Optimal configuration of energy storage based on improved harmony search algorithm and second-order cone relaxation
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School of Information Engineering, Nanchang University, Nanchang 330036, China

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TP18 ;TM734

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    Abstract:

    The large-scale access of distributed power sources in the distribution network aggravates the power fluctuation of the power grid. In order to stabilize the power of the power grid and absorb the power generated by new energy, it is necessary to reasonably allocate energy storage in the power grid. In this paper, the energy storage configuration is divided into the planning layer and the operation layer based on the two-layer programming model, and a hybrid algorithm for energy storage optimization configuration combining the global harmony search algorithm based on trend movement and the second-order cone relaxation theory is proposed. The planning layer uses the minimum sum of the annual investment cost of energy storage and the annual operating cost of the distribution network as the objective function, and studies the economic benefits of energy storage configuration location, power, capacity, etc.; The minimum sum is the objective function, and the optimal charging and discharging power of energy storage in each period is studied. Based on the actual data of a certain place, a 33-node distribution network is taken as an example to conduct a simulation analysis, and the configuration methods of energy storage in different scenarios are studied and the excellence of the hybrid algorithm in this paper is discussed. The results show that the hybrid algorithm proposed in this paper can successfully solve the energy storage configuration problem in this scenario, which proves the effectiveness of the hybrid algorithm in this paper. At the same time, the energy storage configuration method in multiple scenarios is discussed, and it is proved that the use of the two-tier planning model to configure the energy storage is beneficial to reduce the comprehensive cost of the distribution network. Finally, the hybrid algorithm in this paper is compared with the traditional intelligent optimization algorithm to solve such problems, which proves the speed and accuracy of the hybrid algorithm in this paper in solving such problems.

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  • Received:
  • Revised:
  • Adopted:
  • Online: May 16,2024
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