计及风电不确定信息间隙的火电-储能-需求响应多源低碳调峰交易优化模型

doi:10.12204/j.issn.1000-7229.2022.12.014

电力建设 ›› 2022, Vol. 43 ›› Issue (12): 131-140.doi: 10.12204/j.issn.1000-7229.2022.12.014

计及风电不确定信息间隙的火电-储能-需求响应多源低碳调峰交易优化模型

李鹏¹(), 余晓鹏¹(), 周青青²(), 谭忠富², 鞠立伟²(), 乔慧婷³()

1.国网河南省电力公司经济技术研究院,郑州市 450000
2.华北电力大学经济与管理学院,北京市 100026
3.南方电网能源发展研究院有限责任公司技术经济中心,广州市 510530

收稿日期:2022-04-06 出版日期:2022-12-01 发布日期:2022-12-06
通讯作者: 周青青 E-mail:hdlp0830@163.com;xmli97@126.com;503609181@qq.com;183758841@qq.com;qiaohuiting@163.com
作者简介:李鹏(1985),男,博士,高级经济师,主要研究方向为农村能源转型、县域综合能源系统、能源互联网应用技术,E-mail:hdlp0830@163.com;
余晓鹏 (1974),男,硕士,教授级高级工程师,主要研究方向为电网规划,E-mail:xmli97@126.com;
谭忠富(1964),男,博士,教授,主要研究方向为电力经济、综合能源系统;
鞠立伟(1989),男,博士,副教授,主要研究方向为微能源网、综合能源系统、能源系统建模,E-mail:183758841@qq.com;
乔慧婷(1990),女,硕士,高级经济师,主要研究方向为能源经济,E-mail:qiaohuiting@163.com。
基金资助:
中原科技创新领军人才项目“农村能源互联网优化配置关键技术研究及应用”

Multi-Source Low-Carbon Peak-Shaving Transaction Optimization Model for Thermal Power-Energy Storage-Demand Response Considering the Uncertainty Information Gap of Wind Power

LI Peng¹(), YU Xiaopeng¹(), ZHOU Qingqing²(), TAN Zhongfu², JU Liwei²(), QIAO Huiting³()

1. Economic Research Institute, State Grid Henan Electric Power Company, Zhengzhou 450000, China
2. School of Economics and Management, North China Electric Power University, Beijing 100026, China
3. Technical and Economic Center, China Southern Power Grid Energy Development Research Institute Co., Ltd., Guangzhou 510530, China

Received:2022-04-06 Online:2022-12-01 Published:2022-12-06
Contact: ZHOU Qingqing E-mail:hdlp0830@163.com;xmli97@126.com;503609181@qq.com;183758841@qq.com;qiaohuiting@163.com
Supported by:
Zhongyuan Science and Technology Innovation Leading Talent Project

摘要/Abstract

摘要：

将碳排放权交易融入调峰交易中,核算火电调峰产生的碳变动效应,提出多源低碳调峰成本核算方式,构造确定性多源低碳调峰交易优化模型。针对风电不确定性,利用信息间隙决策理论(information gap decision theory,IGDT)反映风电预测值与实际值的信息差距,构造不确定性多源低碳调峰交易优化模型。最后,选取中国西北某局域电网作为仿真系统验证所提模型的有效性和适用性。结果表明,所提多源低碳调峰交易模型可以促进风电并网,实现各参与方共享合作效益,确立不同风险态度决策者的调峰交易方案。

关键词: 低碳调峰, 储能, 需求响应, 信息间歇, 优化模型

Abstract:

This paper proposes to integrate carbon emission trading into peak-shaving trading, to account for the carbon variation effects produced by thermal power peak-shaving, and proposes a multi-source low-carbon peak-shaving cost accounting method. Aiming at the uncertainty of wind power, this paper uses the information gap decision theory (IGDT) to reflect the information gap between the predicted value and the actual value of wind power, and constructs an uncertainty multi-source low-carbon peak-shaving transaction optimization model. Finally, a local power grid in northwest China is selected as the simulation system to verify the correctness and validity of the proposed model. Results show that the proposed multi-source low-carbon peak-shaving transaction model can promote the integration of wind power generation, ensure all participants obtain the cooperation incremental benefits, and establish a peak-shaving transaction plan for decision makers with different risk attitudes.

Key words: low-carbon peak-shaving, energy storage, demand response, information intermittent, optimization model

中图分类号:

TM73

李鹏, 余晓鹏, 周青青, 谭忠富, 鞠立伟, 乔慧婷. 计及风电不确定信息间隙的火电-储能-需求响应多源低碳调峰交易优化模型[J]. 电力建设, 2022, 43(12): 131-140.

LI Peng, YU Xiaopeng, ZHOU Qingqing, TAN Zhongfu, JU Liwei, QIAO Huiting. Multi-Source Low-Carbon Peak-Shaving Transaction Optimization Model for Thermal Power-Energy Storage-Demand Response Considering the Uncertainty Information Gap of Wind Power[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(12): 131-140.

图/表 10

图1 多源联合调峰模式

Fig.1 Multi-source joint peak-shaving mode

图2 火电机组调峰示意图

Fig.2 Schematic diagram of peak-shaving strategy of thermal power units

表1 火电机组运行参数

Table 1 Operational parameters of thermal power units

图3 典型负荷日负荷需求和风电可用出力

Fig.3 Typical daily load demand and available output of wind power

表2 情景1中风电调峰交易优化方案

Table 2 Optimization scheme of wind power peak-shaving transaction in scenario 1

图4 不同预测目标偏差下风电不确定性程度和调峰交易成本

Fig.4 Uncertainty degree of wind power and transaction cost of peak-shaving under different forecast target deviations

图5 火电、储能和DR联合参与风电调峰交易结果

Fig.5 The results of the joint participation of thermal power, energy storage and DR in wind power peak-shaving transactions

表3 4种情景下火电机组参与风电调峰状态(仅考虑启动机组)

Table 3 Participation of thermal power units in wind power peak-shaving under four scenarios (only start-up units are considered)

图6 最恶劣情景下风电调峰交易结果

Fig.6 Wind power peak-shaving transaction results under the worst scenario

图7 不同碳交易价格下风电调峰成本和不确定性程度

Fig.7 Wind power peak-shaving cost and uncertainty under different carbon trading price

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计及风电不确定信息间隙的火电-储能-需求响应多源低碳调峰交易优化模型

Multi-Source Low-Carbon Peak-Shaving Transaction Optimization Model for Thermal Power-Energy Storage-Demand Response Considering the Uncertainty Information Gap of Wind Power

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