Multi-participant operation optimization for charging systems with orderly charging and cooperative game strategies considering carbon capture and uncertainties

In order to further enhance the utilization rate of renewable energy and achieve the goal of carbon emission reduction, this paper establishes a stable and clean energy supply mode of charging system and constructs a three-stage optimization and benefit distribution model. Firstly, the charging system structure is built with employing photovoltaic generator and carbon capture thermal power generator. Secondly, the charging load uncertainty is modeled by using Markov Chain where the differences of speeds of vehicles are considered. Thirdly, a deterministic charging optimization model is constructed in the first stage, with the objectives of economy, environment, energy utilization and load fluctuation; and then, in the second stage, the uncertainty of photovoltaic power generation is considered to formulate two types of information gap decision theory-based models. Finally, a double factor-involved benefit allocation model for the proposed charging system is con-structed in the third stage, based on the Shapley method. The case study shows that: (1) the forecast error of charging load is smaller by considering quantity transfer and speed differences; (2) carbon capture and storage system reduces CO2 emission by 85.08 % and system's cost by 2.60 %; (3) Tripartite cooperation maximizes the system's benefits, and IGDT provides multiple strategies for dealing with PV uncertainty; (5) benefit allocation considering economy and environment is more rational and highlights the contribution of carbon capture system.

Automated summary

This paper aims to enhance the utilization rate of renewable energy and reduce carbon emissions by establishing a stable and clean energy supply mode for a charging system and constructing a three-stage optimization and benefit distribution model.