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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.

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.

The promotion of distributed generation (DG) is a direct consequence of an international drive for sustainable development and advances in technology. At present, the development of environmentally friendly (renewable energy sources) and high efficiency power generation (combined heat and power) has attracted significant attention all over the world. However, traditional supervision and management systems in the power industry prevent a full understanding of the benefits of DG, especially those impacting on the environment, by the policy maker and policy executor. This has therefore, hindered the realisation of the full environmental benefits of DG. In this paper, some indices are introduced to quantitatively analyse the value of the environmental benefits of DG in China. In addition, a new mathematical model for calculating the true generation cost of DG was established based on the environmental benefits indices. In this model, a penalty mechanism was introduced for the pollutant emissions of power generation and the environmental value of pollutant emissions was also considered. Simulation results show that the indices and the new model can correctly reflect the environmental benefits and their application should accelerate the development of DG in China.