# Reallocating Charging Loads of Electric Vehicles in Distribution Networks
## 1 Introduction  
Electric vehicles (EVs) have an important role to play in supporting the UK target of an 80% reduction in CO\({}_{2}\) emissions by 2050s relative to CO\({}_{2}\) emissions in 1990s [1; 2]. The UK short-term goal is to cut emissions by a third by 2020 [1; 2]. The current emphasis of the UK government on greening the private and commercial vehicles can help in decarbonizing a substantial part of the transport sector [1; 2]. Many studies investigated the impact of EV uptake on distribution networks depending on the technical characteristics of the considered EVs and networks. Both temporal and spatial characteristics of (un)controlled EV charging loads have been studied in the literature [3; 4] considering different types of distribution networks. A stochastic method has been used to study the impact of EV charging loads on UK distribution networks using real datasets acquired from smart meters of real trials [5]. It was suggested that the uncertainties of EV charging loads can be reduced with a cooperative framework between aggregators and distribution network operators (DNOs) [5]. The maximum uptakes of EVs have been calculated based on the transformer capacity at neighborhood levels [6]. A simple optimal charging strategy was proposed to efficiently integrate these EVs into the modeled network [6]. Previous studies concluded that, with high uptakes of EVs, existing distribution networks will frequently exceed their limits [7; 8; 9]. The thermal ageing of distribution transformers can significantly deteriorate with high levels of EV charging loads [8]. Results have shown that a 50% uptake of EVs can significantly affect the loss-of-life of distribution transformers [9]. Reinforcement can be used to strengthen the existing networks; however, a widespread adoption of infrastructure upgrades would be very expensive. For example, it has been concluded that the required grid reinforcement may reach up to 15% of the grid cost from a case without EV integrations [10]. Alternatively, smartening the distribution networks have the potential to develop an efficient use of the network components. Responsive loads such as EVs can be used to achieve an optimal energy matching by reshaping the energy demand. Optimal rescheduling strategies of EV (dis)charging loads have been presented with and without renewable energy resources, considering different constraints [11; 12; 13; 14]. Smartening the networks can be implemented using multi-agent systems, adaptive controllers, (de)centralized managements, or coordinated schedules [15; 16; 17; 18; 19]. EV charging loads have been coordinated using agent-based strategies to mitigate their effects on distribution networks [15; 20; 21; 22]. In one study [15], a multi-agent system was experimentally implemented for smart charging of EVs. The agent-based controller was developed using search methods based on neural networks. EV charging loads were designed to follow the low electricity price while maintaining grid constraints. In another study [16], an adaptive controller was proposed to coordinate EV charging schedules. The on-line adaptive controller was used to reduce the EV impact on distribution networks, considering EV charging costs, EV owner preferences, voltage magnitudes, and voltage unbalances. A direct current (DC) power flow was used in the optimization process, whereas an alternating current (AC) power flow has been performed to validate the results. In yet another study [17], load management based on a real-time control method was developed to coordinate EV charging loads within a five-minute time resolution. The cost of energy generation was minimized to achieve the coordination of EV charging loads considering energy losses, voltage fluctuations, and overloads. This cost of energy was further reduced by finding a better local solution using fuzzy theory [18]. An on-line fuzzy coordination algorithm was used for EV charging loads in a smart network with distributed wind generators.