售电公司电力现货交易辅助决策系统关键技术研究  

Research on Key Technologies of Electricity Spot Trading Auxiliary Decision-making System for Electricity Retailers

作　　者：毕可强 屈宝平 范永忠 BI Keqiang;QU Baoping;FAN Yongzhong(SPIC Power Plant Operation Technology(Beijing)Co.,Ltd.,Beijing 102209,China;State Grid of China Technology College,Jinan 250002,China)

机构地区：[1]国家电投集团电站运营技术(北京)有限公司,北京102209 [2]国家电网有限公司技术学院分公司,山东济南250002

出　　处：《山东电力高等专科学校学报》2024年第2期14-18,共5页Journal of Shandong Electric Power College

摘　　要：对电价预测关键技术、用户负荷预测关键技术和零售套餐设计与测算关键技术进行研究。结合基于XGBDT的电价预测算法与基于人工神经网络的电价预测算法,提出了启发式组合电价预测算法,该算法计算简便、预测准确并且能够进行人工调节。将支持向量回归法用于用户负荷预测,用户负荷预测的精度和效率都较高。建立售电公司电力现货交易辅助决策系统,其功能包括市场分析、出清电价预测、用户负荷预测、现货交易决策、中长期交易管理、零售交易管理等,有助于售电公司降低交易风险,增加现货交易收益。The key technologies of electricity price prediction,user load forecasting and retail package design and calculation are investigated.A heuristic combination electricity price prediction algorithm is proposed,which combines the electricity price prediction algorithm based on XGBDT with the electricity price prediction algorithm based on artificial neural network.This algorithm is easy to calculate,accurate in prediction,and can be manually adjusted.Applying support vector regression to user load forecasting results in high accuracy and efficiency.The electricity spot trading auxiliary decision-making system for electricity retailers is established,with the functions such as market analysis,clearing electricity price prediction,user load forecasting,spot trading decision-making,medium and long-term trading management,retail trading management,which helps electricity retailers reduce trading risks and increase spot trading profits.

关 键 词：电力现货市场 人工神经网络 电价预测 用户负荷预测 交易策略 

分 类 号：TM73[电气工程—电力系统及自动化]