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.

This paper studies a multi-objective optimization problem on the allocation problem of photovoltaic (PV) and battery energy storage systems (BESSs) in a community, whereby the aim is to find Pareto optimal solutions according to two different set of objective functions. These objective functions are minimizing the dependency of the whole community or each household on the national grid and minimizing the investment, operation, and maintenance costs of PV and BESS units. A Parallel Multi-Objective Multi-Verse optimization (PMOMVO) algorithm is developed to obtain the Pareto optimal solutions for the problems. The optimization framework is used to determine all Pareto front solutions in a real community and the results are compared to the base case scenario of the community. The Pareto solutions show that by small investment in the BESS units, community can be less dependent on the national grid even with less PV panels installed in the community.

This paper explores the effects of active energy communities, as defined by EU Directive 2019/944, on the energy system using simulations modelled based on a real-world ecological community. The energy flexibility of this community is optimized towards the objectives peak reduction, cost minimization, and CO2 emission minimization. The resulting effects for different stakeholders, such as network operators and the community itself, are investigated using different performance indicators. The results show that energy imports can be reduced by 44% when peak reduction is applied. Conflicting objectives may lead to peak synchronization however, with the risk of deteriorating business cases if the network operator needs to intervene. This results in a risk where energy communities abstain from participation in energy market and its benefits.