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负荷聚合商模式下考虑需求响应的超短期负荷预测
作者:
作者单位:

1.浙江大学电气工程学院,浙江省杭州市 310027;2.广东电网有限责任公司广州供电局,广东省广州市 510600

摘要:

为更好地管理用户侧需求响应资源,减小超短期负荷预测误差,提出了一种在负荷聚合商模式下考虑需求响应的超短期负荷预测方法。首先,分析负荷聚合商的需求响应机制,考虑用户用能习惯、自建光伏、储能行为以及电热耦合,分别对每一类需求响应资源建立优化模型,并通过模糊参数表达用户参与需求响应的不确定性,以改善优化模型;调用CPLEX求解器求解得到综合各类资源后的需求响应信号。然后,在考虑历史负荷数据的基础上引入该需求响应信号,建立迭代预测的长短期记忆网络模型。算例通过3种预测场景的对比,验证了计及需求响应信号能够有效减小预测误差,且考虑需求响应不确定性能够进一步提高预测精度。

关键词:

基金项目:

国家自然科学基金资助项目(51877190)。

通信作者:

作者简介:

郭亦宗(1997—),男,硕士研究生,主要研究方向:综合能源服务与需求响应。E-mail:guoyizong@zju.edu.cn
冯斌(1997—),男,博士研究生,主要研究方向:人工智能在电力系统中的应用。E-mail:11910028@zju.edu.cn
岳铂雄(1995—),男,硕士研究生,主要研究方向:综合能源服务与需求响应。E-mail:21910189@zju.edu.cn
郭创新(1969—),男,通信作者,教授,博士生导师,主要研究方向:智能电网风险调度、综合能源系统。E-mail:guochuangxin@zju.edu.cn


Ultra-short-term Load Forecasting Considering Demand Response in Load Aggregator Mode
Author:
Affiliation:

1.College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;2.Guangzhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China

Abstract:

To better manage the demand response resources on user side and reduce ultra-short-term load forecasting error, an ultra-short-term load forecasting method considering the demand response in the load aggregator mode is proposed. Firstly, the demand response mechanism of load aggregator is analyzed. Considering the energy usage habits of users, self-built photovoltaics, energy storage behavior and electro-thermal coupling, the optimization model for each type of demand response resources is established. Also, the uncertainty of user participation in demand response is denoted by fuzzy parameters to improve the optimization model. The demand response signal after the integration of various resources is obtained by using CPLEX solver. Then, based on the historical load data, a long short-term memory network model for iterative prediction is established with the demand response signal. After the comparison of three prediction scenarios, the example verifies that the prediction error can be effectively reduced considering demand response signal, and the prediction accuracy can be further improved considering the uncertainty of demand response.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 51877190).
引用本文
[1]郭亦宗,冯斌,岳铂雄,等.负荷聚合商模式下考虑需求响应的超短期负荷预测[J].电力系统自动化,2021,45(1):79-87. DOI:10.7500/AEPS20200330014.
GUO Yizong, FENG Bin, YUE Boxiong, et al. Ultra-short-term Load Forecasting Considering Demand Response in Load Aggregator Mode[J]. Automation of Electric Power Systems, 2021, 45(1):79-87. DOI:10.7500/AEPS20200330014.
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  • 收稿日期:2020-03-30
  • 最后修改日期:2020-08-15
  • 录用日期:
  • 在线发布日期: 2021-01-05
  • 出版日期:
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