Abstract
One of the most important issues that must be taken into consideration during the planning of energy storage systems (ESSs) is improving distribution network economy, reliability, and stability. This paper presents a two-layer optimization model to determine the optimal siting and sizing of ESSs in the distribution network and their best compromise between the real power loss, voltage stability margin, and the application cost of ESSs. Thereinto, an improved bat algorithm based on non-dominated sorting (NSIBA), as an outer layer optimization model, is employed to obtain the Pareto optimal solution set to offer a group of feasible plans for an internal optimization model. According to these feasible plans, the method of fuzzy entropy weight of vague set, as an internal optimization model, is applied to obtain the synthetic priority of Pareto solutions for planning the optimal siting and sizing of ESSs. By this means, the adopted fuzzy entropy weight method is used to obtain the objective function’s weights and vague set method to choose the solution of planning ESSs’ optimal siting and sizing. The proposed method is tested on a real 26-bus distribution system, and the results prove that the proposed method exhibits higher capability and efficiency in finding optimum solutions.
Highlights
In recent years, distribution networks using renewable energy such as wind power and photovoltaic power have become one of the research hotspots at home and abroad
ILSSIWBA is a swarm intelligence optimization algorithm based on local iterative search and random inertia weights proposed by Chao Gan [21]
The basic framework of ILSSIWBA is similar to the previous bat algorithm (BA), which is popular in solving optimization problems
Summary
Distribution networks using renewable energy such as wind power and photovoltaic power have become one of the research hotspots at home and abroad. An improved bat algorithm based on non-dominated sorting (NSIBA) is proposed to optimal allocation and sizing of the ESSs in distribution networks. After obtaining the Pareto optimal set, in order to solve the multi-objective decision problem, the fuzzy entropy weight of vague set as a traditional method was used to obtain the best trade-off solutions from the Pareto optimal solution [20]. This method, did not reflect the accuracy of the results because the score function could not fully reflect the relationship between the support, opposition, and neutral target sets.
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