| 引用本文: | 王译旋,杨用春,高长征.计及典型日选取与源荷灵活性调节的优化调度研究[J].电力系统保护与控制,2024,52(10):1-10.[点击复制] |
| WANG Yixuan,YANG Yongchun,GAO Changzheng.Optimal scheduling considering typical day selection and source load flexibility adjustment[J].Power System Protection and Control,2024,52(10):1-10[点击复制] |
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| 摘要: |
| 针对电网面临的反调峰特性和新能源消纳问题，提出一种计及源荷侧灵活性调节资源的电力系统优化调度模型。首先，根据负荷特点采用改进SSE-PFCM聚类算法求取负荷典型日。其次，基于负荷用户对电价变化的不同响应行为，提取出具有需求响应潜力的两类负荷，并且分别为其构建计及乐观响应隶属度的模糊负荷转移率模型以及需求价格弹性模型。然后，计及需求响应不确定性模型以及火电机组深度调峰，以系统综合运行成本最小为目标构建优化调度模型，采用引入步长弹性系数的改进粒子群算法求解。最后，以改进IEEE 30节点系统为例进行多种场景的仿真计算分析，结果表明所提策略能够有效提升系统可再生能源的消纳能力和系统运行的经济性。 |
| 关键词: 新能源消纳 优化调度 典型日选取 需求响应 深度调峰 改进粒子群优化 |
| DOI：10.19783/j.cnki.pspc.230967 |
| 投稿时间：2023-07-27修订日期：2024-04-10 |
| 基金项目:国家自然科学基金项目资助(51607168)；中央高校基本科研业务费项目资助(2021MS068) |
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| Optimal scheduling considering typical day selection and source load flexibility adjustment |
| WANG Yixuan1,2,YANG Yongchun1,GAO Changzheng3 |
| (1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power
University), Baoding 071003, China; 2. Zhangjiakou Power Supply Company, State Grid Jibei Electric Power Co., Ltd.,
Zhangjiakou 075000, China; 3. Electric Power Development Research Institute (CEC Technical and
Economic Consulting Center of Electric Power Construction), Beijing 100053, China) |
| Abstract: |
| This paper proposes an optimization scheduling model for power systems taking into account the flexibility of source load side regulation resource given the characteristics of peak shaving and the problem of new energy consumption faced by the power grids. First, an improved SSE-PFCM clustering algorithm is used to calculate the typical day of the load based on the characteristics of the load. Secondly, based on the different response behaviors of load users to electricity price changes, two types of loads with demand response potential are extracted. Fuzzy load transfer rate models and demand price elasticity models considering optimistic response membership degrees are constructed. Then, taking into account the demand response uncertainty model and the deep peak shaving of thermal power units, an optimal scheduling model is constructed with the objective of minimizing the overall operating cost of the system. An improved particle swarm optimization algorithm with the introduction of step size elasticity coefficient is used to analyze the problem. Finally, taking the improved IEEE 30-bus system as an example, simulation calculations are conducted with various scenarios, and the results show that the proposed strategy can effectively improve the renewable energy consumption capacity of the system and the economy of system operation. |
| Key words: new energy consumption optimal dispatch typical day selection demand response deep peak shaving improved particle swarm optimization |
