Distributed Control of PEV Charging Based on Energy Demand Forecast

Abstract

This paper presents a new distributed smart charging strategy for grid integration of plug-in electric vehicles (PEVs). The main goal is to smooth the daily grid load profile while ensuring that each PEV has a desired state of charge level at the time of departure. Communication and computational overhead, and PEV user privacy are also considered during the development of the proposed strategy. It consists of two stages: 1) an offline process to estimate a reference operating power level based on the forecasted mobility energy demand and base loading profile, and 2) a real-time process to determine the charging power for each PEV so that the aggregated load tracks the reference loading level. Tests are carried out both on primary and secondary distribution networks for different heuristic charging scenarios and PEV penetration levels. Results are compared to that of the optimal solution and other state-of-the-art techniques in terms of variance and peak values, and shown to be competitive. Finally, a real vehicle test implementation is done using a commercial-ofthe-shelf charging station and an electric vehicle.

A fast and efficient coordinated vehicle-to-grid discharging control scheme for peak shaving in power distribution system

“…Classical heuristic charging prioritizing policies can also be applied in charging scheduling. However, the off-peak charging scheduling can be better exploited to achieve a valley-filling behavior, i.e., a grid load profile with lower variance value [20]. This is important for DSOs, because minimizing variance is equivalent to maximizing the load factor and hence, minimizing the losses in the distribution network [27].…”

“…For this purpose, a bidirectional data flow takes place between the aggregator and electric vehicle supply equipments (EVSEs). The decentralized control architecture, on the other hand, allows each PEV to determine its own discharging profile [18][19][20]. It is more flexible in terms of PEV user convenience and easier to implement in the field.…”

Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration

“…There are other type studies, e.g., hosting capacity [31], where realistic operation schemes is addressed using Monte Carlo simulations with stochastic EV demands [32][33][34][35][36]. Thus, this work does not intend to replicate the probabilistic behavior of EV connection in a given period.…”

Online Demand Side Management with PEVs Using Stochastic Optimization

“…the centralized control has an aggregator at the helm of affairs with the responsibility of managing each participating PEV in the V2G service on how and when to charge or discharge of PEV in a coordinated fashion [21], [22]. In the decentralized control each PEV is responsible for its own charge and discharging operation making it convenient and easier to implement practically given room for a localized grid [23]- [25]. One down side of a decentralized operation is a feature of randomness as a result of uncertainties in the arrival and departure time of PEVs' which introduces randomness in the available energy committed by PEVs' thereby causing randomness to the grid.…”

“…The works in [23]- [25], [31], [32] proposes a DSM solution considered uncertainties in demand; [31] and [32] account for system uncertainties in a worst-case scenario. These assumptions may be cost intensive on an operational level.…”