DC Optimal Power Flow With Joint Chance Constraints
| Autor: | Daniel K. Molzahn, Andreas Wächter, Line Roald, Alejandra Pena-Ordieres |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Computer science 020209 energy Energy Engineering and Power Technology Approximation algorithm Sample (statistics) 02 engineering and technology Grid Power (physics) Nonlinear programming Electric power system Test case 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Random variable |
| Zdroj: | IEEE Transactions on Power Systems. 36:147-158 |
| ISSN: | 1558-0679 0885-8950 |
| Popis: | Managing uncertainty and variability in power injections has become a major concern for power system operators due to increasing levels of fluctuating renewable energy connected to the grid. This work addresses this uncertainty via a joint chance-constrained formulation of the DC optimal power flow (OPF) problem, which satisfies all the constraints jointly with a pre-determined probability. The few existing approaches for solving joint chance-constrained OPF problems are typically either computationally intractable for large-scale problems or give overly conservative solutions that satisfy the constraints far more often than required, resulting in excessively costly operation. This paper proposes an algorithm for solving joint chance-constrained DC OPF problems by adopting an S $\ell _1$ QP-type trust-region algorithm. This algorithm uses a sample-based approach that avoids making strong assumptions on the distribution of the uncertainties, scales favorably to large problems, and can be tuned to obtain less conservative results. We illustrate the performance of our method using several IEEE test cases. The results demonstrate the proposed algorithm's advantages in computational times and limited conservativeness of the solutions relative to other joint chance-constrained DC OPF algorithms. |
| Databáze: | OpenAIRE |
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