Machine Learning-based Sizing of a Renewable-Battery System for Grid-Connected Homes with Fast-Charging Electric Vehicle
Journal article, 2023

This paper develops a sizing model of solar photovoltaic (SPV), small wind turbine (SWT) and battery storage system (BSS) for a grid-connected home with a fast-charging plug-in electric vehicle (PEV). The home trades energy with the main grid under time-of-use tariffs for selling and purchasing electricity that affects the energy management. In this paper, a practical rule-based operation strategy is developed for the grid-connected home with fast-charging PEV that enables efficient and cheap energy management. The sizing problem is solved using a supervised machine learning algorithm, which is a feed forward neural network, by minimizing the cost of electricity. While the developed renewable-battery sizing model is general, it is examined using actual data of insolation, wind speed, temperature, load, grid constraints, as well as technical and economic data of BSS, SPV, SWT, and PEV in Australia. Uncertainty analysis is investigated based on ten scenarios of data for wind speed, temperature, load, insolation, and PEV. The effectiveness of the proposed model with fast-charging PEV is verified by comparing to slow charging and uncontrolled fast-charging models, as well as two other machine learning methods and a metaheuristic algorithm. It is found that the proposed model decreases the cost of electricity by 10.1% and 19.6% compared to slow charging and uncontrolled fast-charging models for the grid-connected home with PEV.

Costs

Load modeling

optimal sizing

Tariffs

Batteries

Energy management

distributed renewable energy

Uncertainty

electric vehicle

Degradation

machine learning

fast-charging

Battery

Author

Rahmatollah Khezri

Chalmers, Electrical Engineering, Electric Power Engineering

Peyman Razmi

University of Porto

Amin Mahmoudi

Flinders University

Ali Bidram

University of New Mexico

Mohammad Hassan Khooban

Aarhus University

IEEE Transactions on Sustainable Energy

1949-3029 (ISSN) 19493037 (eISSN)

Vol. 14 2 837-848

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/TSTE.2022.3227003

More information

Latest update

4/26/2023