Day-Ahead Optimal Management of Plug-in Hybrid Electric Vehicles in Smart Homes Considering Uncertainties

Hasankhani, Arezoo; Hakimi, Seyed Mehdi; Bodaghi, Maryam; Shafie‐khah, Miadreza; Osório, Gerardo J.; Catalào, João P. S.

doi:10.1109/powertech46648.2021.9494896

Cited by 2 publications

(1 citation statement)

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“…The intensive development of electromobility, especially the issue of integrating plugin hybrid electric vehicles (PHEVs) into a low-voltage electricity grid, introduces new challenges and opportunities for distribution network operators and PHEV owners. An algorithm for managing PHEV charging with the aim of minimising the total cost and flattening the demand curve is presented in [18]. The results verify the effectiveness of the proposed method in reducing the total costs with the management algorithm for PHEVs, especially when energy from the PHEV storage is discharged (sold to the grid).…”

Section: Introductionmentioning

confidence: 73%

Multicriteria Optimisation of the Structure of a Hybrid Power Supply System for a Single-Family Housing Estate in Poland, Taking into Account Different Electromobility Development Scenarios

Tomczewski

Mikulski

Piotrowski³

et al. 2023

Energies

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This article focuses on determining the optimum structure for a hybrid generation and storage system designed to power a single-family housing estate, taking into account the different number of electric vehicles in use and an assumed level of self-consumption of the generated energy. In terms of generation, two generation sections—wind and solar—and a lithium-ion container storage system will be taken into account. With regards to energy consumption, household load curves, determined on the basis of the tariff for residential consumers and modified by a random disturbance, will be taken into account, as well as the processes for charging electric cars with AC chargers, with power outputs ranging between 3.6 and 22 kW. Analyses were carried out for three locations in Poland—the Baltic Sea coast (good wind conditions), the Lublin Uplands (the best insolation in Poland) and the Carpathian foothills (poor wind and insolation conditions). The mathematical and numerical model of the system and the MOPSO (multiobjective particle swarm optimisation) algorithm were implemented in the Matlab environment. The results include Pareto fronts (three optimisation criteria: minimisation of energy storage capacity, minimisation of energy exchanged with the power grid and maximisation of the self-consumption rate) for the indicated locations and three electromobility development scenarios with determined NPVs (net present values) for a 20-year lifetime. The detailed results relate to the inclusion of an additional expert criterion in the form of a coupled payback period of no more than 10 years, a maximum NPV in the last year of operation and a self-consumption rate of at least 80%. The economic calculations take into account the decrease in PV installation capacity as a function of the year of operation, as well as changes in electricity and petrol prices and variations in energy prices at purchase and sale.

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Section: Introductionmentioning

confidence: 73%