Mostafa F. Shaaban

Accommodating High Penetrations of PEVs and Renewable DG Considering Uncertainties in Distribution Systems

This paper proposes a multi-year multi-objective planning algorithm for enabling distribution networks to accommodate high penetrations of plug-in electric vehicles (PEVs) in conjunction with renewable distributed generation (DG). The proposed algorithm includes consideration of uncertainties and will help local distribution companies (LDC) better assess the expected impacts of PEVs on their networks and on proposed renewable DG connections. The goal of the proposed algorithm is to minimize greenhouse gas emissions and system costs during the planning horizon. An approach based on a non-dominated sorting genetic algorithm (NDSGA) is utilized to solve the planning problem of determining the optimal level of PEV penetration as well as the location, size, and year of installation of renewable DG units. The planning problem is defined in terms of multi-objective mixed integer nonlinear programming. The outcomes of the planning problem represent the Pareto frontier, which describes the optimal system solutions, from which the LDC can choose the system operating point, based on its preferences.

Uncoordinated charging impacts of electric vehicles on electric distribution grids: Normal and fast charging comparison

Plug-in electric vehicles (PEVs) have uncertain penetration in electric grids due to uncertainties in charging and discharging patterns. This uncertainty together with various driving habits makes it difficult to accurately assess the effects on local distribution network. Extra electrical loads due uncoordinated charging of electric vehicles have different impacts on the local distribution grid. This paper proposes a method to evaluate the impacts of uncoordinated PEVs charging on the distribution grid during peak period. Two PEVs charging scenarios are studied, including normal and fast charging. The impact analysis is evaluated in terms of voltage violations, power losses and line loading, which is implemented on a real distribution system in Canada. The results of the analysis indicate that there are significant impacts on distribution networks due to PEVs charging, which limits the accommodation of desired penetration levels of PEVs.

Real-Time Optimal Voltage Regulation for Distribution Networks Incorporating High Penetration of PEVs

This paper proposes a vehicle-to-grid reactive power support (V2GQ) strategy for optimal coordinated voltage regulation in distribution networks with high distributed generation (DG) penetration. The proposed algorithm employs plug-in electric vehicles (PEVs), DG, and on-load tap changer (OLTC) to satisfy PEV charging demand and grid voltage requirements with relaxed tap operation, and minimum DG active power curtailment. The voltage regulation problem is formulated as a nonlinear programming and consists of three consecutive stages, in which successive stages apply the outputs of their preceding stages as constraints. The first stage aims to maximize the energy delivered to PEVs to assure PEV owner satisfactions. The second stage maximizes the DG extracted active power. Third stage minimizes the voltage deviation from its nominal value utilizing the available PEV and DG reactive powers. The main implicit objective of the third stage problem is relaxing the OLTC tap operation. In addition, the conventional OLTC control is replaced by a proposed centralized controller that utilizes the output of the third stage to set its tap position. Real-time simulations are developed to demonstrate the effectiveness of the proposed optimal coordinated algorithm on a typical distribution network using OPAL-RT real-time simulator (RTS) in a hardware-in-the-loop (HIL) application.

Stochastic Centralized Dispatch Scheme for AC/DC Hybrid Smart Distribution Systems

This paper presents a two-stage stochastic centralized dispatch scheme for AC/DC hybrid smart grids. The developed dispatch scheme coordinates the operations of a variety of distributed energy resources (DERs), such as distributed generators (DGs) and energy storage systems (ESSs). It also ensures the coordinated charging of electric vehicles (EVs) and models the degradation of their batteries that occurs due to vehicle-to-grid (V2G). The energy coordination problem has been formulated as a two-stage day-ahead resource scheduling problem, with the intermittent supply; the variable demand, which includes EVs; and the fluctuating real-time energy price modeled as random variables. The first stage produces day-ahead dispatch decisions for the dispatchable DG units. For a set of possible scenarios over the next 24 h, the second stage determines appropriate corrective decisions with respect to the import/export schedule, storage charging/discharging cycles, and EV charging/discharging patterns. The objective is to minimize the expected total operating cost while satisfying operational and technical constraints. The new two-stage stochastic centralized dispatch model has been tested on a 38-bus AC/DC hybrid distribution system. The simulation results demonstrate the effectiveness of the developed scheme for optimally coordinating the various components of future AC/DC hybrid smart grids. To demonstrate the necessity for uncertainty modeling, the value of the stochastic solution (VSS) and the expected value of perfect information (EVPI) have been applied for comparing the stochastic solution obtained and the deterministic one.

Managing Demand for Plug-in Electric Vehicles in Unbalanced LV Systems With Photovoltaics

Although the future impact of plug-in electric vehicles (PEVs) on distribution grids is disputed, all parties agree that mass operation of PEVs will greatly affect load profiles and grid assets. The large-scale penetration of domestic energy storage, such as with photovoltaics (PVs), into the edges of low-voltage grids is increasing the amount of customer-generated electricity. Distribution grids, which are inherently unbalanced, tend to become even more so with the uneven spread of PVs and PEVs. In combination, PEVs and local generation could provide voltage support for distribution networks, and support increased penetration. This paper develops an interactive energy management system for incorporating PEVs in demand response (DR). Using this system, owners can immediately choose whether they want to discharge their PEV battery back into the grid. The system not only provides owners with a flexible scheme for contributing to DR but also ensures that, through real-time collaboration of PEVs and PVs, the three-phase grid operates within acceptable voltage unbalance. An extensive performance evaluation using MATLAB/GAMS simulation of the 123-bus test system verifies the effectiveness of the proposed approach.

Optimal Resource Allocation and Charging Prices for Benefit Maximization in Smart PEV-Parking Lots

The emerging interest in deployment of plug-in electric vehicles (PEVs) in distribution networks represents a great challenge to both system planners and owners of PEV-parking lots. The owners of PEV-parking lots might be interested in maximizing their profit via installing charging units to supply the PEV demand. However, with stringent rules of network upgrades, installing these charging units would be very challenging. Network constraints could be relaxed via controlling the net demand through integrating distributed generation (DG) and/or storage units. This paper presents an optimization model for determining the optimal mix of solar-based DG and storage units, as well as the optimal charging prices for PEVs. The main objective is to maximize the benefit of the PEV-parking lots owner without violating system constraints. Two cases are considered in this paper: uncoordinated and coordinated PEV demand. A novel mathematical model is further developed whereby the behavior of vehicles’ drivers, in response to different charging prices, is considered in generating the energy consumption of PEVs.

Probabilistic modeling of PHEV charging load in distribution systems

This paper proposes an annual probabilistic model for the energy consumed by a fleet of plug-in hybrid electric vehicles (PHEV) based on Monte Carlo simulation (MCS). This model presents the PHEV charging load scenarios and their probabilities for each hour of the year, which can be easily combined with the normal load models. This facilitates the utilization of this model by local distribution companies to quantify the impacts of PHEV charging on their systems and to define the penetration limit of PHEV in each territory.

Impacts of Feeder Reconfiguration on Renewable Resources Allocation in Balanced and Unbalanced Distribution Systems

Abstract In this article, network reconfiguration and distributed generation allocation in distribution networks are dealt with simultaneously while imposing an objective of minimizing energy loss. The proposed method, which is based on a genetic algorithm, takes into consideration the uncertainty related to renewable distributed generation output power and the load variability. Three scenarios are assessed to analyze the superiority of the proposed method. In the first scenario, distributed generation units are allocated using the base configuration, followed by network reconfiguration. In the second scenario, distributed generations are allocated after network reconfiguration. In the third scenario, distributed generations are allocated simultaneously with network reconfiguration. The constraints involved include voltage limits, line current limits, and radial topology. Both balanced and unbalanced distribution systems are used as case studies.

Demand responce through interactive incorporation of plug-in electric vehicles

PEV coordination introduces a significant challenge in demand response programs (DR). In one hand, there is a serious challenge due to uncertainty and dynamics associated with PEVs to be devoted to DR. In another hand, with proper charging and communication infrastructure, PEVs may play a dual role in smart grids; they may eventually either turn into Interruptible Loads (IL) when plugged in for charging or act as grid-able storage responding to the pricing commands. This paper aims to provide an approach that realises DR using aggregated PEVs in parking lots. This approach includes a real-time interaction between the aggregator and the PEV owner, where the aggregator suggests different offers and, accordingly, the owner responds based on his/her preference. A multi-stage optimization solution is proposed here to accommodate properly different offers to the PEV owners, while it is benefiting from an ANN-based forecast model to incorporate the effect of the future PEV arrivals in the decision actions. Implementation results on the 38-bus test system indicate how effectively the proposed solution could help future smart parking lots in DR contribution.

New EMS to Incorporate Smart Parking Lots Into Demand Response

Demand response (DR) seeks to involve end-use customers in modifying their electricity usage and to offer incentive payments to encourage lower electricity use at times of high prices. This paper provides an approach that realizes DR by developing an energy management system for incorporating aggregated plug-in electric vehicles (PEVs) in parking lots. This approach includes real-time interaction between the aggregator and PEV owners, whereby the aggregator proposes a number of offers and the owner responds based on his/her preference. The optimization problem is defined as mixed integer nonlinear programming. An extensive performance evaluation using MATLAB/GAMS simulation of the 38-bus test system verifies the success and effectiveness of the proposed method.