Energy Supply Chain Optimization: A Challenge for Control Engineers?1

Energy systems engineering problems are oftentimes complicated by factors like large amounts of uncertainties and multi-scale nature of decisions. This paper examines a particular aspect of energy systems engineering problems that gives rise to such complications: The coupling between long-term planning decisions like capital investment and policy and shorter-term decisions like production capacity operation and logistics. The paper starts with the discussion of a simple two-stage stochastic program that addresses optimization of an energy supply chain in the presence of uncertainties. The two-stage formulation can handle problems in which all design decisions are made up front and operating parameters act as ‘recourse' decisions that can be varied from one time period to another based on realized values of uncertain parameters. The design of a biodiesel production network in the Southeastern region of the United States is used as an illustrative example. The discussion then moves on to a more complex multi-stage, multi-scale stochastic decision problem in which periodic investment / policy decisions are to be made on a time-scale orders of magnitude slower than that of operating decisions. The problem of energy policy modeling is introduced as an example. In the particular energy policy modeling problem we examine, annual acquisition of energy generation capacities of various types are coupled with hourly energy production and dispatch decisions. The increasing role of renewable sources like wind and solar necessitates the use of a fine-grained time scale for accurate assessment of their values. The use of storage intended to overcome the limitations of the intermittent sources puts further demand on the modeling. Numerical challenges that arise from the multi-scale nature and uncertainties are reviewed and some possible modeling and solution approaches are discussed. Approximate dynamic programming is proposed as a promising algorithmic strategy to handle such challenges.