Financial risk management for new technology integration in energy planning under uncertainty Sajjad Ahmed a,c , Mohamed Elsholkami a , Ali Elkamel a,⇑ , Juan Du b , Erik B. Ydstie b , Peter L. Douglas a a Department of Chemical Engineering, University of Waterloo, Ontario N2L 3G1, Canada b Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA c PPG, Pittsburgh, PA, USA highlights  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. article info Article history: Received 16 October 2013 Received in revised form 5 March 2014 Accepted 25 March 2014 Keywords: Financial risk management Energy planning Optimization New technology integration abstract 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 prob- abilistic 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 technol- ogies and also by considering the integration of new technologies that help achieve stricter carbon reduction requirements. Ó 2014 Elsevier Ltd. All rights reserved. 1. Introduction Mathematical models that incorporate financial risk manage- ment enable decision makers to account for uncertainty in the eval- uation and comparison of alternatives. The formulation helps the decision maker to maximize the expected profit and at the same time minimize the financial risk at every profit level. Stochastic programming is a framework for modeling optimization problems that involve uncertainty. The most widely applied stochastic programming models are two-stage linear programs. In two-stage programming, uncertainty is modeled through a finite number of independent scenarios [1]. Scenarios are formed by random samples taken from the probability distribution of the uncertain parameters as explained by Barbaro and Bagaiewicz [2]. Typical uncertain parameters include prices of raw materials, market demands, process parameters, rate of interest, etc. Recourse is the ability to take corrective action after a random event has taken place [3]. In the planning stage, some decisions are taken before random or uncertain events are known. The remaining decisions are taken only after the uncertain data become known. Stochastic programming started with several methods to deal with uncertainties such as chance-constrained optimization [4], fuzzy programming [5,6] and the design flexibility method [7]. Some references on two-stage stochastic programming include books by Infanger [8], Kall and Wallace [9], Marti and Kall [10], Uryasev and Pardalos [11], Verweij et al. [12], and Neise [13]. Gothe-Lundgren and Persson [14] discussed a production and scheduling problem focusing on planning and scheduling to select the mode of operation to satisfy the demand while minimizing http://dx.doi.org/10.1016/j.apenergy.2014.03.058 0306-2619/Ó 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Address: University of Waterloo, Department of Chemical Engineering, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada. Tel.: +1 519 888 4567. E-mail address: aelkamel@uwaterloo.ca (A. Elkamel). Applied Energy 128 (2014) 75–81 Contents lists available at ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy