Multi-objective day-ahead optimization of distribution system considering low Carbon and demand response with EV cluster

In the context of low-carbon economy, a large-scale integration of electric vehicles (EVs) to the power grid will increase the burden of the grid, with conflicts of interest for the distribution system operator, the EV aggregator and the EV users increased. In order to meet the interests of various stakeholders in the distribution system and to promote low carbon, this paper presents a multi-objective day-ahead optimization strategy considering low carbon and demand response with EV cluster. At first, to improve the traditional charging and discharging model of EV, this paper proposes a charging and discharging model of EV cluster using Minkowski Sum that reduces the variables’ complexity. In addition, based on demand response, a multi-objective model with three stakeholders, distribution system operator, EV aggregator and EV users, is constructed to satisfy all their interests. The model is constructed considering renewable energy, dynamic Time-of-use (TOU) pricing mechanism for charging/discharging and carbon trading. This paper uses a fuzzy optimization method to transform the multi-objective model into a single-objective model, then SLSQP algorithm is used to solve the single-objective model. The results show that the model and solution proposed in this paper can effectively promote the interaction between EVs and the power grid, reduce the burden of the power grid, improve the utilization rate of renewable energy, meet the interests of users, achieve a balance of interests of the distribution system operator, EV aggregator and EV users, and reduce the carbon emission of the system. • A charging and discharging model of EV cluster using Minkowski Sum is proposed. • A multi-objective optimization model with DSO, EVA and EV users is proposed, based on demand response. • Carbon trading, dynamic TOU prices and renewable energy are considered in the model.