Adrian Werner

Multi-horizon stochastic programming

Infrastructure-planning models are challenging because of their combination of different time scales: while planning and building the infrastructure involves strategic decisions with time horizons of many years, one needs an operational time scale to get a proper picture of the infrastructure’s performance and profitability. In addition, both the strategic and operational levels are typically subject to significant uncertainty, which has to be taken into account. This combination of uncertainties on two different time scales creates problems for the traditional multistage stochastic-programming formulation of the problem due to the exponential growth in model size. In this paper, we present an alternative formulation of the problem that combines the two time scales, using what we call a multi-horizon approach, and illustrate it on a stylized optimization model. We show that the new approach drastically reduces the model size compared to the traditional formulation and present two real-life applications from energy planning.

Risk Measures in Multi-Horizon Scenario Trees

Production assurance requirements are used to ensure that the operation of natural gas transportation networks is robust with respect to flow and production disruptions. They also affect strategies for optimal infrastructure investments. Motivated by a combined investment and operational optimization model for natural gas transport, we describe how to address such requirements through risk measure formulations such as Average Value-at-Risk. The large number of operational scenarios required for a meaningful analysis of the risk measures creates a computational challenge. A new scenario tree structure, multi-horizon scenario trees, can improve computational tractability. We investigate properties of the risk measures such as time consistency for such scenario trees and illustrate this discussion with a stylized example.

Stochastic Programming Perspective on the Agency Problems Under Uncertainty

We study the application of the stochastic programming framework to the analysis of complex agency problems under exogenous and endogenous uncertainty, presenting several models that deal with different types of such uncertainty. We demonstrate that the utilization of this framework extends the possibilities for the definition of parameters of incentive schedules. In this paper, we often refer to a model in a telecommunication environment consisting of a regulator (principal) and a service provider (agent) and study different aspects of regulation and licensing. However, the results can easily be generalized to other principal agent relationships.

The Portfolio Perspective in Electricity Generation and Market Operations

Selecting portfolios of electricity production assets, energy sources and market participation strategies facilitates usage and management of complementary resources. It helps also power producers to address uncertainties and to balance profit contributions, costs and risks. Therefore, portfolios should be composed wisely. Our paper will bring concepts of portfolio optimization closer to private energy producers. We highlight important aspects to be considered and outline key value drivers. However, we call also for critical thinking if portfolios of physical assets should be considered a panacea to address uncertainty in power generation and market operations. An example demonstrates that, sometimes, financial instruments rather than diversification into renewables may prove more efficient to hedge risk. In addition to the possibility of hedging through the portfolios, portfolio management can yield benefits for internal physical balancing and market access - but the value in terms of additional profit and risk reduction depends on market conditions.