Abstract
Community Microgrid offers effective energy harvesting from distributed energy resources and efficient energy consumption by employing an energy management system (EMS). Therefore, the collaborative microgrids are essentially required to apply an EMS, underlying an operative control strategy in order to provide an efficient system. An EMS is apt to optimize the operation of microgrids from several points of view. Optimal production planning, optimal demand-side management, fuel and emission constraints, the revenue of trading spinning and non-spinning reserve capacity can effectively be managed by EMS. Consequently, the importance of optimization is explicit in microgrid applications. In this paper, the most common control strategies in the microgrid community with potential pros and cons are analyzed. Moreover, a comprehensive review of single objective and multi-objective optimization methods is performed by considering the practical and technical constraints, uncertainty, and intermittency of renewable energies sources. The Pareto-optimal solution as the most popular multi-objective optimization approach is investigated for the advanced optimization algorithms. Eventually, feature selection and neural network-based clustering algorithms in order to analyze the Pareto-optimal set are introduced.
Highlights
As a response to rapid energy consumption in recent years, microgrids (MGs) appear as an alternative solution in order to reduce the adverse effect of using fossil fuels in conventional power plants and their adverse consequences on the environment
Several papers based on distributed control methods are performed to improve the performance and reliability of P2P control
Among Multiple objective particle swarm optimization (MOPSO), PESAII, and SPEA-II, which are applied to the optimization algorithm, the results show SPEA-II has better performance in this article
Summary
As a response to rapid energy consumption in recent years, microgrids (MGs) appear as an alternative solution in order to reduce the adverse effect of using fossil fuels in conventional power plants and their adverse consequences on the environment. A comprehensive review on MG and virtual power plant concepts was conducted in [24], scheduling problem associated with the formulation and objective functions, solving methods, uncertainty, reliability, reactive power, and demand response is studied. A comprehensive study on the classification of optimized controller approaches concerning the RES integration into MGs and analyzing advanced and conventional optimization algorithms in MG applications is performed by M.A. Hannan et al in [28]. According to the previous academic literature, with respect to the control strategies and EMS framework, the optimization technique and computational approaches play an important role in the efficient and reliable operation of MGs and MGC. The planning and scheduling programs in the MGs application are discussed in order to define the proper optimization problem. Single-objective and multi-objective optimization algorithms are expressed, and eventually, artificial intelligence (AI) application in feather selection and clustering analysis is surveyed
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