Optimal integration of electric vehicles in smart grids with renewables and battery storage systems under uncertainty

There has been a huge trend to integrate Renewable Energies (REs) and Electric Vehicles (EVs) into energy networks (Figure 1). This is mostly due to the shrinking price of their application and the increasingly strict emission policy. However, the integration of REs and EVs brings new challenges to the network operation [1]. For instance, a considerable amount of REs cannot be accommodated in the network and thus has to be curtailed due to technical limitations [2-5]. For overcoming this problem, Battery Storage Systems (BSSs) can be used to store the surplus energy and consequently increase economic benefits [4]. In addition, the storage capacity of EVs can be employed to store an amount of REs and provide it back to the grid when needed [6]. This not only balances the supply and demand but also results in decrease in network losses and improvement of voltage and frequency stability of the grid. However, BSSs and EVs lead to a dynamic power flow for the grid, which is difficult to address. In addition, considering both active and reactive power capability of the EVs and BSSs with flexible operation strategies, as well as maximizing the lifetime of the batteries [7,8] further increase the complexity of the problem. Another significant challenge lies in the fact that REs and EVs are intermittent and uncertain, i.e., their power exchange with the grid cannot be accurately forecasted and thus causes discrepancies between the forecasted and realized values. The uncertainties can lead to constraint violations and thus safety problems if not handled properly [9,10]. Therefore, the network operator has to fast update the operation strategies correspondingly, in order to operate the network economically and safely, i.e., online optimal operation strategies for the whole system are needed [11-16]. Mathematically, it is a task to solve a real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) problem with a huge number of mixed-integer decision variables [16]. The optimization problem aims at minimizing the total grid operation costs and expended life costs of BSSs, while maximizing the benefits for RE and EV owners. Therefore, developing a solution framework for RT-DAR-OPF is of utmost importance for ensuring both optimality and feasibility in the operation of smart grids with BSSs under uncertain EV and RE exchange. The most challenging issue hereby is that a large-scale dynamic stochastic mixed-integer nonlinear programming (MINLP) problem has to be solved in real-time [16]. A multi-phase multi-time-scale solution framework provides a way to solve this complicated optimization problem. As a result, the operation strategies by the online optimization will financially motivate the network operator and energy prosumers to interact optimally in the grid, while satisfying all technical constraints.

Figure 1: A smart grid with BSSs, EVs, and renewable energy generations.

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