This research is focused on microgrid design supporting tools and presents an innovative framework for renewable energy (RE) sources’ site selection, integrating multicriteria decision-making (MCDM) methods with resilience considerations and alignment to the Sustainable Development Goals (SDGs). It addresses present climatic challenges, identifies key causes of possible power failures, and develops strategies to mitigate their effects, while providing tools for energy managers and decision-makers to select suitable RE sources/technologies, based on geographical and sustainability criteria. The framework categorizes criteria into quantitative and qualitative types, adopting a cost ($\mathcal{C}$)- and benefit ($\mathcal{B}$)-based approach. The Analytic Hierarchy Process (AHP) calculates criteria weights to ensure accuracy and compatibility in decision-making, integrating SDG objectives into the evaluation process. This study focuses on five major RE options, photovoltaic (PV), wind, wave, tidal, and geothermal, analyzing more than 50 criteria for each energy type. This evaluation incorporates the expertise of over 50 experts and case studies, making it one of the most extensive research efforts in RE site selection. By systematically addressing resilience challenges and linking them with SDG priorities, this study provides a robust framework for evaluating and optimizing RE options. Its methodologies offer significant contributions to advancing sustainable energy development and enhancing energy systems’ resilience to climate and infrastructural challenges.

This paper introduces an innovative improvement in a in fieri decision support strategy (DSS) aimed at supporting the selection of indicators designed for the energy management strategy for sizing resilient isolated microgrids (RIM) navigating energy challenges within conflict zones. A unique feature of this proposed approach involves a combination of established guidelines for sizing batteries, photovoltaic (PV) systems, and wind turbines, in tandem with the integration of a predictive tool and a consolidated, optimized energy management system. The decisive decision-making process of identifying optimal Days of Autonomy, indicative of the system's capacity to autonomously meet load demands through a careful balance of various technical considerations, constitutes a cornerstone of this investigation. The paper carefully examines the methodology, outcomes, and following discussions, with a particular focus on the deployment of innovative key performance indicators and their synergistic amalgamation. The Kamal Adwan Hospital in Gaza serves as the focal point of the case study, supplemented by a laboratory test bench for validation.

This paper presents an integrated and scalable methodology for assessing the feasibility of Renewable Energy Communities (RECs) in rural and agricultural settings, particularly in areas with limited technical and consumption data. By incorporating Geographic Information System (GIS) data, photovoltaic potential estimation, and energy consumption profiling, the study provides a decision-support framework suitable for various municipalities. A case study conducted in Caorso, a municipality in northern Italy, showcases the framework’s capability to model energy exchanges and estimate self-sufficiency levels for a predominantly rural area. The results highlight seasonal variations in energy production and consumption, identifying opportunities for improvement through energy storage and enhanced energy-sharing strategies. Overall, the proposed approach supports municipalities in the pre-feasibility assessment of RECs by enabling the evaluation of local renewable potential and minimum rooftop utilization thresholds under limited data availability.

Extreme weather events, growing energy demands, and socio-political conflicts are increasingly causing power blackouts across diverse regions. This paper presents the Power Outage Predictor (POP) framework, a unified system that integrates historical blackout data, real-time weather, and conflict identification The framework incorporates climate-zone mapping, Impact-Based Forecasting (IBF), and a Bayesian inference engine to classify blackout likelihood and severity across natural, manmade, and conflict-driven causes. Additionally, a Decision Support System (DSS) is embedded to provide climate-specific microgrid design recommendations based on risk level, including battery duration, autonomy days, and PV-wind suitability. POP is validated using live simulations for six global cities across varied climate zones-demonstrating its effectiveness in anticipating blackouts and guiding resilient energy planning.