Optimal Energy Management in Microgrids: A Demand Response Approach with Monte Carlo Scenario Synthesis and K-Means Clustering - K. Neelashetty; Sonali Goel; Farooqhusain Inamdar; Yeswanth Dintakurthy; L. N. Sastry Varanasi; V. B. Murali Krishna (International Journal of Computational and Experimental Science and Engineering, 2025)
With the increasing integration of renewable energy sources and growing energy demands, microgrids have emerged as a viable solution for enhancing sustainability, efficiency, and resilience in power systems. Effective energy management is crucial to achieving these objectives while maintaining grid stability and minimizing operational costs. This study proposes an advanced energy management strategy for microgrids based on demand response, leveraging Monte Carlo simulations and K-means clustering for scenario-based decision-making. Due to the stochastic nature of photovoltaic (PV) and wind power generation, Monte Carlo simulation is employed to generate multiple potential scenarios that capture the uncertainties associated with renewable energy production. To mitigate computational complexity, K-means clustering is applied for scenario reduction, grouping similar scenarios while preserving the dataset’s representativeness. This approach effectively reduces the microgrid’s operational cost from 14,033 Rs. to 13,785 Rs. without compromising system reliability. Furthermore, the proposed response mechanism actively engages consumers in adjusting their electricity consumption patterns based on real-time pricing signals and system constraints. By dynamically aligning energy demand with supply fluctuations, the microgrid effectively reduces peak loads and enhances cost-efficiency. The results demonstrate that the proposed methodology not only optimizes economic performance but also strengthens the resilience of microgrid operations in the face of renewable energy variability. Renewable energy systems Microgrid Photovoltaic system Wind energy system Energy management