GIS based model to optimize the utilization of renewable energy carriers and related energy flows

A significant part of final energy consumption is demand for space heating. This demand is mainly met by fossil fuels. Given the challenge of climate change and question marks over energy security and import dependency on fossil fuels, improvements in energy efficiency and greater use of renewable energy may be important policy considerations. The paper presents a modelling approach for the optimization of the fulfilment of the heating demand within a defined region of interest, favouring renewable energy carriers - with a particular focus on spatial differentiation. The modelling approach that is presented handles information on geographically disaggregated data describing renewable energy potentials (biomass, solar energy, geothermal energy, ambient heat) on the one hand and geographically disaggregated information on the heating demand on the other hand. This spatial balance is the basis for modelling an optimum spatial utilization of identified renewable energy resources to satisfy the heating demand with respect to the objective function of the model, which is defined as highest economic efficiency with respect to greenhouse gas emissions constraints in the region. To take into account the spatial relevance of the single elements of the energy system in an appropriate way, all relevant spatial data are disaggregated to a consistent spatial resolution. This includes the energy potentials, the demand structure as well as some infrastructure data. The region of interest is segmented into a collection of raster cells, which present the smallest spatial unit in the model. The smallest size of raster cells is 250 m x 250 m. The general model framework within this approach consists of three parts: • The potential model - includes separate models to estimate the potential of individual renewable energy carriers (biomass, solar energy, geothermal energy, ambient heat) in a spatially disaggregated way with their specific characteristics. These separate models are integrated into the overall potential model. • The demand model - illustrates the spatially disaggregated heating demand, expressed as heating degree days. This is the basis for the estimation of the effective demand in relation to the insulation standard of buildings. • The dynamic fulfilment model - is used to derive an optimized setup of the energy system for the fulfilment of the heating demand. For the generation of various scenarios a distinction is made between fixed parameters defined by the system (present situation) and variable parameters (e.g. future costs). The variable parameters (insulation standards, domestic fuel type, natural gas and district heating grid, fuelling of power plants and use of renewable energies) are defined differently for the development of the scenarios. Depending on these definitions, a sensitivity analysis can be carried out. The model is implemented as a linear optimization model realized in the modelling language GAMS. The use of scenario analysis allows the testing for key sensitivities in the model. These outcomes may have important policy implications or provide strategic information to stakeholders.