify that there is a relationship between the development models based on the SLs framework and the planning models of ESSs, which is why it is necessary to create a model that allows projecting the expansion of demand in the systems. Energy planning is based on the use of endogenous resources and development policies. As outlined in Table 3, Ankinyele et al. ^[15] included the largest number of criteria, while the most common criterion was initial capital and lifecycle costs.

References

United Nations Economic Commission for Europe. Measuring Sustainable Develoment. 2014. Available online: https://unece.org/statistics/publications/measuring-sustainable-development (accessed on 14 January 2021).
Dawodu, A.; Cheshmehzangi, A.; Williams, A. Expert-Initiated Integrated Approach to the Development of Sustainability Indicators for Neighbourhood Sustainability Assessment Tools: An African Perspective. J. Clean. Prod. 2019, 240, 117759.
Yang, S.; Zhao, W.; Liu, Y.; Cherubini, F.; Fu, B.; Pereira, P. Prioritizing Sustainable Development Goals and Linking Them to Ecosystem Services: A Global expert’s Knowledge Evaluation. Geogr. Sustain. 2020, 1, 321–330.
Duffy, L.N.; Kline, C.; Swanson, J.R.; Best, M.; McKinnon, H. Community Development through Agroecotourism in Cuba: An Application of the Community Capitals Framework. J. Ecotourism 2017, 16, 203–221.
Gutiérrez-Montes, I.A.; de Imbach, P.B.; Ramírez, F.; Payes, J.L.; Say, E.; Banegas, Y.K. Las Escuelas de Campo del MAP-CATIE: Práctica y Lecciones Aprendidas en la Gestión del Conocimiento y la Creación de Capacidades Locales para el Desarrollo Rural Sostenible; CATIE: Cartago, Costa Rica, 2012; p. 67.
Jacobs, C. Measuring Success in Communities: The Community Capitals Framework; South Dakota State University: Brookings, SD, USA, 2011; p. 3.
FAO. M | Guide for Monitoring and Evaluating Land Administration Programs | Organización de las Naciones Unidas para la Alimentación y la Agricultura. 2021. Available online: http://www.fao.org/in-action/herramienta-Administracion-tierras/glossary/m/Es/ (accessed on 4 April 2020).
DFID. Sustainable Livelihoods Guidance Sheets. 1999. Available online: https://www.unscn.org/en/resource-center/archive/sustainable-food-systems-archive?idnews=1534 (accessed on 5 August 2020).
Cherni, J.A.; Dyner, I.; Henao, F.; Jaramillo, P.; Smith, R.; Font, R.O. Energy Supply for Sustainable Rural Livelihoods. A Multi-Criteria Decision-Support System. Energy Policy 2007, 35, 1493–1504.
Pandey, R.; Jha, S.K.; Alatalo, J.M.; Archie, K.M.; Gupta, A.K. Sustainable Livelihood Framework-Based Indicators for Assessing Climate Change Vulnerability and Adaptation for Himalayan Communities. Ecol. Indic. 2017, 79, 338–346.
Scoones, I. Sustainable Rural Livelihoods a Framework for Analysis; IDS: McLean, VA, USA, 1997; p. 22.
Jordaan, A.J.; Sakulski, D.M.; Mashimbye, C.; Mayumbe, F. Measuring Drought Resilience Through Community Capitals. In Resilience; Elsevier: Amsterdam, The Netherlands, 2018; pp. 105–115.
Nogueira, A.; Ashton, W.S.; Teixeira, C. Expanding Perceptions of the Circular Economy through Design: Eight Capitals As Innovation Lenses. Resour. Conserv. Recycl. 2019, 149, 566–576.
Butler, M. Community Forest Enterprise Governance in the Maya Biosphere Reserve. Ph.D. Thesis, University of Minnesota, Minneapolis, MN, USA, 2020; p. 366.
Akinyele, D.; Belikov, J.; Levron, Y. Challenges of Microgrids in Remote Communities: A STEEP Model Application. Energies 2018, 11, 432.
Bhattarai, P.R.; Thompson, S. Optimizing an off-Grid Electrical System in Brochet, Manitoba, Canada. Renew. Sustain. Energy Rev. 2016, 53, 709–719.
Karthik, N.; Parvathy, A.K.; Arul, R. A Review of Optimal Operation of Microgrids. Int. J. Electr. Comput. Eng. 2020, 10, 2842–2849.
Ribeiro, L.; Saavedra, O.R.; De Lima, S.L.; De Matos, J.G. Isolated Micro-Grids With Renewable Hybrid Generation: The Case of Lençois Island. IEEE Trans. Sustain. Energy 2010, 2, 1–11.
Ahmadi, S.E.; Rezaei, N. A New Isolated Renewable Based Multi Microgrid Optimal Energy Management System Considering Uncertainty and Demand Response. Int. J. Electr. Power Energy Syst. 2020, 118, 105760.
Bhattacharyya, S. Review of Alternative Methodologies for Analysing off-Grid Electricity Supply. Renew. Sustain. Energy Rev. 2012, 16, 677–694.
Zhang, L.; Pang, B.; Yi, R.; Gai, P.; Xin, C.; Yang, L.; Li, H. Multi-Objective Day-Ahead Optimal Scheduling of Isolated Microgrid Considering Flexibility. E3S Web Conf. 2018, 53, 01024.

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CARLOS PEREYRA

Felix Santos Garcia

Víctor S Ocaña-Guevara

Alexander Vallejo-Díaz

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1. Introduction

2. Sustainable Livelihoods Approach Indicators

References