| 引用本文: | 余洋,向小平,李梦璐,李君卫,王卜潇,蔡新雷.面向电网调峰的聚合温控负荷多目标优化控制方法[J].电力自动化设备,2024,44(11):164-170，186. |
| YU Yang,XIANG Xiaoping,LI Menglu,LI Junwei,WANG Buxiao,CAI Xinlei.Grid peaking oriented multi-objective optimal control method for aggregated thermostatically controlled load[J].Electric Power Automation Equipment,2024,44(11):164-170，186. |
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| 面向电网调峰的聚合温控负荷多目标优化控制方法 |
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余洋1,2, 向小平1,2, 李梦璐1,2, 李君卫1,2, 王卜潇1,2, 蔡新雷3
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1.华北电力大学 新能源电力系统国家重点实验室，河北 保定 071003;2.华北电力大学 河北省分布式储能与微网重点实验室，河北 保定 071003;3.广东电网有限责任公司电力调度控制中心，广东 广州 510600
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| 摘要: |
| 为使聚合温控负荷参与电网调峰时兼顾运行的经济性和清洁性，提出面向电网调峰的聚合温控负荷多目标优化控制方法。建立温控负荷聚合模型，在常规模型预测控制的基础上，计及温控负荷聚合特性，设计预测时间自适应优化策略，动态调整域参数以协调处理优化精度与计算时间之间的矛盾，同时建立双闭环反馈机制以实时反馈温控负荷状态信息，提升实现调峰计划的可靠性；引入拉格朗日乘子和松弛因子，将多输出约束条件变成多目标函数的附加部分，提高控制方法的稳定性；提出改进的哈里斯鹰算法，对多目标优化问题进行求解，缩短模型预测控制的执行延时，提高寻优速度和寻优精度。所提控制方法与其他控制方法的仿真对比结果表明，所提控制方法有效降低了电网峰谷差以及运行成本和排放成本，同时兼顾了聚合温控负荷参与电网调峰的技术性、经济性和清洁性。 |
| 关键词: 温控负荷 电网调峰 模型预测控制 改进的哈里斯鹰算法 多目标优化 |
| DOI：10.16081/j.epae.202408014 |
| 分类号:TM73 |
| 基金项目:国家自然科学基金资助项目(52077078)；南方电网公司科技项目(036000KK52220004(GDKJXM20220147)) |
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| Grid peaking oriented multi-objective optimal control method for aggregated thermostatically controlled load |
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YU Yang1,2, XIANG Xiaoping1,2, LI Menglu1,2, LI Junwei1,2, WANG Buxiao1,2, CAI Xinlei3
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1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China;2.Hebei Key Laboratory of Distributed Energy Storage and Micro Grid, North China Electric Power University, Baoding 071003, China;3.Electric Power Dispatching Control Center of Guangdong Power Grid Co.,Ltd.,Guangzhou 510600, China
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| Abstract: |
| In order to balance operational economy and cleanness when aggregated thermostatically controlled load participates in grid peaking, a multi-objective optimization control method for aggregated thermostatically controlled load oriented to grid peaking is proposed. A model of aggregated thermostatically controlled load is established. On the basis of conventional model predictive control, an adaptive optimization strategy for predictive time is designed considering the characteristic of aggregated thermostatically controlled load. The domain parameters are dynamically adjusted to balance the contradiction between optimization accuracy and computation time. Meanwhile, a dual closed-loop feedback mechanism is established to provide real-time feedback on the state information of thermostatically controlled load, which enhances the reliability of realizing peaking plan. Lagrange multipliers and relaxation factors are introduced to transform multiple output constraints into additional components of multi-objective functions, which improves the stability of control method. An improved Harris Hawks optimization algorithm is proposed to solve the multi-objective optimization problem, which reduces the execution delay of model predictive control and improves the optimization speed and accuracy. The simulative and comparison results between the proposed control method and other control methods show that the proposed control method effectively reduces grid peak-to-valley difference and the operational and emission costs, and balances the technicality, economy and cleanness of aggregated thermostatically controlled load participating in grid peaking. |
| Key words: thermostatically controlled load grid peaking model predictive control improved Harris Hawk algorithm multi-objective optimization |
