基于深度学习及多目标优化算法的源荷互动柔性削峰填谷方法,包括以下步骤:S1、多维特征分析;S2、数据预处理;S3、基于强化学习的充电电量预测;S4、基于粒子群算法的削峰填谷计算。本发明提供基于深度学习及多目标优化算法的源荷互动柔性削峰填谷方法,实现充电场站的充分利用,增加利润率,并能根据场站功率预测模型来定价,能够根据历史数据和实时信息,预测未来的充电需求,为服务费定价提供决策支持。帮助充电场站实现最大化的盈利和用户吸引力。
The invention discloses a source-load interaction flexible peak clipping and valley filling method based on deep learning and a multi-objective optimization algorithm. The method comprises the following steps: S1, multi-dimensional feature analysis; s2, data preprocessing; s3, predicting the charging electric quantity based on reinforcement learning; and S4, carrying out peak clipping and valley filling calculation based on a particle swarm algorithm. According to the source-load interaction flexible peak load shifting method based on deep learning and a multi-objective optimization algorithm, full utilization of a charging station is realized, the profit rate is increased, pricing can be carried out according to a station power prediction model, future charging demands can be predicted according to historical data and real-time information, and decision support is provided for service charge pricing. And the charging station is helped to realize maximum profit and user attraction.
基于深度学习及多目标优化算法的源荷互动柔性削峰填谷方法
2024-06-21
Patent
Electronic Resource
Chinese
IPC: | H02J CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER , Schaltungsanordnungen oder Systeme für die Abgabe oder Verteilung elektrischer Leistung / B60L PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES , Antrieb von elektrisch angetriebenen Fahrzeugen / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |