Increasing the Market Value of Wind Power Using Improved Stochastic Process Modeling and Optimization

Increasing the Market Value of Wind Power Using Improved Stochastic Process Modeling and Optimization: A Case Study of a Belgian Wind Power Producer

Deen, Gerrit (TU Delft Technology, Policy and Management)

Verzijlbergh, Remco (mentor)
Cunningham, Scott (graduation committee)
Van Langen, Stijn (graduation committee)
Kuipers, Taco (graduation committee)

Delft University of Technology

Engineering and Policy Analysis

2019-03-29

Wind Power Producers (WPP) incur a negative result through mandatory participation on the balancing market. This study analyzes the performance of multiple improved offering strategies for a WPP offering both on the day-ahead market and the balancing market. It does so on revenue, as well as its impact on the grid and electricity market as a whole. It tests two different stochastic optimization models, one under the price-taker assumption, one under the price-maker assumption and one analytic model, which is a closed-form reformulation of the latter. It also introduces a framework for modeling the stochastic processes, which form the input to the stochastic optimization models. This framework contains an innovation in the form of an extended Gaussian Copula model to directly generate the entire scenario tree, which is tested against a conventionally reduced symmetric tree. It also introduces a framework for evaluating each step of the stochastic process modeling, with the aim of enabling structured improvement. The study shows that when taking the influence a WPP has on the imbalance price into account in the analysis of revenues, proper dependency modeling between the processes as well as endogenizing the impact a strategy has on the imbalance price in the stochastic optimization model are strict requirements for positive real-world performance. Furthermore, it shows that successful strategic offering may prove valuable for the grid and market as a whole.

Stochastic Process
Stochastic Optimization
Wind Power Producers
Electricity Market
Wind Energy
Day-Ahead Market
Balancing Market
Predictive Modeling
Renewable Energy
Decision Making
Decision Support
Advanced Offering Strategies
Optimized Trading
Gaussian Copula
SARIMA
Robust Regression

http://resolver.tudelft.nl/uuid:2ead9b02-640d-40c2-baa5-b349a2990ea5

Student theses

master thesis

© 2019 Gerrit Deen