Optimization scheduling of an integrated energy service system in community under the carbon trading mechanism: A model with reward-penalty and user satisfaction

Abstract

The carbon trading mechanism is considered an effective method for improving energy efficiency, and reducing pollution. In this regard, two methods of considering reward-penalty on the supply and demand sides as well as user satisfaction are the most effective ways to improve the carbon trading mechanism. Herein, this paper proposes a bilayer optimization model of the community-integrated energy service system. The population in the community is mainly divided into office workers and retirees, and their preferences are fully considered. The user considers electric energy reduction and substitution modes to optimize the energy consumption behavior in the reward stage. And the service provider considers the full consumption of wind and photovoltaic power and optimizes the output of each unit in the penalty stage. The effect of reward-penalty factors on the supply and demand sides is analyzed. The algorithm used to achieve bilayer optimization is the hybrid solution algorithm, which adopts the improved particle swarm optimization algorithm and CPLEX solver. The experimental results show that the costs of office workers and retirees are reduced by an average of 18.2% and 12.2%, respectively. And the service provider's profit is increased by 15.28%. The results also provide site selection strategies for energy supply.