An Extensive Optimization Framework for Energy Management within Energy Hubs: A Comparative Analysis of Simulated Multi-Agent Systems, Genetic Algorithms, and Mixed-Integer Linear Programming Incorporating Demand Response Mechanisms

In this paper, we present a framework on Combined Energy System (CES) to optimize energy management in energy hubs (EHs), integrating renewable energy sources (RERs) with demand response (DR) mechanisms. A methodological framework of stochastic scenario-based methodology is used to address the uncertainties associated with wind, solar, and energy prices. Three optimization techniques - Slime Mould Algorithm (SMA), Genetic Algorithm (GA), and Mixed-Integer Linear Programming (MILP) - are compared. SMA is again found to be most cost-efficient by around ۱۰% regarding operational costs compared to GA and MILP. Further incorporation of DR enhances cost reduction by SMA further reiterating the claim of SMA for renewable energy promotion. These results provide definite indications for SMA to be a strong and reliable platform for modern energy systems.

کلیدواژه ها:

Energy Management ، Stochastic Optimization ، Demand Response