Research On The Impact Of Electric Vehicles Broadly Access To The Power System And Their Control Strategy

Persistent growth of the global economy is causing issues relating to energy supply,environmental pollution and dependence on fossil fuels,all of which need to be addressed with a sense of urgency.Electric vehicles(“EVs”),driven wholly or partly by electric energy,are therefore receiving higher attention as an important alternative in response to energy and environmental problems such as carbon emissions cumulation and noise pollution.To better tackle these problems,many countries have been committing to support the construction of EVs charging infrastructures as well as provide incentives to encourage the use of EVs.The increase in power consumed by EVs will pose significant challenges to the stability and economic operation of the existing power systems.On one hand,the increasing levels of EVs charging can lead to higher peak loads,power losses,deterioration of power quality,imbalances between generation and consumption,voltage deviation and an increase in investment costs of network reinforcement in power grids.On the other hand,EVs have the characteristics of ‘storage energy’.They will play a positive role at utilising the current power systems,as long as we can take intelligent and effective charging and discharging measures to promote the good interaction between EVs and the power grid.This dissertation proposes a comprehensive survey for the development of EVs charging technology and an analysis of forecasting regional EVs charging load profiles together with their impact on power grid.This dissertation also provides strategic recommendations on the EVs charging infrastructures and power systems as well as an assessment method for the participation of EVs in demand response.The main contents include the following:(1)Develop a model based on the Trip Chains and Multi-Source Information Considering Travel Patterns and the Decision-Making Process(“TCMSI-BM”)that will be used to forecast the spatial and temporal charging loads of regional EVs.In this work,we will consider situations at both macro and micro levels.At the macro level,the path planning will not be taken into accounting.The TCMSI-BM model will firstly assume several geographically distinct areas based on travel purposes.According to Markov Chain,the interrelationship of multiple trips within one day is investigated to give a detailed roadmap of daily routes.Secondly,it will consider the influence of external conditions on energy consumption and determine charging criterion.Following these,the overall daily charging load forecast at different places is obtained by using a Monte Carlo simulation.At the micro level,the path optimization is considered.Firstly,urban road network,grid information and coupling relationship models are established by using graph theory.Secondly,by combining resident travel databases,trip chains and travel routes,potential stops for charging are put forward.The first start time distribution and the dwell time distribution of each trip destination are fitted by Weibull distribution function respectively.EVs travel path is simulated by using Dijkstra algorithm to obtain travel distance with traffic information being obtained by road grade and traffic congestion,which will lead to the calculations of traveling time and charging load.Subsequently,these will be calculated based on the charging requirements of each travel destination.The Monte Carlo simulation method is adopted to simulate the spatial and temporal charging load distribution of EVs in each functional area.Finally,we will provide an example to verify the effectiveness of the proposed model,by setting different scenarios which predict the charging load curve of the different functional areas and the grid node.The TCMSI-BM model is considered to provide a theoretical basis for the development of an orderly charge and discharge strategy for EVs and evaluate the potential of EVs’ demand response.(2)Analysis on the impact of operating problems if EVs charge on different power systems and research on how to control EVs charging in a coordinated way.To echo with the increasing penetration of EVs,an analysis will be conducted to understand the impact of EVs charging on power system.Scenarios considering charging facilities without EVs and with different levels of penetration of EVs will be demonstrated within the context of IEEE systems,including IEEE 33 nodes distribution system and IEEE 30 nodes system.A control strategy for the residential EVs load management using Variable Start-Time Charging and Variable Power Charging(CS-VSTC/VPC)will be proposed.In addition,a dynamic multi-objective for EVs scheduling problem is formulated which incorporates owners’ requirements on energy consumption,and costs of energy bills together with the voltage at the point of connecting to the grid,the peak load,peak-to-valley load differences,load fluctuations and the network loss.Moreover,an enhanced dynamic multi-objective particle swarm optimization(IDMOPSO)algorithm with various constraints will be proposed for the purposes of solving problems demonstrated above.With introduction of an improved minimum maximum fitness function which is based on crowding entropy and uses the adaptive weight coefficient and learning factors,IDMOPSO can achieve excellent and well-distributed Pareto optimal solutions in objective space.Finally,a daily EVs charging problem in the residential areas will be analysed to verify the performance of IDMOPSO.The numerical results illustrate that the Pareto optimal solutions obtained by IDMOPSO have greater advantages than the comparison algorithms.(3)Research on a novel quantitative evaluation method evaluating the demand response potential of EVs aggregator in consideration of dynamic road network information and fuzzy user participation.Firstly,the topological structure of urban road network is established using graph theory.A dynamic traffic network model considering the traffic time-varying information is also established.Secondly,a trip chain is constructed based on the resident trip statistics with the start time of the first trip and the dwell time of the travel destination being fitted with the probability function.The Floyd algorithm is applied to plan the optimal route of EVs,so as to find the distance and travel time of each trip of EVs.Following these,the Monte Carlo simulation method is used to obtain the temporal-spatial distribution of EVs,charging load and battery charge state,on the basis of the parameters of EVs,charging facilities and the charging requirements of each travel destination.Subsequently,the objective and subjective demand response ability of EVs’ users is analysed,which includes user fuzzy participation response mechanism that is constructed by three factors being the residual dwell time,the residual battery charge state and the incentive electricity price.The fuzzy algorithm is used to calculate the real-time participation degree of a single EV.The demand response potential of each functional block and power grid nodes are therefore evaluated by taking into account the combined coupling of traffic network and power grid.Finally,this dissertation proposed a real-time charging rate control approach for electric vehicles(EVs)management based on a fuzzy logic controller(FCL),where EV user’s charging urgency,the voltage difference and the pricing signal from the utility are taken into consideration.(4)Proposal of a comprehensive scheme of the distributed swapping & centralised charging system(DSCCS)which runs with higher recalling efficiency,less initial investments and lower possible grid impact.Firstly,the traffic conditions are formulated such that the swapping stations and other supporting facilities can be deployed.Secondly,the real-time available batteries and battery demand are investigated using an improved(s,S)inventory management to achieve adequate supply of recharged batteries.Finally,suitable optimization schemes are derived to attain maximum inventory turnovers or minimum impact of charging on the power systems.Simulation results are provided to demonstrate the feasibility of the proposed method and to serve as reference for future work.