Locating bioenergy facilities using a modified GIS-based location–allocation-algorithm: Considering the spatial distribution of resource supply

• Published in: Applied energy Vol. 154; pp. 309 - 316
• Main Authors: Comber, Alexis, Dickie, Jennifer, Jarvis, Claire, Phillips, Martin, Tansey, Kevin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.09.2015
• Subjects: algorithms; anaerobic digesters; bioenergy; case studies; feedstocks; Geo-computation; geographic information systems; Geographic Information Systems (GIS); Location–allocation; Spatial analysis; United Kingdom; watersheds; United Kingdom; Spatial analysis; Geographic Information Systems (GIS); Location–allocation; Geo-computation
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2015.04.128

•Location–allocation models are frequently used to locate bio-energy facilities.•Current approaches do not account for supply competition between potential sites.•We propose an extension to the p-median model that evaluates supply catchments.•It is demonstrated using a case study to optimally locate AD facilities.•Both resource supply and competition are critical when locating bio-energy facilities. This paper proposes a modification to the classic p-median problem that considers the spatial distribution of supply resources and competition for them by potential facility locations. It is illustrated with a simplified case study to optimally locate community scale anaerobic digesters (ADs) in an area in the East Midlands in the UK. The modification evaluates the spatial distribution of the feedstocks needed by each potential AD unit and only includes new locations if their supply catchments do not overlap with the catchments of the current set of locations being considered. The modified algorithm is implemented using the Teitz and Bart search heuristic. This starts with an initial set of locations that it seeks to improve by swapping poor performing locations with better ones. In this case the modification takes account of the spatial distribution of the feedstocks for a typical AD recipe whilst seeking minimise demand weighted distance. The results demonstrate large improvements over the classic p-median model in location selection that eliminate the overlap of facility catchments. Some wider points relating to the robustness of many analyses reported in the bioenergy literature are discussed, with some observations about commonly found methodological deficiencies, before some areas for further research are suggested.