Biblio

An auction-based game is formulated for coordinating the charging of a population of electric vehicles (EVs) over a finite horizon. The proposed auction requires individual EVs to submit bid profiles that have dimension equal to two times the number of time-steps in the horizon. They compete for energy allocation at each time-step. Use of the progressive second price (PSP) auction mechanism ensures that incentive compatibility holds for the auction game. However, due to cross-elasticity between the charging time-steps, the marginal valuation of an individual EV at a particular time is determined by both the demand at that time and the total demand over the entire horizon. This difficulty is addressed by partitioning the allowable set of bid profiles according to the total desired energy over the entire horizon. It is shown that the efficient bid profile over the charging horizon is a Nash equilibrium of the underlying auction game. A dynamic update mechanism for the auction game is designed. A numerical example demonstrates that the auction system converges to the efficient Nash equilibrium.

It has been a hot research topic to research the incorporation of large-scale PEVs into smart grid, such as the valley-fill strategy. However high charging rates under the valley-fill behavior may result in high battery degradation cost. Consequently in this paper, we novelly setup a framework to study a class of charging coordination problems which deals with the tradeoff between total generation cost and the accumulated battery degradation costs for all PEVs during a multi-time interval. Due to the autonomy of individual PEVs and the computational complexity for the system with large-scale PEV populations, it is impractical to implement the solution in a centralized way. Alternatively we propose a novel decentralized method such that each individual submits a charging profile, with respect to a given fixed price curve, which minimizes its own cost dealing with the tradeoff between the electricity cost and battery degradation cost over the charging interval; the price curve is updated based upon the aggregated PEV charging profiles. We show that, following the proposed decentralized price update procedure, the system converges to the unique efficient (in the sense of social optimality) solution under certain mild conditions.

Minimizing the grid impacts of large-scale plug-in electric vehicle (PEV) charging tends to be associated with coordination strategies that seek to fill the overnight valley in electricity demand. However such strategies can result in high charging power, raising the possibility of local overloads within the distribution grid and of accelerated battery degradation. The paper establishes a framework for PEV charging coordination that facilitates the tradeoff between total generation cost and the local costs associated with overloading and battery degradation. A decentralized approach to solving the resulting large-scale optimization problem involves each PEV minimizing their charging cost with respect to a forecast price profile while taking into account local grid and battery effects. The charging strategies proposed by participating PEVs are used to update the price profile which is subsequently rebroadcast to the PEVs. The process then repeats. It is shown that under mild conditions this iterative process converges to the unique, efficient (socially optimal) coordination strategy.

A novel class of auction-based games is formulated to study coordination problems arising from charging a population of electric vehicles (EVs) over a finite horizon. To compete for energy allocation over the horizon, each individual EV submits a multidimensional bid, with the dimension equal to two times the number of time-steps in the horizon. Use of the progressive second price (PSP) auction mechanism ensures that incentive compatibility holds for the auction games. However, due to the cross elasticity of EVs over the charging horizon, the marginal valuation of an individual EV at a particular time is determined by both the demand at that time and the total demand over the entire horizon. This difficulty is addressed by partitioning the allowable set of bid profiles based on the total desired energy over the entire horizon. It is shown that the efficient bid profile over the charging horizon is a Nash equilibrium of the underlying auction game. An update mechanism for the auction game is designed. A numerical example demonstrates that the auction process converges to an efficient Nash equilibrium. The auction-based charging coordination scheme is adapted to a receding horizon formulation to account for disturbances and forecast uncertainty.

As the population of electric vehicles (EVs) grows, coordinating their charging over a finite time horizon will become increasingly important. Recent work established a framework for EV charging coordination where a central node broadcast a price signal that facilitated the tradeoff between the total generation cost and local costs associated with battery degradation and distribution network overloading. This paper considers a completely distributed protocol where the central node is eliminated. Instead, a consensus algorithm is used to fully distribute the price update mechanism. Each EV computes a local price through its estimate of the total EV charging demand, and exchanges this information with its neighbours. A consensus algorithm establishes the average over all the EV-based prices. It is shown that under a reasonable assumption, the price update mechanism is a Krasnoselskij iteration, and this iteration is guaranteed to converge to a fixed point. Furthermore, this iterative process converges to the unique and efficient solution.

This paper considers the coordination of controlled loads in a framework that loads connect to the distribution network through transformers. Our objective is designing a decentralized control method that can motivate selfish loads to achieve global benefits. We formulate this problem as a hierarchical model. In the lower level, each transformer broadcasts a price signal to the loads connect to it, under which loads implement individual best strategies. While in the upper level, transformers communicate with the distribution network and obtain a price reflecting the system generation cost. Each transformer determines a price including this price and another part reflecting individual characteristics. By proposing a dynamic update algorithm, our results build that the system converges to the unique and efficient solution with fast convergence speed.