1,670 research outputs found
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
Multiple Vickrey Auctions for Sustainable Electric Vehicle Charging
Electric vehicles (EVs) are important contributors to a sustainable future. However, uncontrolled EV charging in the smart grid is expected to stress its infrastructure, as it needs to accommodate extra electricity demand coming from EV charging. We propose an auction mechanism to optimally schedule EV charging in a sustainable manner so that the grid is not overloaded. Our solution has lower computational complexity, compared to state-of-the-art mechanisms, making it easily applicable to practice. Our mechanism creates electricity peak demand reduction, which is important for improving sustainability in the grid, and provides optimized charging speed design recommendations so that raw materials are not excessively used. We prove the optimal conditions that must hold, so that different stakeholder objectives are satisfied. We validate our mechanism on real-world data and examine how different trade-offs affect social welfare and revenues, providing a holistic view to grid stakeholders that need to satisfy potentially conflicting objectives
Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks
With the rapid acceleration of transportation electrification, public
charging stations are becoming vital infrastructure in a smart sustainable city
to provide on-demand electric vehicle (EV) charging services. As more consumers
seek to utilize public charging services, the pricing and scheduling of such
services will become vital, complementary tools to mediate competition for
charging resources. However, determining the right prices to charge is
difficult due to the online nature of EV arrivals. This paper studies a joint
pricing and scheduling problem for the operator of EV charging networks with
limited charging capacity and time-varying energy cost. Upon receiving a
charging request, the operator offers a price, and the EV decides whether to
admit the offer based on its own value and the posted price. The operator then
schedules the real-time charging process to satisfy the charging request if the
EV admits the offer. We propose an online pricing algorithm that can determine
the posted price and EV charging schedule to maximize social welfare, i.e., the
total value of EVs minus the energy cost of charging stations. Theoretically,
we prove the devised algorithm can achieve the order-optimal competitive ratio
under the competitive analysis framework. Practically, we show the empirical
performance of our algorithm outperforms other benchmark algorithms in
experiments using real EV charging data
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