A Data-Driven, Survey-Based Bottom-Up Load Modeling and Optimization Framework for Hybrid Renewable Energy Systems in Standalone Microgrids

Abstract

 The precise configuration of standalone hybrid renewable microgrid systems in remote regions is frequently obstructed by a critical lack of historical consumption data. This paper introduces a mathematically rigorous, data-driven framework that bridges this "information gap" by merging bottom-up, appliance-level load modeling with stochastic Monte Carlo simulations. In contrast to conventional top-down modeling, which frequently results in inefficient over-capitalization or compromised energy reliability, this research adopts an approach that captures the inherent stochasticity of both human behavior and environmental fluctuations. By leveraging scenario-driven stochastic optimization grounded in a 95th-percentile (P95) design benchmark, the study identifies an optimal configuration of 1,000 kWp in solar PV capacity coupled with an 800 kWh battery storage system. This specific architecture is engineered to sustain grid stability even during protracted multi-day solar deficits within the project zone. From a techno-economic perspective, the system achieves a levelized cost of energy between $0.17 to $0.52 per kWh and a substantial 87% reduction in carbon footprint. Ultimately, this framework offers a transferable and scalable model for implementing sustainable power solutions in data-limited regions globally.