Scenario-based Robust Scheduling for Electric Vehicle Charging Games

Fele, Filiberto; Margellos, Kostas

doi:10.1109/eeeic.2019.8783413

Cited by 8 publications

(6 citation statements)

References 36 publications

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“…As an example of application of the game K, consider an electric vehicle (EV) charging problem similar to, e.g., [21]. Assume that the ith player is an operator of a fleet of EVs (or example, for car sharing services), and x i represents the charging rate over a temporal horizon of n i = τ ∈ N times slots (e.g., intervals of 5 minutes) for the EVs.…”

Section: Noncooperative Minimax Gamementioning

confidence: 99%

“…From the definition of equilibrium points, we get that max Hence, (21) and the minimax equality imply that (x, ȳ) satisfy Definition 1.…”

Section: Define the Intermediate Functionsmentioning

confidence: 99%

“…This is the case in several applications that span finance, energy systems, transportation networks, and healthcare just to mention a few. For example, in the energy systems context, the problem of finding an optimal charging policy for a fleet of electric vehicles involves uncertainty in the price of energy; indeed, real-time prices are subject to change due to varying demand (which is the decision variable), as well on external factors such as spot market behavior [21].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Saddle Point Problems with Decision-Dependent Distributions

Wood¹,

Dall’Anese²

2022

Preprint

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This paper focuses on stochastic saddle point problems with decision-dependent distributions in both the static and time-varying settings. These are problems whose objective is the expected value of a stochastic payoff function, where random variables are drawn from a distribution induced by a distributional map. For general distributional maps, the problem of finding saddle points is in general computationally burdensome, even if the distribution is known. To enable a tractable solution approach, we introduce the notion of equilibrium points -which are saddle points for the stationary stochastic minimax problem that they induce -and provide conditions for their existence and uniqueness. We demonstrate that the distance between the two classes of solutions is bounded provided that the objective has a strongly-convex-strongly-concave payoff and Lipschitz continuous distributional map. We develop deterministic and stochastic primal-dual algorithms and demonstrate their convergence to the equilibrium point. In particular, by modeling errors emerging from a stochastic gradient estimator as sub-Weibull random variables, we provide error bounds in expectation and in high probability that hold for each iteration; moreover, we show convergence to a neighborhood in expectation and almost surely. Finally, we investigate a condition on the distributional map-which we call opposing mixture dominance-that ensures the objective is strongly-convex-strongly-concave. Under this assumption, we show that primal-dual algorithms converge to the saddle points in a similar fashion.

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Section: Noncooperative Minimax Gamementioning

confidence: 99%

“…From the definition of equilibrium points, we get that max Hence, (21) and the minimax equality imply that (x, ȳ) satisfy Definition 1.…”

Section: Define the Intermediate Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stochastic Saddle Point Problems with Decision-Dependent Distributions

Wood¹,

Dall’Anese²

2022

Preprint

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“…Therefore, improvement in the voltage profile and decrease in the power losses cost can be experimented by the power distribution operator, and customers are expected to make a profit [103]. Likewise, the EV aggregator coordinates charging and discharging strategies, taking into consideration EV driving patterns, features associated with the battery state of health [104], assessment of energy-efficient batteries [105], and unmodeled externalities acting on the energy price [106], with a direct effect on the aggregator's energy bids on the day-ahead market and its profitability [107]. On the other hand, sometimes the term "EV aggregator" is not used, and the DSR programs are directly performed by EV owners.…”

Section: Subperiod 2019mentioning

confidence: 99%

A Chronological Literature Review of Electric Vehicle Interactions with Power Distribution Systems

2020

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In the last decade, the deployment of electric vehicles (EVs) has been largely promoted. This development has increased challenges in the power systems in the context of planning and operation due to the massive amount of recharge needed for EVs. Furthermore, EVs may also offer new opportunities and can be used to support the grid to provide auxiliary services. In this regard, and considering the research around EVs and power grids, this paper presents a chronological background review of EVs and their interactions with power systems, particularly electric distribution networks, considering publications from the IEEE Xplore database. The review is extended from 1973 to 2019 and is developed via systematic classification using key categories that describe the types of interactions between EVs and power grids. These interactions are in the framework of the power quality, study of scenarios, electricity markets, demand response, demand management, power system stability, Vehicle-to-Grid (V2G) concept, and optimal location of battery swap and charging stations.

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“…The non-cooperative formulation is a Stackelberg game, while the cooperative one is a potential game. A charge scheduling problem is studied in [17], where the uncertainty resides in the price determination. The authors adopt a data-driven paradigm, where the demand-response aggregator is included as a player in the arising game.…”

Section: Introductionmentioning

confidence: 99%

Distributed Noncooperative MPC for Energy Scheduling of Charging and Trading Electric Vehicles in Energy Communities

Mignoni,

Carli,

Dotoli

2023

Preprint

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In this paper, we propose a novel control strategy for the optimal scheduling of an energy community constituted by prosumers and equipped with unidirectional vehicle-to-grid (V1G) and vehicle-to-building (V2B) capabilities. In particular, V2B services are provided by long-term parked electric vehicles (EVs), used as temporary storage systems by prosumers, who in turn offer the V1G service to EVs provisionally plugged into charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing them to improve the energy allocation process. Acting as selfish agents, prosumers and EVs interact in a rolling horizon control framework with the aim of achieving an agreement on their operating strategies. The resulting control problem is formulated as a generalized Nash equilibrium problem, addressed through the variational inequality theory, and solved in a distributed fashion leveraging on the accelerated distributed augmented Lagrangian method, showing sufficient conditions for guaranteeing convergence. The proposed model predictive control approach is validated through numerical simulations under realistic scenarios. This preprint has been accepted for publication in IEEE Transactions on Control Systems Technology. How to cite: N. Mignoni, R. Carli and M. Dotoli, "Distributed Noncooperative MPC for Energy Scheduling of Charging and Trading Electric Vehicles in Energy Communities," in IEEE Transactions on Control Systems Technology.

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Scenario-based Robust Scheduling for Electric Vehicle Charging Games

Cited by 8 publications

References 36 publications

Stochastic Saddle Point Problems with Decision-Dependent Distributions

Stochastic Saddle Point Problems with Decision-Dependent Distributions

A Chronological Literature Review of Electric Vehicle Interactions with Power Distribution Systems

Distributed Noncooperative MPC for Energy Scheduling of Charging and Trading Electric Vehicles in Energy Communities

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