Hybrid Power Plant Bidding Strategy for Electricity Market Participation

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In this thesis, the strategies of hybrid power plants (HPPs) in electricity markets to minimize the impacts of wind power uncertainties through storage and demand managements are investigated. Firstly, a commercial Compressed Air Energy Storage (CAES) aggregator equipped with a simple cycle operation mode is correlated with a Wind Power Aggregator (WPA) as an HPP to participate in electricity markets. The WPA utilizes the CAES to tackle wind power forecasting errors and uncertainties associated with different electricity market prices, while CAES can get assistance from WPA to schedule its charging/discharging and simple cycle modes more economically. A three-stage stochastic decision-making method is formulated to model the proposed optimization problem. Besides, conditional value-at-risk (CVaR) is added to the model to control the financial risk of the problem and offer different operation strategies for different financial risk levels. It also provides both bidding quantity and bidding curves to be submitted to the electricity markets. Secondly, an offering strategy with a three-stage stochastic programming is presented for an HPP, which includes a WPA and a Demand Response Aggregator (DRA). Three electricity markets are considered including DA, intraday, and balancing market for the joint operation of WPA and DRA as an HPP. The CVaR is also added to the HPP offering strategy to control the profit risk. The offering strategy for the second case study is tested in a wind farm and electricity market located in Spain. The result shows that the HPP offering strategy can effectively assist the balancing and outage problem of the WPA and increase the overall profit of the joint operation. Finally, an HPP, including a CAES aggregator with a WPA is modeled considering network constraints. Three objective functions are considered including electricity market maximization, congestion management, and voltage stability improvement. In order to accurately model the WPA, pitch control ability is added to wind generator models to control the wind power curtailment level. Multi-objective Pareto front solutions are considered to optimize all the mentioned objective functions properly, and finally, the best solution is suggested using the fuzzy method. The proposed approach is tested on a realistic case study based on a wind farm and electricity market located in Spain, and the IEEE 57 bus test system is used to analyze the network constraint effects on the HPP scheduling for different objective functions.
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