Optimizing energy hubs with a focus on ice energy storage: a strategic approach for managing cooling, thermal, and electrical loads via an advanced slime mold algorithm

Abstract

Abstract Amidst the increasing incorporation of multicarrier energy systems in the industrial sector, this article presents a detailed stochastic methodology for the optimal operation and daily planning of an integrated energy system that includes renewable energy sources, adaptive cooling, heating, and electrical loads, along with ice storage capabilities. To address this problem, it applies the 2 m + 1 point estimation method to accurately assess system uncertainties while minimizing computational complexity. The “2 m + 1 point” technique swiftly evaluates unpredictability through Taylor series calculations, capturing deviations in green energy output, and the demand for both electric and thermal energy across power networks, while also considering the oscillating costs associated with senior energy transmission systems. In addition, this article proposes a novel self-adaptive optimization technique, called the enhanced self-adaptive mucilaginous fungus optimization algorithm (SMSMA), dedicated to overcoming the intricate nonlinear challenges inherent in the optimal daily operation of an energy system. The advanced self-adaptive strategy relies on wavelet theory to enhance the capability and effectiveness of the original mucilaginous fungus algorithm in optimizing daily schedules for an integrated energy system. Numerical analyses demonstrate that the introduced stochastic daily scheduling framework, coupled with the SMSMA optimization algorithm, effectively reduces the operating costs of the energy system.