基于信息差距决策理论的虚拟电厂报价策略

doi:10.11930/j.issn.1004-9649.202308122

中国电力 ›› 2024, Vol. 57 ›› Issue (1): 40-50.DOI: 10.11930/j.issn.1004-9649.202308122

基于信息差距决策理论的虚拟电厂报价策略

谢蒙飞¹(), 马高权¹(), 刘斌¹(), 潘振宁²(), 商云峰³()

1. 昆明电力交易中心有限责任公司，云南昆明　650011
2. 华南理工大学电力学院，广东广州　510641
3. 国家电投山东能源发展有限公司鲁东分公司，山东烟台　264000

收稿日期:2023-08-29 接受日期:2023-12-14 出版日期:2024-01-28 发布日期:2024-01-23
作者简介:谢蒙飞（1989—），男，博士，高级工程师，从事水库优化调度、电力市场化交易、电力电量平衡等研究，E-mail：xiemengfeihust@foxmail.com
马高权（1984—），男，硕士，高级工程师，从事电力市场交易、电力系统运行等研究，E-mail：magaoq@163.com
刘斌（1995—），男，硕士，助理工程师，从事电力市场、电力调度、电力营销等研究，E-mail：lbnculab@qq.com
潘振宁（1984—），男，通信作者，博士，助理研究员，从事电力系统优化运行与控制等研究，E-mail：scutpanzn@163.com
商云峰（1986—），男，高级技师，从事新能源发电系统、风力发电、光伏发电、储能电站设备原理及控制等研究，E-mail：626386690@qq.com
基金资助:
国家自然科学基金资助项目（电力系统智能调度的高泛化性策略模型与元强化学习方法研究，52207105）。

Virtual Power Plant Quotation Strategy Based on Information Gap Decision Theory

Mengfei XIE¹(), Gaoquan MA¹(), Bin LIU¹(), Zhenning PAN²(), Yunfeng SHANG³()

1. Kunming Electric Power Trading Center Co., Ltd., Kunming 650011, China
2. School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
3. Shandong Branch of State Power Investment Shandong Energy Development Co., Ltd., Yantai 264000, China

Received:2023-08-29 Accepted:2023-12-14 Online:2024-01-28 Published:2024-01-23
Supported by:
This work is supported by National Natural Science Foundation of China (Research on High Generalization Strategy Model and Meta Reinforcement Learning Method for Intelligent Dispatching of Power Systems, No.52207105).

摘要/Abstract

摘要：

为进一步提升分布式能源的调节潜力，基于信息差距决策理论，将探讨虚拟电厂（virtual power plant，VPP）在参与需求响应（demand response，DR）策略时的竞价方式分为平衡型、保守型和进取型3种策略模型，并为每种策略设计鲁棒函数和机会函数，分别实现对不同类型决策的优化。同时，设置ε约束模型，考虑了碳排放和利润的权衡关系。采用IEEE 18节点系统作为仿真环境，验证了所提方法的优点和必要性。仿真结果表明，保守型VPP能够保证在未来价格落入最大鲁棒性区间时获得最小关键利润；进取型VPP能够从意外的价格波动中获益，并实现期望的利润。

关键词: 虚拟电厂, 信息差距决策理论, 鲁棒性, 机会函数

Abstract:

To further enhance the regulatory potential of distributed energy resource (DER), based on the information gap decision theory (IGDT), the bidding methods for virtual power plants (VPPs) participating in demand response (DR) strategies are divided into three strategy models: balanced, conservative and aggressive, and the robust and opportunity functions are designed for each strategy to optimize different types of decisions. Meanwhile, a ε-constraint model is set with consideration of the trade-off between carbon emissions and profits. The advantages and necessity of the proposed method were verified using an IEEE18 node system as the simulation environment. The simulation results show that the conservative VPP can ensure the minimum critical profit when the future price falls into the maximum robustness range; the progressive VPP can benefit from unexpected price fluctuations and achieve expected profits.

Key words: virtual power plant, information gap decision theory, robustness, opportunity function deposition

谢蒙飞, 马高权, 刘斌, 潘振宁, 商云峰. 基于信息差距决策理论的虚拟电厂报价策略[J]. 中国电力, 2024, 57(1): 40-50.

Mengfei XIE, Gaoquan MA, Bin LIU, Zhenning PAN, Yunfeng SHANG. Virtual Power Plant Quotation Strategy Based on Information Gap Decision Theory[J]. Electric Power, 2024, 57(1): 40-50.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202308122

https://www.electricpower.com.cn/CN/Y2024/V57/I1/40

图/表 16

图 1 威布尔功率分布

Fig.1 Weibull power distribution

图 2 贝塔功率分布

Fig.2 Beta power distribution

表 1 VPP中DER的主要参数

Table 1 Main parameters of DER in VPP

项目	DG2	DG7	DG8	DG14
最大功率/MW	4	5	5.5	7
最小功率/MW	0	0	0	0
电价/(元·(MW·h)^–1)	280	266	245	315
斜坡速率/(MW·h^–1)	1	1.25	1.375	1.75
碳排放量(kg·(MW·h)^–1)	5.44	4.08	6.8	3.18

图 3 VPP拓扑结构

Fig.3 VPP topology

图 4 典型日的预测价格和λ=0.25的鲁棒性区域

Fig.4 Predicted prices and robustness region with λ= 0.25

图 5 中风险情况下的发电出力

Fig.5 Generation under medium risk scenarios

图 6 最佳鲁棒性变量ξ与利润偏离因子λ

Fig.6 Optimal robustness variable ξ and deviation factor from profit λ

图 7 两种不同临界利润下VPP中部分机组的鲁棒调度

Fig.7 Robust scheduling of some generator units in VPP under two different critical profits

图 8 鲁棒性变量ξ与利润

Fig.8 Robust variable ξ and profit

图 9 机会案例中的利润与θ

Fig.9 Profit and θ in opportunity cases

图 10 两种不同临界利润下VPP中部分机组的机会调度

Fig.10 Opportunity scheduling of some generator units in VPP under two different critical profits

图 11 机会案例中的利润与$ \tau$

Fig.11 Profit and $\tau $ in opportunity cases

图 12 新能源/非新能源的VPP的累积利润和排放

Fig.12 Accumulated profit of VPP for new/ traditional energy and emissions

图 13 收益与排放的帕累托最优解

Fig.13 Pareto optimal solution for accumulated profit and emissions

图 14 收益、排放和各情况下的隶属度函数的帕累托最优解

Fig.14 Pareto optimal solutions for the membership functions of income, emissions, and various scenarios

表 2 VPP中的发电功率和交换功率

Table 2 Generation power and exchange power in VPP

策略模型	发电厂的发电功率/MW						交换功率/MW			削减功率P_DL/MW	收益/元
策略模型	DG2	DG7	DG8	DG14	风电	光伏	GSP1	GSP11	GSP16	削减功率P_DL/MW	收益/元
平衡型	58.90	90.74	123.75	1.75	17.528	29.134	–565.6	350.7	239.1	1.76	93694.51
保守型（Pareto No.7）	89.99	112.50	46.80	25.32	17.528	29.134	–586.4	390.1	221.0	1.79	91256.83

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基于信息差距决策理论的虚拟电厂报价策略

Virtual Power Plant Quotation Strategy Based on Information Gap Decision Theory

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基于信息差距决策理论的虚拟电厂报价策略

Virtual Power Plant Quotation Strategy Based on Information Gap Decision Theory

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

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参考文献 32

相关文章 15

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