| 引用本文: | 陈丽娜,张智晟,于道林.基于广义需求侧资源聚合的电力系统短期负荷预测模型[J].电力系统保护与控制,2018,46(15):45-51.[点击复制] |
| CHEN Lina,ZHANG Zhisheng,YU Daolin.Short-term load forecasting model of power system based on generalized demand side resources aggregation[J].Power System Protection and Control,2018,46(15):45-51[点击复制] |
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| 摘要: |
| 需求响应计划的广泛实施对电力系统短期负荷预测将产生一定的影响。为了获得理想的预测精度，需要准确计及需求响应因素的实时变化，并将其融入短期负荷预测模型的构建中。首先提出了一种能够通过电价合同实现的需求响应调度方式，该方式借助负荷聚合商机构实现了广义需求侧资源的最优调度，并能够以需求响应信号的形式提供给系统调度员利用。以此为基础，构建了基于广义需求侧资源聚合的电力系统短期负荷预测模型，将需求响应因素融入到短期负荷预测模型的构建中。仿真结果表明，构建的短期负荷预测模型能够有效弥补传统负荷预测模型的不足，有利于提升模型的预测精度。 |
| 关键词: 短期负荷预测 广义需求侧资源 聚合商 径向基函数神经网络 电力系统 |
| DOI：10.7667/PSPC171751 |
| 投稿时间：2017-12-01修订日期：2018-02-10 |
| 基金项目:国家自然科学基金项目资助(51477078)；智能电网教育部重点实验室开放研究基金(2018) |
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| Short-term load forecasting model of power system based on generalized demand side resources aggregation |
| CHEN Lina,ZHANG Zhisheng,YU Daolin |
| (College of Electrical Engineering, Qingdao University, Qingdao 266071, China;College of Electrical Engineering, Qingdao University, Qingdao 266071, China;Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China;State Grid Laiyang Electric Power Supply Company, Laiyang 265226, China) |
| Abstract: |
| The extensive implementation of the demand response plan will have a negative influence on short-term load forecasting of power system. To acquire satisfactory forecasting accuracy, it is necessary to consider real-time changes of the demand response factors, which can be blended in the building of the short-term load forecasting model. Firstly, a demand response scheduling method which can be realized depending on the electricity price contract is presented. With the aid of load aggregation, it achieves the optimal scheduling of the generalized demand side resources and then be employed by the dispatcher in the form of demand response signal. On this basis, a short-term load forecasting model of power system based on generalized demand side resources aggregation is constructed, and the demand response factors are integrated into the course. The simulation result shows that the model newly built effectively makes up for the shortage of the traditional load forecasting models, improving the forecasting accuracy. This work is supported by National Natural Science Foundation of China (No. 51477078) and Open Research Fund of Key Laboratory of Smart Grid of Ministry of Education (2018). |
| Key words: short-term load forecasting generalized demand side resources load aggregation radical basis function-neural networks power system |
