Enhanced Genetic Algorithm based computation technique for multi-objective Optimal Power Flow solution

This study proposes an improved particle swarm optimization method to find optimal power flow by using the power transmission loss as an objective function. In the literature, PSO is a well — known intelligent search method to handle the solution of optimal power flow problem. A novel scheme which is called Improved PSO (IPSO) is defined by modifying the initialization step of PSO algorithm and choosing the load bus voltages, generator active and reactive powers, line flow capacities as penalty functions in the objective function. PSO and IPSO-based optimal power flow solutions are compared with each other on IEEE 118 and 300 bus systems. According to the test results, the power loss obtained by IPSO-based solution has less power loss than PSO-based solution. This proposed method can be used to obtain faster desirable solutions and better power loss results for the optimal power flow problem in case of power loss minimization.

In this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.

This paper deals with solving optimal power flow problems using one of the new meta-heuristic optimizations inspired by the behavior of grey wolves (Canis lupus). Optimal power flow (OPF) faced several challenged such as increased of fuel cost of generation units, increased the real power losses at transmission lines, increased of voltage deviation of each bus, and uncontrolled of voltage stability for whole system. Therefore, in this paper, the objective functions that have been optimized include generation cost (GC), power loss (PL), voltage deviation (VD), and voltage stability index (VSI). The GWO algorithm has been applied on the Iraqi Super Grid High Voltage 400 kV (ISGHV 400 kV) to set the optimal control variables. The results obtained by this algorithm have been compared with the results obtained by the initial study case to determine the percentage of reduction rate. This comparison proved the efficiency and superiority of the GWO algorithm when applied to the power system networks. The percentages of reduction rates compared with the initial case are 45.08%, 27.28%, 70.64%, and 15.40% of generation cost (GC), power loss (PL), voltage deviation (VD), and voltage stability index (VSI), respectively.KeywordsOptimal Power Flow (OPF)Grey Wolf Optimizer (GWO)Iraqi Super Grid High Voltage (ISGHV) 400 kV

This paper investigates potential of modified differential evolution (MDE) algorithm, to find solution for optimal power flow (OPF) problem of power system. The performance of the proposed MDE algorithm is experienced on the modified IEEE-30 bus test systems integrating flexible ac transmission systems (FACTS) devices, that is, thyristor controlled phase shifter (TCPS) and thyristor controlled series capacitor (TCSC) at preset locations. The OPF problem of the present work is formulated with five different objective functions viz. (a) fuel cost (with valve point effect) minimization, (b) power transmission losses minimization, (c) emission reduction (d) fuel cost (without valve point effect) minimization and (e) minimization of combined economic and environmental cost. The proposed implementation exploits MDE, resulting in a much shorter execution time while retaining the best possible solution of OPF problem.

Optimal power flow solution using fuzzy evolutionary and swarm optimization

This paper presents the solution of optimal power flow (OPF) using particle swarm optimization (PSO).The objective function is minimize power loss by adjustment the power control variables and at the same time satisfying the equality and inequality constraints.The proposed particle swarm optimization (PSO) method is compared with newton's Raphson method (conventional method) approach on the standard IEEE 14 bus system.The analysis indicated that Particle Swarm Optimization (PSO) method was the most efficient method in terms of minimizing the power loss.This can be concluded that the Artificial Intelligence (AI) method such as Particle Swarm Optimization (PSO) is the most suitable and efficient method for analysing the Optimal Power Flow (OPF) problem in terms of minimizing power loss.

Multi-objective adaptive clonal selection algorithm for solving optimal power flow considering multi-type FACTS devices and load uncertainty

Optimal power flow using gravitational search algorithm

Although a significant amount of research has been directed to developing necessary and sufficient conditions for the optimal solutions of several engineering problems, there is little work on the development of a necessary and sufficient condition for optimal power flow (OPF) solutions. This paper seeks to develop the following: (1) a necessary and sufficient condition for a local OPF solution, and (2) a necessary and sufficient condition for an OPF Karush-Kuhn-Tucker (KKT) solution. The derived necessary and sufficient conditions were numerically verified on IEEE 30-, 57- and 118-bus systems. Computationally, the derived necessary and sufficient condition is also useful in assisting existing OPF methods to obtain OPF solutions instead of KKT solutions (i.e., KKT saddle points).

The optimal power flow (OPF) problem is a well-known non-convex optimization problem that aims to minimize the cost of electric power generation subject to consumer demand, the physics of power flow, and technological constraints. To find an optimal solution to this problem, local search techniques such as interior point methods are typically used. However, due to the non-convex nature of the problem, these methods are likely to result in a sub-optimal solution. The goal of this paper is to characterize the worst-case performance of local search on the OPF problem. To accomplish this, we formulate the OPF problem as a canonical quadratically-constrained quadratic program (QCQP). Then, we study the problem of finding the worst-case local minimum of this QCQP, which is non-convex and hard to solve in general. We find a relaxation of this problem into a semidefinite program (SDP) and show that it is exact for certain cases. Using some test cases which are known to have multiple local minima, we demonstrate the effectiveness of the proposed relaxation to bound the worst-case local minimum. We compare the obtained upper bound on local minima to the lower bound provided by the standard SDP relaxation of the OPF problem to understand how much SDP outperforms local search for a given problem.

Recent years have witnessed the success of employing convex relaxations of the AC optimal power flow (OPF) problem to find global or near-global optimal solutions. The majority of the effort has focused on solving problem formulations where variables live in continuous spaces. Our focus here is in the extension of these results to the co-optimization of network topology and the OPF problem. We employ binary variables to model topology reconfiguration in the standard semidefinite programming (SDP) formulation of the OPF problem. This makes the problem non-convex, not only because the variables are binary, but also because of the presence of bilinear products between the binary and other continuous variables. Our proposed convex relaxation to this problem incorporates the bilinear terms in a novel way that improves over the commonly used McCormick approximation. We also address the exponential complexity associated with the discrete variables by partitioning the network graph in a way that minimizes the impact on the optimal value of the relaxation. As a result, the problem is broken down into several parallel mixed-integer problems, reducing the overall computational complexity. Simulations in the IEEE 118-bus test case demonstrate that our approach converges to solutions which are very close to the lower bound of the mixed-integer OPF problem.

In this work is proposed a solution method for the optimal power flow (OPF) problem, including the synchronous generator capability curve constraints (SGCC). OPF is a non-convex, non-linear, and hard to solve optimization problem. When synchronous generator operational limits are not concerned, OPF solution could not be applicable in real scenarios, taking the machine to operate with winding currents above acceptable values. The SGCC used in this research includes current and power limits allowed in the generator, preventing the machine operation against overheating and excessive mechanical efforts. Convex relaxation methods show a great performance, allowing the problem to be solved in a simpler and way. Thus, OPF is formulated over a second order cone programming (SOCP) approach from convex relaxation techniques, aiming the active power generators cost minimization, subject to the electrical transmission network constraints. SGCC used has a convex form, which allows the convex programming method application, with no convexification need for the optimization model. The proposed method was implemented in MATLAB® environment and applied to IEEE 30-bus test system. Simulation results demonstrate the applicability and good performance of the proposed method, so that solutions do not violate the constraints imposed by the actual operational limits of the generators.

A new hybrid evolutionary algorithm for multi-objective optimal power flow in an integrated WE, PV, and PEV power system

In this paper, a new method to incorporating FACTS devices in optimal power flow (OPF) problem is presented. This method can provide an enhanced economic solution with the use of controllable FACTS devices. Two types of FACTS devices, thyristor controlled series compensators (TCSC) and thyristor controlled phase shifters (TCPS), are considered in this method. The specified power flow control constraints due to the use of FACTS devices are included in the OPF problem. The proposed method decomposes the solution of such modified OPF problem into two subproblems' iteration. The first subproblem is a power flow control problem and the second subproblem is a conventional OPF problem. The two subproblems are solved iteratively until convergence. Case studies are presented to show the effectiveness of the proposed method.

For operating electrical power networks, the Optimal Power Flow (OPF) problem plays a central role. The problem is nonconvex and NP hard. Therefore, designing efficient solution algorithms is crucial, though their global optimality is not guaranteed. Existing semi-definite programming relaxation based approaches are restricted to OPF problems for which zero duality holds, whereas for non-convex problems there is a lack of solution methods of provable performance. In this paper, an efficient novel method to address the general nonconvex OPF problem is investigated. The proposed method is based on alternating direction method of multipliers combined with sequential convex approximations. The global OPF problem is decomposed into smaller problems associated to each bus of the network, the solutions of which are coordinated via a light communication protocol. Therefore, the proposed method is highly scalable. The convergence properties of the proposed algorithm are mathematically and numerically substantiated.