Chance-constrained stochastic congestion management of power systems considering uncertainty of wind power and demand side response. (May 2019)

• Record Type: Journal Article
• Authors: Wu, Jiasi; Zhang, Buhan; Jiang, Yazhou; Bie, Pei; Li, Hang
• Is Part Of: International journal of electrical power & energy systems. Volume 107(2019)
• Page Start: 703
• Page End: 714
• Publication Date: 2019-05
• Subjects: Chance-constrained optimization -- Congestion management -- Demand response -- Uncertainty -- Wind power; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2018.12.026
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A stochastic optimization model based on chance constraints is proposed for congestion management in the day-ahead market. An iteration method is proposed to consider transmission losses in the model for congestion management. Probabilistic power flow based on cumulant method is used to validate the determined dispatch of generators and demand responsive loads. The proposed model considers the correlation of load demands at different buses and the correlation of the wind power outputs for congestion management. Abstract: This study proposes a new stochastic model based on chance constraints for network congestion management in the day-ahead power market. By jointly considering the uncertainty of wind power and demand side response, the proposed optimization model can help determine the optimal daily dispatch of generators and demand responsive loads to minimize the risk of transmission congestion. To this end, transmission line congestion probability (TLCP) is constructed in chance constraints to ensure system congestion less than a certain level. The indexes of load loss probability (LLP) and wind curtailment probability (WCP) are proposed and incorporated as chance constraints to achieve a high reliability of power supply and high utilization of wind generation, respectively. In the optimization process, the proposed stochastic optimization model is transformed to an equivalent deterministic model by using the probability distribution of random variables, and the influence of transmission system loss on transmission congestion management is considered. To calculate the satisfaction degree of chance constraints under the determined dispatch of generators and demand responsive loads, the probabilistic power flow based on the cumulant method is used, and the risk of transmission congestion level is quantified by the index, congestion risk value (CRV). Simulation results of a modified PJM 5-bus system and an IEEE 118-bus test system demonstrate the effectiveness of the proposed approach for congestion management in the day-ahead power market.