IEEE Access (Jan 2024)
A Multi-Objective Hybrid Game Pricing Strategy for Integrated Energy Operator-Load Aggregator Alliances Considering Integrated Demand Response
Abstract
In recent years, with significant changes in electricity demand, load aggregators have begun to integrate customer demand-side resources and participate in market transactions as independent entities. This study aims to establish an alliance of integrated energy service providers and load integrators, which optimizes the operation of the integrated energy system and maximizes in order tothe interests of all participants through the collaborative operation mechanism of new Power to Gas (P2G) and Interruptible Demand Response (IDR). To this end, we first constructed a master-slave game model that considers the bargaining game between load integrators with a large number of photovoltaic users in the model, to solve the problem of competition and cooperation. We establish a multi-objective model to address the low-carbon economic equilibrium problem faced by comprehensive energy service providers in the game process, and use the Conditional Value at Risk (CVaR) theory to measure the uncertainty of renewable energy. Secondly, to achieve cooperation between load aggregators, we use Nash bargaining theory to decompose the cooperative game model into two subproblems: maximizing alliance benefits and allocating cooperation benefits. Finally, we use the Alternative Direction Method of Multipliers (ADMM) algorithm combined with dichotomy and compromise programming theory to solve the model, to maximize the interests of all parties in the continuous interaction process. The research results indicate that after considering the electricity trading and carbon emission targets between load aggregators, the interests of all parties are protected, fully promoting the exchange of interests between photovoltaic producers and sellers, and alleviating the energy supply pressure of IESO. At the same time, a comparison with Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) shows that the fuzzy compromise programming theory used in this paper has a faster solving speed and a smoother solving process.
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