提升新能源承载力的电网需求响应与储能配置协同优化方案

doi:10.3969/j.issn.2097-0706.2026.07.010

综合智慧能源 ›› 2026, Vol. 48 ›› Issue (7): 98-109.doi: 10.3969/j.issn.2097-0706.2026.07.010

• 储能规划配置 • 上一篇

提升新能源承载力的电网需求响应与储能配置协同优化方案

欧圣¹(), 蒋锐琳¹, 张钰¹, 韩自奋²(), 宋关羽³(), 董海鹰¹^,^*()

¹ 兰州交通大学新能源与动力工程学院，兰州 730070
² 国网甘肃省电力公司，兰州 730030
³ 天津大学电气自动化与信息工程学院，天津 300072

收稿日期:2025-12-30 修回日期:2026-03-17 出版日期:2026-07-25
通讯作者: * 董海鹰（1966），男，教授，博士，从事新能源电力系统规划、分析、运行与控制技术等方面的研究，hydong@mail.lzjtu.cn。
作者简介:欧圣（1999），男，硕士生，从事新能源与储能等方面的研究，17729876052@163.com；
韩自奋（1976），男，正高级工程师，博士，从事水电、新能源与储能调度管理等方面的工作，hanzf@gs.sgcc.com.cn；
宋关羽（1990），男，高级工程师，博士，从事分布式发电与微电网等方面的研究，gysong@tju.edu.cn。
基金资助:
中国工程院院地合作项目(2025-GS-XZ-02)

Coordinated planning of demand response and energy storage configuration for increasing new energy hosting capacity of power grids

OU Sheng¹(), JIANG Ruilin¹, ZHANG Yu¹, HAN Zifen²(), SONG Guanyu³(), DONG Haiying¹^,^*()

¹ School of New Energy and Power Engineering， Lanzhou Jiaotong University， Lanzhou 730070， China
² State Grid Gansu Electric Power Company， Lanzhou 730030， China
³ School of Electrical Automation and Information Engineering， Tianjin University， Tianjin 300072， China

Received:2025-12-30 Revised:2026-03-17 Published:2026-07-25
Supported by:
China Academy of Engineering Regional Cooperation Project(2025-GS-XZ-02)

摘要/Abstract

摘要：

新能源的快速发展给电网的安全稳定运行和区域电网的新能源承载能力带来挑战。在保证供电可靠性的前提下，提出综合考虑需求响应与储能优化配置的新能源承载能力提高方案。采用Copula理论和K-means聚类算法生成风光出力典型场景，构建基于价格型激励的需求响应模型，并通过价格信号优化负荷曲线。其次，构建考虑经济性和稳定性的储能双层优化模型，上层以储能投资成本最小为目标优化储能容量、下层以系统综合运行成本最低为目标开展联合调度，以失负荷概率（LOLP）为约束评估电网的最大新能源承载能力，通过配置储能优化装机容量，提升新能源的承载能力。依托电力系统分析综合程序（PSASP）开展潮流计算，量化储能接入对节点电压的影响，对区域电网进行安全性校验。以甘肃某区域电网为例，设置有无考虑需求响应两类情景对能源系统进行仿真。仿真结果表明，需求响应优化后，同等承载力下储能配置容量由5 090 MW降至4 320 MW，储能投资成本减少11.01亿元；配置4 320 MW储能可将新能源装机占比由74.0% 提升至83.3%；储能接入后关键节点电压越限概率显著下降，节点电压标幺值稳定维持在0.95~1.05；但新能源占比超88.9%后，储能投资成本快速攀升，经济性大幅衰减。分时电价驱动的需求响应可平滑负荷曲线、降低储能配置需求；储能与需求响应协同能从源荷储协同提升电网调节能力，有效提高新能源承载力、降低失负荷风险。二者联合可抑制节点电压波动，改善电网稳态安全水平，该优化方案可为新能源富集区域储能规划提供参考。

关键词: 需求响应, 储能配置, 双层优化, 新能源承载力, 供电可靠性, 失负荷概率

Abstract:

The rapid development of new energy poses challenges to the secure and stable operation of power grids and the new energy hosting capacity of regional power grids. Therefore， a new energy hosting capacity improvement scheme considering demand response and optimal energy storage configuration was proposed while ensuring power supply reliability. Typical wind and photovoltaic power output scenarios were generated using Copula theory and the K-means clustering algorithm， and a demand response model based on price incentives was constructed to optimize the load curve through price signals. Subsequently， a bi-level optimization model for energy storage considering economic efficiency and stability was established. The upper level aimed to minimize energy storage investment costs to optimize energy storage capacity， while the lower level conducted coordinated scheduling with the objective of minimizing the comprehensive system operation cost. The loss of load probability （LOLP） was used as a constraint to evaluate the maximum new energy hosting capacity of the power grid， and the installed energy storage capacity was optimized through energy storage configuration to improve new energy hosting capacity. Power flow calculations were performed based on the Power System Analysis Software Package （PSASP） to quantify the impact of energy storage integration on nodal voltages and conduct security verification of the regional power grid. Taking a regional power grid in Gansu Province as an example， two scenarios with and without considering demand response were established to simulate the energy system. Simulation results showed that， after demand response optimization， the energy storage configuration capacity decreased from 5 090 MW to 4 320 MW under the same hosting capacity， and the energy storage investment cost was reduced by 1.101 billion yuan. The configuration of 4 320 MW energy storage increased the proportion of installed new energy capacity from 74.0% to 83.3%. After energy storage integration， the probability of voltage limit violations at key nodes decreased significantly， and the per-unit values of nodal voltages remained stable ranging from 0.95 to 1.05. However， when the proportion of new energy exceeded 88.9%， the energy storage investment cost increased rapidly， and the economic efficiency decreased substantially. Time-of-use price-driven demand response could smooth the load curve and reduce the demand for energy storage configuration. The coordination of energy storage and demand response can enhance grid regulation capability through source-load-storage coordination， effectively improve new energy hosting capacity， and reduce the risk of load loss. The coordination of the two approaches can suppress nodal voltage fluctuations and improve the steady-state security level of power grids. The proposed optimization scheme can provide references for energy storage planning in regions with abundant new energy resources.

Key words: demand response, energy storage configuration, bi-level optimization, new energy hosting capacity, power supply reliability, loss of load probability

中图分类号:

TK01

欧圣, 蒋锐琳, 张钰, 韩自奋, 宋关羽, 董海鹰. 提升新能源承载力的电网需求响应与储能配置协同优化方案[J]. 综合智慧能源, 2026, 48(7): 98-109.

OU Sheng, JIANG Ruilin, ZHANG Yu, HAN Zifen, SONG Guanyu, DONG Haiying. Coordinated planning of demand response and energy storage configuration for increasing new energy hosting capacity of power grids[J]. Integrated Intelligent Energy, 2026, 48(7): 98-109.

图/表 18

图1

图2

图3

表1

图4

图5

表2

图6

图7

表3

图8

图9

表4

图10

表5

表6

图11

表7

参考文献 26

[1]	郭剑波, 王铁柱, 罗魁, 等. 新型电力系统面临的挑战及应对思考[J]. 新型电力系统, 2023, 1(1): 32-43.
	GUO Jianbo, WANG Tiezhu, LUO Kui, et al. Development of new power systems: Challenges and solutions[J]. New Type Power Systems, 2023, 1(1): 32-43.
[2]	李立浧, 罗兵, 董旭柱, 等. 高电压技术发展现状及展望[J]. 高电压技术, 2025, 51(8): 3682-3720.
	LI Licheng, LUO Bing, DONG Xuzhu, et al. Advances and prospects of high voltage technology[J]. High Voltage Engineering, 2025, 51(8): 3682-3720.
[3]	任智君, 郭红霞, 杨苹, 等. 含高比例可再生能源配电网灵活资源双层优化配置[J]. 太阳能学报, 2021, 42(9): 33-38.
	REN Zhijun, GUO Hongxia, YANG Ping, et al. Double-layer optimal configuration of flexible resources with high proportion of renewable energy distribution network[J]. Acta Energiae Solaris Sinica, 2021, 42(9): 33-38.
[4]	张忠华, 李春山, 胡杰, 等. 东北地区碳排放现状分析与“双碳”目标下新能源发展建议[J]. 东北电力技术, 2022, 43(8):1-5.
	ZHANG Zhonghua, LI Chunshan, HU Jie, et al. Analysis of current situation of carbon emission and suggestions on new energy development under dual-carbon goal in Northeast China[J]. Northeast Electric Power Technology, 2022, 43(8):1-5.
[5]	马会萌, 李相俊, 吴荣宇, 等. 兼顾新能源消纳和主动支撑电网能力提升的多类型储能容量优化配置[J]. 电力建设, 2024, 45(6): 111-119. doi: 10.12204/j.issn.1000-7229.2024.06.011
	MA Huimeng, LI Xiangjun, WU Rongyu, et al. Optimal capacity configuration method for multi-type energy storage systems enhancing new energy consumption and actively supporting the grid[J]. Electric Power Construction, 2024, 45(6): 111-119. doi: 10.12204/j.issn.1000-7229.2024.06.011
[6]	艾欣, 周树鹏, 赵阅群. 考虑风电不确定性的用户侧分时电价研究[J]. 电网技术, 2016, 40(5): 1529-1535.
	AI Xin, ZHOU Shupeng, ZHAO Yuequn. Study on time of use pricing of user side considering wind power uncertainty[J]. Power System Technology, 2016, 40(5): 1529-1535.
[7]	王守相, 张善涛, 王凯, 等. 计及分时电价下用户需求响应的分布式储能多目标优化运行[J]. 电力自动化设备, 2020, 40(1): 125-132.
	WANG Shouxiang, ZHANG Shantao, WANG Kai, et al. Multi-objective optimal operation of distributed energy storage considering user demand response under time-of-use price[J]. Electric Power Automation Equipment, 2020, 40(1): 125-132.
[8]	ISMEIL M A, ALFOULY A, HUSSEIN H S, et al. Improved inverter control techniques in terms of hosting capacity for solar photovoltaic energy with battery energy storage system[J]. IEEE Access, 2023, 11:140161-140173. doi: 10.1109/ACCESS.2023.3340035
[9]	胡鹏, 艾欣, 张朔, 等. 基于需求响应的分时电价主从博弈建模与仿真研究[J]. 电网技术, 2020, 44(2): 585-592.
	HU Peng, AI Xin, ZHANG Shuo, et al. Modelling and simulation study of TOU Stackelberg game based on demand response[J]. Power System Technology, 2020, 44(2): 585-592.
[10]	李益民, 董海鹰, 丁坤, 等. 考虑长期负荷概率预测的储能多阶段优化配置[J]. 综合智慧能源, 2024, 46(2): 19-27. doi: 10.3969/j.issn.2097-0706.2024.02.003
	LI Yimin, DONG Haiying, DING Kun, et al. Multi-stage optimal allocation of energy storage considering long-term load probability prediction[J]. Integrated Intelligent Energy, 2024, 46(2): 19-27. doi: 10.3969/j.issn.2097-0706.2024.02.003
[11]	孙伟卿, 罗静, 张婕. 高比例风电接入的电力系统储能容量配置及影响因素分析[J]. 电力系统保护与控制, 2021, 49(15): 9-18.
	SUN Weiqing, LUO Jing, ZHANG Jie. Energy storage capacity allocation and influence factor analysis of a power system with a high proportion of wind power[J]. Power System Protection and Control, 2021, 49(15): 9-18.
[12]	ZENHOM Z M, ALEEM S H E A, ZAHAB E A, et al. Synergistic hierarchical bi-level optimization of demand response programs and smart inverter Volt/Var control for maximizing hosting capacity of power systems with renewable-based distributed generators[J]. IEEE Access, 2025, 13:15390-15409. doi: 10.1109/ACCESS.2025.3530557
[13]	陈家杨, 郝斌, 邓志辉, 等. 光储直柔系统储能设施优化配置方法研究[J]. 供用电, 2025, 42(4): 34-41, 58.
	CHEN Jiayang, HAO Bin, DENG Zhihui, et al. Research on optimal allocation method of energy storage facilities in PEDF system[J]. Distribution & Utilization, 2025, 42(4): 34-41, 58.
[14]	马丽, 李伟, 裴玮, 等. 含高比例光伏配电网中计及需求侧响应的混合储能配置优化[J]. 高电压技术, 2024, 50(4): 1416-1425.
	MA Li, LI Wei, PEI Wei, et al. Hybrid energy storage configuration optimization in distribution network with high-proportion PV considering demand response[J]. High Voltage Engineering, 2024, 50(4): 1416-1425.
[15]	储国良, 邵竹星, 孙文兵, 等. 一种用于新能源承载力分析的改进粒子群算法[J]. 西安工业大学学报, 2022, 42(5): 513-519, 526.
	CHU Guoliang, SHAO Zhuxing, SUN Wenbing, et al. Improved particle swarm optimization algorithm for analyzing renewable energy hosting capacity[J]. Journal of Xi'an Technological University, 2022, 42(5): 513-519, 526.
[16]	罗雪梅, 马昕峥, 张淑, 等. 计及电能质量全指标的承载力评估方法[J]. 科技创新与应用, 2024, 14(27): 158-162.
	LUO Xuemei, MA Xinzheng, ZHANG Shu, et al. Evaluation method of bearing capacity considering all indexes of power quality[J]. Technology Innovation and Application, 2024, 14(27): 158-162.
[17]	王智伟, 张振宇, 李征, 等. 提升高渗透率新能源电网承载能力的DSSC优化配置[J]. 电力系统保护与控制, 2023, 51(6): 179-187.
	WANG Zhiwei, ZHANG Zhenyu, LI Zheng, et al. Optimized DSSC configuration to enhance load-carrying capacity of the new energy grid with high permeability[J]. Power System Protection and Control, 2023, 51(6): 179-187.
[18]	杨世博, 孙亮, 陈立东, 等. 计及分时电价的含冷热电联供型微网的配电网系统协调优化调度[J]. 电力自动化设备, 2021, 41(4): 15-23.
	YANG Shibo, SUN Liang, CHEN Lidong, et al. Coordinated optimal scheduling of distribution network with CCHP-based microgird considering time-of-use electricity price[J]. Electric Power Automation Equipment, 2021, 41(4): 15-23.
[19]	张智晟, 于道林. 考虑需求响应综合影响因素的RBF-NN短期负荷预测模型[J]. 中国电机工程学报, 2018, 38(6): 1631-1638.
	ZHANG Zhisheng, YU Daolin. RBF-NN based short-term load forecasting model considering comprehensive factors affecting demand response[J]. Proceedings of the CSEE, 2018, 38(6): 1631-1638.
[20]	张元曦, 杨国华, 马龙腾, 等. 基于改进DE算法的园区微电网风光储优化配置[J]. 综合智慧能源, 2025, 47(9): 71-79. doi: 10.3969/j.issn.2097-0706.2025.09.008
	ZHANG Yuanxi, YANG Guohua, MA Longteng, et al. Optimized wind-solar-storage configuration of industrial park microgrids based on improved differential evolution algorithm[J]. Integrated Intelligent Energy, 2025, 47(9): 71-79. doi: 10.3969/j.issn.2097-0706.2025.09.008
[21]	陈艳波, 马彦虎, 郑国栋, 等. 计及需求响应的多CHP机组热电解耦协调规划[J]. 电网技术, 2022, 46(10): 3821-3830.
	CHEN Yanbo, MA Yanhu, ZHENG Guodong, et al. Coordinated planning of thermo-electrolytic coupling for multiple CHP units considering demand response[J]. Power System Technology, 2022, 46(10): 3821-3830.
[22]	徐玉琴, 方楠. 基于分段线性化与改进二阶锥松弛的电-气互联系统多目标优化调度[J]. 电工技术学报, 2022, 37(11): 2800-2812.
	XU Yuqin, FANG Nan. Multi objective optimal scheduling of integrated electricity-gas system based on piecewise linearization and improved second order cone relaxation[J]. Transactions of China Electrotechnical Society, 2022, 37(11): 2800-2812.
[23]	李明扬, 蒋媛媛. 考虑煤耗率的火电机组灵活调峰对风电消纳的影响效果研究[J]. 热力发电, 2020, 49(2): 45-51.
	LI Mingyang, JIANG Yuanyuan. Effect of flexible load regulation of thermal power units considering coal consumption rate on wind power utilization[J]. Thermal Power Generation, 2020, 49(2): 45-51.
[24]	梁振锋, 李召, 彭书涛, 等. 提升新能源承载能力的抽水蓄能容量配置研究[J]. 电网技术, 2024, 48(10): 4206-4214, I0094-I0098, I0093.
	LIANG Zhenfeng, LI Zhao, PENG Shutao, et al. Research on capacity configuration of pumped storage to improve the carrying capacity of new energy[J]. Power System Technology, 2024, 48(10): 4206-4214, I0094-I0098, I0093.
[25]	王超, 徐政, 高鹏, 等. 大电网可靠性评估的指标体系探讨[J]. 电力系统及其自动化学报, 2007, 19(1): 42-48.
	WANG Chao, XU Zheng, GAO Peng, et al. Reliability index framework for reliability evaluation of bulk power system[J]. Proceedings of the CSU-EPSA, 2007, 19(1): 42-48.
[26]	刘文飞, 曾嘉志, 潘海涛, 等. 计及电力安全事故风险的输电网可靠性评估[J]. 电力系统及其自动化学报, 2018, 30(8): 126-132.
	LIU Wenfei, ZENG Jiazhi, PAN Haitao, et al. Evaluation on the reliability of power transmission network considering risk of electric power safety accidents[J]. Proceedings of the CSU-EPSA, 2018, 30(8): 126-132.

时段		电价
峰时	06∶00 — 08∶00， 18∶00 — 23∶00	0.75
平时	23∶00 — 06∶00， 08∶00 — 10∶00， 16∶00 —18∶00	0.50
谷时	10∶00 —16∶00	0.26

场景	场景概率
1	0.19
2	0.24
3	0.14
4	0.16
5	0.13
6	0.12

参数	数值
火电耗煤系数${\mathit{a}}_{\mathrm{c}},\mathrm{ }{\mathit{b}}_{\mathrm{c}},\mathrm{ }{\mathit{c}}_{\mathrm{c}}$	0.226，30.42，786.8
碳捕集成本/(元·t^-1)	80
失负荷成本/(元·kW^-1)	8
储能单位容量成本/[元·（kW·h）^-1]	1 200
储能单位功率成本/(元·kW^-1)	600
充电效率	0.9
放电效率	0.9
最大荷电状态	0.9
最小荷电状态	0.1
失负荷概率/%	0.14

项目	新能源承载能力/MW	储能容量/MW	储能成本/亿元
需求响应前	67 951	5 090	72.79
需求响应后	69 465	4 320	61.78

新能源装机占比/%	火电机组运行成本	碳排放成本	储能总成本
83.3	245.19	26.57	61.78
86.1	234.92	26.30	71.36
88.9	225.71	26.03	80.51
90.0	227.56	25.76	82.07

提升新能源承载力的电网需求响应与储能配置协同优化方案

Coordinated planning of demand response and energy storage configuration for increasing new energy hosting capacity of power grids

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 18

参考文献 26

相关文章 15

编辑推荐

Metrics

本文评价

新能源承载能力/MW	储能容量/MW	配置储能前LOLP/%	配置储能后LOLP/%
69 465	4 320	0.84	0.13
71 639	4 990	1.80	0.14
80 213	5 630	2.40	0.14
83 477	5 739	8.00	0.57

场景	电压越限概率/%
场景	未含储能	新增4 320 MW储能
1	68.23	61.05
2	57.65	31.52
3	59.47	36.42
4	31.14	22.37

[1]	郭秉霖, 张竞宇, 臧启勇. 储能参与的电能量-调频市场双层交易决策模型[J]. 综合智慧能源, 2026, 48(5): 10-18.
[2]	张元曦, 杨国华, 马龙腾, 马鑫, 刘耀泽. 基于改进DE算法的园区微电网风光储优化配置[J]. 综合智慧能源, 2025, 47(9): 71-79.
[3]	陈浩, 马刚, 钱达, 马健, 彭乐瑶. 绿证-碳交易机制下考虑阶梯需求响应的区域综合能源系统优化调度[J]. 综合智慧能源, 2025, 47(5): 21-30.
[4]	张文博, 秦文萍, 刘佳昕, 陈玉梅, 刘博洋, 赵桉霆. 绿证-电-碳耦合市场下计及需求响应与租赁共享储能的VPP鲁棒优化调度策略[J]. 综合智慧能源, 2025, 47(5): 31-40.
[5]	季振亚, 郑帅帅, 许金星, 刘晓峰, 包宇庆. 计及碳-绿证联合交易的含需求响应的综合能源系统调度[J]. 综合智慧能源, 2025, 47(5): 41-50.
[6]	程乐峰, 刘奕杭, 邹涛. 博弈论视角下智能电网需求响应研究综述[J]. 综合智慧能源, 2025, 47(4): 1-22.
[7]	邸亮, 董杰, 闫馨月, 甄成, 田喆, 牛纪德. 用于市场准入条件判定的建筑需求响应潜力量化方法[J]. 综合智慧能源, 2025, 47(3): 15-22.
[8]	江美慧, 许镇江, 牛统科, 朱虹谕, 李想. 考虑综合需求响应的矿区综合能源系统日内多时间尺度滚动优化调度[J]. 综合智慧能源, 2025, 47(3): 73-83.
[9]	崔再岳, 杨扬, 王立地. 考虑负荷需求响应的配电网三相不平衡优化[J]. 综合智慧能源, 2025, 47(3): 92-101.
[10]	陈晓祺, 张敏, 孙周, 刘斌, 毛勇, 陶永晋. 高速公路微网的储能容量配置与调度优化策略[J]. 综合智慧能源, 2025, 47(2): 29-40.
[11]	苏炜琦, 黄宇轩, 陈飞雄, 邵振国, 胡昆熹, 吴鸿斌. 考虑碳势-电价联合响应机制的电力系统源-荷协同低碳经济调度[J]. 综合智慧能源, 2025, 47(11): 1-13.
[12]	卞礼杰, 马刚, 陈旭东, 詹孝升. 基于动态弹性分级与5G通信延迟补偿的虚拟电厂紧急切负荷优化策略[J]. 综合智慧能源, 2025, 47(11): 62-71.
[13]	邱文婷, 董家乐, 吴笛, 苏文静, 宗毅. 基于NSGA-Ⅱ的含氢多能源系统经济与低碳协同优化调度[J]. 综合智慧能源, 2025, 47(10): 34-44.
[14]	高建生, 汪马翔, 邢鹏生, 肖柱. 计及钢铁企业参与的含风电电力系统协同调度策略[J]. 综合智慧能源, 2025, 47(10): 52-59.
[15]	徐强. 基于NSGA-Ⅲ算法的分布式光伏储能系统优化配置方法[J]. 综合智慧能源, 2025, 47(1): 26-33.