| 引用本文: | 李卫国,陈立铭,张师,等.分时电价下考虑储能调度因素的短期负荷预测模型[J].电力系统保护与控制,2020,48(7):133-140.[点击复制] |
| LI Weiguo,CHEN Liming,ZHANG Shi,et al.Short-term load forecasting model considering energy storage scheduling factors under time-sharing price (1. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China;[J].Power System Protection and Control,2020,48(7):133-140[点击复制] |
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| 摘要: |
| 影响电力系统短期负荷预测的因素有多种，因此在进行短期负荷预测时，考虑的因素种类越多，预测的精度越高。在考虑环境因素的基础上，构建分时电价下考虑储能调度因素的改进鲸鱼算法优化Elman神经网络模型。在智能电网下，由于储能调度能够使传统的负荷曲线发生改变，首先在基于分时电价的基础上构建储能调度模型，对储能用户在各时段的充放电行为进行具体分析。然后由于Elman神经网络具有收敛速度慢、容易陷入局部最优等缺点，提出了一种改进的鲸鱼算法(MWOA)用于优化神经网络的权值和阈值，进一步提高了神经网络的收敛速度和全局寻优能力。最后构建考虑储能调度因素的短期负荷预测模型，通过对某地电网2018年7月至8月的数据为例进行仿真分析，并与所提到的其他预测模型进行比较。通过误差结果分析可知所提方法的预测精度更高，收敛速度更快。 |
| 关键词: 短期负荷预测 储能调度 Elman神经网络 鲸鱼算法 分时电价 |
| DOI：10.19783/j.cnki.pspc.190423 |
| 投稿时间：2019-04-16修订日期：2019-07-11 |
| 基金项目:国家电网公司科技项目资助(SGTJDK00DWJS1700034)“面向随机性电源的多元负荷主动响应及预测控制技术研究与应用” |
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| Short-term load forecasting model considering energy storage scheduling factors under time-sharing price (1. School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China; |
| LI Weiguo,CHEN Liming,ZHANG Shi,XU Bei,WANG Xuguang,LIU Hongwei |
| (, CHEN Liming;, WANG Xuguang) |
| Abstract: |
| There are many factors affecting the short-term load forecasting of power systems, therefore, when performing short-term load forecasting, the more types of factors are considered, the higher the accuracy of predicting is. On the basis of considering environmental factors, the improved whale algorithm optimization Elman neural network model considering the factors of energy storage scheduling under time-of-use price is constructed. Under the smart grid, because the energy storage scheduling can change the traditional load curve, firstly, based on the time-sharing price, the energy storage scheduling model is constructed to analyze the charging and discharging behavior of the energy storage users in each period. Then, because the Elman neural network has the disadvantages of slow convergence and easy to fall into local optimum, a Modified Whale Optimization Algorithm (MWOA) is proposed to optimize the weight and threshold of the neural network, which further improves the convergence and global search of the neural network excellent ability. Finally, constructing a short-term load forecasting model considering energy storage scheduling factors, the analysis of the data of a certain power grid from July to August of 2018 is carried out and compared with other prediction models mentioned. The error results analysis shows that the proposed method has higher prediction accuracy and faster convergence. This work is supported by Science and Technology Project of State Grid Corporation of China (No. SGTJDK00D WJS1700034) “Research and Application of Stochastic Power Sources Oriented Multiple Load Active Response and Predictive Control Technology”. |
| Key words: short-term load forecasting energy storage scheduling Elman neural network whale algorithm time-sharing price |
