Abstract
Investments in power generation assets are multi-year projects with high costs and multi-decade lifetimes. Since market circumstances can significantly change over time, investments into such assets are risky and require structured decision-support systems. Investment decisions and dispatch in electricity spot markets are connected, thus requiring anticipation of expected market outcomes. This strategic situation can be described as a bilevel optimization problem. At the upper level, an investor decides on investments while anticipating the market results. At the lower level, a market operator maximizes welfare given consumer demand and installed generation assets as well as producer price bids. In this paper, we formulate this problem as a mathematical program with equilibrium constraints (MPEC). We consider this model to include a dynamic, rolling-horizon optimization. This structure splits the investment process into multiple stages, allowing the modification of wait-and-see decisions. This is a realistic representation of actors making their decision under imperfect information and has the advantage of allowing the players to adjust their data in between rolls. This more closely models real-world decision-making and allows for learning and other feedback in between rolls. The rolling-horizon formulation also has the beneficial byproduct of computational advantage over a fixed-horizon stochastic optimization formulation since smaller problems are solved and we provide supporting numerical results to this point.
Highlights
1.1 MotivationInvestments in power generation assets are multi-year projects with high associated costs and lifetimes that usually last several decades
One contribution of our work is to evaluate the usefulness of a rolling-horizon optimization and the benefit of allowing recourse action within the rolling-horizon paradigm
We have introduced a dynamic investment mathematical program with equilibrium constraints (MPEC) that can be solved using a rolling-horizon optimization or as a fixed-horizon stochastic optimization
Summary
Investments in power generation assets are multi-year projects with high associated costs and lifetimes that usually last several decades. Investments into generation assets are risky in competitive markets and require structured decision-support systems. With asset lifetimes that can reach half a century and more, the computational efforts required to solve optimization problems can be significant. Methods for reducing these efforts may be required, while at the same time limiting the detrimental effect on solution quality. The rolling-horizon approach is a more realistic representation of actors making their decision under imperfect information and has the advantage of allowing the players to adjust their data in between rolls This more closely models real-world decision-making and allows for learning and other feedback in between rolls
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