Two-Level Optimal Bidding Strategy for Load Aggregator Based on a Data-Driven Approach Combined With LSTM-Based Forecasting and Agent-Based Models

Demand response (DR) is an economical way of addressing the challenges faced by the massive penetration of distributed energy resources, such as renewable energy. Residential consumers account for a significant proportion of electricity consumption. However, their behavior is highly random and uncertain, meaning it is difficult to quantify the impact of DR programs in which they participate. This paper presents a two-level optimal bidding strategy framework for load aggregators that combines a data-driven forecasting model and a data-driven agent-based model (D-ABM) to provide a realistic estimate of the impact of DR. First, the aggregated load of all consumers and market prices are predicted via a long short-term memory (LSTM) autoencoder forecasting model. Then, the proposed D-ABM estimates and quantifies the difference rate in terms of total load due to DR. Since D-ABM is a bottom-up approach, each consumer can be treated as a heterogeneous agent and changes in individual electricity usage patterns due to DR can be estimated. Changes in collective electricity consumption patterns can also be quantified by considering the estimated individual behavior and the interactions defined by the basic rules. In addition, assumptions about biases and preferences that explain the irrationality of individual decision-making are given to agents, and the uncertainty of DR participation is considered more realistically. Finally, based on these uncertainties addressed at each level, various bidding strategies for load aggregators can be obtained. The numerical simulation results indicate that our framework provides a more realistic estimation of the impact of total load under DR, minimizes any deviations from bidding strategies, and ensures maximum profits for load aggregators.