Solving Optimal Power Flow Problem Research Articles

Distributed optimal power flow for smart grid transmission system with renewable energy sources

Utilizing renewable energy sources to reduce carbon emission and minimizing the fuel cost for energy saving in the OPF (optimal power flow) problem will contribute to reducing the global warming effect from the power generation sector. In this paper, we propose a DPOPF (distributed and parallel OPF) algorithm for the smart grid transmission system with renewable energy sources to account for the fast variation of the power generated by renewable energy sources. The proposed DPOPF algorithm is a combination of the recursive quadratic programming method and the Lagrange projected gradient method; it can achieve the complete decomposition and can be executed in the smart grid transmission system to make distributed and parallel computation possible. We also propose Petri nets to control the computational synchronization of the DPOPF algorithm under the asynchronous data arrival in the smart grid transmission system.The proposed DPOPF algorithm is applied to solve OPF problems in a smart grid transmission system with renewable energy sources on a 26-bus test system. The test results demonstrate the computational efficiency of the proposed DPOPF algorithm, which is fast enough to cope with the fast variation of the power generated by renewable energy sources, and justify the accuracy of the obtained solutions.

Optimal power flow solution using improved harmony search method

This paper describes the improved harmony search method (IHS) to solve optimal power flow (OPF) problems. The harmony search is one of meta-heuristic search methods inspired by the improvisation of musicians developed by Geem (2001) [23]. The proposed algorithm was tested with five standard IEEE test systems (6-bus, 14-bus, 30-bus, 57-bus and 118-bus test systems). The tests were divided into smooth and non-smooth fuel-cost cases. The comparisons among solutions obtained by sequential quadratic programming (SQP), genetic algorithms (GA) and IHS were conducted. As revealed from the simulated results, the effectiveness of the IHS for solving OPF problems was confirmed.

Computational Analysis of Different Artificial Intelligence Based Optimization Techniques for Optimal Power Flow and Economic Load Dispatch Problem

The purpose of this paper is to present a computational Analysis of various Artificial Intelligence based optimization Techniques used to solve OPF problems. The various Artificial Intelligence methods such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO), Bacterial Foraging Optimization(BFO), ANN are studied and analyzed in detail. The objective of an Optimal Power Flow (OPF) algorithm is to find steady state operation point which minimizes generation cost and transmission loss etc. or maximizes social welfare, load ability etc. while maintaining an acceptable system performance in terms of limits on generators’ real and reactive powers, power flow limits, output of various compensating devices etc. Traditionally, Classical optimization methods were used effectively to solve optimal power flow. But, recently due to the incorporation of FACTS devices and deregulation of power sector the traditional concepts and practices of power systems are superimposed by an economic market management and hence OPF have become more complex. So, in recent years, Artificial Intelligence (AI) methods have been emerged which can solve highly complex OPF problems at faster rate.

Application of Key Cutting Algorithms for Optimal Power Flow Problems

This paper illustrates an application of Key Cutting Algorithm (KCA) to optimal power flow (OPF) problems in comparative with some effective mathematical and evolutionary optimization methods. The KCA is one of intelligence algorithms (AI), which was just developed since 2009. This algorithm emulates the work of locksmiths to open the lock. The best key that matches a given lock is pretended to be an optimal solution of a relevant optimization problem. The basic structure of the key cutting algorithm is as simple as that of genetic algorithms in which a string of binary numbers is employed as a key to open the lock. The proposed algorithm was tested with four mathematical test functions and three standard IEEE test power systems (6-bus, 14-bus and 30-bus test systems). The test power systems were divided into two cases. The first test case was given by applying a quadratic function to generators’ fuel-cost curve whereas a non-smooth fuel-cost function was assigned to the second. The comparisons among solutions obtained by sequential quadratic programming (SQP), genetic algorithms (GA), particle swarm optimization (PSO) and key cutting algorithm (KCA) were carried out. As revealed from the simulated results, the effectiveness of the KCA algorithm for solving OPF problems was confirmed.

Evolutionary Programming Based Optimal Power Flow for Units with Non-Smooth Fuel Cost Functions

This article presents the solution of optimal power flow (OPF) problem of generator units with ramp rate limits and non-smooth fuel functions. In this article evolutionary programming (EP) algorithm is devoted to solve the OPF problem with non-smooth fuel cost functions like quadratic, piece-wise, valve point loading and combined cycle cogeneration plants. In the proposed EP algorithm, mutation is changing non-linearly with respect to the number of generations to avoid premature condition. The proposed EP algorithm is demonstrated to solve OPF problem for IEEE-30 bus system and Indian utility-62 bus system with line flow constraints. The line flows in MVA are computed directly from Newton Raphson method. The test results prove that the EP method is simpler and more efficient for solving OPF problem with non-smooth fuel cost functions with many constraints.

Hybrid algorithm of chaos optimisation and SLP for optimal power flow problems with multimodal characteristic

Optimal power flow (OPF) is an important tool in an electricity market Despite traditional optimisation techniques being implemented successfully in the existing power systems, there are still difficulties. One of these difficulties is that it is easy to be caught by a local minimum solution because OPF is a mathematically nonconvex problem with multimodal characteristics. The paper proposes a hybrid optimisation algorithm for solving OPF problems with multimodal characteristics. The hybrid algorithm is structured in two stages. The first stage uses the chaos optimisation algorithm (COA), while successive linear programming (SLP) is employed in the second stage. A linear predictor–corrector primal–dual interior point (PCPDIP) method is applied to solving a sequence of linearised sub-problems. The hybrid algorithm involves two levels of optimisation, namely global search by the COA and local search by the linear interior point method, which co-operates in a global process of optimisation. It can provide more robust convergence on OPF problems than either algorithm alone. IEEE-14, -30 and -57 bus test systems with complex objective functions are run. Numerical results illustrate the feasibility and potential of the proposed hybrid algorithm.

Optimal power flow using particle swarm optimization

This paper presents an efficient and reliable evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs particle swarm optimization (PSO) algorithm for optimal settings of OPF problem control variables. Incorporation of PSO as a derivative-free optimization technique in solving OPF problem significantly relieves the assumptions imposed on the optimized objective functions. The proposed approach has been examined and tested on the standard IEEE 30-bus test system with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. The proposed approach results have been compared to those that reported in the literature recently. The results are promising and show the effectiveness and robustness of the proposed approach.

Optimal Power Flow Using Tabu Search Algorithm

This paper presents an efficient and reliable tabu search (TS)-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs TS algorithm for optimal settings of the control variables of the OPF problem. Incorporation of TS as a derivative-free optimization technique in solving OPF problem significantly reduces the computational burden. One of the main advantages of TS algorithm is its robustness to its own parameter settings as well as the initial solution. In addition, TS is characterized by its ability to avoid entrapment in local optimal solution and prevent cycling by using flexible memory of search history. The proposed approach has been examined on the standard IEEE 30-bus test system with different objectives and generator cost curves. The results are promising and show the effectiveness and robustness of the proposed approach.