Master-slave game-based optimal scheduling strategy for integrated energy systems with carbon capture considerations

This study proposes a master-slave game-based optimisation strategy for pricing and scheduling within integrated electric-heat energy systems. Initially, the transactional model between integrated energy operators and load aggregators is presented. Second, a master-slave game model for the integrated electricity-heat energy system is developed. The integrated energy operator assumes the role of leader, aiming to maximize revenue. To achieve this, carbon capture and power-to-gas technologies are introduced. Based on the energy usage strategies reported by users, the operator determines both the pricing strategy for end users and the output levels for each piece of equipment. The load aggregator, acting as a follower with the aim of maximising consumer surplus, takes into account both electrical and heat demand response from the customer side. It then optimises its own load power participating to the demand response based on the pricing strategy of the leader. Finally a distributed algorithm integrating a genetic algorithm with a CPLEX solver is employed to address the pricing strategy of an integrated energy operator and the energy consumption strategy of a load aggregator. The simulation results demonstrate that the proposed effectively reduces system carbon emissions while effectively balancing the interests of both parties involved.