Deliverable D2.1 Control Strategies for V2X Integration in Houses

João Mateus, Samuel Matias, Francisco Branco, Hugo Morais, Manuel Pereira, Cindy Lascano, Herbert Amezquita, Mattia Marinelli, Charalampos Ziras, Matej Zajc, Igor Mendek, António Furtado, Tarcísio Silva, Carlos Martins, Andreja Smole

Research output: Book/ReportReportCommissioned

56 Downloads (Pure)


The Control Strategies for V2X Integration in Houses deliverable aims to create a decision-making model capable of integrating Vehicle-to-Everything (V2X) and Distributed Energy Resources (DER) / Renewable Energy Sources (RES) aspects in a Home Energy Management System (HEMS). The resulting decision-making model will be considered in the Portuguese demonstrator of the EV4EU project, in São Miguel Island, Azores, to test Vehicle-to-Home (V2H) smart charging and discharging techniques that benefit both Electric Vehicle (EV) using homeowners and utilities.

The methodology for the design and development of the proposed decision-making model includes
the following steps:
1. Data Collection: Post-processing information on EV usage behaviours, load demand profiles, network tariff frameworks, grid service activation, and weather conditions specific to the case of São Miguel Island. The aforementioned data serve as input for the decision-making model.
2. Forecast Module: The decision-making model includes a forecast module that features machine learning capabilities to predict EV user behaviour, weather conditions (i.e., solar PV power output), household energy consumption, and grid service activation. This module generates predictive day-ahead data.
3. Daily Planning Module: The decision-making model also includes a daily planning module computationally rooted in optimization algorithms. This module aims to produce control instructions that optimize a predefined goal, such as minimizing the overall operating cost of a household with an EV.
4. Real-time operation Module: Based on real-time data, previous control decisions, and the control instructions from the daily planning module, this module leverages information exchange and communication capabilities to control and monitor multiple EV charging and discharging actions.

To that effect, the main findings of this work are related to the capabilities of the decision-making algorithm for the integration of EVs into the energy system, with a focus on V2X integration in houses. The inclusion of a forecast and an optimization module can boost economic benefits for distinct parameters, namely, EV usage, load demand, and grid service participation. These benefits are enhanced as the flexibility and complexity of variables increase. Nevertheless, the computation time can escalate, which may limit the attainment of global optimums within the designated time window.

The capabilities of the developed control strategies have shown promising results in achieving substantial cost reductions. These optimization-based approaches have proven to be particularly effective, especially in terms of leveraging the sale of exported energy to the power grid. Implementing such algorithms can lead to enhanced financial benefits for the stakeholders involved in load flexibility. Also, this V2X integration into the energy system can reduce carbon emissions and promote sustainable energy practices.

Two main recommendations emerge as the inclusion of comprehensive studies on the overall operational cost (e.g., battery degradation) and forms of compensation for participation in grid services. The results of these studies on battery degradation would not only strengthen existing know-how relative to the techno-economic feasibility of V2X technology but also support the creation of new rules and penalties to be respectively integrated into the rule-based and optimization algorithms of the daily planning module, enabling a more complete decision-making model. Different forms of compensation are also envisaged, particularly those of economic nature, for the provision of EV supported grid services, which should preferably be conducted in association with flexibility operators and EV users.

These conclusions highlight the potential benefits, challenges, and areas for further research and development in the integration of EVs into the energy system, namely within the household scope, using the proposed decision-making algorithm.
Original languageEnglish
PublisherInstituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento
Number of pages43
Publication statusPublished - 2023


Dive into the research topics of 'Deliverable D2.1 Control Strategies for V2X Integration in Houses'. Together they form a unique fingerprint.

Cite this