With a significant number of states in the U.S. and countries around the world trading electricity in restructured markets, a sizeable proportion of capacity expansion in the future will have to take place in market-based environments. However, since a majority of the literature on capacity expansion is focused on regulated market structures, there is a critical need for comprehensive capacity expansion models targeting restructured markets. In this research, we develop a two-level game-theoretic model, and a novel solution algorithm that incorporates risk due to volatilities in profit (via CVaR), to obtain multi-period, multi-player capacity expansion plans. To solve the matrix games that arise in the generation expansion planning (GEP) model, we first develop a novel value function approximation based reinforcement learning (RL) algorithm.
Currently there exist only mathematical programming based solution approaches for two player games and the N-player extensions in literature still have several unresolved computational issues. Therefore, there is a critical void in literature for finding solutions of N-player matrix games. The RL-based approach we develop in this research presents itself as a viable computational alternative. The solution approach for matrix games will also serve a much broader purpose of being able to solve a larger class of problems known as stochastic games. This RL-based algorithm is used in our two-tier game-theoretic approach for obtaining generation expansion strategies. Our unique contributions to the GEP literature include the explicit consideration of risk due to volatilities in profit and individual risk preference of generators. We also consider transmission constraints, multi-year planning horizon, and multiple generation technologies.
The applicability of the twotier model is demonstrated using a sample power network from PowerWorld software. A detailed analysis of the model is performed, which examines the results with respect to the nature of Nash equilibrium solutions obtained, nodal prices, factors affecting nodal prices, potential for market power, and variations in risk preferences of investors. Future research directions include the incorporation of comprehensive cap-and-trade and renewable portfolio standards components in the GEP model.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-3117 |
| Date | 01 June 2009 |
| Creators | Nanduri, Vishnuteja |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
Page generated in 0.0019 seconds