The Research on Planning Methods for Energy Storage in Distributed Renewable Energy Distribution Networks

Authors

  • Ziyi Xuan
  • Yonggang Li

DOI:

https://doi.org/10.54097/7pmdgm32

Keywords:

Distribution network; Distributed renewable energy; Energy storage planning; Renewable energy integration.

Abstract

In order to accelerate the green transformation of the power grid, improve the absorption capacity of renewable energy, reduce distribution network operation costs, and balance the voltage stability issues caused by supply and demand fluctuations during different operating periods, this paper proposes an energy storage planning method for distribution networks with integrated distributed renewable energy.First, the load and renewable energy (wind and solar) data for the year are clustered to generate four temporal scenarios. Then, considering various economic costs associated with the operation of the distribution network as well as the investment and operational costs required for energy storage planning.Building upon this, an optimization model for the distribution network, both prior to and following the integration of energy storage planning, is developed, along with the associated constraints. Next, an optimization solution is sought using a weight-adaptive particle swarm optimization (PSO) algorithm. Finally, the effectiveness and feasibility of the energy storage planning are validated through simulation on the IEEE 33-bus test system. The simulation results show that after the introduction of energy storage planning, the overall operational cost of the distribution network with integrated distributed renewable energy is significantly reduced, while the voltage stability at each node during different periods is effectively improved.

Downloads

Download data is not yet available.

References

[1] XIE Xiaorong, HE Jingbo, MAO Hangyin, et al. New issues and classification of power system stability with high shares of renewables and power electronics[J]. Proceedings of the CSEE, 2021, 41(02): 465-475.

[2] LI Hui, LIU Dong, YAO Danyang. Analysis and reflection on the development of power system towards the goal of carbon emission peak and carbon neutrality[J]. Proceedings of the CSEE, 2021, 41(18): 6245-6250.

[3] S.H, M.Z M, B.B. Optimai allocation and sizing of synchronous condensers in weak grids with increased penetration of wind and solar farms[J]. IEEE Journal on Emergingand Selected Topics in Circuits and Systems, 2021, 11(01): 199-205.

[4] MA Shaokang, GENG Hua, LIU Lu, et al. Grid-synchronizationstability improvement of large scale wind farm during severe gridfault[J]. IEEE Transactions on Power Systems, 2018, 33(01): 217-220.

[5] CAO Y, WEI W, WANG JH, et al. Capacity planning of energy hub in multi carrier energy networks: A data-driven robust stochastic programming approach[J]. IEEE Transactions on Sustainable Energy, 2020, 11(1): 3-14.

[6] S.W.Hadley, T.K.Stovall. Customer-owned utilities and distributed energy: Potentials and benefits[S], Oak Ridge National Laboratory, Tennessee, February, 2006: 50-70.

[7] Ju Liwei, Yu Chao, Tan Zhongfu. Two-stage Dispatching Optimization Model and Solution Algorithm for Wind Power Energy Storage Considering Demand Response[J], Power System Technology, 2015, 39(05): 1287-1293.

[8] MA Hengrui, WANG Bo, GAO Wenzhong, et al. Operation Optimization of Energy Storage Equipment Participating in Ancillary Services in Regional Integrated Energy System[J]. Automation of Electric Power Systems, 2019, 43(08): 34-46+68.

[9] LI Peng, ZHONG Hanming, MA Hongwei, et al. Multi-time Scale Source-Load-Storage Co-learning Optimal Control of Active Distribution Network Based on Deep Reinforcement Learning[J/OL]. Transactions of China Electrotechnical Society, 2024, 1-17, https://doi.org/10.19595/j.cnki.1000-6753.tces.240316.

[10] Gao Hongjun, Liu Junyong. Coordinated planning considering different types of DG and load in active distribution network[J]. Proceedings of the CSEE, 2016, 36(18): 4911-4922+5115.

[11] Zhang Y, Ren S, Dong Z Y, et al. Optimal placement of battery energy storage in distribution networks considering conservation voltage reduction and stochastic load composition[J]. IET Generation, Transmission & Distribution, 2017, 11(15): 3862-3870.

[12] Bael S, Kim JC, Singh C. Optimal operating strategy for distributed generation considering hourly reliability worth. IEEE Transactions on Power Systems, 2004, 19(1): 287-292.

[13] Jia Qingquan, Zhao Meichao, Sun Lingling, et al. Planning for grid-connection of distributed PVs considering the sequential feature and correlation in active distribution network[J]. Proceedings of the CSEE, 2018, 38(6): 1719-1728+1908.

[14] SUN Donglei, ZHAO Long, SUN Kaiqi, et al. Research on Optimization Strategy of Location and Capacity Optimization of Hybrid Energy Storage in Distribution System under New Energy Access[J]. Journal of Solar Energy, 2024. 45(01): 423-432.

[15] Ding Ming, Wu Jianfeng, Zhu Chengzhi, et al. Real-time Smoothing Control Strategy for Energy Storage System with State-of-Charge Regulation[J]. Proceedings of the CSEE, 2013, 33(01): 22-29.

[16] TAO Qiong, SANG Bingyu, YE Jilei, et al. Optimal Allocation Method of Distributed Energy Storage System in High Photovoltaic Permeability Distribution Network[J]. High Voltage Engineering, 2016, 42(07): 2158-2165.

[17] Zhang Zhonghui, Lei Dayong, Jiang Changhui, et al.A Bi-level Planning Model and Its Solution Method of AC/DC Hybrid Distribution Network Based on Second-order Cone Programming and NNC Method[J]. Proceedings of the CSEE, 2023, 43(01): 70-85.

[18] Gan L, Li N, Topcu U, et al. Exact convex relaxation of optimal power flow in radial networks[J]. IEEE Transactions on Automatic Control, 2015, 60(1): 72-87.

[19] National Bureau of Statistics, China Energy Statistical Yearbook 2022[M]. Beijing: China Statistics Press, 2023.

Downloads

Published

29-07-2025

How to Cite

Xuan, Z., & Li, Y. (2025). The Research on Planning Methods for Energy Storage in Distributed Renewable Energy Distribution Networks. Highlights in Science, Engineering and Technology, 149, 171-181. https://doi.org/10.54097/7pmdgm32