Bidding Strategies of Load Aggregators for Day-Ahead Market With Multiple Uncertainties
Lingyu Chen, Jian Xu, Yuanzhang Sun, Siyang Liao, Deping Ke, Liangzhong Yao, Beilin Mao
Abstract
Developing an appropriate bidding strategy for the day-ahead market is key for load aggregators. Demand-side distributed renewable energy sources (DRESs) are increasingly connected to the grid, and each is a source of uncertainty. These uncertainties together with the coupling relationship between load and electricity price uncertainty caused by demand response finally result in the complex uncertainty situation. With the goal of obtaining robust profit, the optimal bidding decision in this complex uncertainty situation for a load aggregator when considering time-shiftable loads and DRESs is investigated in this article. The bidding process is decomposed into two stages: an energy consumption model and a bidding model. Scenarios and information gap decision theory (IGDT) are used in the two models, respectively, to handle the uncertainties in electricity prices and DRESs. To solve the established bilevel nonlinear model, a non-KKT method and backward solving algorithm are creatively proposed; they transform the bilevel model into a single-level model and greatly increase the solving speed.