研究目的
To investigate optimal scheduling of a central concentrating solar power (CSP) plant in the presence of uncertainties such as power market price and solar irradiation, using a hybrid IGDT-stochastic method to maximize profit and analyze risk-based strategies.
研究成果
The hybrid IGDT-stochastic method effectively handles uncertainties in CSP plant scheduling, providing reliable results. Risk-averse strategy reduces profit to mitigate uncertainty, while risk-taker strategy increases profit by leveraging favorable conditions. Integration of thermal energy storage enhances dispatchability. The method supports decision-making under uncertainty, with potential for further improvements by incorporating additional factors like demand response.
研究不足
The study relies on specific case study data and assumptions (e.g., 50 scenarios for market price, normal distribution). It does not account for other uncertainties like equipment failures or environmental factors. The computational method may have limitations in scalability or real-time application. Future work could include demand response programs and other uncertainties.
1：Experimental Design and Method Selection:
A hybrid information gap decision theory (IGDT)-stochastic method is used, which is a mixed-integer linear programming (MILP) approach. The IGDT method models solar irradiation uncertainty, while stochastic programming with 50 scenarios models market price uncertainty. The objective is profit maximization under different risk strategies (risk-averse, risk-neutral, risk-taker).
2：Sample Selection and Data Sources:
A realistic case study based on historical data from the CSP plant of IBERDROLA in Puertollano, Spain, for a typical sunny day in summer. Market price scenarios are generated using normal distribution.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned; the study is computational and theoretical, focusing on modeling and optimization without physical equipment.
4：Experimental Procedures and Operational Workflow:
The MILP model is solved iteratively (10 iterations) starting from deterministic results, with profit adjustments of $500 per step for robustness and opportunity functions. Constraints include power output limits, thermal storage dynamics, and operational constraints like ramp rates and minimum up/down times.
5：Data Analysis Methods:
Results are analyzed for three strategies: risk-averse (using robustness function), risk-neutral (deterministic case), and risk-taker (using opportunity function). Profit, thermal power, electrical power, and storage usage are compared across strategies.
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