{
  "id": "2105.05336",
  "version": "v2",
  "published": "2021-05-11T20:31:20.000Z",
  "updated": "2021-11-11T15:07:07.000Z",
  "title": "Efficient Solution Strategy for Chance-Constrained Optimal Power Flow based on FAST and Data-driven Convexification",
  "authors": [
    "Ren Hu",
    "Qifeng Li"
  ],
  "categories": [
    "math.OC",
    "cs.SY",
    "eess.SY"
  ],
  "abstract": "The uncertainty of multiple power loads and renewable energy generations (PLREG) in power systems increases the complexity of power flow analysis for decision-makers. The chance-constrained method can be applied to model the optimization problems of power flow under uncertainty. This paper develops a novel solution approach for chance-constrained AC optimal power flow (CCACOPF) problem based on the data-driven convexification of power flow and a fast algorithm for scenario technique (FAST). This method is computationally effective for mainly two reasons. First, the original nonconvex AC power flow (ACPF) constraints are approximated by a set of learning-based quadratic convex ones. Second, FAST is an advanced scenario-based solution method (SSM) that doesn't rely on the pre-assumed probability distribution, using far less scenarios than the conventional SSM. Eventually, the CCACOPF is converted into a computationally tractable convex optimization problem. The simulation results on IEEE test cases indicate that 1) the proposed solution method can outperform the conventional SSM in computational efficiency, 2) the data-driven convexification of power flow is effective in approximating original complex AC power flow.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2021-11-11T15:07:07.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "49-02",
      "I.2.6"
    ],
    "keywords": [
      "chance-constrained optimal power flow",
      "efficient solution strategy",
      "data-driven convexification",
      "ac optimal power flow",
      "nonconvex ac power flow"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}