We propose an emission-oriented charging scheme to evaluate the emissions of electric vehicle (EV) charging from the electricity sector at the region of Electric Reliability Council of Texas (ERCOT). We investigate both day- and night-charging scenarios combined with realistic system load demand under the emission-oriented vs direct charging schemes. Our emission-oriented charging scheme reduces carbon emissions in the day by 13.8 on average. We also find that emission-oriented charging results in a significant CO2 reduction in 30 of the days in a year compared with direct charging. Apart from offering a flat rebate for EV owners, our analysis reveals that certain policy incentives (e.g. pricing) regarding EV charging should be taken into account in order to reflect the benefits of emissions reduction that haven't been incorporated in the current market of electricity transactions.
Myriad studies discuss the environmental impacts of EV charging, but most neglect the fact that power plants are heterogeneous and there are different emission rates associated with these plants. Several reports that analyze the benefits of EV adoption assume that the electricity used to recharge the vehicles is generated from a particular type of power plant @cite_3 . Their studies conclude that cleaner power plants yield greater environmental benefits, which is as expected. These analyses are not useful in understanding the sensitivity in emission with respect to changes in EV charging demand at specific locations on the grid and times-of-the-day. A typical study in @cite_4 uses a regression method to estimate marginal emissions across different regions in the U.S. and the specific times of day. Although this is an improved method in quantifying GHG emission, it has drawbacks in that, for example, the marginal emission rate does not depend on the electricity demand estimated from regression. An another relevant work is that @cite_2 evaluate standard, delayed, off-peak, and continuous charging on emissions for Xcel in Colorado, which can be considered as an early attempt to understand the emission results due to various charging schemes.
@cite_4 @cite_3 @cite_2
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The vehicle electrification will have a significant impact on the power grid due to the increase in electricity consumption. It is important to perform intelligent scheduling for charging and discharging of electric vehicles (EVs). However, there are two major challenges in the scheduling problem. First, it is challenging to find the globally optimal scheduling solution which can minimize the total cost. Second, it is difficult to find a distributed scheduling scheme which can handle a large population and the random arrivals of the EVs. In this paper, we propose a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging. We first formulate a global scheduling optimization problem, in which the charging powers are optimized to minimize the total cost of all EVs which perform charging and discharging during the day. The globally optimal solution provides the globally minimal total cost. However, the globally optimal scheduling scheme is impractical since it requires the information on the future base loads and the arrival times and the charging periods of the EVs that will arrive in the future time of the day. To develop a practical scheduling scheme, we then formulate a local scheduling optimization problem, which aims to minimize the total cost of the EVs in the current ongoing EV set in the local group. The locally optimal scheduling scheme is not only scalable to a large EV population but also resilient to the dynamic EV arrivals. Through simulations, we demonstrate that the locally optimal scheduling scheme can achieve a close performance compared to the globally optimal scheduling scheme. While electric vehicles (EVs) are expected to provide environmental and economical benefit, judicious coordination of EV charging is necessary to prevent overloading of the distribution grid. Leveraging the smart grid infrastructure, the utility company can adjust the electricity price intelligently for individual customers to elicit desirable load curves. In this context, this paper addresses the problem of predicting the EV charging behavior of the consumers at different prices, which is a prerequisite for optimal price adjustment. The dependencies on price responsiveness among consumers are captured by a conditional random field (CRF) model. To account for temporal dynamics potentially in a strategic setting, the framework of online convex optimization is adopted to develop an efficient online algorithm for tracking the CRF parameters. The proposed model is then used as an input to a stochastic profit maximization module for real-time price setting. Numerical tests using simulated and semi-real data verify the effectiveness of the proposed approach. The penetration of electric vehicles becomes a catalyst for the sustainability of Smart Cities. However, unregulated battery charging remains a challenge causing high energy costs, power peaks or even blackouts. This paper studies this challenge from a socio-technical perspective: social dynamics such as the participation in demand-response programs, the discomfort experienced by alternative suggested vehicle usage times and even the fairness in terms of how equally discomfort is experienced among the population are highly intertwined with Smart Grid reliability. To address challenges of such a socio-technical nature, this paper introduces a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness. In contrast to related work, a novel autonomous software agent exclusively uses local knowledge to generate energy demand plans for its vehicle that encode different battery charging regimes. Agents interact to learn and make collective decisions of which plan to execute so that power peaks and energy cost are reduced system-wide. Evaluation with real-world data confirms the improvement of drivers' comfort and fairness using the proposed planning method, while this improvement is assessed in terms of reliability and cost reduction under a varying number of participating vehicles. These findings have a significant relevance and impact for power utilities and system operator on designing more reliable and socially responsible Smart Grids with high penetration of electric vehicles. The electricity and transportation industries are the main sources of greenhouse gas emissions on Earth. Renewable energy, mainly wind and solar, can reduce emission from the electricity industry (mainly from power plants). Likewise, next-generation plug-in vehicles, which include plug-in hybrid electric vehicles (EVs) and EVs with vehicle-to-grid capability, referred to as “gridable vehicles” (GVs) by the authors, can reduce emission from the transportation industry. GVs can be used as loads, energy sources (small portable power plants), and energy storages in a smart grid integrated with renewable energy sources (RESs). Smart grid operation to reduce both cost and emission simultaneously is a very complex task considering smart charging and discharging of GVs in a distributed energy source and load environment. If a large number of GVs is connected to the electric grid randomly, peak load will be very high. The use of traditional thermal power plants will be economically and environmentally expensive to support the electrified transportation. The intelligent scheduling and control of GVs as loads and or sources have great potential for evolving a sustainable integrated electricity and transportation infrastructure. Cost and emission reductions in a smart grid by maximum utilization of GVs and RESs are presented in this paper. Possible models for GV applications, including the smart grid model, are given, and results are presented. The smart grid model offers the best potential for maximum utilization of RESs to reduce cost and emission from the electricity industry.