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A Multi-Objective Optimization Method on Consumer's Benefit in Peer-to-peer Energy Trading

Authors:
Mitsue Imahori
Ryo Hase
Norihiko Shinomiya

Keywords: Peer-to-Peer Energy Trading, Evolutionary Algorithm, Multi-objective Optimization Problem, Graph Theory.

Abstract:
In recent years, many countries have been promoting shift from centralized energy systems to distributed ones for clean energy utilization. Direct energy trading among consumers has drawn increasing interest in the development of efficient distributed energy systems. However, a part of consumers might not be able to receive electricity from their preferred suppliers since some suppliers have limited capacity of supplying electricity. This occasion leads to a decrease in the consumer's benefit. Therefore, a mechanism that satisfies not only balance between supply and demand but considering market participants' preference is required. In this paper, as a market mechanism, an energy resource allocation problem is proposed to improve each consumer's benefit. Simulation results show that Optimized MultiObjective Particle Swarm Optimization (OMOPSO) and Generalized Differential Evolution-III (GDE3) found solutions optimized for only one of two objective functions, and other evolutionary algorithms such as Non-dominated Sorting Genetic Algorithms-II (NSGA-II), Speed-constrained Multiobjective Particle Swarm Optimization (SMPSO), Strength Pareto Evolutionary Algorithm-II (SPEA2) and ε-MultiObjective Evolutionary Algorithm (ε- MOEA) discovered solutions dense in Pareto front.

Pages: 56 to 61

Copyright: Copyright (c) IARIA, 2020

Publication date: February 23, 2020

Published in: conference

ISSN: 2308-4243

ISBN: 978-1-61208-771-9

Location: Lisbon, Portugal

Dates: from February 23, 2020 to February 27, 2020