Optimal Design and Operation of a Multi-Energy Microgrid Using Mixed-Integer Nonlinear Programming: Impact of Carbon Cap and Trade System and Taxing on Equipment Selections

Abstract

Multi-energy microgrids comprise various energy sources such as solar, wind, hydro, biomass, oil, gas, and coal. Optimal configuration and scheduling of multi-energy microgrids enhance energy efficiency and reduce carbon dioxide emissions. In this work, a novel and detailed mixed-integer nonlinear programming (MINLP) model is proposed to compare the effects of carbon emission taxing and cap and trade system on the optimal equipment selection and scheduling under the regulatory effects of the Paris Agreement. The candidate equipment to be selected by the MINLP model includes two solar panel arrays, two wind turbine farms, two integrated gasification combined cycles, two combined heat and power units, two conventional generators, one biogenerator, one battery, two bio-gasifiers, and one power-to-gas system. The impact of carbon dioxide price, taken as 50, 75, and 100 $/ton, is investigated for both emission taxing and carbon dioxide cap and trade system. Equipment se-lections with the multi-period seasonal data and the carbon dioxide prices of 50 $/ton and 75 $/ton in stand-alone mode are the same for both cases without selecting renewable sourced generators. For the same condi-tions and with a carbon dioxide price of 100 $/ton, the MINLP model selects one wind turbine farm, one bio-generator, and one bio-gasifier. Moreover, the power-to-gas system is chosen in electricity grid-connected mode under emission taxing with the carbon dioxide price of 100 $/ton. For both emission taxing and cap and trade system, the trials using the seasonal data and the yearly average data with carbon dioxide price of 75 $/ton in stand-alone mode contributes to different equipment selections. Results show that carbon dioxide price may be a crucial parameter for equipment selection and must be taken into account in the design phase for optimal design and management. Finally, the MINLP solvers SCIP, BARON, and DICOPT, are compared in terms of CPU times and equipment selections.