| Title | AI-Driven Security Constrained Unit Commitment Using Eigen Decomposition And Linear Shift Factors |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Iqbal, Talha, Banna, Hasan Ul, Feliachi, Ali |
| Conference Name | 2021 North American Power Symposium (NAPS) |
| Keywords | artificial intelligence, composability, compositionality, Data analysis, decomposition, Eigen Value Decomposition, Electricity supply industry, Generation Shift Factor, machine learning, Metrics, North America, Optimization, Planning, Power systems, Power Transfer Distribution Factor, pubcrawl, security, Security Constrained Unit Commitment |
| Abstract | Unit Commitment (UC) problem is one of the most fundamental constrained optimization problems in the planning and operation of electric power systems and electricity markets. Solving a large-scale UC problem requires a lot of computational effort which can be improved using data driven approaches. In practice, a UC problem is solved multiple times a day with only minor changes in the input data. Hence, this aspect can be exploited by using the historical data to solve the problem. In this paper, an Artificial Intelligence (AI) based approach is proposed to solve a Security Constrained UC problem. The proposed algorithm was tested through simulations on a 4-bus power system and satisfactory results were obtained. The results were compared with those obtained using IBM CPLEX MIQP solver. |
| DOI | 10.1109/NAPS52732.2021.9654579 |
| Citation Key | iqbal_ai-driven_2021 |
AI-Driven Security Constrained Unit Commitment Using Eigen Decomposition And Linear Shift Factors