Financial risk management for new technology integration in energy planning under uncertainty

• Published in: Applied energy Vol. 128; pp. 75 - 81
• Main Authors: Ahmed, Sajjad, Elsholkami, Mohamed, Elkamel, Ali, Du, Juan, Ydstie, Erik B., Douglas, Peter L.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.09.2014; Elsevier
• Subjects: Applied sciences; carbon; case studies; Electricity; electricity generation; Energy; Energy management; Energy planning; Exact sciences and technology; Financial risk management; fuels; linear programming; Mathematical models; New technology; New technology integration; Ontario; Optimization; planning; power generation; prices; Programming; Risk management; Stochasticity; Uncertainty; Ontario; Financial risk management; Optimization; Energy planning; New technology integration; Power production; Planning; Risk management
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2014.03.058

•Financial risk associated with over or underproduction of electricity is studied.•A two-stage stochastic model that considers parameter uncertainties is developed.•The model was applied to a real case to meet projected electricity demand of a fleet of generating stations.•Incorporation of financial risk resulted in an increase in electricity cost.•The selection of technologies was the same as that obtained from a deterministic model. This paper proposes a new methodology to include financial risk management in the framework of two-stage stochastic programming for energy planning under uncertainties in demand and fuel price. A deterministic mixed integer linear programming formulation is extended to a two-stage stochastic programming model in order to take into account random parameters that have discrete and finite probabilistic distributions. This was applied to a case study focusing on planning the capacity supply to meet the projected electricity demand for the fleet of electricity generation stations owned and operated by Ontario Power Generation (OPG). The objective of the proposed mathematical model is to minimize cost subject to environmental constraints. The case study is investigated by considering only existing technologies and also by considering the integration of new technologies that help achieve stricter carbon reduction requirements.