Optimal location and operation of waste-to-energy plants when future waste composition is uncertain

Jaroslav Pluskal (), Radovan Šomplák, Dušan Hrabec, Vlastimír Nevrlý and Lars Magnus Hvattum
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Jaroslav Pluskal: Brno University of Technology – VUT Brno
Radovan Šomplák: Brno University of Technology – VUT Brno
Dušan Hrabec: Tomas Bata University in Zlín
Vlastimír Nevrlý: Brno University of Technology – VUT Brno
Lars Magnus Hvattum: Molde University College

Operational Research, 2022, vol. 22, issue 5, No 34, 5765-5790

Abstract: Abstract In many countries, waste management is increasingly geared towards a circular economy, aiming for a sustainable society with less waste generation, fewer landfills, and a higher rate of recycling. Waste-to-Energy (WtE) plants, which convert waste into heat and energy, can contribute to the circular economy by utilizing types of waste that cannot be recycled. Due to the varying quality of sorting and socio-economic conditions in individual regions, the waste composition differs between regions and has an uncertain future development. Waste composition significantly affects the operation of WtE plants due to differences in energy potential. This paper supports strategic capacity planning for waste energy recovery by introducing a two-stage stochastic mixed-integer linear programming model that captures waste composition uncertainty through scenarios of possible future development. The results of the model provide insights into the economics of operation and identify important factors in the sustainability of the waste handling system. The model is demonstrated on an instance with six scenarios for waste management in the Czech Republic for the year 2030. The solution of the proposed model is to build 14 new WtE plants with a total capacity of 1970 kt in addition to the four existing plants with a capacity of 831 kt. The annual energy recovery capacity is expected to increase almost four times to satisfy EU directives that restrict waste landfilling.

Keywords: Stochastic programming; Facility location; Multi-commodity; Mixing approach; Lower heating value; MILP (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12351-022-00718-w

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