Vehicle-to-grid management for multi-time scale grid power balancing

The mitigation of peak-valley difference and transient power fluctuation are both of great significance to the economy and stability of the power grid. This paper proposes a novel vehicle-to-grid behavior management method that can provide peak-shaving and fast power balancing service to the grid simultaneously. Firstly, a multi-time scale vehicle-to-grid behavior management framework is designed to enable large-scale optimization and real-time control at the same time in vehicle-to-grid scheduling. Then, the grid peak-shaving requirement is modeled as an optimization problem in a centralized V2G state coordinator, where the charging behavior of all grid-connected electric vehicles can be synergistically scheduled. The optimization variable is designed as a group of vehicle-to-grid state control signals that can respond to grid peak-shaving requirements. Further, a V2G power controller is designed to manage the vehicle charging power in real time based on the sampled grid frequency state and discrete state control signals. In the developed scheduling method, the charging power of grid-connected electric vehicles is scheduled by the cooperation between the V2G state coordinator and the power controller. The effectiveness of the proposed methodologies is verified on a microgrid system, and results indicate that the V2G scheduling can achieve multi-time scale grid power balancing. This work can bring dual benefits, enabling system operators to use cheap solutions to manage energy networks and allowing vehicle owners to gain profits from providing V2G services to the grid.</p

Distributed charging management of multi‐class electric vehicles with different charging priorities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166242/1/gtd2bf02710.pd

Vehicle-to-Grid Integration for Enhancement of Grid: A Distributed Resource Allocation Approach

In the future grids, to reduce greenhouse gas emissions Electric Vehicles (EVs) seems to be an important means of transportation. One of the major disadvantages of the future grid is the demand-supply mismatch which can be mitigated by incorporating the EVs into the grid. The paper introduces the concept of the Distributed Resource Allocation (DRA) approach for incorporating a large number of Plug-in EV (PEVs) with the power grid utilizing the concept of achieving output consensus. The charging/discharging time of all the participating PEVs are separated with respect to time slots and are considered as strategies. The major aim of the paper is to obtain a favorable charging strategy for each grid-connected PEVs in such a way that it satisfies both grid objectives in terms of load profile smoothening and minimizing of load shifting as well as economic and social interests of vehicle owners i.e. a fair share of the rate of charging for all connected PEVs. The three-fold contribution of the paper in smoothening of load profile, load shifting minimization, and fair charging rate is validated using a representative case study. The results confirm improvement in load profile and also highlight a fair deal in the charging rate for each PEV

A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks

The growing diffusion of electric vehicles connected to distribution networks for charging purposes is an ongoing problem that utilities must deal with. Direct current networks and storage devices have emerged as a feasible means of satisfying the expected increases in the numbers of vehicles while preserving the effective operation of the network. In this paper, an innovative probabilistic methodology is proposed for the optimal sizing of electrical storage devices with the aim of maximizing the penetration of plug-in electric vehicles while preserving efficient and effective operation of the network. The proposed methodology is based on an analytical solution of the problem concerning the power losses minimization in distribution networks equipped with storage devices. The closed-form expression that was obtained is included in a Monte Carlo simulation procedure aimed at handling the uncertainties in loads and renewable generation units. The results of several numerical applications are reported and discussed to demonstrate the validity of the proposed solution. Also, different penetration levels of generation units were analyzed in order to focus on the importance of renewable generation

Distributed Voltage Control in Distribution Networks with Electric Vehicle Charging Stations and Photovoltaic Generators

The developments of distributed generators (DGs) and electric vehicles (EVs) are dramatical due to the rapid increase of friendly environment desire. While on another hand, the proliferation of distributed generators (DGs) and electric vehicle charging stations (EVCSs) has brought voltage regulation challenges to distribution systems due to their high generations and heavy loads. In this thesis, a distributed control strategy is proposed which mainly consisted by a reactive compensation algorithm to dispatch surplus reactive power from DGs and EVCSs for proper voltage regulation without violating their converters’ capacity limits or stressing conventional voltage control devices, i.e., on-load tap changers (OLTCs), and an active power curtailment algorithm for DGs to properly integrate OLTC in voltage regulation when the reactive power compensation is deficient. The proposed control algorithms rely on consensus theory and sensitivity analysis, thus, minimizing the active and reactive powers needed for voltage support, and decreasing the net cost of voltage regulation. In the proposed control strategy, three distributed voltage regulation algorithms, as well as a distributed control method for OLTC, are developed and coordinated to realize adequate voltage maintaining effects. Simulation results of a typical distribution system confirm the effectiveness and robustness of the proposed distributed control strategy in continuously maintaining proper voltage regulation for the whole distribution system with minimum power demands from DGs and EVCSs, and reduced tap operation for OLTC, within every 24 hours

Charge scheduling optimization of plug-in electric vehicle in a PV powered grid-connected charging station based on day-ahead solar energy forecasting in Australia

Optimal charge scheduling of electric vehicles in solar-powered charging stations based on day-ahead forecasting of solar power generation is proposed in this paper. The proposed algorithm’s major objective is to schedule EV charging based on the availability of solar PV power to minimize the total charging costs. The efficacy of the proposed algorithm is validated for a small-scale system with a capacity of 3.45 kW and a single charging point, and the annual cost analysis is carried out by modelling a 65 kWp solar-powered EV charging station The reliability and cost saving of the proposed optimal scheduling algorithm along with the integration and the solar PV system is validated for a charging station with a 65 kW solar PV system having charging points with different charging powers. A comprehensive comparison of uncontrolled charging, optimal charging without solar PV system, and optimal charging with solar PV system for different vehicles and different time slots are presented and discussed. From the results, it can be realized that the proposed charging algorithm reduces the overall charging cost from 10−20% without a PV system, and while integrating a solar PV system with the proposed charging method, a cost saving of 50−100% can be achieved. Based on the selected location, system size, and charging points, it is realized that the annual charging cost under an uncontrolled approach is AUS $28,131. On the other hand, vehicle charging becomes completely sustainable with net-zero energy consumption from the grid and net annual revenue of AUS$28,134.445 can be generated by the operator. New South Wales (NSW), Australia is selected as the location for the study. For the analysis Time-Of-Use pricing (ToUP) scheme and solar feed-in tariff of New South Wales (NSW), Australia is adopted, and the daily power generation of the PV system is computed using the real-time data on an hourly basis for the selected location. The power forecasting is carried out using an ANN-based forecast model and is developed using MATLAB and trained using the Levenberg−Marquardt algorithm. Overall, a prediction accuracy of 99.61% was achieved using the selected algorithm

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

Active and Reactive Power Control of Flexible Loads for Distribution-Level Grid Services

Electric vehicle (EV) charging/discharging can take place in any P-Q quadrants, which means EVs could provide reactive power at any state-of-charge (SOC). This dissertation shows four-quadrant operation of EVs and aggregation of EVs for support of grid operations. First, this work develops hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed EVs incorporating distribution grid level constraints. This work demonstrates benefits of coordinated dispatch of active and reactive power from EVs using a 33-node distribution feeder with large number of EVs (more than 5,000). Case studies demonstrate that, in constrained distribution grids, coordinated charging reduces the average cost of EV charging if the charging takes place at non-unity power factor mode compared to unity power factor. Similarly, the results also demonstrate that distribution grids can accommodate charging of increased number of EVs if EV charging takes place at non-unity power factor mode compared to the unity power factor. Next, this work utilizes detailed EV battery model that could be leveraged for its four-quadrant operations. Then, the developed work coordinates the operations of EVs and distribution feeder to support voltage profile on the grid in real time. The grid level problem is devised as a distribution optimal power flow model to compute voltage regulation signal to dispatch active/reactive power set points of individual EVs. The efficacy of the developed models are demonstrated by using a LV secondary feeder, where EVs\u27 operating in all four quadrants are shown to compensate the feeder voltage fluctuations caused by daily time varying residential loads, while honoring other operational constraints of the feeder. Furthermore, a novel grid application, called virtual power plant (VPP), is developed. Traditional nonlinear power flow problems are nonconvex, hence, time consuming to solve. In order to be used in real time simulation in VPP, an efficient linearized optimal power flow model is developed. This linearization method is used to solve a 534-bus power system with 3 VPPs in real-time. This work also implements VPP scheduling in real-time using OPAL-RT\u27s simulator in hardware-in-the-loop (HIL), where the loads are emulated using micro-controller devices

Enhancing System Reliability Utilizing Private Electric Vehicle Parking Lots Accounting for the Uncertainties of Renewables

Integration of renewable energy sources into electric grids comes with significant challenges. The produced energy from renewable sources such as wind and solar is intermittent, non-dispatchable and uncertain. The uncertainty in the forecasted renewable energy will consequently impact the accuracy of the forecasted generation. That, in turn, will increase the difficulties for the grid operators to meet the demand-supply balance in the grids. Moreover, the shift to electric vehicles (EV) also adds complications to grid operators planning since their demand profiles are unlike anything that is currently connected to the grid. With the advent of the smart grid, many new interesting and practical technologies will become a reality. Unfortunately, most of these elements will not be physically realized in systems for the immediate future. This thesis will maintain an overarching constraint of only using aspects of the smart grid that can be implemented, given today’s infrastructure, within the next three to five years. For that reason, all loads connected to the system are considered to be uncontrollable except EVs when they are connected to a “commercial parking lot”. The goal of this body of work is to investigate the benefits of the utility providing incentives, in terms of reducing the price of electricity for charging EVs through a commercial parking lot, for the sole goal of enhancing reliability through optimized scheduling of charging time periods. Moreover, since the penetration of renewable energy is only predicted to increase over time, their impact will also be investigated. Since both EVs and renewables add uncertainty and randomness whenever connected, these elements need to be accurately and adequately modelled. There will be five electrical components that will need stochastic models built. The first two, base electric load and dispatchable/distributed generators, will be modelled based on the IEEE - Reliability Test System (IEEE-RTS), with the later using a Monte-Carlo (MC) simulation. The next two, solar and wind energy, will be extrapolated from historical weather patterns through a Markov-Chain Monte-Carlo (MCMC) simulation. Finally, the EV will be virtually generated from historical commercial parking lot data also using a MCMC. Three scheduling algorithms were implemented in this work. The first is a base case, in which the EVs charged in a first-come first-serve basis, this situation that would arise if no information at all is shared with the parking lot owner. The results of the simulation had the value of Expected Energy Not Served (EENS) came out to be 38.05 MWh/year (the lower the better). With the second algorithm, a basic Demand Side Management (DSM) algorithm was implemented, with the result being that the EENS decreased by 8.35 %. The information shared was the demand shape of all consumers for a given day. Lastly, an algorithm that will be called Grid and Demand Side Management (GDSM) by this thesis proved to be even more successful, having the EENS reach a value of 29.85 MWh/year, a decrease of 21.55 %. The GDSM scheduling algorithm needs the grid to share not only the consumer behavior but also the expected generator behavior. Based on these results, recommendations are made to the electric utility in terms of the benefits it may reap if it expands and develops a communication infrastructure that includes information of generator availability