面向需求响应的园区虚拟电厂优化调度策略

doi:10.11930/j.issn.1004-9649.202410059

中国电力 ›› 2025, Vol. 58 ›› Issue (6): 112-121.DOI: 10.11930/j.issn.1004-9649.202410059

• 基于数据驱动的电力系统安全稳定分析与控制 • 上一篇下一篇

面向需求响应的园区虚拟电厂优化调度策略

魏春晖¹(), 单林森¹(), 胡大栋¹(), 高乾恒¹(), 张新松²(), 薛晓岑²()

1. 国网浙江省电力有限公司绍兴供电公司，浙江绍兴　312000
2. 南通大学电气与自动化学院，江苏南通　226019

收稿日期:2024-10-18 发布日期:2025-06-30 出版日期:2025-06-28
作者简介:
魏春晖（1977），男，硕士，高级工程师，从事新型能源体系研究，E-mail：13567552631@163.com
单林森（1981），男，硕士，高级工程师，从事需求响应、新型电力系统研究，E-mail：sanls1981@163.com
胡大栋（1983），男，硕士，高级工程师，从事电网规划研究，E-mail：45880309@qq.com
高乾恒（1992），男，硕士，工程师，从事变电站运维研究，E-mail：gaoqianheng163@163.com
张新松（1980），男，教授，博导，从事新能源并网、新型电力系统规划等研究，E-mail：zhang.xs@ntu.edu.cn
薛晓岑（1986），男，博士，讲师，通信作者，从事综合能源系统研究，E-mail：xiaocenxue@ntu.edu.cn
基金资助:
国家自然科学基金资助项目（52377104）；国网绍兴供电公司科技项目（SGZJSX00FZJS2311395）。

Optimal Scheduling Strategy of Park-level Virtual Power Plant for Demand Response

WEI Chunhui¹(), SHAN Linsen¹(), HU Dadong¹(), GAO Qianheng¹(), ZHANG Xinsong²(), XUE Xiaocen²()

1. Shaoxing Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Shaoxing 312000, China
2. School of Electrical Engineering and Automation, Nantong University, Nantong 226019, China

Received:2024-10-18 Online:2025-06-30 Published:2025-06-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.52377104) & Technology Project of State Grid Shaoxing Power Supply Company (No.SGZJSX00FZJS2311395).

摘要/Abstract

摘要：

将工业园区内部多元化灵活可调资源聚合为园区虚拟电厂，可充分挖掘工业园区的需求响应潜力，提高自身收益。为优化电碳联合市场背景下的园区虚拟电厂需求响应策略，提出了考虑碳排放成本的园区虚拟电厂日前-日内两阶段优化调度模型，并采用GAMS软件求解。日前阶段，采用改进径向基函数神经网络进行光伏发电功率预测，并以需求响应净收益最大为优化目标，确定需求响应投标容量。日内阶段，根据光伏实时出力，以运行成本最小为优化目标对日前调度计划进行修正，尽可能减少日前光伏出力预测误差对需求响应收益的不利影响。最后，对某园区虚拟电厂进行仿真分析，结果表明：所提策略可在计及碳排放成本前提下，最大化园区虚拟电厂参与需求响应获取的净收益。

关键词: 园区虚拟电厂, 灵活资源, 需求响应, 优化调度, 碳排放

Abstract:

The park-level virtual power plant (PVPP) can aggregate diversified and flexible resources with the industrial parks to fully explore demand response potential, thus improving the profits of the PVPP. In order to optimize the demand response strategy of the PVPP in the electricity carbon joint market, a two-stage optimal scheduling model of the PVPP is developed here, in which, carbon emission costs are included in the optimization objects. The developed model includes two stages, i.e., a day-ahead stage and a real-time stage, and can be solved by the GAMS software. In the day-ahead stage, photovoltaic (PV) generation power is predicted by the improved radial basis function neural network, and the demand response bidding capacities are determined aiming to maximize the net benefits of the PVPP resulted from the demand response. In the real-time stage, the day-ahead schedules are revised according to real PV generation power, thus minimizing the negative effects of the PV power generation forecasting errors on the net benefits of the PVPP resulted from the demand response. Finally, the simulation analysis based on a PVPP is carried out. The results demonstrate that the strategy developed in this paper can maximize the net benefits of the PVPP resulted from the demand response in the context of considering the carbon emission costs.

Key words: park-level virtual power plant, flexible resources, demand response, optimal schedule, carbon emission

魏春晖, 单林森, 胡大栋, 高乾恒, 张新松, 薛晓岑. 面向需求响应的园区虚拟电厂优化调度策略[J]. 中国电力, 2025, 58(6): 112-121.

WEI Chunhui, SHAN Linsen, HU Dadong, GAO Qianheng, ZHANG Xinsong, XUE Xiaocen. Optimal Scheduling Strategy of Park-level Virtual Power Plant for Demand Response[J]. Electric Power, 2025, 58(6): 112-121.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202410059

https://www.electricpower.com.cn/CN/Y2025/V58/I6/112

图/表 9

图 1 PVPP基础架构

Fig.1 Basic structure of the PVPP

图 2 PVPP需求响应流程

Fig.2 Flow chart of demand response of the PVPP

图 3 PVPP两阶段优化调度策略

Fig.3 Two-stage optimal scheduling strategy of the PVPP

表 1 PVPP内部单元运行参数

Table 1 Operational parameters of units within the PVPP

可调节资源		参数
PV/MW		17
GT/MW		10
BESS/(MW·(MW·h)⁻¹)		1.2/2.4

表 2 当地峰谷电价

Table 2 Local peak and valley electricity price

时段		电价/(元·(kW·h)^–1)
尖峰（09:00—11:00、15:00—17:00）		1.2840
高峰（08:00—09:00、17:00—23:00）		1.0701
平段（13:00—15:00、23:00—24:00）		0.6485
低谷（00:00—08:00、11:00—13:00）		0.2918

图 4 光伏发电功率预测值与实际值

Fig.4 Prediction and actual values of the PV output

图 5 3种场景下的日前用电优化曲线

Fig.5 Day-ahead electricity consumption optimization curves in three scenarios

图 6 场景2下日前和日内实时优化结果

Fig.6 Day-ahead and intra-day real-time optimization results in scenario 2

表 3 3种场景下的PVPP运行收益

Table 3 Operating revenue of PVPP in three scenarios

需求响应收入/元			净收益/元
场景1	场景2	场景3	场景1	场景2	场景3
58 206	54 387	58 206	49 168	36 858	35 735

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面向需求响应的园区虚拟电厂优化调度策略

Optimal Scheduling Strategy of Park-level Virtual Power Plant for Demand Response

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面向需求响应的园区虚拟电厂优化调度策略

Optimal Scheduling Strategy of Park-level Virtual Power Plant for Demand Response

RichHTML

PDF (PC)

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