Evaluating the effect of electric vehicle parking lots in transmission-constrained AC unit commitment under a hybrid IGDT-stochastic approach

Abstract

Power network operators have recently faced new challenges due to an increase in the penetration of non-dispatchable renewable energy sources in power grids.Incorporating emerging flexible resources like electric vehicle parking lots (EVPLs) and demand response programs (DRPs) into power systems, could be a good solution to deal with inherent uncertainties imposed by these resources to the power grid. EVPLs can improve power system operating conditions by active and reactive power injection capabilities. The participation of consumers in DRPs can also improve energy consumption management by decreasing or shifting loads to other periods. This paper proposes a hybrid information gap decision theory (IGDT)stochastic method to solve a transmission-constrained AC unit commitment model integrated with electric vehicle (EV), incentive-based DRP, and wind energy. The behavioural uncertainty related to EV owners is modelled using a scenario-based method. Additionally, an IGDT method is applied to manage wind energy uncertainty under a two-level optimization model. Verification of the proposed model is done under several case studies. Based on the results achieved, the proposed risk-based hybrid model allows the operator to differentiate between the risk level

A Comprehensive Review on Brushless Doubly-Fed Reluctance Machine

“…In addition to wind turbines, BDFRM is a potential solution for the development of Electric Vehicles (EVs), which are an alternative to conventional vehicles. EVs are developing due to the energy crisis and air pollution issues [142,143]. BDFRM can accelerate the development of EVs due to its advantages, such as lower maintenance and manufacturing cost, higher reliability, lower losses, and robustness feature.…”

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“…λ is a random variable generated using the normal distribution function with a mean value of 0 and a standard deviation of 1 for each demand level. µ and σ are the predicted values of the load demand level and its standard deviation, respectively [25,26].…”

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“…3. For every leaf node R jC , better fitness value of EV parameters such as battery SOC, engine torque and speed, EM torque and speed, and battery power are calculated via Equation (58).…”