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

Emadi, Mohamad; Massrur, Hamid Reza; Rokrok, Esmaeel; Samanfar, Amin

doi:10.24200/sci.2024.63816.8639

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

Articles in Press

Document Type : Research Article

Authors

Mohamad Emadi ¹

Hamid Reza Massrur ²

Esmaeel Rokrok ³

Amin Samanfar ¹

¹ Department of Electrical Engineering, Khorramabad Branch, Islamic Azad University, Khorramabad, Iran

² Electrical Engineering Department, Sharif University of Technology, Tehran, Iran

³ Department of Electrical Engineering, Engineering Faculty, Lorestan University, Iran

10.24200/sci.2024.63816.8639

Abstract

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.

Keywords

Demand Response

Energy Hub

Microgrid

Multi-Energy Generation

Point Estimate Method

Renewable Energy

Risk-Based Scheduling

Self-Adaptive Algorithm

Slime Mould Algorithm

Subjects