Merchant Operations of Energy and Commodity Conversion Assets Considering Market Incompleteness

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

This award will promote the development of national prosperity and economic welfare by investigating effective operations of critical energy and commodity infrastructure systems. Merchant operations must manage the operational flexibility embedded in physical energy conversion assets, such as power plants, refineries, and storage and transport facilities. These systems form the backbone of energy and commodity physical markets, which in turn form the basis for the functioning of their associated financial markets. Incompleteness of these financial markets reduces the productivity of existing energy and commodity physical assets and hampers investment in new assets. This project explicitly addresses market incompleteness and supports the development of integrated operations and hedging practices to help asset managers achieve improved valuation, operations, and risk management. A better understanding of the impact of reduced energy and commodity market incompleteness on economic efficiency can also inform related policy making. The associated educational plan fosters interest in energy and commodity markets analytics based on curriculum innovation and electronic archives. This research will develop a decision-making framework that directly accounts for market incompleteness when managing on a merchant basis energy and commodity conversion assets modeled as real options. It will have potential applicability for the valuation, exercise, and hedging of both financial and real options that arise in contexts that transcend the energy and commodity industries. This approach contrasts common tactics that assume away market incompleteness when devising operating policies, effectively decoupling their optimization from that of financial hedging policies. It is also distinct from schemes that rely on the calibration of risk propensity parameters. The methodology in this project will extend the realm of applicability of quadratic hedging, integrating it with the optimization of operating policies for energy and commodity infrastructure. It will produce tractable algorithms for the computation of near optimal operating and financial hedging policies relying on and extending state-of-the-art approximate dynamic programming techniques. The performance of these methods will be assessed using available market and operational data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.