A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks

Abstract

The growing diffusion of electric vehicles connected to distribution networks for charging purposes is an ongoing problem that utilities must deal with. Direct current networks and storage devices have emerged as a feasible means of satisfying the expected increases in the numbers of vehicles while preserving the effective operation of the network. In this paper, an innovative probabilistic methodology is proposed for the optimal sizing of electrical storage devices with the aim of maximizing the penetration of plug-in electric vehicles while preserving efficient and effective operation of the network. The proposed methodology is based on an analytical solution of the problem concerning the power losses minimization in distribution networks equipped with storage devices. The closed-form expression that was obtained is included in a Monte Carlo simulation procedure aimed at handling the uncertainties in loads and renewable generation units. The results of several numerical applications are reported and discussed to demonstrate the validity of the proposed solution. Also, different penetration levels of generation units were analyzed in order to focus on the importance of renewable generation.

Effective Deterministic Methodology for Enhanced Distribution Network Performance and Plug-in Electric Vehicles

“…The participation of flexible resources elevates the accommodation of PEVs by 10%. [26] optimized the size and location of the energy storage system to maximize PEV penetration in the DC network using Monte Carlo simulations. The outcome of the study shows that 40 PEVs are safely accommodated considering the voltage constraint, while only 25 PEVs are accommodated considering current constraint violations.…”

“…It necessitates the adoption of a compromised strategy to attain an optimum solution. The existing studies maximized the accommodation of PEVs [19,25,26,28,[30][31][32][33][34][35] while others reduced network power losses [21][22][23]27,29,[36][37][38][39][40][41]. Thus, the combined handling of both objectives within a single framework has been ignored, which is not a practical approach.…”

Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation

“…To address this, in [31], a smart charging algorithm is proposed that improves the power system stability and robustness, using PV and wind. The authors of [32] proposed a probabilistic approach for managing unavoidable uncertainties in loads, plug-in EVs and renewable generation, including PV and Wind, based on the sizing of storage devices.…”

Integration of statistical methods and neural networks for temperature regulation parameter optimization

“…Moreover, the application of hybrid optimization techniques extends to diverse domains such as transportation systems, vehicle architectures, and energy systems. Researchers have explored hybrid design methodologies for automated generation and optimization of vehicle architectures, powertrains, and renewable energy systems, showcasing the potential for enhanced performance and efficiency through integrated optimization strategies [20]- [25]. Overall, the emerging trend of hybrid optimization methodologies represents a significant advancement in the field of optimization, offering a synergistic approach that leverages the strengths of different techniques to address complex optimization challenges effectively.…”