Kejun Qian

Modeling of Load Demand Due to EV Battery Charging in Distribution Systems

This paper presents a methodology for modeling and analyzing the load demand in a distribution system due to electric vehicle (EV) battery charging. Following a brief introduction to the common types of EV batteries and their charging characteristics, an analytical solution for predicting the EV charging load is developed. The method is stochastically formulated so as to account for the stochastic nature of the start time of individual battery charging and the initial battery state-of-charge. A comparative study is carried out by simulating four EV charging scenarios, i.e., uncontrolled domestic charging, uncontrolled off-peak domestic charging, “smart” domestic charging and uncontrolled public charging-commuters capable of recharging at the workplace. The proposed four EVs charging scenarios take into account the expected future changes to the electricity tariffs in the electricity market place and appropriate regulation of EVs battery charging loads. A typical U.K. distribution system is adopted as an example. The time-series data of EV charging loads is taken from two commercially available EV batteries: lead-acid and lithium-ion. Results show that a 10% market penetration of EVs in the studied system would result in an increase in daily peak demand by up to 17.9%, while a 20% level of EV penetration would lead to a 35.8% increase in peak load, for the scenario of uncontrolled domestic charging-the “worst-case” scenario.

Modeling of the Cost of EV Battery Wear Due to V2G Application in Power Systems

This paper presents an analysis of the cost of utilizing battery electric vehicle (BEV) batteries as energy storage in power grids [also known as vehicle-to-grid (V2G)] associated with lessening battery cycle life due to more frequent charging and discharging activities and utilization in elevated ambient temperature. Comparison is made between V2G in the U.K., where annual electricity peak demand is reached in winter, and in China, where peak demand is reached in summer due to the air conditioning load. This paper presents mathematical correlations between charging-discharging, ambient temperature, depth of discharge (DoD), and the degradation of electric vehicle batteries based on manufacturers data. Simulation studies were carried out for V2G in both the U.K. and China. Numerical results show that ambient temperature and DoD of a BEV battery play a crucial role in the cost of battery wear. Lead-acid and NiMH battery powered BEVs are not cost effective in V2G use due to the present electricity tariff. Under the present electricity tariff structure, no vehicles would be cost effective for the peak power sources in China. However, lithium-ion battery powered BEVs are cost effective in the U.K. due to a much longer cycle life.

A Methodology for Optimization of Power Systems Demand Due to Electric Vehicle Charging Load

This paper presents a methodology of optimizing power systems demand due to electric vehicle (EV) charging load. Following a brief introduction to the charging characteristics of EV batteries, a statistical model is presented for predicting the EV charging load. The optimization problem is then described, and the solution is provided based on the model. An example study is carried out with error and sensitivity analysis to validate the proposed method. Four scenarios of various combinations of EV penetration levels and charging modes are considered in the study. A series of numerical solutions to the optimization problem in these scenarios are obtained by serial quadratic programming. The results show that EV charging load has significant potential to improve the daily load profile of power systems if the charging loads are optimally distributed. It is demonstrated that flattened load profiles may be achieved at all EV penetration levels if the EVs are charged through a fast charging mode. In addition, the implementation of the proposed optimization is discussed with analyses on the impact of travel pattern and the willingness of customers.

Load model for prediction of electric vehicle charging demand

Increasing environmental concerns, the decarbonisation of future auto industry, the consequent regulatory requirements and the depletion of oil have made the fuel independent battery electric vehicle (EV), with zero emission increasingly more attractive as practical and economical alternative to the gasoline fuelled car. The expected increasing number of EV connected to power systems for charging will have significant impact on power systems, such as generation capacity, transformer loading level, line congestion level and load profile, among which, the impact of EV charging load on the system load profile claims most attention. This paper develops a methodology to determine the EV battery charging load on the power system load profile. Three scenarios were simulated, comprising uncontrolled charging, controlled off-peak charging and smart charging. The proposed method in this paper takes into account the initial state of charge and start time of EV battery charging. Results show that uncontrolled charging will impose a new peak to the system and may cause congestion issues to local network. Controlled off-peak charging can shift EVs charging load to an off-peak time, however, EV can also introduce a new peak or near peak in early off-peak time. Smart charging method which optimises the start time of EVs charging is the most beneficial charging method to both distribution network operator and EV users.

Load modelling in distributed generation planning

Increasing the penetration level of Distributed Generation (DG) into the distribution system is a new challenge for traditional electric power systems. Although it is generally recognised that DG has the potential of reducing energy losses in power systems, inappropriate modelling can lead to a misleading predictions for power loss reduction in DG planning. This paper presents an investigation into the impact of load models on the calculation of energy loss. Following a brief introduction the paper proposes detailed modelling of load in DG planning. Load is divided into three categories: residential, industrial and commercial rather than characterised as the traditional constant PQ. A comparative study of real and reactive power losses for various load models and load levels is carried out using the methodology proposed in this paper. In addition, a long term forecasting model is developed to forecast the future customer demand for residential, commercial and industrial sectors in 2020 for the UK, allowing consideration of various factors and aspects, such as, historical load demand, weather data, economic growth and demographic information. A sample power system is adopted to analyse the system performance under various DG scenarios and at various load levels. Simulation results indicate that load models can significantly affect the load losses calculation in DG planning.

Demand Response for Optimisation of Power Systems Demand Due to EV Charging Load

This paper presents a novel demand response program for optimising power systems demand due to electric vehicle charging load. Based on a mathematical solution to the problem of power systems demand optimization, a demand response program which includes multiple tariffs for different groups of customers is proposed. This program takes into account consumer behaviour with and without external incentives (e.g. time-varying electricity prices). It also prices electricity over 24 hours according to the wholesale market energy price which reflects the generation marginal cost. A comparative study is carried out to evaluate the performance of the program. Three scenarios are considered, i.e. standard tariff, single-tariff and multi-tariff programs. The results show that the multi-tariff program could help utilities reduce daily cost by 1.5% and help customers save electricity bills by 7% compared to the standard tariff.