刘衣萍,于鹤洋,王晨旭,马骏超,耿光超,江全元
LIU Yiping,YU Heyang,WANG Chenxu,MA Junchao,GENG Guangchao,JIANG Quanyuan

• 1:浙江大学电气工程学院
• 2:国网浙江省电力有限公司电力科学研究院

摘要(Abstract)：

居民侧需求响应是保持电力系统源荷供需平衡的重要补充手段，但用户行为的不确定性导致难以实现需求响应的精准控制。以居民空调负荷为研究对象，建立了完整的需求响应分布式控制模型，提出了针对空调负荷未知性、多变性问题的解决方案。首先将需求响应中的负荷控制建模为一个多周期随机过程，将用户参与需求响应的行为抽象为马尔可夫链。然后基于线性回归和高斯过程建立用户热动力学模型，归纳并定义了居民住宅舒适度，计算得到马尔可夫转移概率；针对集中式控制存在的高成本、隐私性差等问题，以削峰需求响应为经典目标设计了分布式控制算法。最后采用实际负荷数据验证算法，取得了良好的削峰效果。
Residential side demand response is an important supplementary means to maintain the supply-demand balance of source-load in the power system. However, the uncertainty of user behavior makes it difficult to accurately control demand response. With residential air conditioning load being a research object, a complete distributed control model of demand response is established. A solution to unknown and variable air conditioning load is proposed. Firstly, the load control in demand response is modeled as a multi-period stochastic process, and the behavior of users' participation in demand response is abstracted as a Markov chain. Afterward, a thermodynamic model of the user is established based on linear regression and the Gaussian process to generalize and define residential comfort and calculate Markov transfer probability. For the problems of high cost and poor privacy of centralized control, a distributed control algorithm is designed with peak-shaving demand response as a classical object. Finally, the algorithm is validated using actual load data, achieving an excellent peak-shaving effect.

关键词(KeyWords)： 需求响应;高斯过程;机器学习;分布式算法
demand response;Gaussian process;machine learning;distributed algorithm

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司科技项目（B311DS210010）

作者(Author): 刘衣萍,于鹤洋,王晨旭,马骏超,耿光超,江全元
LIU Yiping,YU Heyang,WANG Chenxu,MA Junchao,GENG Guangchao,JIANG Quanyuan

DOI: 10.19585/j.zjdl.202303001

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