Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source

• Published in: Energy (Oxford) Vol. 36; no. 11; pp. 6490 - 6507
• Main Authors: Moghaddam, Amjad Anvari, Seifi, Alireza, Niknam, Taher, Alizadeh Pahlavani, Mohammad Reza
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.11.2011; Elsevier
• Subjects: algorithms; Applied sciences; batteries; Chaotic search; electricity; Energy; energy efficiency; Exact sciences and technology; Micro-grid; Multi-operation management; operating costs; PSO (Particle swarm optimization); renewable energy sources; RESs (Renewable energy sources); socioeconomic development; system optimization; Micro-grid; Chaotic search; PSO (Particle swarm optimization); Multi-operation management; RESs (Renewable energy sources)
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2011.09.017

As a result of today’s rapid socioeconomic growth and environmental concerns, higher service reliability, better power quality, increased energy efficiency and energy independency, exploring alternative energy resources, especially the renewable ones, has become the fields of interest for many modern societies. In this regard, MG (Micro-Grid) which is comprised of various alternative energy sources can serve as a basic tool to reach the desired objectives while distributing electricity more effectively, economically and securely. In this paper an expert multi-objective AMPSO (Adaptive Modified Particle Swarm Optimization algorithm) is presented for optimal operation of a typical MG with RESs (renewable energy sources) accompanied by a back-up Micro-Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the surplus of energy when it’s needed. The problem is formulated as a nonlinear constraint multi-objective optimization problem to minimize the total operating cost and the net emission simultaneously. To improve the optimization process, a hybrid PSO algorithm based on a CLS (Chaotic Local Search) mechanism and a FSA (Fuzzy Self Adaptive) structure is utilized. The proposed algorithm is tested on a typical MG and its superior performance is compared to those from other evolutionary algorithms such as GA (Genetic Algorithm) and PSO (Particle Swarm Optimization). ► Optimal operation of distribution networks and Clean Air Act Amendments in November 1990. ► Use of renewable energy power plants. ► Save energy and environmental pollution.