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.
Highlights
In efforts to ensure sustainable societies, smart technologies have been identified as significant contributors to meet the expected increment of energy that will be required due to the human activities in urban areas [1,2]
The outputs of the proposed method are the maximum penetration of plug-in electric vehicles (PEVs) and the corresponding optimal sizes of the energy storage systems (EESSs) that allow balancing load and generation efficiently and satisfying the constraints of the network. These outputs are derived based on the pdfs of the line currents, bus voltages, imported power and EESSs sizes resulting from the application of the Monte Carlo procedure
A new probabilistic optimization methodology was proposed in this paper to manage unavoidable unavoidable uncertainties in loads, PEVs and renewable production in DC distribution networks
Summary
In efforts to ensure sustainable societies, smart technologies have been identified as significant contributors to meet the expected increment of energy that will be required due to the human activities in urban areas [1,2]. In [29], an augmented time-series energy balance was used to determine the size of the battery in a charging station that would reduce the impact of the chargers on the electrical network; the author used a chance constraint-based method to deal with the uncertainties in the network’s load, generation, and the number of PEVs. In [30], a search-based algorithm was used to solve the problem of determining the optimal storage sizing, and it was formulated in terms of the mixed integer optimization problem aimed at satisfying the load demand entirely by renewable energy sources.
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