期刊
IEEE ACCESS
卷 9, 期 -, 页码 28237-28250出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3058746
关键词
Optimization; Distribution networks; Pareto optimization; Genetic algorithms; Renewable energy sources; Minimization; Linear programming; Battery energy storage system (BESS); hybrid metaheuristic algorithm; distribution networks; multiobjective optimization; renewable energy; whale optimization algorithm; genetic algorithm; pareto optimal solutions
资金
- Council for Scientific and Industrial Research, Pretoria, South Africa, through the Smart Networks collaboration initiative
- IoT-Factory Programme [Department of Science and Innovation (DSI), South Africa]
- South Africa's National Research Foundation [114626, 112248, 129311]
- Korea Agency for Infrastructure Technology Advancement (KAIA) [129311] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper introduces a multi-stage categorical framework to handle the integration of DG units and BESS in a distribution network, utilizing a new hybrid metaheuristic technique to address multiple objectives including technical, economic, and environmental considerations.
Distributed generation (DG) units are power generating plants that are very important to the architecture of present power system networks. The primary benefits of the addition of these units are to increase the power supply and improve the power quality of a power grid while considering the investment cost and carbon emission cost. Most studies have simultaneously optimized these objectives in a direct way where the objectives are directly infused into the multiobjective framework to produce final values. However, this method may have an unintentional bias towards a particular objective; hence this paper implements a multi-stage framework to handle multiple objectives in a categorical manner to simultaneously integrate DG units and Battery Energy Storage System (BESS) in a distribution network. A new hybrid metaheuristic technique is developed and combined with the Technique Order for Preference by Similarity to Ideal Solution (TOPSIS) approach and the crowding distance technique to produce Pareto optimal solutions from the multiple collective objectives, namely technical, economic, and environmental. Compared to the conventional direct way approach in multiobjective handling, the proposed categorical approach reduces bias towards a set of objective(s) and efficiently handles more objectives. Results also show that the Whale Optimization Algorithm and Genetic Algorithm (WOAGA) produces the smallest power loss of 101.6 kW compared to Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA), which produces 105.1 kW and 105.8 kW respectively. The algorithm, although does not have a faster convergence than the WOA, has a better computational time than the WOA and GA. The multiobjective WOAGA also performs better than the Non-dominating Sorted Genetic Algorithm (NSGA-II) and the multiobjective WOA in terms of the quality of Pareto optimal solutions.
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