Online scheduling for vehicle-to-grid regulation service

Electric vehicle (EV) fleets can provide ancillary services, such as frequency regulation, to the utility grid, if their charging/discharging schedules are coordinated appropriately. In this paper, a multi-level architecture for bidirectional vehicleto-grid regulation service is proposed. In this architecture, aggregators coordinate the charging/discharging schedules of EVs in order to meet their shares of regulation demand requested by the grid operator. Based on this architecture, the scheduling problem of V2G regulation is then formulated as a convex optimization problem, which in turn degenerates to an online scheduling problem for charging/discharging of EVs. It requires only the current and past regulation profiles, and does not depend on the accurate forecast of regulation demand. A decentralized algorithm, which enables every EV to solve its local optimization problem and obtain its own schedule, is applied to solve the online scheduling problem. Based on the household driving pattern and regulation signal data from the PJM market, a simulation study of 1,000 EVs has been performed. The simulation results show that the proposed online scheduling algorithm is able to smooth out the power fluctuations of the grid by coordinating the EV schedules, demonstrating the potential of V2G in providing regulation service to the grid.published_or_final_versio

Optimal Scheduling With Vehicle-to-Grid Regulation Service

In a vehicle-to-grid (V2G) system, aggregators coordinate the charging/discharging schedules of electric vehicle (EV) batteries so that they can collectively form a massive energy storage system to provide ancillary services, such as frequency regulation, to the power grid. In this paper, the optimal charging/discharging scheduling between one aggregator and its coordinated EVs for the provision of the regulation service is studied. We propose a scheduling method that assures adequate charging of EVs and the quality of the regulation service at the same time. First, the scheduling problem is formulated as a convex optimization problem relying on accurate forecasts of the regulation demand. By exploiting the zero-energy nature of the regulation service, the forecast-based scheduling in turn degenerates to an online scheduling problem to cope with the high uncertainty in the forecasts. Decentralized algorithms based on the gradient projection method are designed to solve the optimization problems, enabling each EV to solve its local problem and to obtain its own schedule. Our simulation study of 1000 EVs shows that the proposed online scheduling can perform nearly as well as the forecast-based scheduling, and it is able to smooth out the real-time power fluctuations of the grid, demonstrating the potential of V2G in providing the regulation service.published_or_final_versio

Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems

Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management

Provision of secondary frequency regulation by coordinated dispatch of industrial loads and thermal power plants

Demand responsive industrial loads with high thermal inertia have potential to provide ancillary service for frequency regulation in the power market. To capture the benefit, this study proposes a new hierarchical framework to coordinate the demand responsive industrial loads with thermal power plants in an industrial park for secondary frequency control. In the proposed framework, demand responsive loads and generating resources are coordinated for optimal dispatch in two-time scales: (1) the regulation reserve of the industrial park is optimally scheduled in a day-ahead manner. The stochastic regulation signal is replaced by the specific extremely trajectories. Furthermore, the extremely trajectories are achieved by the day-ahead predicted regulation mileage. The resulting benefit is to transform the stochastic reserve scheduling problem into a deterministic optimization; (2) a model predictive control strategy is proposed to dispatch the industry park in real time with an objective to maximize the revenue. The proposed technology is tested using a real-world industrial electrolysis power system based upon Pennsylvania, Jersey, and Maryland (PJM) power market. Various scenarios are simulated to study the performance of the proposed approach to enable industry parks to provide ancillary service into the power market. The simulation results indicate that an industrial park with a capacity of 500 MW can provide up to 40 MW ancillary service for participation in the secondary frequency regulation. The proposed strategy is demonstrated to be capable of maintaining the economic and secure operation of the industrial park while satisfying performance requirements from the real world regulation market

Incentive Design for Direct Load Control Programs

We study the problem of optimal incentive design for voluntary participation of electricity customers in a Direct Load Scheduling (DLS) program, a new form of Direct Load Control (DLC) based on a three way communication protocol between customers, embedded controls in flexible appliances, and the central entity in charge of the program. Participation decisions are made in real-time on an event-based basis, with every customer that needs to use a flexible appliance considering whether to join the program given current incentives. Customers have different interpretations of the level of risk associated with committing to pass over the control over the consumption schedule of their devices to an operator, and these risk levels are only privately known. The operator maximizes his expected profit of operating the DLS program by posting the right participation incentives for different appliance types, in a publicly available and dynamically updated table. Customers are then faced with the dynamic decision making problem of whether to take the incentives and participate or not. We define an optimization framework to determine the profit-maximizing incentives for the operator. In doing so, we also investigate the utility that the operator expects to gain from recruiting different types of devices. These utilities also provide an upper-bound on the benefits that can be attained from any type of demand response program.Comment: 51st Annual Allerton Conference on Communication, Control, and Computing, 201