The growing complexity of micro-grid management and the demand for resilient, sustainable energy systems require solutions that go beyond traditional management strategies. This paper introduces a Multi-Objective Energy Management System (MOEMS) designed for micro-grids and energy communities, emphasizing resilience and sustainability. Unlike conventional Energy Management Systems (EMS), which mainly focus on cost and efficiency, MOEMS takes a user-centered approach. It incorporates a democratic decision-making process that involves all stakeholders, enabling personalized energy management tailored to user preferences and environmental considerations. MOEMS is used to address grid challenges like power congestion and voltage issues while balancing diverse stakeholder goals. The optimization problem is formulated as a mixed-integer non-linear program and adopted to be solved using a free and open-source solver. The proposed framework leverages the results of a multi-objective optimization model, allowing users to define their preferences. By specifying an acceptable solution space, the central controller in the micro-grid can optimize operations while ensuring that the selected solutions align with user expectations. The system is validated through simulations and a real-world micro-grid case study, demonstrating its adaptability to different setups. To evaluate its effectiveness, MOEMS is compared with traditional EMS approaches, including profile steering and methods that prioritize economic and environmental factors. A real-time implementation in the Kezo micro-grid further demonstrates its capability to dynamically manage energy flows, meet user energy demands, and adapt to real-time fluctuations in supply and demand. Significantly, MOEMS achieved up to 22% higher annual electricity cost savings and a 37% reduction in CO2 emissions compared to traditional EMS methods.

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.

Centralized optimization methods have been widely used to manage the operation of distribution systems. However, these methods have some restrictions, such as the high computational cost, the reliance on a centralized computer system, and the lack of data privacy. To overcome these limitations, decentralized optimization methods have been proposed in recent years. Decentralized methods divide the control variables of the centralized optimization problem over several controllers, and each controller solves its own subproblem independently. This paper presents a multi-objective optimization framework for managing the operation of distribution systems. The primary objective of the proposed method is to optimize the tap positions of voltage regulators and charging and discharging powers for energy storage devices locally through a decentralized coordination process. The used objective functions take into account the voltage profile within the network, the lifetime of the devices, and the energy losses in the systems. Two decentralized methods, based on the Advanced Arithmetic Optimizer algorithm and the Profile Steering approach, are proposed to address the limitations of centralized optimization methods. The decentralized methods aim to improve the reliability and efficiency of the optimization process, while also minimizing communication and computational costs. The proposed methods are evaluated and compared to a centralized approach using the IEEE 33 and 69 bus systems. The results demonstrate that the proposed decentralized methods can effectively resolve voltage problems, minimize energy losses, and find high-quality solutions with improved computational efficiency compared to the centralized approach.