Abstract
In this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.
Highlights
Local and distributed generation is on the rise due to the constant evolution and improvement of technologies related to the production of electricity using renewable energies, the storage in batteries or other energy storage systems and the intelligent management systems
This section describes the MG used in this work: loads’ profiles considered, Distributed Generation (DG) and types of generators used, distances separating the nodes in the grid, etc
An assessment of the proposed Mo-SL-Coral Reefs Optimization (CRO) algorithm is presented, providing results obtained with different combinations of substrate layers and comparing them with the outcomes of well-known multi-objective algorithms (NSGA-II, Multi-objective Harmony Search)
Summary
Local and distributed generation is on the rise due to the constant evolution and improvement of technologies related to the production of electricity using renewable energies, the storage in batteries or other energy storage systems and the intelligent management systems These advances are leading to the development of Microgrids (MGs), small-sized networks of electricity users (loads) with local sources of electricity supply and, sometimes, even containing energy storage systems [1]. These MGs can operate connected to the main national grid or in islanded mode [2]. From the design determining the optimal power generation mix selection (renewable, diesel, etc.) to satisfy demand for a particular area [7,8,9], the sizing and siting problem [10,11,12,13,14,15,16] or the scheduling to minimize operational costs, environmental impact, quality, etc. while covering the demand [17,18,19,20]
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