A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources. (15th August 2015)

• Record Type: Journal Article
• Authors: Quan, Hao; Srinivasan, Dipti; Khambadkone, Ashwin M.; Khosravi, Abbas
• Is Part Of: Applied energy. Volume 152(2015:Aug. 15)
• Page Start: 71
• Page End: 82
• Publication Date: 2015-08-15
• Subjects: Uncertainty integration -- Scenario generation -- Renewable energy -- Prediction interval -- Unit commitment -- Genetic algorithm; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2015.04.103
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A computational framework is proposed for uncertainty integration. A new scenario generation method is proposed for renewable energy. Prediction intervals are used to capture uncertainties of wind and solar power. Load, wind, solar and generator outage uncertainties are integrated together. Different generation costs and reserves are discussed for decision making. Abstract: The penetration of intermittent renewable energy sources (IRESs) into power grids has increased in the last decade. Integration of wind farms and solar systems as the major IRESs have significantly boosted the level of uncertainty in operation of power systems. This paper proposes a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs. Different sources of uncertainties in DPSs such as electrical load, wind and solar power forecasts and generator outages are covered by the proposed framework. Load forecast uncertainty is assumed to follow a normal distribution. Wind and solar forecast are implemented by a list of prediction intervals (PIs) ranging from 5% to 95%. Their uncertainties are further represented as scenarios using a scenario generation method. Generator outage uncertainty is modeled as discrete scenarios. The integrated uncertainties are further incorporated into a stochastic security-constrained unit commitment (SCUC) problem and a heuristic genetic algorithm is utilized to solve this stochastic SCUC problem. To demonstrate the effectiveness of the proposed method, five deterministic and four stochastic case studies are implemented. Generation costs as well as different reserve strategies are discussed from the perspectives of system economics and reliability. Comparative results indicate that the planned generation costs and reserves are different from the realized ones. The stochastic models show better robustness than deterministic ones. Power systems run a higher level of risk during peak load hours.