Optimal Bidding/Offering Strategy for EV Aggregators under a Novel Business Model

Symbolism, signs, and accounts of electric vehicles in California

An empirically-validated methodology to simulate electricity demand for electric vehicle charging

Optimal coordination of plug-in hybrid electric vehicles: A stochastic market-based approach

With the increasing penetration of renewable energy resources and plug-in electric vehicles (PEV) in the smart grid, the problem of phase imbalance in a three-phase distribution system becomes more challenging. In this paper, we propose a phase balancing (PB) scheme performed by PEVs. With PB remunerations introduced as financial incentives, PEV owners are encouraged to assist the distribution system operator in reducing the phase imbalance. This is achieved by exploiting the charging flexibility of PEV batteries. We formulate this problem into a non-smooth non-cooperative game, where each PEV owner autonomously minimizes his/her charging cost minus the PB remuneration, considering decisions of other PEV owners. Through transforming this game into a smooth extended one, we are able to solve this challenging problem. Specifically, existence of its Nash equilibrium (NE) is demonstrated, and a distributed algorithm is developed to achieve the NE. Convergence proof of this algorithm is also provided. Simulation results validate our proposed scheme, and illustrate that both PEV owners and the distribution system operator will benefit financially. Moreover, the power quality and system reliability of the distribution network can also be improved.

As more battery electric vehicles and plug-in hybrid electric vehicles are connected to the microgrid, plug-in electric vehicles have a major impact on the microgrid. This paper proposes a multi-objective optimization energy management model including plug-in electric vehicles and other distributed generations. By analyzing the powertrain structure of different kinds of plug-in electric vehicles, the engine fuel consumption model, the charging model, the discharge model, and the battery state of charge model of plug-in electric vehicle in microgrids are given. The proposed model considers the plug-in electric vehicle battery state of charge constraints to prevent the battery from overcharging and over-discharging and gives the state of charge curve in microgrids. Simultaneously, an improved gray wolf algorithm, introducing optimization control factors and greedy strategies to better balance the mining and exploration capabilities of the gray wolf algorithm, is proposed to solve this multi-objective optimization energy management model. Compared with particle swarm optimization and traditional gray wolf algorithm, the improved algorithm further improves the accuracy and convergence speed. Besides, the improved algorithm is applied to three scheduling schemes, and the results show that plug-in hybrid electric vehicles have more advantages in energy economy in some special cases.

This article proposes a new planning framework for optimal allocation of parking lot (PL)-based charging infrastructures to facilitate the efficient integration of plug-in electric vehicles (PEVs). Unlike existing works, the present article explicitly considers the uncertain implications of incentive policy on PEV owners’ charging behaviors and its effects in PL planning. For this aim, a regret-matching technique is introduced to model the bounded rationality of PEV owners in deciding the choice to use different charging options for recharging their vehicle, as a dependency with respect to the incentive value and the accessibility of the charging service in long-term horizon. Such endogenous uncertainties are considered simultaneously with the inherent exogenous randomness of PEV demand and captured by the proposed PL planning model using a proper scenario generation method. The resulting model turns out to be a two-stage stochastic programming with decision-dependent uncertainties and it is solved by using the genetic algorithm. Numerical studies based on an illustrative test system verify the effectiveness of the proposed model and the approach.

Due to the energy savings and environmental protection they provide, plug-in electric vehicles (PEVs) are increasing in number quickly. Rapid development of PEVs brings new opportunities and challenges to the electricity distribution network’s dispatching. A high number of uncoordinated charging PEVs has significant negative impacts on the secure and economic operation of a distribution network. In this paper, a bi-level programming approach that coordinates PEVs’ charging with the network load and electricity price of the open market is presented. The major objective of the upper level model is to minimize the total network costs and the deviation of electric vehicle aggregators’ charging power and the equivalent power. The subsequent objective of the lower level model after the upper level decision is to minimize the dispatching deviation of the sum of PEVs’ charging power and their optimization charging power under the upper level model. An improved particle swarm optimization algorithm is used to solve the bi-level programming. Numerical studies using a modified IEEE 69-bus distribution test system including six electric vehicle aggregators verify the efficiency of the proposed model.

The number of offboard fast charging stations is increasing as plug-in electric vehicles (PEVs) are more widespread in the world. Additional features on the operation of chargers will result in more benefits for investors, utility companies, and PEV owners. This paper investigates reactive power support operation using offboard PEV charging stations while charging a PEV battery. The topology consists of a three-phase ac-dc boost rectifier that is capable of operating in all four quadrants. The operation modes that are of interest are power-factor-corrected charging operation, and charging and capacitive/inductive reactive power operation. This paper also presents a control system for the PQ command following of a bidirectional offboard charger. The controller only receives the charging power command from a user and the reactive power command (when needed) from a utility, and it adjusts the line current and the battery charging current correspondingly. The vehicle's battery is not affected during the reactive power operation. A simulation study is developed utilizing PSIM, and the control system is experimentally tested using a 12.5-kVA charging station design.

Author(s): Muehlegger, Erich; Rapson, David | Abstract: This research project explores the plug-in electric vehicle (PEV) market, including both Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), and the sociodemographic characteristics of purchasing households. The authors use detailed micro-level data on PEV purchase records to answer two primary research questions. Their results confirm that low-income households exhibit a lower share of PEV purchases than they do for conventional, internal combustion engine (ICE) vehicles. Households with annual income less than $50,000 comprise 33 percent of ICE purchases and only 14 percent of PEVS. By comparison, high-income households earning more than $150,000 annually comprise only 12 percent of ICE purchases and 35 percent of PEV purchases over their sample period. Similarly, unsurprising patterns can be seen across ethnicities. For example, non-Hispanic Whites represent 41 percent of ICE purchases but 55 percent of PEV purchases, as compared to Hispanics (38 percent of ICE and 10 percent of PEVs) and African Americans (3 percent of ICEs and 2 percent of PEVS). These differences naturally raise questions about barriers to PEV adoption among low-income and minority ethnic populations. By comparing outcomes in the ICE, hybrid, and PEV markets across income and ethnic groups, the authors are able to test whether price discrimination and barriers to market access are higher in PEV markets for low-income and minority ethnic groups. The authors find that, overall, they are not, although there are mixed results for the used PEV market. In general, non-white, low-income populations face higher prices in the used PEV market, relative to a baseline, than they do in the new PEV market. While some people travel farther to buy used PEVs than they do to buy used ICE vehicles, there is not a pattern that would indicate systematic discrimination (e.g. Hispanics travel farther to buy used PHEVs but less far to buy used BEVs). While the authors admit that their empirical approach cannot control for all potential vehicle composition effects, the authors view their results as being most consistent with a market that provides access to all ethnicities and income groups.View the NCST Project Webpage

Plug-in hybrid electric vehicles and smart grids: Investigations based on a microsimulation

This dissertation contributes to our understanding of how plug-in hybrid electric vehicles (PHEVs) and plug-in battery-only electric vehicles (EVs)—collectively termed plug-in electric vehicles (PEVs)—could be successfully integrated with the electric power system. The research addresses issues at a diverse range of levels pertaining to light-duty vehicles, which account for the majority of highway vehicle miles traveled, energy consumed by highway travel modes, and carbon dioxide emissions from on-road sources. Specifically, the following topics are investigated: (i) On-board power electronics topologies for bidirectional vehicle-to-grid and grid-to-vehicle power transfer; (ii) The estimation of the electric energy and power consumption by fleets of light-duty PEVs; (iii) An operating framework for the scheduling and dispatch of electric power by PEV aggregators; (iv) The pricing of electricity by PHEV aggregators and how it affects the decision-making process of a cost-conscious PHEV owner; (v) The impacts on distribution systems from PEVs under aggregator control; (vi) The modeling of light-duty PEVs for long-term energy and transportation planning at a national scale.

A multi-objective optimization algorithm is proposed in this paper to increase the penetration level of renewable energy sources (RESs) in distribution networks by intelligent management of plug-in electric vehicle (PEV) storage. The proposed algorithm is defined to manage the reverse power flow (PF) from the distribution network to the upstream electrical system. Furthermore, a charging algorithm is proposed within the proposed optimization in order to assure PEV owner’s quality of service (QoS). The method uses genetic algorithm (GA) to increase photovoltaic (PV) penetration without jeopardizing PEV owners’ (QoS) and grid operating limits, such as voltage level of the grid buses. The method is applied to a part of the Danish low voltage (LV) grid to evaluate its effectiveness and capabilities. Different scenarios have been defined and tested using the proposed method. Simulation results demonstrate the capability of the algorithm in increasing solar power penetration in the grid up to 50%, depending on the PEV penetration level and the freedom of the system operator in managing the available PEV storage.

Coordinated energy management plays a main role in increasing the performance and economic benefits of future smart homes. This article focuses on the energy management of a smart home equipped with a plug-in electric vehicle (PEV), household battery storage, and photovoltaics (PV), and it proposes an energy price tag (EPT) for all energy storage devices connected to the smart home system. A hybrid approach that consists of optimization and a rule-based prioritization is presented. The proposed algorithm establishes a priority order between the PEV, the household battery, and the imported power from the grid based on the EPT of energy sources. The proposed energy management algorithm seeks for the minimum overall energy cost for the smart home and PEV owner while satisfying household power demand and charging requirements of the storage devices. The performance of the energy management algorithm is examined on a typical smart home over a 24-h period based on time-varying grid electricity price.

In this article, we propose a decentralized energy management system to minimize the daily operating cost of microgrid and the net charging cost of individual plug-in electric vehicles (PEVs) while maintaining comfort level and privacy. A novel approach based on stochastic dynamic programming (SDP) with N-person noncooperative game-theoretic approach is used for real-time scheduling of heterogeneous PEVs both ways-in the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) mode. We use the SDP method for full enumeration of all possible decisions while game theory provides quick convergence to Nash-equilibrium. The proposed approach considers uncertainties of renewable generation as well as load demand and offers autonomy to the microgrid operators and PEVs owners to optimize their objectives individually. The effectiveness of the proposed approach has been tested on modified LV CIGRE and modified IEEE 33 bus test networks. The results have been compared with the deterministic and stochastic bi-level optimization. The simulation results exhibit that the proposed algorithm outperforms the other two algorithms in terms of operating cost, incentive to the PEVs owner, robustness to uncertainties, and convergence. The operating cost reduction achieved for the modified CIGRE model and modified IEEE 33 bus with the proposed approach is 20.02% and 36.53%, respectively, as compared to the deterministic framework, whereas 7.64% and 16.35% are the values obtained in comparison with the bi-level approach.

Plug-in electric vehicles (PEVs) are becoming increasingly popular. In the next several years, all major vehicle manufacturers will introduce PEVs, either as battery electric vehicles (BEVs), such as the Nissan Leaf, or plug-in hybrid electric vehicles (PHEVs) with a dual powertrain, such as the General Motors Volt and the Toyota Prius. This new generation of vehicles relies on electricity as a power source and a battery as an energy storage medium. Naturally, for PEVs, refueling occurs when the battery connects to an electrical outlet linked to a distribution network. The primary objective of this paper is to study optimal locations for building facilities (i.e., smart garages) that will maximize integrated benefits from the transportation system (i.e., parking fee) and the electric power system [i.e., revenue from vehicle-to-building (V2B) services]. A deterministic smart garage development problem (SGDP) is formulated and modeled as a bilevel program and solved by using a genetic algorithm. The results of a simple numerical example show the sensitivity of the model results with respect to parameters, including PEV penetration rate and battery capacity.