Dynamic pricing strategy and regional energy consumption optimization based on different stakeholders

This paper focuses on the pricing strategy of agents and the optimization of business performance of different stakeholders. The pricing strategy of agents directly affects the energy consumption cost on the demand side. It is an important planning problem to formulate a reasonable pricing strategy to meet the energy consumption needs of different stakeholders. In order to increase the profit of agents and reduce the energy consumption cost on the demand side, a multi-objective bilevel optimization model is established based on the principle of Stackelberg game. The upper layer takes the maximum day ahead real-time profit of agents as the optimization objective, and the lower layer is the microgrid area with the lowest operation cost. By introducing Karush-Kuhn-Tucker (KKT) condition, the bilevel problem is simplified, and the optimization model is converted into a mixed integer linear programming model by combining duality theorem, linear relaxation, etc., which solves the difficult problem of solving the bilevel nesting problem. Wind, photovoltaic and user load forecasting scenarios are formed in the form of opportunity constraints. The simulation process is carried out without considering wind, photovoltaic and user loads forecasting errors and forecasting errors respectively. The simulation results show that demand side dynamic response pricing strategy can greatly improve the profit of distribution network agents and reduce the operating costs of microgrid operators. Achieve win-win results; finally, the influencing factors of optimization objective are analyzed to provide reference for power market decision-making.

Automated summary

This paper focuses on the pricing strategy of agents and the optimization of business performance of different stakeholders. A multi-objective bilevel optimization model is proposed, which shows that using demand side dynamic response pricing strategy can increase the profit of agents and reduce the operating costs of microgrid operators.