Integration of large-scale cluster electric vehicles (EVs) and their spatial-temporal transfer randomness are likely to affect the safety and economic operation of the distribution network. This paper investigates the spatial-temporal distribution prediction of EVs' charging load and then evaluates the reliability of the distribution network penetrated with large-scale cluster EVs. To effectively predict the charging load, trip chain technology, Monte Carlo method, and Markov decision process (MDP) theory are employed. Moreover, a spatial-temporal transfer model of EVs is established, and based on which, an EV energy consumption model and a charging load prediction model are constructed with consideration of temperature, traffic condition and EV owner's subjective willingness in different scenarios. With the application of sequential Monte Carlo method, the paper further evaluates distribution network reliability in various charging scenarios. In the evaluation, indices including per unit value (PUV), fast voltage stability index (FVSI), loss of load probability (LOLP), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and expected energy not supplied (EENS) are incorporated. To validate the proposed prediction model and evaluation method, a series of numerical simulations are conducted on the basis of taking the traffic-distribution system of a typical city as an example. The result demonstrates that the proposed spatial-temporal transfer model is more practical in charging load prediction than the popularly used Dijkstra's shortest path algorithm. Moreover, high temperature, congestion and the increment of EV penetration rate will further weaken distribution network reliability. INDEX TERMS Electric vehicle, reliability evaluation, trip chain technology, Markov decision process, sequential Monte Carlo method, spatial-temporal transfer randomness NOMENCLATURE ABBREVIATIONS EV Electric vehicle MDP Markov decision process SOC State of charge TNN Traffic network node DNN Distribution network node DCP Dispersion of charging power PARAMETERS μ Mean of the beginning time of each trip Variance of the beginning time of each trip v i,j max Maximum speed allowed at the road (i, j) x EV's position in the travel path D x Distance between the origin of the trip and x C EV Capacity of EV battery
To comprehensively consider the actual spatial-temporal transfer process of electric vehicles (EVs) and enhance the computation efficiency of scheduling, this article proposes a spatial-temporal transfer model of EVs and an improved Lagrange dual relaxation method (ILDRM) for the decentralized scheduling of a charging-storage station (CSS). Specifically, with the application of trip chain technology, Monte Carlo, and Markov decision process (MDP), the spatialtemporal transfer model of EVs is constructed, taking into account multiple factors including temperatures, traffic conditions, and transfer randomness. Subsequently, by introducing ILDRM, a decentralized optimization model is proposed which converts the traditional centralized optimization model into a set of sub-problems. Moreover, the optimization model aims to maximize the profit of CSS under the constraints of vehicle-to-grid behavior and the operation of both CSS and distribution network. To validate the proposed spatialtemporal transfer model and the decentralized optimization method for CSS, a series of simulations in various scenarios are performed regarding the load curve and computation efficiency. The comprehensive and systematical study indicates that the proposed spatial-temporal transfer model enables to reflect EVs transfer randomness and it is more factually practical than the classical Dijkstra algorithm. Besides, ILDRM can provide a high computationally efficient solution to the operation of CSS.
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