A Self-Adaptive Risk-Based Optimization for a Multi-Carrier Energy Microgrid Incorporating Renewable Energy Sources, Energy Storage Systems, and Responsive Thermal, Cooling, and Electrical Demands

The optimal daily operation of multi-carrier energy hub systems is a tremendous challenge for operators due to the mutual impact of different energies, non-constant efficiency and partial loads of several internal equipment, various energy purchasing prices, and different energy demands at the hub output. In addition, the uncertainties of various renewable energy sources, energy purchasing prices, and energy demands may create serious risks for the hub’s operating costs. This paper presents a comprehensive risk-based decision-making framework for multi-carrier energy hub systems to address the mentioned challenges. In the proposed framework, the effects of the responsibility of cooling, thermal and electrical energy demands, and different kinds of energy storage, particularly ice storage, as well as the integration of several renewable energy sources, are investigated. The well-known CVaR method and the 2m+1 Point Estimate Method (PEM), which is a fast uncertainty analysis method based on the Taylor series, are employed to evaluate the risks associated with the system uncertainties. Moreover, to solve the complex non-linear problem of risk-based daily scheduling of an integrated energy hub, a new self-adaptive optimization method based on the Wavelet theory named Self-adaptive Modified Slime Mould Algorithm (SMSMA) is introduced to ensure moving toward the global optimum.