Keywords
renewable energy, regional energy systems, modelling
Start Date
1-7-2008 12:00 AM
Abstract
During the last years an increasing energy demand, rising prices for fossil fuels, thechallenge of meeting the objectives of the Kyoto protocol as well as a certain uncertainty ofenergy supply resulted in the two main aspects arising within the design of our futureenergy system, which are sustainability and security of supply.To meet these challenges, the paper presents a modelling approach that handles informationon geographically disaggregated data of renewable energy potentials as well asgeographically disaggregated information on energy demand structures. The comparison ofthe identified energy potentials of the modelling process to the relative energy consumptionstructure results in a “balance grid” that represents the energy excess or shortage in everycell of the grid. The balance grid is the basis for modelling self-sustaining regions andallows a differentiated geographical consideration of energy production and consumptionpotentials.Processing this information the model approach identifies optimized energy flows tobalance all energy demand hot spots. This is applied for a special region of interest with theobjective of finding one optimized setup for the whole prospected area. The final outcomeof the model shows an ideally balanced energy flow structure for the whole examinedregion. In its simplest realization the energy flows only consider balanced flows for a fullyear timescale. Nevertheless these flows could also be treated on an arbitrary differenttimescale.Based on these outcomes a possible sub-regionalisation in terms of energetic independencywithin the considered region of interest can be identified. This is reflected by clustering theregion of interest into single self sustaining sub regions.The model itself is a linear optimization model realised in the modelling language GAMS.There is an interface implemented to connect the model to common GIS software. In thecurrent model all input and result data are administrated and visualised in ArcGIS.
GIS based Model to optimize possible self sustaining regions in the context of a renewable energy supply
During the last years an increasing energy demand, rising prices for fossil fuels, thechallenge of meeting the objectives of the Kyoto protocol as well as a certain uncertainty ofenergy supply resulted in the two main aspects arising within the design of our futureenergy system, which are sustainability and security of supply.To meet these challenges, the paper presents a modelling approach that handles informationon geographically disaggregated data of renewable energy potentials as well asgeographically disaggregated information on energy demand structures. The comparison ofthe identified energy potentials of the modelling process to the relative energy consumptionstructure results in a “balance grid” that represents the energy excess or shortage in everycell of the grid. The balance grid is the basis for modelling self-sustaining regions andallows a differentiated geographical consideration of energy production and consumptionpotentials.Processing this information the model approach identifies optimized energy flows tobalance all energy demand hot spots. This is applied for a special region of interest with theobjective of finding one optimized setup for the whole prospected area. The final outcomeof the model shows an ideally balanced energy flow structure for the whole examinedregion. In its simplest realization the energy flows only consider balanced flows for a fullyear timescale. Nevertheless these flows could also be treated on an arbitrary differenttimescale.Based on these outcomes a possible sub-regionalisation in terms of energetic independencywithin the considered region of interest can be identified. This is reflected by clustering theregion of interest into single self sustaining sub regions.The model itself is a linear optimization model realised in the modelling language GAMS.There is an interface implemented to connect the model to common GIS software. In thecurrent model all input and result data are administrated and visualised in ArcGIS.
