Optimization of Micro-Grid Electricity Market Based on Multi Agent Modeling Approach

Optimization of Micro-Grid Electricity Market Based on Multi Agent Modeling Approach

Alireza Heidari, Mehdi Moradi, Alireza Aslani, Ahmad Hajinezhad
Copyright: © 2018 |Pages: 23
DOI: 10.4018/IJEOE.2018070101
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Abstract

Micro-grids are the key technologies known to solve challenges such as increased electric demand, fatigue electric installations, electrical leakage and pressures and opposition from environmental advocacy groups. The current article is presenting an improved optimization algorithm based on a differential evolution algorithm to achieve the optimal response for managing distributed energy resources in micro-grids. The simulation results show that: 1) The final cost of network management in systems based on the agent is very favorable compared to a network regardless of the agent and also are economically much more useful and effective in coordinating various energy sources. 2) The results of the proposed algorithm are much better in comparison with the results of the Fireflies optimization algorithm, a differential evolution algorithm and the particle swarm algorithm. This comparison proves the high performance of the algorithm.
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Literature Review

Two novel issues are discussed in the current study include presenting a simulation model by MAS approach besides introducing a new optimization model based on random search methods. An ideal simulation tool can provide useful information for legislators, decision-makers of the electricity market, and market players. This will lead to develop and improve the overall structure of the electricity market. Thus, many recent researches are conducted in electricity industry restructuring as well as electricity financial markets in order to achieve the simulation tools in the electricity markets (Adom, 2017; Gal, Milstein, Tishler, & Woo, 2017; Sharifi, Fathi, & Vahidinasab, 2017; Shokri et al., 2017; Yousefi, MakhdoomiKaviri, Latify, & Rahmati, 2017; Xavier, Goncalves, Dias, & Borba, 2017; Vinagre, et al., 2017). In this way, simulating the market environment and predicting the behavior of participants in various electrical markets will be useful and effective to select the suitable model and appropriate structure for legislators and decision makers in the electricity markets.

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