Optimal Energy Management of Hybrid Power Plants in Electricity Markets

Hybrid Power Plants (HPPs), which combine multiple renewable generation technologies and storage capabilities have raised interest in academia and industry worldwide. Compared with individual renewable power plants, HPPs can produce more reliable and flexible electricity. Therefore, HPPs are expected to participate in electricity markets and provide ancillary services to support the green transition of energy systems. However, according to International Energy Agency Wind TCP Task 50, the research on optimization control tools for HPPs participating in electricity markets is still limited.
This thesis studies the optimal energy management system (EMS) of HPPs in electricity markets, especially for spot markets and balancing markets. For this purpose, this thesis firstly reviews the existing research on EMS for grid-connected utility-scale renewable HPPs and identifies knowledge gaps. In the literature review, the different configurations of HPPs are summarized as they are highly relevant to EMS models. The market participation and uncertainties considered in the EMS are then classified. Five dominated EMS methodologies including rule-based, mathematical optimization, model predictive control, deep reinforcement learning, and stochastic dynamic programming are introduced and described in detail. At last, the challenges and opportunities of EMS for HPPs are summarized. This thesis provides the state-of-the-art of the current development on EMS for HPPs which might be beneficial both for researchers and industrialists working with EMS on HPPs.
Based on the literature review, the thesis proposes a framework of optimal EMS for HPPs in spot markets and balancing markets. The framework consists of day-ahead optimization, hour-ahead optimization, and re-dispatch optimization. A semi-empirical battery degradation model is incorporated into the framework to quantify the non-linear degradation process of batteries. This framework enables HPPs to maximize profits in markets while providing reliable generation. Based on the framework, this thesis proposes a deterministic EMS, where all optimizations are modeled without considering uncertainties of wind power and market prices, namely, point forecasts of wind power and market prices are incorporated into the optimization models. Then the thesis examines the impacts of forecasting errors on the profitability of HPPs in markets. By comparing the profits with imperfect forecasts and with perfect forecasts, it is found that forecasting errors lead to significant reductions in profits in markets. More specifically, wind power forecasting errors contribute more than spot price forecasting errors to these reductions.

Therefore, considering uncertainties in the EMS is crucial to improve the profitability of HPPs in markets. The thesis then focuses on improving the performance of day-ahead optimization by proposing a data-driven optimization model for HPPs participating in spot markets. The considered uncertainties are wind power, spot price, and regulating price. Distributionally robust optimization is utilized to model wind power uncertainty while incorporating price scenarios considering the correlation between the spot price and regulating price. The model demonstrates extraordinary performance in improving the profits of HPPs in markets compared with the deterministic optimization model. Even more, the model also performs better than the stochastic optimization model in a market environment with a high share of renewable energy. This highlights the application potential of the model in the future market environment.

Last but not least, the thesis also improves the performance of hour-ahead optimization by utilizing robust optimization for HPPs’ offering and operation in balancing markets (BMs). Unlike spot markets where financial settlements are used to assess the deviation between actual generation and the spot market schedule, the main challenge in BM is that if HPPs can not deliver or deliver non-conforming activated regulating power in BMs, transmission system operators may quarantine the HPPs from participating in auctions. Therefore, apart from the uncertainties of wind power and regulating price, the uncertainty of activated regulating volumes is also incorporated in the proposed model. This model demonstrates extraordinary performance in providing reliable balancing service. All the activated regulating volumes can be achieved by HPPs based on the model. This prevents the HPPs from being quarantined by transmission system operators from participating in auctions.

Overall, the thesis contributes to the field by proposing a holistic optimal EMS for HPPs participating in spot markets and balancing markets. The effectiveness has been verified in the simulation environment. As HPPs participating in markets is in their early stage and few realistic experiences are available, this research can be significant to pave the way for economic operation of HPPs in electricity markets.