(459f) A Two-Stage Stochastic MINLP Model for Design and Scheduling of a Multi-Energy Microgrid Under the Sanctions of the Paris Agreement

A two-stage stochastic MINLP model for design and scheduling of a multi-energy microgrid under the sanctions of the Paris Agreement

Handan Akulker ^a,b , Erdal Aydin^a,c,d

^aDepartment of Chemical Engineering, Bogazici University, Istanbul 34342, Turkey

^bDepartment of Chemical Engineering, Ondokuz Mayis University, Samsun 55139, Turkey

^cDepartment of Chemical and Biological Engineering, Koç University, Istanbul 34457, Turkey

^dKoç University TUPRAS Energy Center (KUTEM), Koç University, Istanbul 34450, Turkey

eaydin@ku.edu.tr

Abstract

Paris Agreement, signed in 2016, enforces the countries of the European Union to make legally binding sanctions on global warming. Accordingly, two main regulation policies for carbon dioxide emission, the leading actor of global warming, have been discussed. These are carbon emission taxing and cap and trade system. Carbon emission taxing implements taxes for carbon dioxide emission to energy producers, whereas cap and trade system permits selling or purchasing carbon dioxide emission limits (World Bank (2022)).

Multi-energy microgrids include various energy sources, such as solar, wind, hydro, biomass, oil, gas, and coal. Optimal configuration and scheduling of multi-energy microgrids may improve energy efficiency and reduce carbon dioxide emissions. Yet, design studies are challenging owing to the uncertainties associated with uncertain future carbon dioxide regulation policies and the intermittency of renewable energy sources. Stochastic programming handles those uncertainties to make more robust and reliable design and scheduling (Han and Lee (2021)). In addition, mixed-integer nonlinear programming (MINLP) considers the nonlinear nature of modeling problems (Elsido et al. (2017)).

This study aims to optimally design and schedule a multi-energy microgrid by formulating a two-stage stochastic MINLP problem to meet the electricity demand of a specific location. The objective function is to minimize the microgrid's total installation and operational costs over the plant life. The optimization model determines which types of equipment to be installed, their installation capacities, and the energy production plan through the lifetime under eight different scenarios with five-year temporal resolution. Candidate equipment are two wind turbine farms, two solar panel arrays, two integrated gasification combined cycles, two conventional generators, three combined heat and power generators, one battery, two bio-gasifiers, one bio-generator, and one power-to-gas system. Power-to-gas systems use excess electricity and carbon dioxide from flue gases to produce synthetic natural gas.

This study aims two significant contributions: The first is the determination of optimal diameters and heights of wind turbines, optimal tilt angles of PV panels, and optimal extend of methanation reaction simultaneously. The second is the implementation of two different carbon dioxide regulation policies in a two-stage stochastic MINLP model. Although many studies under this research area consider carbon taxing (Azimian et al. (2020), Ikäheimo et al. (2022)), cap and trade system has not been included yet. The optimal net present cost is determined as 4.92 billion dollars. The electricity production plans over five years for each scenario are obtained. The model chooses one solar panel array, two integrated gasification combined cycles, one bio-generator, two bio-gasifiers, and one combined heat and power generator.

Keywords: Mixed-integer nonlinear programming, two-stage stochastic programming, cap and trade system, carbon taxing, optimal design and scheduling.

References

World Bank. https://www.worldbank.org/en/programs/pricing-carbon; [accessed 1 January 2022].

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