Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

Abstract

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

Decentralized scheduling optimization for charging‐storage station considering multiple spatial‐temporal transfer factors of electric vehicles

“…30 It is assumed that socEV s,i follows the uniform distribution of 0.8-0.9, which can be generated with the application of Monte Carlo method. 31 The unit scheduling period of CSS is 15 minutes. And all the simulations are conducted based on MATLAB 2016a and Gurobi.…”

“…The purpose of EVs' trips can be generally divided into five types, including going back home (H), working (W), shopping and eating (SE), social interaction and recreation (SR), and others (O). 31 Places in these trips are assumed to be the locations where EVs can be charged or discharged. Moreover, the five trip types can form two modes of the trip chain in this article, namely, simple trip chain and complex trip chain.…”

“…Thus let P st denote the transition probability, st {2, 3, …, n − 1, n, n + 1}. Then the transition probability from node 1 to node r, that is, p (rj1, a r ), can be derived via Equations (2) and (3), where r {2, 3, …,n-1, n, n + 1} 31 :…”

“…Here, the aim is to update ζ according to Equation (31). It needs to be pointed out that the explicit meaning of s (m) in the decentralized optimization model can be expressed in Equation 39:…”

“…If it is, stop the whole iteration. Otherwise, update the Lagrange multiplier according to Equation (31) and recalculate each sub-problem.…”