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  • A Comprehensive Framework for Optimal Stochastic Operating of Energy Hubs Integrated with Responsive Cooling, Thermal and Electrical Loads, and Ice Storage System by an Improved Self-Adaptive Slime Mold Optimization Algorithm

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Manuscript ID : TEEGES-2210-1043 (R1) Visit : 212 Page: 77 - 95

10.30486/teeges.2022.1969195.1043

Article Type: Original Research

A Comprehensive Framework for Optimal Stochastic Operating of Energy Hubs Integrated with Responsive Cooling, Thermal and Electrical Loads, and Ice Storage System by an Improved Self-Adaptive Slime Mold Optimization Algorithm

Subject Areas : Power Engineering

Mohamad Emadi 1 ( Department of Electrical Engineering, Khorramabad Branch, Islamic Azad University, Khorramabad, Lorestan, Iran )
Hamid Reza Massrur 2 ( Department of Electrical Engineering, Sharif University of Technology )
Esmaeel Rokrok 3 ( Department of Electrical Engineering, Lorestan University, Khorramabad, Lorestan, Iran )
Amin Samanfar 4 ( Department of Electrical Engineering, Khorramabad Branch, Islamic Azad University, Khorramabad, Lorestan, Iran )

Received: 2022-10-12 Accepted : 2022-12-19 Published : 2023-05-22

Keywords: Stochastic energy hub management, Integrated energy hub, Point estimate method, Multi-energy generation, Slime mold algorithm,

Abstract :

Following the expansion of the use of multi-carrier energy hubs in industries, this paper presents a comprehensive stochastic framework for optimal management and daily scheduling of an energy hub integrated with renewable energy sources and responsive cooling, thermal and electrical loads, and ice storage system. To solve this challenge, the 2m+1 Point Estimation Method (PEM) is used to accurately evaluate the system's uncertainties with low computational complexity. The 2m+1 PEM is a fast uncertainty analysis method based on the Taylor series. This method considers the uncertainty of renewable energy sources, the cooling, electrical and thermal loads, and the exchange price with different upstream energy distribution networks. This paper also presents a new self-adaptive optimization method called the Self-adaptive Modified Slime Mold optimization Algorithm (SMSMA) to solve the complex nonlinear problem of optimal daily scheduling of an energy hub. The improved self-adaptive method is based on the wavelet theory, which improves the capability and ability of the original slime mold algorithm to solve the daily optimal scheduling problem of an integrated energy hub. Numerical results show that the proposed daily stochastic scheduling framework, together with the proposed SMSMA algorithm, effectively reduces the operating costs of energy hubs.

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