{
  "id": "1909.12763",
  "version": "v1",
  "published": "2019-09-27T16:02:33.000Z",
  "updated": "2019-09-27T16:02:33.000Z",
  "title": "Solving Optimal Power Flow for Distribution Networks with State Estimation Feedback",
  "authors": [
    "Yi Guo",
    "Xinyang Zhou",
    "Changhong Zhao",
    "Yue Chen",
    "Tyler Summers",
    "Lijun Chen"
  ],
  "categories": [
    "math.OC",
    "cs.SY",
    "eess.SY"
  ],
  "abstract": "We propose an algorithm to solve an optimal power flow (OPF) problem with state estimation (SE) feedback for distribution networks with limited sensor allocation for system monitoring. The framework integrates state estimation into traditional optimal power flow. Instead of physically monitoring all states, here we consider an estimation algorithm acting as a feedback loop within an online gradient-based OPF controller to monitor and feed-in the real-time information. The estimation algorithm reduces uncertainty on unmeasured grid states based on a few appropriate online state measurements and noisy \"pseudo-measurements\". We analytically investigate the convergence of the proposed algorithm. The numerical results demonstrate that this approach is more robust to large pseudo measurement variability and inherent sensor noise in comparison to the other frameworks without SE feedback.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-09-27T16:02:33.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "solving optimal power flow",
      "state estimation feedback",
      "distribution networks",
      "appropriate online state measurements",
      "framework integrates state estimation"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}