Two-stage Characteristic Recognition of Demand Side Resource for Load Aggregator Based on Bias-SVD Matrix Factorization and Bayesian Inference

Abstract

As the agent and risk taker of demand side resources, inaccurate recognition of user characteristics will lead to large trial-and-error cost of load aggregator (LA) at the early stage of market-oriented demand response (DR). In this work, the typical invitation-based DR program implemented in China's provincial DR trading pilot is first introduced, and a bidding and scheduling strategy optimization model is built as a simulation tool for LA's decision-making in the DR transactions. Then, a two-stage user characteristic recognition method is proposed for LA to improve its market strategies based on a labelled demand side resources database named demand resource pool (DRP). In the first stage of the method, the DRP data completion algorithm based on Bias- singular value decomposition matrix factorization is proposed to preliminarily recognize the potential demand side resources with unknown characteristic labels. In the second stage, the Bayesian inference-based modification algorithm is proposed to realize a refined recognition of user characteristic labels according to LA's empirical conclusions and the new samples of each round of the DR transactions. Simulations based on the data from the emerging DR pilot in Zhejiang province, China illustrate that the proposed method has lower error in user characteristic recognition, which is conducive to LA to bear lower loss risk and enhance its income at the early stage of DR trading.