Reactive Power Optimization Model for Distribution Networks Based on the Second-Order Cone and Interval Optimization

Abstract

Traditional reactive power optimization mainly considers the constraints of active management elements and ignores the randomness and volatility of distributed energy sources, which cannot meet the actual demand. Therefore, this paper establishes a reactive power optimization model for active distribution networks, which is solved by a second-order cone relaxation method and interval optimization theory. On the one hand, the second-order cone relaxation technique transforms the non-convex optimal dynamic problem into a convex optimization model to improve the solving efficiency. On the other hand, the interval optimization strategy can solve the source–load uncertainty problem in the distribution network and obtain the interval solution of the optimization problem. Specially, we use confidence interval estimation to shorten the interval range, thereby improving the accuracy of the interval solution. The model takes the minimum economy as the objective function and considers a variety of active management elements. Finally, the modified IEEE 33 node arithmetic example verifies the feasibility and superiority of the interval optimization algorithm.

Research on Reactive Power Optimization Based on Hybrid Osprey Optimization Algorithm

“…In the IOOA, random numbers subject to Weibull distribution are introduced into the position update process of phase 1 of the osprey optimization algorithm as a step factor. The probability density function of Weibull distribution is shown in Equation (11):…”

“…The traditional mathematical methods include the linear programming method [5], nonlinear programming method [6], simplified gradient method [7], sequential quadratic programming method [8], Newton method [9], interior point method [10], etc. A second-order cone programming model was established in reference [11] to transform non-convex optimization problems into convex optimization problems to make more effective use of distributed generation and reduce network losses and voltage fluctuations, and its effectiveness was verified with the IEEE33 system. Each of these methods has certain adaptability and advantages, but the mathematical methods also have great disadvantages, such as not dealing with discrete variables so well, and an easy to fall into the local optimal.…”