A novel user-centric decentralized multi-objective energy management system for energy communities

Journal Article (2025)
Author(s)

B. Ahmadi (University of Twente)

Aditya Pappu (University of Twente)

Gerwin Hoogsteen (University of Twente)

Marco E.T. Gerards (University of Twente)

Johann L. Hurink (University of Twente)

Affiliation
External organisation
DOI related publication
https://doi.org/10.1016/j.segan.2025.101936
More Info
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Publication Year
2025
Language
English
Affiliation
External organisation
Volume number
44

Abstract

This paper presents a decentralized energy management approach based on a Multi-Objective Energy Management System called DMOEMS, designed for Energy Communities (ECs), aiming to create resilient and sustainable energy systems. DMOEMS integrates a multi-objective optimization framework that aggregates conflicting goals–minimizing electricity cost and CO
2, reducing Photovoltaic (PV) curtailment, and maximizing self-consumption–by converting them into a single objective using user-defined weight factors. Each local controller optimizes the operation of distributed assets based on localized constraints and user preferences, while an EC controller coordinates aggregated power profiles through an iterative feedback mechanism. This coordination dynamically adjusts weight factors and curtailment strategies to resolve grid congestion without compromising individual privacy. Simulation studies on the realistic Aardehuizen EC demonstrate that DMOEMS effectively mitigates overloading scenarios across diverse operating conditions (high EV charging, normal demand, and excess PV generation), enhances user satisfaction, reduces operational costs, and lowers CO
2 emissions. The proposed framework highlights the potential of a democratic, decentralized approach to energy management in modern ECs. The numerical results for asset management using DMOEMS indicate improvements in different aspects such as reduction of 20 % in CO
2 emissions, improvement of 4 % in electricity cost savings, and a 30 % reduction in PV curtailment relative to baseline scenarios. Furthermore, the proposed mechanism in the DMOEMS shows improvement in computational cost by converging faster to resolve grid congestion compared to conventional approaches.

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