| 引用本文: | 刘 林,李成鑫,赵林昕.考虑用户多次响应的空调负荷集群控制与调度策略[J].电力系统保护与控制,2025,53(18):39-51.[点击复制] |
| LIU Lin,LI Chengxin,ZHAO Linxin.Cluster control method and scheduling strategy of air-conditioning loads considering multi-round participation of users in demand response[J].Power System Protection and Control,2025,53(18):39-51[点击复制] |
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| 摘要: |
| 空调负荷是重要的需求侧资源，但现有基于温度控制方式的空调可调节能力研究多着眼于温度设定值调整期间的系统性能研究，不涉及温度设定值回调，只考虑了空调一次性参与需求响应，这不利于充分利用空调的可调节能力。为了系统描述空调集群在温度设定值调整和恢复全过程中的聚合功率特性，针对温度设定值恢复阶段扩充了在温度控制方式中被广泛应用的安全协议(safe protocol, SP)，建立了完整描述同质空调参与需求响应全过程的聚合功率模型。在此基础上，考虑空调用户舒适度的一致性，提出了以舒适度因子为排序指标的用户选择模型，用以确定空调用户分时分批、多次轮换参与需求响应的调控策略。仿真结果表明，所提调控策略能保证空调用户多次参与调控，在减小用户间舒适度差异的同时，具有更高的功率控制精度和更小的功率反弹波动。 |
| 关键词: 需求响应 空调负荷 聚合功率模型 负荷调度 反弹平抑 |
| DOI：10.19783/j.cnki.pspc.241563 |
| 投稿时间：2024-11-23修订日期：2025-01-13 |
| 基金项目:四川省自然科学基金项目资助(2022NSFSC0206) |
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| Cluster control method and scheduling strategy of air-conditioning loads considering multi-round participation of users in demand response |
| LIU Lin,LI Chengxin,ZHAO Linxin |
| (College of Electrical Engineering, Sichuan University, Chengdu 610065, China) |
| Abstract: |
| Air conditioning loads (ACLs) are important demand-side resources. However, existing researches on the adjustable capability of ACLs based on temperature control mainly focus on system performance during the adjustment of temperature setpoints, without addressing the recovery of setpoints. These approaches typically assume one-time participation of ACLs in demand response, which limits the full utilization of the flexibility. To systematically describe the aggregated power characteristics of air conditioner clusters throughout the entire process of temperature setpoint adjustment and recovery, the widely used safe protocol (SP) in temperature control is expanded to the recovery phase. Based on this, an aggregated power model that fully describes the process of homogeneous ACLs participation in demand response is established. Furthermore, considering the consistency of air conditioning users’ comfort, a user selection model based on a comfort factor ranking index is proposed to determine the regulation strategy of air conditioning users participating in demand response in batches and multiple rotations. Simulation results show that the proposed control strategy enables multiple rounds of user participation, reduces differences in comfort levels among users, and achieves higher power control accuracy with smaller power rebound fluctuations. |
| Key words: demand response air conditioning loads aggregated power model load scheduling rebound flatten |
