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The literature covering Plug-in Electric Vehicles (EVs) contains many charging/discharging strategies. However, none of the review papers covers such strategies in a complete fashion where all patterns of EVs charging/discharging are identified. Filling a gap in the literature, we clearly and systematically classify such strategies. After providing a clear definition for each strategy, we provide a detailed comparison between them by categorizing differences as follows: complexity; economics and power losses on the grid side; ability to provide ancillary services for integrity of the power grid; operation aspects (e.g., charging timing); and detrimental impact on the EV, the power grid, or the environment. Each one of these comparison categories is subdivided into even more detailed aspects. After we compare the EV charging/discharging strategies, we further provide recommendations on which strategies are suitable for which applications. Then, we provide ratings for each strategy by weighting all aspects of comparison together. Our review helps authors or aggregators explore likely choices that might suit the specific needs of their systems or test beds.

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The increased integration of Electric Vehicles (EVs) into the distribution network can create severe issues—especially when demand response programs and time-varying electricity prices are applied, EVs tend to charge during the off-peak time to minimize the electricity cost. Hence, another peak demand might be created, and other solutions are required. Many researchers tried to solve the problem; however, limitations exist because of the decentralized topology of the network. The system operator is not allowed to control the end-users’ load due to security and privacy issues. To overcome this situation, we propose a novel data-energy management algorithm on the transformer’s level that controls the power demand profiles of the householders and exchange energy between them without violating their privacy and security. Our method is compared to an existing one in the literature based on a decentralized control strategy. Simulations show that our approach has reduced the electricity cost of the end-users by 3%, increased the revenue of the system operator, and reduced techno-economic losses by 50% and 42%, respectively. Our strategy shows better performance even with a 100% penetration level of EVs on the network, in which it respects the network’s constraints and maintains the voltage within the recommended limits.

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In the literature, many optimization algorithms were developed to control electrical loads, especially Electric Vehicles (EVs) in buildings. Despite the success of the existing algorithms in improving the power profile of charging EVs and reducing the total electricity bill of the end-users, these algorithms didn’t show significant contribution in improving the voltage profile on the network, especially with the existence of highly inductive loads. The control of the active power may not be sufficient to regulate the voltage, even if sophisticated optimization algorithms and control strategies are used. To fill the gap in the literature, we propose a new algorithm that is able to control both the active and reactive power flows using electric vehicles in buildings and homes. The algorithm is composed of two parts; the first part uses optimization to control the active power and minimize the electricity bill, while the second part controls the reactive power using the bidirectional converter in the EV in a way that the voltage profile on the distribution transformer respects its limits. The new approach is validated through a comparative study of four different scenarios, (i) without EV, (ii) with EV using uncoordinated charging, (iii) with EV using coordinated charging, (iv) with EV using our proposed algorithm. Results show that our algorithm has maintained the voltage within the recommended limits, and it has minimized the peak load, the electricity cost, and the techno-economic losses on the network.

Load as well as power flow in tie-line are continuously varying in interconnection power systems. This paper presents an efficient method based on artificial intelligence control for automatic generation control (AGC) of a three-area power network. The control method implements Artificial Neural Network (ANN) to damp the frequency deviation and the fluctuation in the tie line power caused by load disturbances. The performance of the proposed controller is compared with classical control methods (PI and PID). The results showed that ANN-based control method is more efficient than others approaches. In this paper, MATLAB/SIMULINK package is used to investigate the results.

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