Stochastic optimal charging of electric-drive vehicles with renewable energy

Pantoš, Miloš

doi:10.1016/j.energy.2011.09.006

Cited by 82 publications

(34 citation statements)

References 20 publications

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“…The batteries of V2G vehicles are charged during a low demand and discharged during a shortage of a power generation in the system [41]. Analysis and testing for V2G were carried out using various models [42][43][44]. The smart charge model that includes V2G in the production system, allows charging V2G vehicles at the time of available CEEP and available battery capacity.…”

Section: Smart Charge Using V2gmentioning

confidence: 99%

The integration of renewable energy sources and electric vehicles into the power system of the Dubrovnik region

et al. 2015

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Background: In order to reduce greenhouse gas emissions, governments seek to replace conventional fuels by renewable ones. Nowadays, most attention is paid to electric vehicles in the transport systems and the use of renewable energy in the power systems. The aim of this work is to achieve a 100 % renewable and sustainable system and to examine the impact of electrification in the transport sector on the power curve and the integration of renewable energy into the power systems of the Dubrovnik region up to 2050. Methods: The analyses of different charging regulation models for the electric vehicles were derived in the EnergyPLAN, which is a computer model for Energy Systems Analysis of the major energy systems and runs on an hourly basis. Calculations were done for selected years-2020, 2030 and 2050. Charging models provided in the EnergyPLAN were dumb charge, flexible demand, smart charge and smart charge with vehicle-to-grid. For each year, two different charging models were selected. Charging regulations according to three tariff models, based on a lower and higher electricity price, with different distributions, were also done for 2050, i.e. tariff model 1, 2 and 3. Results: The results for the year 2020 showed no difference between the models. In 2030, smart charge gained better results than a flexible demand. In 2050, the flexible demand allowed to achieve better results than the smart charge with vehicle-to-grid and the tariff model 1, while tariff model 3 provided the best results for 2050. It is also shown that the energy systems which include electric vehicles have a greater impact on the reduction of a critical excess electricity production than the systems excluding electric vehicles.

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Section: Smart Charge Using V2gmentioning

confidence: 99%

The integration of renewable energy sources and electric vehicles into the power system of the Dubrovnik region

et al. 2015

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“…An EV can be considered a flexible load that can be charged throughout the day instead of following a rigid charging schedule [9,10]. The flexibility of the EV demand will improve the operation of power systems in terms of flattening the load curve on main substation transformers, providing, in addition, peak shaving services, reduced power system losses, reduced aging of transformers and lines, and increased renewable energy penetration [11] as well as providing financial support [9][10][11][12][13][14][15][16][17][18]. In an uncontrolled, or "dumb", scenario, EVs should be charged when the owner arrives at his home [16,19]; however, potential problems such as sudden peak demand or sudden overloading could be shaped or flattened by using a smart charging schedule for the EV's batteries [20].…”

Section: Introductionmentioning

confidence: 99%

“…The study was performed by taking into account the PEV owner's preferred fixed charging periods. The authors of reference [12] coordinated the charging of EVs to maximise the use of renewable energy in the transportation sector. Furthermore, they consider stochastic driving patterns and employ simple linear programming.…”

Section: Introductionmentioning

confidence: 99%

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

et al. 2014

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Transportation electrification has become an important issue in recent decades and the large scale deployment of electric vehicles (EVs) has yet to be achieved. The smart coordination of EV demand addresses an improvement in the flexibility of power systems and reduces the costs of power system investment. The uncertainty in EV drivers' behaviour is one of the main problems to solve to obtain an optimal integration of EVs into power systems. In this paper, an optimisation algorithm to coordinate the charging of EVs has been developed and implemented using a Genetic Algorithm (GA), where thermal line limits, the load on transformers, voltage limits and parking availability patterns are taken into account to establish an optimal load pattern for EV charging-based reliability. This methodology has been applied to an existing residential low-voltage system. The results indicate that a smart charging schedule for EVs leads to a flattening of the load profile, peak load shaving and the prevention of the aging of power system elements.

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“…The economic viability and business models of V2G technology have already been addressed [3], [4], and models have been presented regarding EV bidding and optimal charging strategies [5]. Some work has also been focused on the problem of optimally managing a microgrid including vehicle-to-grid interactions [6], [7], and in [8] a stochastic model was used to optimize the use of renewable sources to charge EVs. However, few studies address V2G benefits while analyzing DER investments at microgrids.…”

Section: Introductionmentioning

confidence: 99%

Stochastic programming of vehicle to building interactions with uncertainty in PEVs driving for a medium office building

Cardoso

Städler

Bozchalui³

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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Abstract-The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained with the stochastic formulation of the problem.

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Stochastic optimal charging of electric-drive vehicles with renewable energy

Cited by 82 publications

References 20 publications

The integration of renewable energy sources and electric vehicles into the power system of the Dubrovnik region

The integration of renewable energy sources and electric vehicles into the power system of the Dubrovnik region

Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms

Stochastic programming of vehicle to building interactions with uncertainty in PEVs driving for a medium office building

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