Bi-Level Planning Model for Distributed Energy Storage Based on Accommodation-Supply Security Game

doi:10.12096/j.2096-4528.pgt.25195

Abstract

Abstract:

Objectives To address the conflicting objectives between “maximizing renewable energy accommodation” and “ensuring power supply reliability” in distributed energy storage systems, a coordinated planning method that balances economic efficiency and resilience is proposed. Methods A dynamic Stackelberg game-driven bi-level planning model is established, integrating Stackelberg game theory with robust optimization to address the cross-level coupling problem between planning and operation. The model coordinates the interests of planning, operation, and market stakeholders to optimize energy storage capacity allocation and charging/discharging strategies, thereby enhancing power supply reliability. Through bi-level optimization, coordination between planning and operation is achieved, and the trade-off between accommodation and supply security is quantified, with Pareto-optimal solutions derived to achieve the lowest investment costs and highest renewable energy accommodation rates. Results A rapid solution algorithm for a bi-level model incorporating multi-objective optimization is developed, and a “dynamic pricing-capacity subsidy” linkage mechanism is proposed. This mechanism effectively balances the conflicting interests among multiple stakeholders in planning and operation. By integrating Stackelberg game theory with robust optimization, the cross-level coupling problem between planning and operation is solved. The simulation cases demonstrate that the proposed bi-level game model significantly outperforms conventional planning methods, validating the long-term economic efficiency of the dynamic game mechanism. Furthermore, a nonlinear trade-off relationship between renewable energy accommodation and power supply security is revealed. Conclusions The bi-level planning model based on accommodation-supply security game coordination can significantly enhance the system’s renewable energy accommodation rate and power supply reliability, while reducing energy storage system investment costs and user electricity costs.

Key words: distributed energy storage planning, accommodation-supply security game, bi-level optimization model, renewable energy accommodation, power supply reliability

CLC Number:

TK 01

Feng CHEN, Xiaomin LU, Bing SHEN, Junpeng WANG. Bi-Level Planning Model for Distributed Energy Storage Based on Accommodation-Supply Security Game[J]. Power Generation Technology, 2025, 46(6): 1133-1143.

Figures/Tables 10

Fig. 1 Dynamic process of “prioritized planning-operation response”

Fig. 2 Flowchart of model construction and solution algorithm

Fig. 3 Time-series comparison between market clearing price and energy storage bidding price

Fig. 4 Logistic curve of subsidy intensity and return on investment

Fig. 5 Output characteristic curves of wind and solar power

Fig. 6 Load time-series characteristic curves

Fig. 7 Trade-off curves of accommodation rate and reliability under different game strategies

Fig. 8 Differences in effectiveness between dynamic mechanisms and conventional subsidies

Fig. 9 Sensitivity range of CESS, max

Fig. 10 Influence of policy parameter variations on game equilibrium

References 26

[1]	邬东格，常馨月，薛屹洵，等．基于动态碳熵的主动配电网低碳能量管理策略[J/OL]．上海交通大学学报，2025：1-20．．
	WU D G， CHANG X Y， XUE Y X，et al ．Low-carbon energy management strategy for active distribution networks based on dynamic carbon entropy[J/OL]．Journal of Shanghai Jiao Tong University，2025：1-20．．
[2]	黄兴华，范元亮，张功林，等.含分布式光伏中低压配电网多时间尺度协同无功优化策略[J]．智慧电力，2024，52(11）：8-15．
	HUANG X H， FAN Y L， ZHANG G L，et al ．Multi-time scale collaborative reactive power optimization strategy for medium and low voltage distribution networks with PV[J]．Smart Power，2024，52(11)：8-15．
[3]	佟曦，侯朗博，孙昊，等.高比例光伏和电动汽车接入配电网的无功优化[J]．智慧电力，2023，51(10)：31-37．
	TONG X， HOU L B， SUN H，et al ．Reactive power optimization of distribution networks with high proportion of PV and EVs[J]．Smart Power，2023，51(10)：31-37．
[4]	李明扬，张智.基于强化学习的含分布式风-光-储虚拟电厂优化调度[J]．智慧电力，2024，52(8)：50-56．
	LI M Y， ZHANG Z ．Optimal dispatch of distributed wind-solar-storage virtual power plants based on reinforcement learning[J]．Smart Power，2024，52(8)：50-56．
[5]	王朋，张迪，张勇军，等．新型电力系统数智化关键技术应用研究与展望[J]．电力系统保护与控制，2025，53(6)：175-187．
	WANG P， ZHANG D， ZHANG Y J，et al ．Research and prospects for key digital-intelligent technology applications in new power systems[J]．Power System Protection and Control，2025，53(6)：175-187．
[6]	单思珂，刘含笑，刘美玲，等．我国火电行业碳足迹评估综述[J]．发电技术，2024，45(4)：575-589．
	SHAN S K， LIU H X， LIU M L，et al ．Review of carbon footprint for thermal power industry in China[J]．Power Generation Technology，2024，45(4)：575-589．
[7]	王辉，梁凌，李乃慧，等．考虑交通流量预测的光-氢-电耦合系统规划[J]．现代电力，2024，41(6)：1100-1108．
	WANG H， LIANG L， LI N H，et al ．Photovoltaics-hydrogen-electricity coupling system planning considering traffic flow prediction[J]．Modern Electric Power，2024，41(6)：1100-1108．
[8]	郭晓雅，郑李西，李庚达，等．大型水光互补电站的光伏捆绑容量规划[J]．中国农村水利水电，2023(12)：273-279．
	GUO X Y， ZHENG L X， LI G D，et al ．Optimal sizing of photovoltaic power generation for integration into a large hydropower plant[J]．China Rural Water and Hydropower，2023(12)：273-279．
[9]	余本东，樊苗苗，颜承初．基于运维视角的低碳建筑实现路径及关键技术[J]．南京工业大学学报(自然科学版)，2023，45(5)：467-477．
	YU B D， FAN M M， YAN C C ．Low-carbon implementation path and key technologies based on building operation and maintenance perspective[J]．Journal of Nanjing Tech University (Natural Science Edition)，2023，45(5)：467-477．
[10]	郝俊红，郭俣，王星策，等．有限信息下典型用能行业能效碳效诊断研究进展[J]．中国电机工程学报，2023，43(S1)：174-197．
	HAO J H， GUO Y， WANG X C，et al ．Research progress on energy efficiency and carbon efficiency diagnosis of typical energy-using industries under limited information[J]．Proceedings of the CSEE，2023，43(S1)：174-197．
[11]	李军徽，董福财，郭琦，等．考虑调峰特性的抽水蓄能电站综合效能量化评估[J]．全球能源互联网，2024，7(5)：567-578．
	LI J H， DONG F C， GUO Q，et al ．Quantitative assessment of the integrated efficiency of pumped storage power stations considering peak shifting characteristics[J]．Journal of Global Energy Interconnection，2024，7(5)：567-578．
[12]	张贝贝．基于Simulink的集中型馈线自动化控制策略仿真系统设计[J]．东北电力技术，2024，45(9)：41-45．
	ZHANG B B ．Design of centralized feeder automation control strategy simulation system based on Simulink[J]．Northeast Electric Power Technology，2024，45(9)：41-45．
[13]	杨馥源，田雪沁，余潜跃，等．面向电网互动的绿电转氨单元变工况特性和互动能力评估研究[J]．中国电机工程学报，2024，44(S1)：180-190．
	YANG F Y， TIAN X Q， YU Q Y，et al ．Study on off-design characteristics and interactive capability evaluation of green power to ammonia conversion unit for power grid interaction[J]．Proceedings of the CSEE，2024，44(S1)：180-190．
[14]	易俊，孙浩然，林伟芳，等．电力系统碳排放核算标准对比分析[J]．电网技术，2025，49(3)：920-933．
	YI J， SUN H R， LIN W F，et al ．Comparative analysis of carbon emission accounting standards for power systems[J]．Power System Technology，2025，49(3)：920-933．
[15]	董军，章静，张瑞松，等．“双碳” 战略背景下不锈钢复合钢板结构减碳前景分析[J]．南京工业大学学报(自然科学版)，2024，46(4)：378-386．
	DONG J， ZHANG J， ZHANG R S，et al ．Analysis of carbon emission reduction prospects for stainless-clad bimetallic steel structures under the carbon peak and carbon neutrality goals[J]．Journal of Nanjing Tech University (Natural Science Edition)，2024，46(4)：378-386．
[16]	辛曦，欧阳森，黄祎，等．考虑储能容量衰减的多保供电型微网最优经济配置及可靠性评估[J]．电力建设，2024，45(10)：100-113．
	XIN X， OUYANG S， HUANG Y，et al ．Optimal economic configuration and reliability evaluation of multiple power supply ensuring microgrid considering energy storage capacity attenuation[J]．Electric Power Construction，2024，45(10)：100-113．
[17]	朱润泽，王德军，张佑，等．数字化转型下新能源场站智能管控平台研建[J]．科技和产业，2023，23(15)：203-210．
	ZHU R Z， WANG D J， ZHANG Y，et al ．Research on the construction of intelligent monitoring platform for new energy stations under digital transformation[J]．Science Technology and Industry，2023，23(15)：203-210．
[18]	李雅丹，邓大为，吴振田，等．新型电力系统生产设备智能化管理平台建设关键技术与应用[J]．全球能源互联网，2023，6(4)：437-444．
	LI Y D， DENG D W， WU Z T，et al ．Key technologies and applications of intelligent management platform construction for production equipment in new power system[J]．Journal of Global Energy Interconnection，2023，6(4)：437-444．
[19]	魏震波，李银江，张雯雯，等．基于改进Myerson值法的云储能双层优化运营模型[J]．中国电力，2023，56(7)：198-206．
	WEI Z B， LI Y J， ZHANG W W，et al ．Two-layer optimization operation model of cloud energy storage based on improved myerson value method[J]．Electric Power，2023，56(7)：198-206．
[20]	张晓瑞，纪陵，范仲鸣．基于云边融合的新一代配电物联运维技术研究[J]．综合智慧能源，2022，44(12)：25-32．
	ZHANG X R， JI L， FAN Z M ．Research on the new power distribution IoT operation and maintenance technology based on cloud-edge collaboration[J]．Integrated Intelligent Energy，2022，44(12)：25-32．
[21]	张冲，荣娜．基于改进粒子群算法的新能源侧储能容量配置[J]．电网与清洁能源，2022，38(10)：98-105．
	ZHANG C， RONG N ．Energy storage capacity allocation of renewable energy side based on improved particle swarm optimization[J]．Power System and Clean Energy，2022，38(10)：98-105．
[22]	马钊，张恒旭，赵浩然，等．双碳目标下配用电系统的新使命和新挑战[J]．中国电机工程学报，2022，42(19)：6931-6945．
	MA Z， ZHANG H X， ZHAO H R，et al ．New mission and challenge of power distribution and consumption system under dual-carbon target[J]．Proceedings of the CSEE，2022，42(19)：6931-6945．
[23]	肖伟栋，刘耀，蒋纯冰，等．面向源荷互动的建筑-电网数据共享现状与展望[J]．暖通空调，2023，53(12)：76-85．
	XIAO W D， LIU Y， JIANG C B，et al ．Current status and prospects of data sharing between buildings and power grids for source-load interaction[J]．Heating Ventilating & Air Conditioning，2023，53(12)：76-85．
[24]	许丹莉，朱文，彭超逸，等．基于云边协同的虚拟电厂调度系统设计[J]．电力信息与通信技术，2023，21(7)：44-50．
	XU D L， ZHU W， PENG C Y，et al ．Design of cloud edge collaborative dispatching system for virtual power plant[J]．Electric Power Information and Communication Technology，2023，21(7)：44-50．
[25]	陈曦，司恒斌，张良，等．基于图神经网络的电-热联合能源系统优化调度研究[J]．智慧电力，2023，51(7)：67-73．
	CHEN X， SI H B， ZHANG L，et al ．Optimal scheduling of electricity-thermal combined energy system based on graph neural network[J]．Smart Power，2023，51(7)：67-73．
[26]	栗峰，丁杰，周才期，等．新型电力系统下分布式光伏规模化并网运行关键技术探讨[J]．电网技术，2024，48(1)：184-199．
	LI F， DING J， ZHOU C Q，et al ．Key technologies of large-scale grid-connected operation of distributed photovoltaic under new-type power system[J]．Power System Technology，2024，48(1)：184-199．