Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks

García-Villalobos, J.; Zamora, I.; Knezović, Katarina; Marinelli, Mattia

doi:10.1016/j.apenergy.2016.07.110

Cited by 77 publications

(44 citation statements)

References 26 publications

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“…Although the approach proposed in the paper is effective in optimising the two objectives, the interaction with the grid was not investigated since no charging scheduling was implemented. Load variance and charging cost were minimised with a weighted sum method in [20] with a decentralised approach. Although some measures to reduce battery degradation were mentioned, i.e.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services

et al. 2020

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Electric vehicles and renewable energy sources are collectively being developed as a synergetic implementation for smart grids. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are seen as a way forward to achieve economic, technical and environmental benefits. The implementation of these technologies requires the cooperation of the end-electricity user, the electric vehicle owner, the system operator and policy makers. These stakeholders pursue different and sometime conflicting objectives. In this paper, the concept of multi-objective-techno-economicenvironmental optimisation is proposed for scheduling electric vehicle charging/discharging. End user energy cost, battery degradation, grid interaction and CO2 emissions in the home micro-grid context are modelled and concurrently optimised for the first time while providing frequency regulation. The results from three case studies show that the proposed method reduces the energy cost, battery degradation, CO2 emissions and grid utilisation by 88.2%, 67%, 34% and 90% respectively, when compared to uncontrolled electric vehicle charging. Furthermore, with multiple optimal solutions, in order to achieve a 41.8% improvement in grid utilisation, the system operator needs to compensate the end electricity user and the electric vehicle owner for their incurred benefit loss of 27.34% and 9.7% respectively, to stimulate participation in energy services. Highlights  Optimisation of energy cost, battery degradation, grid utilisation and CO2 emission  The conflicts among objectives were addressed with multi-objective optimisation  A multi-criteria decision making process was tailored to the stakeholders

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Section: Introductionmentioning

confidence: 99%

Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services

et al. 2020

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“…The decentralized strategies allow vehicle owners to schedule their charging. The centralized charging employs a controller that schedules the charging of multiple vehicles [173,174]. The centralized controller can be placed in the charging station or, in some cases, can be a separate entity known as an aggregator.…”

Section: Controlled Chargingmentioning

confidence: 99%

“…The controller is responsible for acquiring data about electricity prices, the grid situation as well as the state of charge (SOC) of the vehicles under control. The decentralized strategy can be easily scaled up to match the number of participating vehicles besides it is more tolerant to faults and requires less 275 computational power and communication lines [173]. On the other hand, the centralized strategy ensures optimal charging of EVs and provides more services to the grid operator and the electricity market [174].…”

Section: Controlled Chargingmentioning

confidence: 99%

Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review

Shepero

Widén

Bishop

et al. 2018

Renewable and Sustainable Energy Reviews

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Photovoltaics (PV) and electric vehicles (EVs) are promising technologies for increasing energy efficiency and the share of renewable energy sources in power and transport systems. As regards the deployment, use and system integration of these technologies, spatio-temporal modeling of PV power production and EV charging is of importance for several purposes such as urban planning and power grid design and operation. There is an abundance of studies and reviews on modeling of PV power production and EV charging available in the literature. However, there is a lack of studies that review the opportunities for combined modeling of the power consumption and production associated with these technologies. This paper aims to fill this research gap by presenting a review of previous research regarding modeling of spatio-temporal PV power production and charging load of EVs. The paper provides a summary of previous work in both fields and the combination of the fields. Finally, research gaps that need to be further explored are identified.This survey revealed some research gaps that need to be further addressed. Improving the accuracy of PV power production ramp-rate modeling in addition to quantifying the aggregate clear-sky index on cityscale are two priorities for the PV potential studies. For the EV charging load models, differences in model assumptions, such as charging locations, charging powers and charging profiles, need to be studied more extensively. Moreover, there is an imminent need for metering the load of charging stations. This is essential in developing accurate models and time series forecasting techniques. For studies exploring both the PV and EV impacts, local weak points in a spatial network need to be discovered, especially for the city-scale studies. Cooperation between eminent researchers in the PV and EV fields might propagate state-of-the-art models from the separate fields to the combined studies.vehicles has risen rapidly in the world, with over 565,000 plug-in cars sold globally 2011-2015 [6]. In the year 2016, the global sales of plug-in cars exceeded 750,000 vehicles [7]. Electric vehicles (EVs) is a term applied to hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs) [8]. In this paper, EVs refer only to PHEVs and BEVs.As an example, a target of 20 million EVs and fuel cell vehicles, on the road was set by the Electric 15 vehicle initiative (EVI) to be achieved in 2020 [9]. This target is expected to be surpassed if the 78% growth rate achieved in the year 2015 was maintained [10]. However in the year 2016, the annual growth rate of EV sales has fallen below 50% for the first time since 2010, and the annual growth rate of publicly available charging has increased by 72% [11]. The Electric and plug-in hybrid roadmap aims that EVs represent 50% of the sales of vehicles worldwide by 2050 [12]. This is to say that the current research is investigating 20 penetration levels of EVs that are expected to take place within decades, which is ...

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“…On the other hand, the decentralized architecture manages the charging process of the EVs locally. Decentralized controls present several advantages such as scalability, constant computational effort, and reduced communication requirements [11]. Besides, it allows each individual EV to minimize their respective charging costs, but, collectively, this may not be the optimal solution for the region, since there could be instabilities when all controllers react simultaneously to the measurements.…”

Section: Introductionmentioning

confidence: 99%

Strategies Comparison for Voltage Unbalance Mitigation in LV Distribution Networks Using EV Chargers

et al. 2019

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The increasing penetration of Electric Vehicles (EVs) in LV distribution networks can potentially cause voltage quality issues such as voltage unbalance and under-voltage conditions. According to the EV charger characteristics, some strategies can be adopted to mitigate the aforementioned effects. Smart decentralized charging controls seem to be a more practical solution than centralized controls, since there is no need for communication because they rely only on local measurements. The four most relevant decentralized charging strategies, two for single-phase and two for three-phase EV chargers, have been implemented in a typical three-phase four-wire European LV distribution network. Simulations have been carried out for scenarios with single-phase EV chargers, three-phase EV chargers, and a combination of both. Single-phase controls are aimed at under-voltage regulation, while three-phase controls are focused on mitigating voltage unbalance. Results show that the implementation of a decentralized EV charging control is an adequate solution for Distribution System Operators (DSOs) since it improves the reliability and security of the network. Moreover, even though decentralized charging control does not use any communication, the combination of three-phase and single-phase controls is able to mitigate voltage unbalance while preventing the under-voltage condition.

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Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks

Cited by 77 publications

References 26 publications

Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services

Multi-objective techno-economic-environmental optimisation of electric vehicle for energy services

Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review

Strategies Comparison for Voltage Unbalance Mitigation in LV Distribution Networks Using EV Chargers

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