Optimal Dispatch And Charging Station Planning Of Electric Vehicles In Distribution Systems With Renewable Generation

The climate change has aroused international awareness about the negative influence of using fossil fuels in recent years. Development of electric vehicles (EV) and renewable energy industry is regarded as an important measure to reduce emissions. Rapid development of EVs could transfer vehicle energy demands for oil to electricity, which will reduce pollution and the dependence of foreign oil. However, EVs will also bring up new technical problems to power systems and the existing power system may not well prepared to meet the EVs’charging demands. Renewable generation raises another challenge to power system operation. Because of the uncertain nature of wind speed or solar irradiance, renewable generation output tends to be intermittent, which may cause noticeable negative impacts on power system operation. Meanwhile, at the initial stage, lack of sufficient charging infrastructures is the most critical barrier to successful deployment of EVs at large scale.Given these facts, this dissertation explores the methods to reduce the fluctuations of loads and renewable generations by utilizing the storage potential of EV batteries and the optimal planning of EV charging stations. The detailed contents of this dissertation are as follows:(1) A new dispatch model to reduce the fluctuations of daily loads is proposed based on the probabilistic analysis of energy consumption and charge/discharge behaviors of PHEVs. The cross-entropy (CE) method is employed to solve this optimal dispatch model. The feasibility and efficiency of the developed dispatch model is demonstrated with a33-bus distribution network. The result shows that compared with random charging, optimal dispatch of PHEVs can reduce the load curve fluctuations and improve distribution network stability.(2) A large number of idle PHEVs can potentially be employed to form a distributed energy storage system for supporting renewable generation. To reduce the negative effects of unsteady renewable generation outputs, a stochastic optimization based dispatch model, capable of handling uncertain outputs of PHEVs and renewable generation, is formulated. The probability distributions of the wind and photovoltaic (PV) generation outputs are derived based on the assumption that the wind speed follows the Rayleigh distribution and solar irradiance follows the Beta distribution. The mathematical expectations, second-order central moments and variances of wind and PV generation outputs are then derived analytically. Incorporated all the derived uncertainties, a novel generation shifting objective is proposed. A genetic algorithm (GA) method is adopted to solve this optimization problem. Multiple patterns of renewable generation depending on location, season, and renewable market share are investigated. The developed optimal dispatch model as well as the GA method are demonstrated with a distribution system.(3) To facilitate large-scale EV application, optimal locating and sizing of charging stations in smart grids become essential. A multi-objective EV charging station planning method which can ensure charging service while reducing power losses and voltage deviations of distribution systems is proposed. A battery capacity constrained EV flow capturing location model (EFCLM) is proposed to maximize the EV traffic flow that can be charged given a candidate construction plan of EV charging stations. Subsequently, a well-established particle swam optimization (PSO) method is utilized to solve the planning problem. The simulation results have demonstrated the effectiveness of the proposed method based on a case study consisting of a33-node distribution system and a25-node traffic network system.(4) A data envelopment analysis (DEA) method is utilized to make final decision to find the best planning results from Pareto solutions of EV charging station, which is critical for multi-objective optimizations. Determination of final decision based on the DEA method can reflect more objective and comprehensive physical significance. Meanwhile, the DEA method is also utilized in the evaluation of multistage EV charging station planning performance considering various future uncertain scenarios and chooses the best planning scheme. In the multistage planning problem, the uncertainties of charging station/battery swapping station type, the location of future loads and EV flows, are treated as scenarios.