4.5 Article

Incorporating energy storage and user experience in isolated microgrid dispatch using a multi-objective model

期刊

IET RENEWABLE POWER GENERATION
卷 13, 期 6, 页码 973-981

出版社

INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-rpg.2018.5862

关键词

evolutionary computation; fuzzy set theory; distributed power generation; Pareto optimisation; power generation dispatch; energy storage; demand side management; user experience; isolated microgrid dispatch; multiobjective model; multiple different scheduling objectives; multiobjective dynamic optimal dispatch model; isolated microgrids; spinning reserve services; consumer satisfaction indicator; dominance based evolutionary algorithm; Pareto-optimal solutions; energy storage; microturbine units; two-step solution methodology; multiobjective optimisation; heuristic optimisation algorithm; demand side management; quality of user experience; evolutionary algorithm; decision analysis; fuzzy C-means clustering; grey relation projection

资金

  1. China Scholarship Council (CSC) [201608220144]

向作者/读者索取更多资源

In order to coordinate multiple different scheduling objectives from the perspectives of economy, environment, and users, a practical multi-objective dynamic optimal dispatch model incorporating energy storage and user experience is proposed for isolated microgrids. In this model, besides microturbine units, energy storage is employed to provide spinning reserve services for microgirds; and furthermore, from the perspective of demand side management, a consumer satisfaction indicator is developed to measure the quality of user experience. A two-step solution methodology incorporating multi-objective optimisation and decision analysis is put forward to address this model. First, a powerful heuristic optimisation algorithm, called the -dominance based evolutionary algorithm, is used to find a well-distributed set of Pareto-optimal solutions of the problem. Thereby, the best compromise solutions are identified from the entire solutions with the use of decision analysis by integrating fuzzy C-means clustering and grey relation projection. The simulation results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab microgrid test system demonstrate the effectiveness of the proposed approach.

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