Journal of Electrical Engineering and Automation
Journal of Electrical Engineering and Automation. 2025; 4: (1) ; 10.12208/j.jeea.20250004 .
总浏览量: 29
1 南京邮电大学自动化学院/人工智能学院 江苏南京
2 国网常州供电公司设备监控班 江苏常州
*通讯作者： 刘程子,单位： 南京邮电大学自动化学院/人工智能学院 江苏南京；
本文研究了在多市场环境下，结合条件风险价值（CVaR）和碳成本的虚拟电厂（VPP）优化调度策略。虚拟电厂由分布式燃气轮机、储能设备、风力发电、光伏发电及非柔性负荷组成，研究的核心目标是在基于多场景下VPP通过参与电力期货市场、日前市场、实时市场以及碳市场交易，分析最大化收益下的VPP内部优化调度结果。在实际操作中，当日前市场的风电和光伏发电无法满足非柔性负荷需求时，VPP优先满足期货市场的负荷需求，并通过与实时市场的博弈，决定是购电还是启动燃气轮机发电，以平衡供需。本文详细分析了多种风电、光伏出力及负荷场景下的优化调度结果，旨在为决策者在极端天气条件下提供科学的决策支持。同时为了应对风电和光伏出力的波动带来的不确定性，本文采用蒙特卡罗模拟结合CVaR方法，对出力波动的不确定性进行量化，并将其视为相关成本加以考虑。通过分析不同偏差容忍度下的调度优化结果，研究表明，合理设置偏差容忍度不仅能够在多市场环境中实现较高收益，还能有效降低运营风险，从而推动构建更加低碳和高效的能源系统。
This paper investigates the optimization scheduling strategy of Virtual Power Plant in a multi-market environment, integrating Conditional Value-at-Risk (CVaR) and carbon cost considerations. The VPP comprises distributed gas turbines, energy storage devices, wind power, photovoltaic generation, and non-flexible loads. The core objective of this research is to analyze the internal optimization scheduling results of VPPs that maximize profits through participation in electricity futures markets, day-ahead markets, real-time markets, and carbon markets under a multi-scenario approach. In practical operations, when wind and photovoltaic generation in the day-ahead market cannot meet non-flexible load demands, the VPP prioritizes satisfying load demands in the futures market. Through strategic interactions with the real-time market, the VPP determines whether to purchase electricity or activate gas turbines for power generation to balance supply and demand. This paper presents a detailed analysis of optimization scheduling results under various wind power, photovoltaic output, and load scenarios, aiming to provide scientific decision support for decision-makers under extreme weather conditions. Furthermore, to address the uncertainties caused by fluctuations in wind and photovoltaic outputs, this study employs Monte Carlo simulation combined with the CVaR method to quantify output volatility, which is considered as a relevant cost factor. By analyzing optimization results under different deviation tolerance levels, the research demonstrates that setting reasonable deviation tolerance not only achieves higher returns in multi-market environments but also effectively reduces operational risks, thereby promoting the development of a more low-carbon and efficient energy system.
[1] 《高举中国特色社会主义伟大旗帜为全面建设社会主义现代化国家而团结奋斗——在中国共产党第二十次全国代表大会上的报告》[J]. 九江学院学报(自然科学版), 2023, 38(1): 2.
[2] 国家能源局组织发布《新型电力系统发展蓝皮书》[J]. 农村电气化, 2023(7): 3.
[3] 黄震, 谢晓敏, 张庭婷. “双碳”背景下我国中长期能源需求预测与转型路径研究[J]. 中国工程科学, 2022, 24(6): 8-18.
[4] MINAI A F，KHAN A A, KITMO, 等. Evolution and role of virtual power plants: Market strategy with integration of renewable based microgrids[J/OL]. Energy Strategy Reviews, 2024, 53: 101390.
[5] 汪洋叶, 赵力航, 常伟光, 等. 基于模型预测控制的虚拟电厂储能系统能量协同优化调控方法[J]. 智慧电力, 2021, 49(7): 16-22.
[6] 张天策,李庚银,王剑晓,等. 基于可行域投影理论的新能源电力系统协同运行方法 [J]. 电工技术学报, 2024, 39 (09): 2784-2796.
[7] 李翔宇,赵冬梅.分散架构下多虚拟电厂分布式协同优化调度[J].电工技术学报,2023,38(07):1852-1863.
[8] 沈广, 庄晓丹, 孙瑜. 基于需求侧响应的虚拟电厂自动优化调度系统[J]. 自动化技术与应用, 2024, 43(5): 158-162.
[9] 刘雨佳,樊艳芳,白雪岩,等.基于优化计算型区块链系统的虚拟电厂模型与调度策略[J].电工技术学报,2023,38(15):4178-4191.
[10] 郑冉,夏彦辉,赵学茂,等.基于云-边-端协同控制的综合型虚拟电厂[J].电气技术,2023,24(09):40-48.
[11] 张高, 王旭, 蒋传文, 等. 采用双层优化调度的虚拟电厂经济性分析[J]. 电网技术, 2016, 40(8): 2295-2302.
[12] 段翩, 朱建全, 刘明波. 基于双层模糊机会约束规划的虚拟电厂优化调度[J]. 电工技术学报, 2016, 31(9): 58-67.
[13] 秦婷, 刘怀东, 王锦桥, 等. 基于碳交易的电—热—气综合能源系统低碳经济调度[J]. 电力系统自动化, 2018, 42(14): 8-13+22.
[14] 张大海, 贠韫韵, 王小君, 等. 计及风光不确定性的新能源虚拟电厂多时间尺度优化调度[J]. 太阳能学报, 2022, 43(11): 529-537.
[15] 徐康轩, 郭超, 包铭磊, 等. 市场环境下考虑多元不确定性的热电联合虚拟电厂竞标策略[J]. 电网技术, 2022, 46(9): 3354-3365.
[16] 于东民, 王晓鹏, 孙钦斐, 等. 基于VPP碳流计算的多目标多时间尺度优化调度[J]. 智慧电力, 2024, 52(1): 30-38.
[17] 谢鹏林, 王瑜, 王浙, 等. 基于CNN-BiLSTM负荷预测方法的VPP低碳调度策略[J]. 华北电力大学学报(自然科学版): 1-10.
[18] 李虹, 林兰心. 基于碳排放流理论的供需联合优化调度策略[J]. 电力科学与工程, 2023, 39(11): 1-11
[19] MOHY-UD-DIN G, MUTTAQI K M, SUTANTO D. A Bi-level Energy Management Model for Energy Transactions from VPP based Integrated Energy Systems under System Uncertainties[C/OL]//2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES). Jaipur, India: IEEE, 2020: 1-6[2023-11-16].
[20] Wang Y, Wu X, Liu M, et al. Bidding strategy of the virtual power plant considering green certificates and carbon trading[J]. Energy Reports, 2023, 9: 73-84.
[21] MASHHOUR E, MOGHADDAS-TAFRESHI S M. Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation[J]. IEEE Transactions on Power Systems, 2011, 26(2): 949-956.
[22] MASHHOUR E, MOGHADDAS-TAFRESHI S M. Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part II: Numerical Analysis[J]. IEEE Transactions on Power Systems, 2011, 26(2): 957-964.
[23] 祖文静, 杜易达, 李鹏, 等. 计及不确定性与相关性的虚拟电厂参与主辅市场联合交易优化研究[J]. 智慧电力, 2022, 50(10): 70-77+86.
[24] 税纪钧, 彭道刚, 宋炎侃, 等. 计及风光不确定性含碳排放和碳惩罚的虚拟电厂优化调度策略[J]. 系统仿真学报, 2024, 36(2): 305-319.
[25] 景喜平. 计及不确定性的虚拟电厂竞价研究[D]. 广西大学, 2023.
[26] 孙辉, 范轩轩, 胡姝博, 等. 虚拟电厂参与日前电力市场的内外协调竞标策略[J]. 电网技术, 2022, 46(4): 1248-1262.