Controlled Electric Vehicle Charging for Mitigating Impacts on Distribution Assets

Abstract

This paper proposes a two-step methodology for scheduling electric vehicle (EV) charging, which limits the burden on distribution and transmission assets while ensuring all the vehicles are charged. In the first step, the number of vehicles to be charged during each hour is optimized based on day-ahead requests for charging. The second step determines the maximum number of vehicles that can be charged based on operating conditions during the next hour to ensure distribution reliability requirements are met. A numerical example is given to illustrate the EV charging management scheme. This technique will limit the effects of EV charging on distribution assets.Index Terms-Distribution system, electric vehicles (EVs) charging, greenhouse gas (GHG) emission, loss of life (LOL) of components reliability.

An Improved Optimal Forecasting Algorithm for Comprehensive Electric Vehicle Charging Allocation

“…[40] The charging strategies play a significant role in reducing grid pressure and are determined by the battery state of charge. [41] Most literatures have studied the influence of dumb or uncoordinated random charging, which studies the impact of random EVs plug in; tariff-based time-of-use (TOU) electricity price-based control, which influences the user to charge at off-peak at low price hours; charge-at-park that enables the users to charge EV after each trip. In the study by Vagropoulos et al, [42] the EVs' impact on the grid with smart and direct charging solution is analyzed on the insular system in Greece with the objective of cost minimization and energy schedules.…”

“…Generally, charging schedule is determined by two factors, i.e., charging capacity and charging strategy . The charging strategies play a significant role in reducing grid pressure and are determined by the battery state of charge . Most literatures have studied the influence of dumb or uncoordinated random charging, which studies the impact of random EVs plug in; tariff‐based time‐of‐use (TOU) electricity price–based control, which influences the user to charge at off‐peak at low price hours; charge‐at‐park that enables the users to charge EV after each trip.…”

“…In previous studies, researchers were unable to propose a global practical scheduling solution due to less accuracy of proposed averaging similar day approach or regression and time series–based forecasting method . 3) The proposed solution is scalable to handle Large EVs population through self‐scheduling and require minimum communication for charging control or price adjustment between EV users and utility . The proposed charging strategy is a multi‐objective comprehensive solution scheme to favor both EV users and utilities; for optimal parameter configuration, a binary feature constraint handling mechanism is used.…”

PV-based EV charging station with vehicle-to-grid services for business premises

“…Known in advance [47,52,53,58,136,140,141,145,147,148] n A,k and n D,k modeled by time-invariant Normal distributions [138] n k+ modeled by time-variant Normal distribution [85] n k+ at each bus of the grid is predefined [137] To keep track of the transition of X k+ , it is assumed the timeslot-wise X A,k is known in the papers mentioned in Table 2.30. Contrarily, X A,k is predicted from daily travel distance using the similar equation to (2.12), whereby the travel distance has been modeled by a time-invariant Lognormal distribution in some other papers.…”

“…Equation (6.9) is solved for G by formulating a linear problem as depicted in (6.10): Step 2 -calculation of impact indices from g-parameter: During the real-time charging process, for the k-th sample, first, net grid load is calculated from (6.2). Then, the matrix  is computed by 140 assuming y = y k , for the known g-parameters. The ECDFs of the nodal voltages and feeder currents are approximated by (6.7), using the estimated values of the matrix  .…”