Integration of V2X in Energy Communities Management

7 de fevereiro de 2025

The deliverable D2.5 – "Integration of V2X in Energy Communities Management" aims to propose various methodologies for the optimal management of energy communities, considering user's needs. The proposed methodologies include deterministic, stochastic, and metaheuristic optimization. These approaches are tailored for an energy community comprising battery energy storage systems (BESS), photovoltaic systems (PV), electric vehicles (EVs), electric vehicles supply equipment (EVSEs), generators, main grid import/export power, loads, and offers of up and down power reserves. Additionally, the vehicle-to-everything (V2X) capabilities of EVs and EVSEs are incorporated into the optimization strategies. The methodology for the integration of V2X in energy communities’ management includes i) technical specifications related to loads, PV systems, generators, BESSs, EVs, and EVSEs. Key data points include power consumption, contracted power, peak PV power production, and charging/discharging capacities for BESSs and EVs. Additionally, it involves processing user behaviour data, such as arrival and departure times, energy requirements, initial state of charge (SoC), willingness to utilize V2X technology, as well as data related to energy prices; ii) energy community management considering a deterministic optimisation considering day-ahead scheduling and real-time control; iii) energy community management considering metaheuristic model in which the Dandelion Optimizer algorithm is analysed; iv) Energy community management is addressed through a stochastic optimization framework that incorporates up and down power reserve services from both grid imports and BESSs. By analyzing the obtained results, the performance of the proposed models can be validated. A use case (UC) representing an energy community with 20 consumers/providers was included. Notably, only the stochastic model incorporates up and down reserve power offers across 25 scenarios (combined 5 for load consumption and 5 for power generation). When compared to the metaheuristic and stochastic models, the deterministic model yields the best results due to the minimal uncertainty it addresses. It demonstrates optimal management of BESSs and EVs for charging and discharging. On the other hand, the metaheuristic model shows the poorest performance, consistently resulting in load reduction and curtailment. It also displays inefficiencies in the management of BESS and EV charging/discharging operations. The stochastic model, like the deterministic model, delivers optimal results in managing BESS and EV power. It prioritizes charging during periods of higher generator availability. However, due to the uncertainties it addresses, the stochastic model reduces BESS consumption. Nevertheless, it maintains the energy demand for EVs at the same level, ensuring that the comfort of e-mobility users is preserved.

Autores da comunidade :

• 

  H

  Hugo Gabriel Valente Morais

  ist428549

  Afiliações e fontes

  IST > DEEC

  Departamento de Engenharia Electrotécnica e de Computadores

  IST > INESC-ID

  Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento em Lisboa

10.57859/ulisboa-istinesc-id.000024

eng - Inglês

Entidade financiadora da bolsa/projeto

European Climate, Infrastructure and Environment Executive Agency

Nome da bolsa/projeto: Electric Vehicles Management for carbon neutrality in Europe

Fonte de financiamento: HORIZON EUROPE

Identificador da Entidade Financiadora: https://doi.org/10.13039/501100021050

Tipo de identificador da Entidade Financiadora: Crossref Funder

Número de bolsa/projeto: 101056765