Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand-supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors such as driver behavior, location of charging stations, electricity pricing etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors which influence the charging demand of EVs. Several studies have modelled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This paper aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using NetLogo is employed in this paper to closely mimic the human aggregate behaviour and its influence on the load demand due to charging of EVs.
New telecommunication towers are installed in remote/rural areas to facilitate the increasing connectivity worldwide. With concerns over environmental issues, such towers are to be environmentally friendly. Conventionally, diesel generator supply power to towers in remote/rural areas, which leads to carbon emission. Also, the operation of diesel generator entails considerable operating cost (fuel and maintenance costs). Thus, a wind-photovoltaic (PV) based DC microgrid is proposed for supplying power to telecommunication towers in remote/rural areas ensuring reliable, economical, and green power supply. Therefore, techno-economic analysis is carried out here to determine the feasibility and cost of electricity (COE) per unit of the proposed DC microgrid. A non-dominated sorting genetic algorithm II is implemented to solve the multi-objective optimal sizing problem to achieve a trade-off between the cost and the reliability. Hourly solar irradiation and wind speed data is used for longterm analysis equivalent to the lifespan of the battery. Further, de-rating factor and maximum power point tracking factor are considered while modelling the renewable resources. The loss of power supply probability, excess energy, and COE are calculated and different scenarios are studied to examine the techno-economic feasibility of the proposed DC microgrid.
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