Strategic joint bidding and pricing of load aggregators in day-ahead demand response market

Load aggregators (LAs) purchase demand response (DR) capacity from users by incentive compensation and then resell them by bidding in the day-ahead DR market to gain profits. Existing related research fails to adequately integrate bidding and pricing decision-makings and lacks sufficient consideration of uncertain user response behaviors, resulting in unstable trading returns. To this end, we propose a novel joint optimization method for bidding and pricing of the LA. First, a two-stage joint optimization framework is constructed to coordinate the temporally coupled bidding and pricing decisions, where these two strategies are simultaneously optimized to form the DA bidding strategy at first, and the pricing adjustment is then introduced after market-clearing to develop the formal user incentive scheme. Second, to account for the risks from uncertain response behaviors, the first stage incorporates DR settlement rules and the hedging effect of the aggregated response uncertainty to develop a relatively aggressive bidding strategy. After that, a user selection mechanism is then introduced to filter out users with both low response costs and reliable responses for participation. Numerical studies are presented to validate the proposed methodology in formulating optimal trading strategies that effectively tradeoff transaction revenue and risks.