Optimal battery management for vehicle-to-home and vehicle-to-grid operations in a residential case study
- 1. Fondazione LINKS - Leading Innovation & Knowledge for Societ, Via Pier Carlo Boggio, 61, 10138, Torino (Italy)
- 2. Politecnico di Torino - Dipartimento Energia "Galileo Ferraris", Corso Duca Degli Abruzzi, 24, 10129, Torino (Italy)
- 3. École Supérieure D'Électricité - SUPELEC, 3 Rue Joliot Curie, 91190, Gif-sur-Yvette (France)
Description
Highlights: • Optimal scheduling of EV battery to reduce electricity supply cost of households. • Monte Carlo simulation for considering statistical driver's habits and mobility needs. • Increase of PV self-consumption through Vehicle-to-Home operation. • Reduction of electricity supply cost by means of V2G and V2H operation. -- Abstract: In the mobility sector Electric Vehicles represent one of the main opportunities to ensure strong reduction of local pollution. However, their higher costs compared to gas-fuelled cars are still a barrier for their large diffusions. One possible solutions to increase EVs penetration is their use as storage within households equipped with Renewable Energy Sources enabling a flexible energy management, for instance by the Vehicle-to-Grid and/or Vehicle-to-Home scheme. The aim of this paper is to investigate possible management of the EV battery through an optimization approach capable to minimize the electricity supply costs for an Italian residential end-user with PV, considering battery constraints, such as driving habits. A statistical approach of driver behavior is integrated within the optimization approach to define some possible daily driving patterns. The optimization takes into consideration mobility needs, prices for selling and purchasing electricity and hourly electrical, heating and cooling load profiles of the household. On the basis of these constraints the optimizer identifies when PV overproduction can be either used to charge batteries or to partially cover the load demand of the household. Finally, economic and energy evaluations are performed under Monte Carlo simulations to highlight reliability of potential benefits for the household case study.
Additional details
Identifiers
- DOI
- 10.1016/j.energy.2019.03.113;
- PII
- S0360544219305171;
Publishing Information
- Journal Title
- Energy (Oxford)
- Journal Volume
- 175
- Journal Page Range
- p. 704-721
- ISSN
- 0360-5442
- CODEN
- ENEYDS
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 55017595
- Subject category
- S29: ENERGY PLANNING, POLICY AND ECONOMY;
- Descriptors DEI
- AUTOMOBILES; COMPUTERIZED SIMULATION; ELECTRICITY; ELECTRIC-POWERED VEHICLES; ENERGY MANAGEMENT; HEATING; MONTE CARLO METHOD; OPTIMIZATION; PRICES; RENEWABLE ENERGY SOURCES
- Descriptors DEC
- CALCULATION METHODS; ENERGY SOURCES; MANAGEMENT; SIMULATION; VEHICLES
Optional Information
- Copyright
- Copyright (c) 2019 Elsevier Ltd. All rights reserved.