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Distributed Generation (DG) is gaining in significance due to the keen public awareness of the environmental impacts of electric power generation and significant advances in several generation technologies which are much more environmentally friendly (wind power generation, micro-turbines, fuel cells, and photovoltaic) than conventional coal, oil and gas-fired plants. Accurate assessment of energy losses when DG is connected is gaining in significance due to the developments in the electricity market place, such as increasing competition, real time pricing and spot pricing. However, inappropriate modelling can give rise to misleading results. This paper presents an investigation into the effect of load models on the predicted energy losses in DG planning. Following a brief introduction the paper proposes a detailed voltage dependent load model, for DG planning use, which considers three categories of loads: residential, industrial and commercial. The paper proposes a methodology to study the effect of load models on the assessment of energy losses based on time series simulations to take into account both the variations of renewable generation and load demand. A comparative study of energy losses between the use of a traditional constant load model and the voltage dependent load model and at various load levels is carried out using a 38-node example power system. Simulations presented in the paper indicate that the load model to be adopted can significantly affect the results of DG planning.

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.

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.