Bi-directional coordination of plug-in electric vehicles with economic model predictive control

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-To-vehicle (G2V) and vehicle-To-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%

Effectiveness of smart charging of electric vehicles under power limitations

This article investigates charging strategies for plug-in hybrid electric vehicles (PHEV) as part of the energy system. The objective was to increase the combined all-electric mileage (total distance driven using only the traction batteries in each PHEV) when the total charging power at each workplace is subject to severe limitations imposed by the energy system. In order to allocate this power optimally, different input variables, such as state-of-charge, battery size, travel distance, and parking time, were considered. The required vehicle mobility was generated using a novel agent-based model that describes the spatiotemporal movement of individual PHEVs. The results show that, in the case of Helsinki (Finland), smart control strategies could lead to an increase of over 5% in the all-electric mileage compared to a no-control strategy. With a high prediction error, or with a particularly small or large battery, the benefits of smart charging fade off. Smart PHEV charging strategies, when applied to the optimal allocation of limited charging power between the cars of a vehicle fleet, seem counterintuitively to provide only a modest increase in the all-electric mileage. A simple charging strategy based on allocating power to PHEVs equally could thus perform sufficiently well. This finding may be important for the future planning of smart grids as limiting the charging power of larger PHEV fleets will sometimes be necessary as a result of grid restrictions.Peer reviewe

Optimal Scheduling of Home Energy Management System with Plug-in Electric Vehicles Using Model Predictive Control

abstract: With the growing penetration of plug-in electric vehicles (PEVs), the impact of the PEV charging brought to the utility grid draws more and more attention. This thesis focused on the optimization of a home energy management system (HEMS) with the presence of PEVs. For a household microgrid with photovoltaic (PV) panels and PEVs, a HEMS using model predictive control (MPC) is designed to achieve the optimal PEV charging. Soft electric loads and an energy storage system (ESS) are also considered in the optimization of PEV charging in the MPC framework. The MPC is solved through mixed-integer linear programming (MILP) by considering the relationship of energy flows in the optimization problem. Through the simulation results, the performance of optimization results under various electricity price plans is evaluated. The influences of PV capacities on the optimization results of electricity cost are also discussed. Furthermore, the hardware development of a microgrid prototype is also described in this thesis.Dissertation/ThesisMasters Thesis Engineering 201

Mining electric vehicle adoption of users

Rodrigues, R., Albuquerque, V., Ferreira, J. C., Dias, M. S., & Martins, A. L. (2021). Mining electric vehicle adoption of users. World Electric Vehicle Journal, 12(4), 1-31. [233]. https://doi.org/10.3390/wevj12040233 ------------------------------------------------------------------------------------- Funding Information: Funding: This research was funded by the Foundation for Science and Technology (FCT) through ISTAR-IUL’s project UIDB/04466/2020 and UIDP/04466/2020. Funding Information: Acknowledgments: J.C.F. received support from the Portuguese National Funds through FITEC— Programa Interface, with reference CIT INOV—INESC INOVAÇÃO—Financiamento Base. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The increase of greenhouse gas emissions into the atmosphere, and their adverse effects on the environment, have prompted the search for alternative energy sources to fossil fuels. One of the solutions gaining ground is the electrification of various human activities, such as the transport sector. This trend has fueled a growing need for electrical energy storage in lithium batteries. Precisely knowing the degree of degradation that this type of battery accumulates over its useful life is necessary to bring economic benefits, both for companies and citizens. This paper aims to answer the current need by proposing two research questions about electric motor vehicles. The first focuses on habits EV owners practice, which may harm the battery life, and the second on factors that may keep consumers from purchasing this type of vehicle. This research work sought to answer these two questions, using a methodology from data science and statistical analysis applied to three surveys carried out on electric vehicle owners. The results allowed us to conclude that, except for the Year variable, all other factors had a marginal effect on the vehicles’ absolute autonomy degradation. Regarding obstacles of the adoption of electric vehicles, the biggest encountered was the insufficient coverage of the network of charging stations.publishersversionpublishe

Mining electric vehicle adoption of users

The increase of greenhouse gas emissions into the atmosphere, and their adverse effects on the environment, have prompted the search for alternative energy sources to fossil fuels. One of the solutions gaining ground is the electrification of various human activities, such as the transport sector. This trend has fueled a growing need for electrical energy storage in lithium batteries. Precisely knowing the degree of degradation that this type of battery accumulates over its useful life is necessary to bring economic benefits, both for companies and citizens. This paper aims to answer the current need by proposing two research questions about electric motor vehicles. The first focuses on habits EV owners practice, which may harm the battery life, and the second on factors that may keep consumers from purchasing this type of vehicle. This research work sought to answer these two questions, using a methodology from data science and statistical analysis applied to three surveys carried out on electric vehicle owners. The results allowed us to conclude that, except for the Year variable, all other factors had a marginal effect on the vehicles’ absolute autonomy degradation. Regarding obstacles of the adoption of electric vehicles, the biggest encountered was the insufficient coverage of the network of charging stations.info:eu-repo/semantics/publishedVersio

Design of a controller for a building from the tertiary sector associated to electric vehicles.

Currently the transport sector is dominated by the fossil fuels, bringing a high dependence on this specific source. The introduction of the electric vehicle offers an opportunity for avoiding this dependence, as there is more diversification on the energysources for this new type of transport. However, its introduction also brings new challenges and risks, a new type of load appears and it cannot be left unmanaged because the electricity demand will increase significantly. In order to confront these issues energy management systems will be required together with the load forecast, both will play an important role in a near future for ensuring the correct operation of the grid. In this work, the energy consumption from a building fromthe tertiary sectorwhich hasintroduced bidirectional chargersis studie