Day-Ahead Scheduling of Multi-Energy Microgrids Based on a Stochastic Multi-Objective Optimization Model

Seyed Reza Seyednouri, Amin Safari, Meisam Farrokhifar (), Sajad Najafi Ravadanegh, Anas Quteishat and Mahmoud Younis
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Seyed Reza Seyednouri: Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz 5375171379, Iran
Amin Safari: Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz 5375171379, Iran
Meisam Farrokhifar: Department of the Built Environment, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
Sajad Najafi Ravadanegh: Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz 5375171379, Iran
Anas Quteishat: Electrical Engineering Department, Faculty of Engineering Technology, Al Balqa Applied University, Al-Salt 19117, Jordan
Mahmoud Younis: Department of Electrical and Computer Engineering, Sohar University, P.O. Box 44, Sohar 311, Oman

Energies, 2023, vol. 16, issue 4, 1-17

Abstract: Dealing with multi-objective problems has several interesting benefits, one of which is that it supplies the decision-maker with complete information regarding the Pareto front, as well as a clear overview of the various trade-offs that are involved in the problem. The selection of such a representative set is, in and of itself, a multi-objective problem that must take into consideration the number of choices to show the uniformity of the representation and/or the coverage of the representation in order to ensure the quality of the solution. In this study, day-ahead scheduling has been transformed into a multi-objective optimization problem due to the inclusion of objectives, such as the operating cost of multi-energy multi-microgrids (MMGs) and the profit of the Distribution Company (DISCO). The purpose of the proposed system is to determine the best day-ahead operation of a combined heat and power (CHP) unit, gas boiler, energy storage, and demand response program, as well as the transaction of electricity and natural gas (NG). Electricity and gas are traded by MGs with DISCO at prices that are dynamic and fixed, respectively. Through scenario generation and probability density functions, the uncertainties of wind speed, solar irradiation, electrical, and heat demands have been considered. By using mixed-integer linear programming (MILP) for scenario reduction, the high number of generated scenarios has been significantly reduced. The ɛ-constraint approach was used and solved as mixed-integer nonlinear programming (MINLP) to obtain a solution that meets the needs of both of these nonlinear objective functions.

Keywords: energy hub; stochastic day-ahead operation; multi-microgrid; energy storages; Pareto front (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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