10.1007/s40095-022-00509-1

Improving the initialization of a stochastic AC-QP optimal power flow algorithm

  • Daniel Piccoli1,
  • Jennifer Marley*1,
  1. Electrical and Computer Engineering Department, Valparaiso University, Valparaiso, IN, 46383, US

Published in Issue 2022-06-10

How to Cite

Piccoli, D., & Marley, J. (2022). Improving the initialization of a stochastic AC-QP optimal power flow algorithm. International Journal of Energy and Environmental Engineering, 14(2 (June 2023). https://doi.org/10.1007/s40095-022-00509-1
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Abstract

Abstract As the penetration of renewable generation in electricity networks increases, so does the challenge of maintaining the reliability of those networks. This work utilizes a scenario-based approach to solving a stochastic AC optimal power flow (OPF) problem. In addition to producing an optimal operating point, this method offers a-posteriori theoretical guarantees on the probability of violation for any arbitrary wind scenario that may be encountered in real-time operation. This work expands upon prior formulations by proposing six methods of initializing the stochastic AC-QP OPF algorithm. Results are presented for both the IEEE 14-bus and 3012wp networks; each network was augmented with two wind nodes. The performance of all initialization methods is assessed with regard to the total number of support scenarios, total execution time, and cost of operation. Each is shown to outperform a random initialization process. Trade-offs among these methods with respect to computational complexity and efficacy are also discussed.

Keywords

  • Renewable generation,
  • Forecast uncertainty,
  • K-means clustering,
  • AC optimal power flow

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