In this paper, an electric vehicle (EV) charging competition, among EV aggregators that perform coordinated EV charging, is explored while taking into consideration potential non-ideal actions of the aggregators. In the coordinated EV charging strategy presented in this paper, each aggregator determines EV charging start time and charging energy profiles to minimize overall EV charging energy cost by including consideration of the actions of the neighboring aggregators. The competitive interactions of the aggregators are modeled by developing a two-stage non-cooperative game among the aggregators. The game is then studied under prospect theory to examine the impacts of non-ideal actions of the aggregators in selecting EV charging start times according to subjectively evaluating their opponents' actions. It is shown that the non-cooperative interactions among the aggregators lead to a subgame perfect @math -Nash equilibrium when the game is played with either ideal, or non-ideal, actions of the aggregators. A case study presented demonstrates that the benefits of the coordinated EV charging strategy, in terms of energy cost savings and peak-to-average ratio reductions, are significantly resilient to non-ideal actions of the aggregators.
Game theory is also used in the context of distributed optimal operation in EV charging systems @cite_30 @cite_22 @cite_0 @cite_29 . Based on game theory, @cite_22 explores optimal price competition among EV charging stations with renewable energy generation to attract EVs. Using a non-cooperative game-theoretic approach, @cite_0 investigates an optimal demand response method for effective scheduling of plug-in hybrid EV charging. By investigating a non-cooperative game among self-interested EV owners, an optimal valley filling pricing mechanism to coordinate EV charging is proposed in @cite_1 . Using a non-cooperative game among EVs at a parking lot, @cite_18 studies an effective way of obtaining EV charging strategies at a unique Nash equilibrium under the constrained distribution transformer capacity. The non-cooperative Stackelberg game-theoretic approach in @cite_26 investigates the optimal bi-level grid-to-vehicle coordination between the power grid and plug-in EV groups with limited energy supply. Most of the game-theoretic demand-side management literature optimizes important aspects of various energy markets conforming to conventional game theory that fundamentally assumes rational choices by users.
@cite_30 @cite_18 @cite_26 @cite_22 @cite_29 @cite_1 @cite_0
2120121765 2444333355 2065343413 2162593703 1987809617 1980166891
This paper describes the development and experimental implementation of an energy management controller for hybrid electric vehicles (HEVs) based on the application of game theory (GT). This controller is constructed as a feedback Stackelberg equilibrium in the noncooperative game between the driver and the powertrain with the cost penalizing fuel consumption, NOx emissions, battery state of charge deviation, and vehicle operating conditions deviation. This control policy is drive-cycle and time independent. A description of the controller implementation with ancillary strategy elements is given. Experimental results from tests in a parallel HEV prototype vehicle are presented and compared with the existing baseline controller in terms of fuel consumption and NOx emissions. The HEV powertrain configuration is advanced and includes a high-speed diesel engine, two electric motors, and automated converterless transmission. Over the New European Driving Cycle (NEDC), the GT controller, with minimal calibration effort, demonstrates better performance than the existing baseline controller that is calibrated from the deterministic dynamic programing solution over NEDC. We also demonstrate that the GT controller substantially outperforms the baseline controller over other real-world-focused driving cycles while providing good drivability. Parking-lot electric vehicle (EV) charging promises reduced on-board battery capacity for commuters, which would decrease the payback time. However, the parking-lot EV charging scenario is rendered complicated by the large number of agents involved and highly dynamic price of electricity during the day. This study solves the parking-lot EV charging scheduling problem through a noncooperative game approach that considers the coupled constraint therein. The total charging amount is restrained by the transformer capacity. Such a coupled constraint makes the parking-lot EV charging game distinct from other EV charging scenarios. The theoretical framework of the Rosen–Nash normalized equilibrium is applied to deal with such a problem. The Nikaido–Isoda relaxation algorithm is used to calculate the equilibrium point. The dynamic game extension is then provided. Numerical simulation validates the proposed framework. Moreover, the impact of major parameters of the EV charging game on the equilibrium point that can be achieved is investigated. In this paper, the problem of grid-to-vehicle energy exchange between a smart grid and plug-in electric vehicle groups (PEVGs) is studied using a noncooperative Stackelberg game. In this game, on the one hand, the smart grid, which acts as a leader, needs to decide on its price so as to optimize its revenue while ensuring the PEVGs' participation. On the other hand, the PEVGs, which act as followers, need to decide on their charging strategies so as to optimize a tradeoff between the benefit from battery charging and the associated cost. Using variational inequalities, it is shown that the proposed game possesses a socially optimal Stackelberg equilibrium in which the grid optimizes its price while the PEVGs choose their equilibrium strategies. A distributed algorithm that enables the PEVGs and the smart grid to reach this equilibrium is proposed and assessed by extensive simulations. Further, the model is extended to a time-varying case that can incorporate and handle slowly varying environments. In this paper, we study the price competition among electric vehicle charging stations (EVCSs) with renewable power generators (RPGs). As electric vehicles (EVs) become more popular, a competition among EVCSs to attract EVs is inevitable. Thereby, each EVCS sets its electricity price to maximize its revenue by taking into account the competition with neighboring EVCSs. We analyze the competitive interactions between EVCSs using game theory, where relevant physical constraints such as the transmission line capacity, the distance between EV and EVCS, and the number of charging outlets at the EVCSs are taken into account. We show that the game played by EVCSs is a supermodular game and there exists a unique pure Nash equilibrium for best response algorithms with arbitrary initial policy. The electricity price and the revenue of EVCSs are evaluated via simulations, which reveal the benefits of having RPGs at the EVCSs. This paper proposes an operating framework for aggregators of plug-in electric vehicles (PEVs). First, a minimum-cost load scheduling algorithm is designed, which determines the purchase of energy in the day-ahead market based on the forecast electricity price and PEV power demands. The same algorithm is applicable for negotiating bilateral contracts. Second, a dynamic dispatch algorithm is developed, used for distributing the purchased energy to PEVs on the operating day. Simulation results are used to evaluate the proposed algorithms, and to demonstrate the potential impact of an aggregated PEV fleet on the power system.