Optimal hedging for flexible fuel energy conversion networks

We demonstrate an alternative approach for the hedging of flexible energy conversion networks appropriate for use in campus scale and municipal scale utilities with complex energy requirements, fuel sources, and operational flexibility. This new approach utilizes empirical modeling of price models for alternative commodity fuels coupled with a previously reported class of bilinear models for estimating the efficiency of complex and flexible energy conversion networks. The coupling of financial and operational hedging provides the utility operator with additional mechanisms for mitigating price volatility in the energy markets. In this paper, we use a steady state bilinear model introduced in an earlier paper to model a power plant that can utilize either coal or natural gas. The model demonstrates the complex tradeoff between using a lower cost but less efficient fuel versus a more expensive fuel with higher conversion efficiency. This class of bilinear models incorporates first and second law principles from finite-time thermodynamics to predict energy conversion networks. In the hedging framework, the bilinear models are used to compute deterministic optimal operating conditions which are functions of fuel prices. Historical fuel price data are collected for coal and natural gas, and a stochastic price model is fit to capture the joint distribution of fuel prices. The fit is used to generate a fuel cost uncertainty model. The model is used to project a joint distribution of fuel prices over a single period planning horizon. Economic optimization is performed over many fuel price realizations. These results are aggregated to obtain a distribution of prices of fuels and fuel costs of the energy conversion network on the planning horizon. A fuel ´Cost at Risk´ model is suggested, and it shows how one may choose to take position in coal inventory or in natural gas futures in order to reduce ´Cost at Risk´. In summary, this paper presents three main results: (1) A- stochastic fuel price model is constructed using historical data. (2) Economic optimal process conditions are determined for a flexible fuel energy conversion network model and realizations of future prices using the uncertain fuel price model. (3) Determination of an optimal hedging strategy for the flexible energy conversion network using Monte Carlo techniques.