4,967 research outputs found
Distributed smart charging of electric vehicles for balancing wind energy
To meet worldwide goals of reducing CO2 footprint, electricity production increasingly is stemming from so-called renewable sources. To cater for their volatile behavior, so-called demand response algorithms are required. In this paper, we focus particularly on how charging electrical vehicles (EV) can be coordinated to maximize green energy consumption. We present a distributed algorithm that minimizes imbalance costs, and the disutility experienced by consumers. Our approach is very much practical, as it respects privacy, while still obtaining near-optimal solutions, by limiting the information exchanged: i.e. consumers do not share their preferences, deadlines, etc. Coordination is achieved through the exchange of virtual prices associated with energy consumption at certain times. We evaluate our approach in a case study comprising 100 electric vehicles over the course of 4 weeks, where renewable energy is supplied by a small scale wind turbine. Simulation results show that 68% of energy demand can be supplied by wind energy using our distributed algorithm, compared to 73% in a theoretical optimum scenario, and only 40% in an uncoordinated business-as-usual (BAU) scenario. Also, the increased usage of renewable energy sources, i.e. wind power, results in a 45% reduction of CO2 emissions, using our distributed algorithm
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Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California
This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits
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Utilizing Highway Rest Areas for Electric Vehicle Charging: Economics and Impacts on Renewable Energy Penetration in California
California policy is incentivizing rapid adoption of zero emission electric vehicles for light-duty and freight applications. This project explored how locating charging facilities at California’s highway rest stops might impact electricity demand, grid operation, and integration of renewables like solar and wind into California’s energy mix. Assuming a growing population of electric vehicles to meet state goals, state-wide growth of electricity demand was estimated, and the most attractive rest stop locations for siting chargers identified. Using a California-specific electricity dispatch model developed at UC Davis, the project estimated how charging vehicles at these stations would impact renewable energy curtailment in California. It estimated the impacts of charging infrastructures on California’s electricity system and how they can be utilized to decrease the duck curve effect resulting from a large amount of solar energy penetration by 2050.View the NCST Project Webpag
The impact of domestic plug-in hybrid electric vehicles on power distribution system loads
The market for Plug-in Hybrid Electric Vehicle (PHEVs) is expected to grow significantly over the next few years and a number of new products are soon to come onto the market, such as the Toyota Prius plug-in version, . The charging demand of wide-scale use of PHEVs may have a significant impact on domestic electricity loads and could risk of overloading the power system if appropriate charging strategies not applied to prevent this. A Monte Carlo Simulation (MCS) model of domestic PHEV use and availability has been developed based on probabilistic characterisations obtained from UKTUS and quantifies charging demand of PHEVs as a function of time of day. The MCS model has been developed in order to simulate the impact on the electricity distribution system. This article also discusses the potential for responsive battery charging load from PHEVs
The Innovation Interface: Business model innovation for electric vehicle futures
There is huge potential to link electric vehicles, local energy systems, and personal mobility in the city. By doing so we can improve air quality, tackle climate change, and grow new business models. Business model innovation is needed because new technologies and engineering innovations are currently far ahead of the energy system’s ability to accommodate them. This report explores new business models that can work across the auto industry, transport infrastructure and energy systems
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Uncertainty, Innovation, and Infrastructure Credits: Outlook for the Low Carbon Fuel Standard Through 2030
California’s low carbon fuel standard (LCFS) specifies that the state’s transportation fuel supply achieve a 20% reduction in carbon intensity (CI) below 2011 levels by 2030. Reaching the standard will require substantive changes in the fuel mix, but the specifics and the cost of these changes are uncertain. We assess if and how California is likely to achieve the standard, and the likely impact of infrastructure credits on this compliance outlook. We begin by projecting a distribution of fuel and vehicle miles demand under business-as-usual economic and policy variation and transform those projections into a distribution of LCFS net deficits for the entire period from 2019 through 2030. We then construct a variety of scenarios characterizing LCFS credit supply that consider different assumptions regarding input markets, technological adoption over the compliance period, and the efficacy of complementary policies. In our baseline scenario for credit generation, LCFS compliance would require that between 60% and 80% of the diesel pool be produced from biomass. Our baseline projections have the number of electric vehicles reaching 1.3 million by 2030, but if the number of electric vehicles reaches Governor Jerry Brown’s goal of 5 million by 2030, then LCFS compliance would require substantially less biomass-based diesel. Outside of rapid zero emission vehicle penetration, compliance in 2030 with the $200 credit price may be much more difficult. New mechanisms to allow firms to generate credits by building electric vehicle charging stations or hydrogen fueling stations have minor implications for overall compliance because the total quantity of infrastructure credits is restricted to be relatively small
Large Scale Integration of Electric Vehicles into the Power Grid and Its Potential Effects on Power System Reliability
In this thesis, the potential effects of large scale integration of electric vehicles into the power grid are discussed in both the beneficial and detrimental aspects. The literature review gives a comprehensive introduction about the existing smart charging algorithms. According to the system structure and market mechanism, the smart charging algorithms can be divided into centralized and distributed method. With the knowledge of driving patterns and charging characteristics of electric vehicles, both the centralized and decentralized smart charging algorithms are studied in this research.
Based on the smart charging pricing and sequential price update mechanism, a multi-agent based distributed smart charging algorithm is used in this research to flatten the load curve and therefore mitigate the potential detrimental effects caused by uncoordinated charging. Each EV agent has some extent of intelligence to solve its own charging scheduling problem. The optimization method used in this research is the binary hybrid GSA-PSO algorithm, which combines the merits of particle swarm optimization (PSO) and gravitational search algorithm (GSA), and has very good exploration and exploitation abilities. A V2G enabled centralized smart charging algorithm is also introduced in this thesis, each EV can earn revenues by discharging power into the grid. The dominant search matrix is used to resolve the \u27\u27curse of dimensionality\u27\u27 problem existing in the centralized optimization problems. Numerical case studies show both the distributed and V2G enabled smart charging algorithms can effectively transfer the charging load from the peak load period to the load valley hours.
Because of the limited integration ratio of electric vehicles, most power system reliability methods do not evaluate the charging load of EVs separately in their analytical procedures. However, with a fast increasing integration level, the potential effects of large scale integration of EVs on the power system reliability should be comprehensively evaluated. The effects of EV charging on power system reliability in the planning phase is analyzed in this research based on the RBTS. The results show the uncontrolled charging will deteriorate the reliability level while the smart charging can effectively decrease the detrimental effect. The potential application of aggregated EV providing operating reserve to the grid as a kind of ancillary service is also discussed, and the related effects on power system reliability in operating phase are calculated using the modified PJM method. The case study shows the unit commitment risk of the system can decrease to a very low level with the additional operating reserve capacity provided by aggregated EVs, which can not only improve the system\u27s reliability level but also save the cost
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