考虑电-气-热耦合和需求响应的虚拟电厂优化调度策略

doi:10.11930/j.issn.1004-9649.202309050

中国电力 ›› 2024, Vol. 57 ›› Issue (1): 101-114.DOI: 10.11930/j.issn.1004-9649.202309050

考虑电-气-热耦合和需求响应的虚拟电厂优化调度策略

王世杰¹(), 冯天波², 孙宁³, 何可¹, 李嘉文¹, 杨程¹, 崔昊杨¹()

1. 上海电力大学电子与信息工程学院，上海　201306
2. 国网上海市电力公司信息通信公司，上海　200122
3. 国网上海市电力公司培训中心，上海　200438

收稿日期:2023-09-12 接受日期:2023-11-07 出版日期:2024-01-28 发布日期:2024-01-23
作者简介:王世杰（1998—），男，硕士研究生，从事新能源与电力系统优化运行研究，E-mail：799067980@qq.com
崔昊杨（1978—），男，通信作者，博士，教授，从事电力设备状态监测研究，E-mail：cuihy@shiep.edu.cn
基金资助:
国家自然科学基金面上资助项目（52177185）；上海自然科学基金资助项目（23ZR1424400）。

Optimal Scheduling Strategy for Virtual Power Plant Considering Electricity-Gas-Heat Coupling and Demand Response

Shijie WANG¹(), Tianbo FENG², Ning SUN³, Ke HE¹, Jiawen LI¹, Cheng YANG¹, Haoyang CUI¹()

1. College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China
2. State Grid Shanghai Electric Power Company Information and Communication Company, Shanghai 200122, China
3. Training Center of State Shanghai Municipal Electric Power Company, Shanghai 200438, China

Received:2023-09-12 Accepted:2023-11-07 Online:2024-01-28 Published:2024-01-23
Supported by:
This work is supported by General Projects of National Natural Science Foundation of China (No.52177185), Natural Science Foundation of Shanghai (No.23ZR1424400).

摘要/Abstract

摘要：

热电联产机组以热定电的工作方式无法同时满足冬季供暖效率最大化和电力调峰需求，存在发电出力调节能力不足的问题。针对上述问题，提出了考虑电、气、热能源耦合特性以及需求响应的虚拟电厂优化调度策略。首先，为提升热电联产机组向下调峰能力，引入电制气设备和碳捕集技术，构建新型的热电联产耦合模型。其次，为提升系统运行的灵活性，考虑峰谷分时电价、热价，建立综合需求响应机制。然后，为减少系统发电成本，引入电、热储能装置，以系统总成本和电、热储能运行成本最小化为目标建立虚拟电厂双层优化模型，并根据下层优化模型的KKT（Karush-Kuhn-Tucher，KKT）条件将双层模型转为单层并线性化处理进行求解。结果表明，所提方法的碳排放、运行成本以及新能源消纳率达到最优，提升了热电机组向下调峰能力，满足了系统低碳性、经济性的需求。

关键词: 热电耦合, 电、热储能, 需求响应, 双层优化, 虚拟电厂

Abstract:

The combined heat and power (CHP) units can not meet the maximum heating efficiency and power peak shaving demand in winter at the same time, and there exit problems of insufficient power generation output regulation ability. In view of the above problems, an optimal scheduling strategy for virtual power plant (VPP) is proposed considering electricity-gas-thermal energy coupling and demand response. Firstly, in order to improve the downward peak shaving capacity of CHP units, the P2G equipment and carbon capture technology are introduced to construct a new CHP coupling model. Secondly, in order to improve the operation flexibility of the system, considering the peak-valley time-of-use electricity price and heat price, a comprehensive demand response mechanism is established. And then, in order to reduce the generation cost of the system, the electric and thermal energy storage devices are introduced, and a VPP bi-level optimization model is established with the goal of minimizing the total cost of the system and the operation cost of the electric and thermal energy storage devices. According to the Karush-Kuhn-Tucher ( KKT ) condition of the lower-level optimization model, the bi-level model is transformed into a single level and linearized for solution. The results show that the carbon emissions, operation cost and new energy consumption rate of the proposed method are optimal, which improves the downward peak shaving capacity of the CHP units and meets the low-carbon and economic requirements of the system.

Key words: heat and power coupling, electric-thermal energy storage, demand response, bi-level optimization, virtual power plant

王世杰, 冯天波, 孙宁, 何可, 李嘉文, 杨程, 崔昊杨. 考虑电-气-热耦合和需求响应的虚拟电厂优化调度策略[J]. 中国电力, 2024, 57(1): 101-114.

Shijie WANG, Tianbo FENG, Ning SUN, Ke HE, Jiawen LI, Cheng YANG, Haoyang CUI. Optimal Scheduling Strategy for Virtual Power Plant Considering Electricity-Gas-Heat Coupling and Demand Response[J]. Electric Power, 2024, 57(1): 101-114.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202309050

https://www.electricpower.com.cn/CN/Y2024/V57/I1/101

图/表 12

图 1 虚拟电厂系统结构

Fig.1 Virtual power plant system structure

图 2 PCC与CHP运行特性对比

Fig.2 Comparison of PCC and CHP operation characteristics

图 3 PCC模型运行特性曲线

Fig.3 The operating characteristic curve of PCC model

图 4 双层优化模型求解过程

Fig.4 The solving process of the bi-level optimization model

图 5 风电、光伏出力及多种负荷需求预测曲线

Fig.5 Forecast curves of wind power, photovoltaic output and multi-load demand

图 6 分时电热价格曲线

Fig.6 The time-of-use electric heating price curve

图 7 热电联产机组电出力对比

Fig.7 Comparison of power output of cogeneration units

表 1 4种模式调度结果

Table 1 Scheduling results of different methods

项目	模式1	模式2	模式3	模式4
风电利用率/%	64.45	91.46	97.24	98.37
光伏利用率/%	65.43	74.75	100.00	100.00
碳排放量/t	1382.65	1376.09	1246.97	1230.24
VPP运行成本/元	537792.14	533556.79	435768.54	403244.09
热电机组成本/元	64309.70	98940.94	81200.49	75478.69
MT成本/元	90435.45	97438.67	105081.20	105175.28
电制冷机成本/元	16445.10	15772.40	12509.42	11715.27
碳交易成本/元	43378.30	43194.20	39135.53	38510.22
弃风弃光成本/元	309780.03	112570.50	11976.44	7055.58
购电购气成本/元	9346.84	3702.28	0	0
VPP系统收益/元	3846.18	5222.37	7752.12	9127.76

图 8 VPP内部各单元调度结果

Fig.8 VPP internal unit scheduling result

图 9 P2G耗电量和CCS系统碳捕集量

Fig.9 P2G power consumption and carbon capture of CCS system

图 10 电、热需求响应优化结果

Fig.10 Optimization results of electric-thermal demand response

图 11 电、热储能优化运行结果

Fig.11 Optimal operation results of electric and thermal energy storage

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考虑电-气-热耦合和需求响应的虚拟电厂优化调度策略

Optimal Scheduling Strategy for Virtual Power Plant Considering Electricity-Gas-Heat Coupling and Demand Response

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