Research On Optimal Scheduling Method Of Virtual Power Plant Considering Electric Vehicle Demand Response Under Market Environment

China’s increasing reliance on non-renewable energy sources such as coal,oil and natural gas has raised concerns regarding sustainable development.To address this issue,the construction of a new type power system with new energy as the primary source has become imperative.However,the large-scale grid connected power generation of distributed clean energy presents new challenges in terms of centralized dispatching and the cost of auxiliary services.Moreover,the rapid growth of electric vehicle usage also poses challenges to the demand side of the power system.The charging behavior of electric vehicle users is highly random,and their charging time coincides with peak electricity consumption periods.This results in a random multi-peak charging load that coincides with the daily peak of residents’ electricity usage,which lead to greater pressure on power grid dispatching.It is therefore necessary to develop effective strategies to manage these challenges and ensure the efficient and reliable operation of the power system.Virtual power plant can effectively concentrate distributed energy and load while reducing dispatching pressure of power system,so it becomes a competitive subject in power market.Electric vehicles,due to their flexibility,large volume,and low cost,can be combined with demand response technology to provide assistance for auxiliary services in the power system.Therefore,this thesis proposes a method to increase the dispatching revenue of virtual power plant.First of all,it integrates electric vehicle and distributed energy through virtual power plant technology.Then,the demand response technology is used to adjust the operation of the electric vehicle,thus reducing the peakvalley difference of the system load and promoting the consumption of renewable energy grid-connected power generation.To achieve this goal,the modeling and demand response strategy of electric vehicles must be carefully considered.This thesis first analyzes the usage habits and charging preferences of three types of typical electric vehicle users based on real data.Then,by combining Weber Fishner’s law with user behavior modeling,the electric vehicle operation model is established to reflect the actual situation.The load forecast and schedulable potential forecast of electric vehicles are obtained by constructing the operation model of an electric vehicle cluster.Based on the trading mechanism of the electricity market,an optimal scheduling model of virtual power plants is established,including day-ahead and real-time market transactions,with the aim of maximizing profit.To optimize the incentive level of electric vehicles,this thesis constructs the risk management strategy based on the opportunity constraint theory and the conditional risk value theory.The improved two-level particle swarm optimization algorithm is used to solve the model.The results demonstrate that electric vehicle participation in demand response can effectively improve the benefits of virtual power plants.Moreover,risk management can substantially enhance the anti-risk capability of virtual power plants.Overall,this study proposes a novel approach that combines virtual power plants,electric vehicles,and demand response technology to improve the efficiency and sustainability of the power system.