“…Primal-Dual interior point based solution methods have been applied to the KKT-conditions (3) of non-linear OPF-problem formulations [9,10,11,12,13,14,15]. All these approaches solve a linear system of equations per Newton-Raphson step.…”
Section: Class B2: Interior Pointmentioning
confidence: 99%
“…The goal of this algorithm is the solution of the transformed optimality conditions as presented in (11). This solution is achieved by a simple Newton-Raphson algorithm as derived in the following subsection using no logarithmic barrier parameter and no step length control to stay within the feasible space (note: the feasible space is infinite for all variables).…”
A non-linear Optimal Power Flow (OPF) algorithm is presented which allows to solve the KarushKuhn-Tucker (KKT) optimality conditions using a pure Newton-Raphson solution procedure. The method is similar to interior point algorithms. However, due to a simple transformation, the variable space becomes unlimited (Unlimited Point) and variables do not need to be forced to stay within the feasible region during all OPF iterations as is the case for interior point algorithms. As a consequence only a pure Newton-Raphson iterative process to algebraically transformed KKT conditions is applied. The algorithm has been successfully applied to various networks up to size 700 buses with the active power loss objective function.
“…Primal-Dual interior point based solution methods have been applied to the KKT-conditions (3) of non-linear OPF-problem formulations [9,10,11,12,13,14,15]. All these approaches solve a linear system of equations per Newton-Raphson step.…”
Section: Class B2: Interior Pointmentioning
confidence: 99%
“…The goal of this algorithm is the solution of the transformed optimality conditions as presented in (11). This solution is achieved by a simple Newton-Raphson algorithm as derived in the following subsection using no logarithmic barrier parameter and no step length control to stay within the feasible space (note: the feasible space is infinite for all variables).…”
A non-linear Optimal Power Flow (OPF) algorithm is presented which allows to solve the KarushKuhn-Tucker (KKT) optimality conditions using a pure Newton-Raphson solution procedure. The method is similar to interior point algorithms. However, due to a simple transformation, the variable space becomes unlimited (Unlimited Point) and variables do not need to be forced to stay within the feasible region during all OPF iterations as is the case for interior point algorithms. As a consequence only a pure Newton-Raphson iterative process to algebraically transformed KKT conditions is applied. The algorithm has been successfully applied to various networks up to size 700 buses with the active power loss objective function.
“…Classical optimization methods, such as gradient based optimization algorithm [1,2], quadratic programming, non linear programming [3] and interior point method [4][5][6][7].…”
Abstract-This paper proposes an efficient differential evolution (DE) algorithm for the solution of the optimal reactive power dispatch (ORPD) problem. The main objective of ORPD is to minimize the total active power loss with optimal setting of control variables. The continuous control variables are generator bus voltage magnitudes. The discrete control variables are transformer tap settings and reactive power of shunt compensators. In DE algorithm the other form of differential mutation operator is used. It consists to add the global best individual in the differential mutation operator to improve the solution. The DE algorithm solution has been tested on the standard IEEE 30-Bus test system to minimize the total active power loss without and with voltage profile improvement. The results have been compared to the other heuristic methods such as standard genetic algorithm and particle swarm optimization method. Finally, simulation results show that this method converges to better solutions.
“…In [63] , a modifi ed IP DAS algorithm was proposed. In [64] , an interior point method was proposed for linear and convex quadratic programming. It is used to solve power system optimization problems such as economic dispatch and reactive power planning.…”
Section: Development Of Optimization Techniques In Opf Solutionsmentioning
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