Stochastic AC optimal power flow: A data-driven approach

• Published in: Electric power systems research Vol. 189; p. 106567
• Main Authors: Mezghani, Ilyes, Misra, Sidhant, Deka, Deepjyoti
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2020; Elsevier Science Ltd; Elsevier
• Subjects: Algorithms; Chance constraints; Data-driven optimization; ENGINEERING; Fluid dynamics; Iterative methods; Monte Carlo; Optimization algorithms; Power flow; Probability; Scenario optimization; Sparse regression; Stochastic AC-OPF; Stochastic models; Chance constraints; Sparse regression; Scenario optimization; Monte Carlo; Stochastic AC-OPF; Data-driven optimization
• ISSN: 0378-7796; 1873-2046; 1873-2046
• DOI: 10.1016/j.epsr.2020.106567

There is an emerging need for efficient solutions to stochastic AC Optimal Power Flow (AC-OPF) to ensure optimal and reliable grid operations in the presence of increasing demand and generation uncertainty. This paper presents a highly scalable data-driven algorithm for stochastic AC-OPF that has extremely low sample requirement. The novelty behind the algorithm’s performance involves an iterative scenario design approach that merges information regarding constraint violations in the system with data-driven sparse regression. Compared to conventional methods with random scenario sampling, our approach is able to provide feasible operating points for realistic systems with much lower sample requirements. Furthermore, multiple sub-tasks in our approach can be easily paralleled and based on historical data to enhance its performance and application. We demonstrate the computational improvements of our approach through simulations on different test cases in the IEEE PES PGLib-OPF benchmark library.