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The development of electric vehicle (EV) charging stations has been a key consideration for enabling the evolution of EV technology and continues to support the fosterage of this technology. Notably, fast charging enhances the EV user's adaptability by reducing the charging time and supporting long-mile travel. The optimal operation and erratic power demand of a fast charging station (FCS) are still challenging. It is necessary to understand EV charging scheduling and FCS management, which can jointly overcome the problem of EV users on account of optimal operation. However, joint optimization needs detailed future information, which is a formidable task for prediction. This paper aims to address the joint optimization issue using combined game theory and the Lyapunov optimization approach. This hybrid approach could ease the data forecast requirement and minimize the operating costs of FCSs while optimally dispatching EVs to FCSs and satisfying their energy demand. Further, the problem is decomposed into three subproblems. The first subproblem addresses a network of FCSs that try to maximize their revenue through a dynamic pricing game with EV customers who have different behavioral responses to the prices. The pricing game determines the electricity selling prices in a distributed manner as well as the energy demand of users. Subsequently, EVs are assigned to local FCSs, taking into account the distance from and the queue at the stations. Finally, the third subproblem exploits Lyapunov optimization to control the operation cost of each FCS, considering the impact of demand charges. In this paper, the proposed method is validated through a numerical analysis using the real data of FCSs in Boulder, Colorado. Moreover, the presented results revealed that the proposed method is efficient regarding dynamic pricing and optimal allocation of EVs to stations.

Joint optimization of electric vehicle (EV) fast charging and DC fast charging (DCFC) station problems is presented in this paper. An electro-thermal-aging model is used for modeling EV's battery cell. The model is then expanded from a single cell to a battery pack based on the structure of commercially available EV batteries in the market. Moreover, the limitations imposed by the DCFC station are properly considered in the problem's constraints, including restrictions due to the cost of electricity, especially demand charge, and port power rating of chargers. The station's profit is maximized, and its cost is minimized considering demand charge and energy charge tariffs. Finally, the problem is discretized and numerically evaluated in a detailed case study.

In this paper, an optimal power allocation strategy of a fast charging station (FCS) is presented to maximize the quality of service (QoS) of electric vehicle (EV) charging when the charging demand is greater than the available power at the station. In this regard, the problem is formulated in the form of a Lyapunov optimization problem that enables solving the problem online while eliminating the need for future information. The QoS is modeled through Jain's fairness index to account for the charging time increase for each customer due to the power limit. Moreover, each EV's demand is characterized by the amount of charging power. The power is calculated with the help of a second-order equivalent circuit model of the battery pack of the EV. Lastly, the problem is evaluated in a case study.

© 2019 Elsevier Ltd As the number of electric vehicles (EVs) is steadily increasing, their aggregation can offer significant storage to improve the electric system operation in many aspects. To this end, a comprehensive stochastic optimization framework is proposed in this paper for the joint operation of a fleet of EVs with a wind power producer (WPP) in a three-settlement pool-based market. An aggregator procures enough energy for the EVs based on their daily driving patterns, and schedules the stored energy to counterbalance WPP fluctuations. Different sources of uncertainty including the market prices and WPP generation are modeled through proper scenarios, and the risk is hedged by adding a risk measure to the formulation. To obtain more accurate results, the battery degradation costs are also included in the problem formulation. A detailed case study is presented based on the Iberian electricity market data as well as the technical information of three different types of EVs. The proposed approach is benchmarked against the disjoint operation of EVs and WPP. Numerical simulations demonstrate that the proposed strategy can effectively benefit EV owners and WPP by reducing the energy costs and increasing the profits.

© 2017 IEEE. Offering strategy of a price-maker demand response aggregator (DRA) in a two-settlement market is presented in this paper. The aggregator minimizes its cost by offering energy and price bids in the day-ahead market and energy bids in the balancing market. On the other hand, DRA optimally manages the aggregated demands of a large number of electric vehicles and properly distributes them through the time. The problem is formulated as a stochastic mixed-integer nonlinear optimization problem. The risk of the problem is managed by conditional value-at-risk measure and finally, the proposed approach is numerically evaluated through a detailed case study.

© 2017 IEEE. This paper presents a new framework for risk consideration of participants in electricity markets. Current approaches add risk measures to stochastic problem formulation in order to control the variation of the profits (costs). However, in the proposed method, cumulative distribution function of profit is utilized and it is shaped based on the preferred risk levels of the producer (buyer). The features of this new method are compared with the current approaches based on a detailed case study performed on the problem of a producer participating in a three settlement market.