Optimized network planning of mini-grids for the rural electrification of developing countries

Research output: Contribution to conferencePaper

Abstract

1.2 billion people, predominantly living in remote rural regions in countries of the Global South, currently live without access to any modern source of energy. Options for electrification of these communities include extending existing national grid infrastructure, deploying mini-grids, and installing standalone home systems (SHS). Deriving the most cost effective means of delivering energy to these consumers is a complex, multidimensional problem that normally requires determination on a case-by-case basis. However, optimization of the network planning may help to maximize the socio-economic return of the installed energy system. This paper presents an optimization process that minimizes the installation cost of a mix of generation sources for a rural mini-grid using a multi-objective particle swarm optimization (MOPSO) technique. Minimizing the cost of distribution layout is first formulated as a capacitated minimum spanning tree (CMST) problem and solved using the Esau-Williams method. Multiple cable sizes and source locations are then added to create a multi-level capacitated minimum spanning tree (MLCMST) problem, solved via a Genetic Algorithm (GA) employing Prim-Pred encoding. The method is applied to a case study village in India.

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Developing countries
Planning
Costs
Particle swarm optimization (PSO)
Cables
Genetic algorithms
Economics

Keywords

  • mini grids
  • renewable energy
  • sustainability
  • rural electrification
  • Africa
  • India

Cite this

Nolan, Steven ; Strachan, Scott ; Rakhra, Puran ; Frame, Damien. / Optimized network planning of mini-grids for the rural electrification of developing countries. Paper presented at 2017 IEEE PES PowerAfrica Conference, Accra, Ghana.6 p.
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title = "Optimized network planning of mini-grids for the rural electrification of developing countries",
abstract = "1.2 billion people, predominantly living in remote rural regions in countries of the Global South, currently live without access to any modern source of energy. Options for electrification of these communities include extending existing national grid infrastructure, deploying mini-grids, and installing standalone home systems (SHS). Deriving the most cost effective means of delivering energy to these consumers is a complex, multidimensional problem that normally requires determination on a case-by-case basis. However, optimization of the network planning may help to maximize the socio-economic return of the installed energy system. This paper presents an optimization process that minimizes the installation cost of a mix of generation sources for a rural mini-grid using a multi-objective particle swarm optimization (MOPSO) technique. Minimizing the cost of distribution layout is first formulated as a capacitated minimum spanning tree (CMST) problem and solved using the Esau-Williams method. Multiple cable sizes and source locations are then added to create a multi-level capacitated minimum spanning tree (MLCMST) problem, solved via a Genetic Algorithm (GA) employing Prim-Pred encoding. The method is applied to a case study village in India.",
keywords = "mini grids , renewable energy, sustainability, rural electrification, Africa, India",
author = "Steven Nolan and Scott Strachan and Puran Rakhra and Damien Frame",
note = "(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.; 2017 IEEE PES PowerAfrica Conference : Harnessing Energy, Information and Communications Technology (ICT) for Affordable Electrification of Africa ; Conference date: 27-06-2017 Through 30-06-2017",
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doi = "10.1109/PowerAfrica.2017.7991274",
language = "English",
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Optimized network planning of mini-grids for the rural electrification of developing countries. / Nolan, Steven; Strachan, Scott; Rakhra, Puran; Frame, Damien.

2017. Paper presented at 2017 IEEE PES PowerAfrica Conference, Accra, Ghana.

Research output: Contribution to conferencePaper

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AU - Nolan, Steven

AU - Strachan, Scott

AU - Rakhra, Puran

AU - Frame, Damien

N1 - (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

PY - 2017/7/27

Y1 - 2017/7/27

N2 - 1.2 billion people, predominantly living in remote rural regions in countries of the Global South, currently live without access to any modern source of energy. Options for electrification of these communities include extending existing national grid infrastructure, deploying mini-grids, and installing standalone home systems (SHS). Deriving the most cost effective means of delivering energy to these consumers is a complex, multidimensional problem that normally requires determination on a case-by-case basis. However, optimization of the network planning may help to maximize the socio-economic return of the installed energy system. This paper presents an optimization process that minimizes the installation cost of a mix of generation sources for a rural mini-grid using a multi-objective particle swarm optimization (MOPSO) technique. Minimizing the cost of distribution layout is first formulated as a capacitated minimum spanning tree (CMST) problem and solved using the Esau-Williams method. Multiple cable sizes and source locations are then added to create a multi-level capacitated minimum spanning tree (MLCMST) problem, solved via a Genetic Algorithm (GA) employing Prim-Pred encoding. The method is applied to a case study village in India.

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