The development of an integrated GIS-based optimization framework for power generation from municipal solid waste-to-energy facilities

Abstract

Waste-to-energy (WtE) is one of the municipal solid waste (MSW) management pathways that can reduce waste volumes while recovering energy. There has been considerable focus on using MSW for energy production; however, there are few integrated assessments of the optimal location and scale of the conversion of MSW to energy. This research develops a novel framework that integrates the analysis of candidate sites for MSW gasification facilities using a geographical information system (GIS) through a multi-criterion decision-making (MCDM) model with a mixed-integer linear programming (MILP) model to determine optimal scale and location. The most suitable candidate locations were identified through a GIS-based location-allocation analysis. A MILP model integrated with GIS was then used to determine the minimum per-unit electricity production costs by optimizing the numbers, locations, and sizes of the proposed WtE and preprocessing facilities. The key inputs to the optimization model are actual road and railway transportation distances, WtE gasification and preprocessing facility capital and installation costs, operation and maintenance costs, gate fee and tipping fees (the fee for disposal of ash at the landfill), and the municipal guidelines. The proposed integrated framework was applied in a case study for Alberta, a western province in Canada. The results indicate that at a 10% rate of return, electricity production is cost-competitive with current electricity prices. The per-unit cost of electricity production (COEP) was found to be only 16.14 $/MWh when the gate fee was treated as a revenue component. The optimal plant location was in Edmonton and the optimum plant size was 175 MW. Sensitivity and uncertainty analysis was conducted to understand the critical input parameters that impact the COEP. The results indicate that the COEP is most sensitive to changes in tipping fees, plant efficiency, capital cost, discount rate, and transportation cost. The developed framework is critical for investment decisions and policy development, and it can be applied globally with adjustments to data and assumptions.