City-level energy planning aimed at emission reduction in residential sector with the use of decision support model andgeodata

Abstract

The article presents an energy-economic model that can be used for decision support in developing strategies aimed at reduction of air pollutant emissions from the residential sector. The model was developed with the use of the General Algebraic Modelling System (GAMS). It is a bottom-up model coupled with the GIS-based tool making use of the georeferenced datasets describing buildings. These data sets include, among the others, the building boundaries, utility types, location, number of floors. At first, an energy demand for space heating was estimated at the building level. Then, the model solved the Mixed Integer Programming (MIP) problem by taking measures: (i) on the supply side (e.g. fuel and/or technology switch) and (ii) on the demand side (e.g. replacement of windows, improving insulation of the building envelope and the roof). The objective function minimized by the model wasthe total cost of covering heat demand. A number of user-constraints were defined, including, e.g. limitations of emissions of air pollutants, dedicated budget for emissions reduction, priority given to either demand-side or supply-side measures. The applicability of the model was demonstrated in a case-study done for a town in Poland. Several scenarios were considered to show the strategies to decrease emissions and the respective implementation costs. The results showed that emission reduction could be achieved with negative costs due to investments in thermomodernization of energy intensive buildings.