Learning-assisted optimization for transmission switching

In recent years, continuous and reliable electric energy supply is the objective of any power system operation. FACTS devices are promising devices from the past ten years and are becoming popular. Also FACTS devices are very effective solution for many power system transmission problems. FACTS controllers can be used for steady state voltage regulation and control, steady state power flow can be controlled on a transmission line, transient stability enhancement, along with this it also reduces the problem of sub synchronous resonance. In this regard, Flexible Alternating Current Transmission Systems (FACTS) devices play a key role in enhancing controllability and to increase power transfer capability of the network. Thyristor Controlled Series Compensator (TCSC) is an upcoming FACTS device which can be used to meet this objective. The conventional methods in solving optimization problems in power systems suffer from several limitations due to necessities of derivative existence, providing suboptimal solutions, etc. computational Intelligence plays an important role in finding the optimal solutions for multi-objective functions. A combinatorial analysis problem in power systems can be solved by modern heuristic methods in finding optimal solution. Thus, in this paper, Genetic Algorithm, a type of evolutionary computation encapsulated with heuristic approach, is proposed for finding optimal location of TCSC. IEEE standard 30bus system is considered to test the credibility of the proposed algorithm and the results obtained are subjected to analysis to determine the optimal location.

This paper presents a new algorithm to solve economic dispatch (ED) as an optimization problem in power systems. A simple iterated-based algorithm has been developed. It starts with a guess and converges to the optimal point in such a way that the total generation cost of a microgrid is minimized. Since microgrid is considered as a lossless small network, the algorithm is supposed to solve the lossless economic dispatch problem. The proposed method applied on a microgrid consisting of three distributed energy resources (DERs) with convex type of cost functions. The problem was also solved by CPLEX solver as a quadratic programming problem (QP) and the obtained results of proposed algorithm were compared with its results. The comparison confirmed the capability of proposed method to solve ED problem since it was easy to implement with acceptable speed and accuracy.

Optimal power dispatch in hybrid power system for medium- and large-scale practical power systems using self-adaptive bonobo optimizer algorithm

Short term hydro–wind–thermal scheduling based on particle swarm optimization technique

The dynamic economic dispatch (DED) problem is an extension of the conventional static load dispatch problem in the context of electrical power generation. In this paper, issues related to the implementation of the several soft computing techniques are highlighted for a successful application to solve dynamic economic dispatch (DED) problem, which is a constrained optimization problem in power systems. First of all, a survey covering the basics of the techniques is presented and then implementation of the techniques in the DED problem is discussed. The soft computing techniques, namely multi-layered perceptron neural network (MLP NN), genetic algorithm (GA), Imperialist Competitive Algorithm(ICA), particle swarm (PSO) and are applied to solve the DED problem. The Evolutionary Algorithms are tested on power system consisting 3 generating units and the results are compared together. Suggestion is presented to improve techniques.

A new honey bee mating optimization algorithm for non-smooth economic dispatch

International audience

This paper examines how two techniques of the Particle Swarm Optimization method (PSO) can be used to solve the Economic Power Dispatch (EPD) problem. The mathematical model of the EPD is a nonlinear one, PSO algorithms being considered efficient in solving this kind of models. Also, PSO has been successfully applied in many complex optimization problems in power systems. The PSO techniques presented here are applied to three case studies, which analyze power systems having four, six, respectively twenty generating units.

Economic load dispatch is one of the optimization problems in power systems. This paper presents an improved genetic algorithm for economic load dispatch with valve-point loadings. New crossover and mutation operations are introduced. The solutions of the economic load dispatch with valve-point loadings under three cases are solved by the improved genetic algorithm. Test results are given and compared with those from different published genetic algorithms. It is shown that the proposed improved genetic algorithm performs better than the published genetic algorithms.

The optimization of dynamic economic dispatching with valve-point effect (DED-VPE) is a complex non-convex and nonlinear optimization problem in power systems. Responding to these challenges, an improved artificial fish swarm algorithm (IAFSA) was proposed to cope with the DED-VPE problem. The classical artificial fish swarm algorithm (AFSA) has slow convergence speed and obtains solutions of low quality because of the structure defects, the fixed visual and moving step. So, this paper proposes two strategies to overcome these defects and improve the optimization ability of the AFSA, one is self-adaptive visual and moving step, and the other is a new scheme of the preying behavior. Two test systems were used to verify the effectiveness of the proposed method. The findings of case study show the feasibility and effectiveness of the IAFSA in the optimization of DED-VPE problem.

Dynamic economic dispatch (DED) that considers valve-point effects is a complex nonconvex and nonsmooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. In this paper, an adaptive differential evolution based on simulated annealing algorithm is proposed to solve the DED problem with valve-point effects. Simulated annealing (SA) algorithm is employed to carry out an adaptive selection mechanism in which the mutation operators of differential evolution (DE) are selected adaptively based on their historical performance. A mutation operator pool consisting of five operators is built to make each operator show its strength at different stages of the evolutionary process. Moreover, a heuristic strategy is introduced to transform infeasible solutions towards feasible ones to enhance the convergence rate of the proposed algorithm. The effectiveness of the proposed methods is demonstrated first on 10 popular benchmark functions with 100 dimensions, in comparison with the classic DE and five variants. Then, it is used to solve four DED problems with 10, 15, 30, and 54 units, which consider the valve-point effects, transmission loss, and prohibited operating zones. The simulation results are compared with those of state-of-the-art algorithms to clarify the significance of the proposed method and verify its performance. Three systems with 100-500 generators are also tested to confirm the advantages of the proposed method on large-scale DED problem.

Unit Commitment is one of the most complex and difficult optimization problem in power systems. The objective of the optimal commitment is to determine the on/off states of the units in the system to meet the load demand and spinning reserve requirements at each time period, such that the overall cost of generation is minimized, while satisfying the various operational constraints. This paper presents the application of an improved genetic algorithm (GA) with crossover, mutation, and advanced operators such as repair and swap mutation to determine the commitment order of the thermal units in power generation. The proposed GA has been successfully applied to 10 and 26 generating-unit systems. Robustness of the proposed GA is demonstrated by comparison to the dynamic programming algorithm. The comparison results show the effectiveness of the proposed GA in solving the unit commitment problem with sufficient accuracy and low computational time.

NETOMAC program is one of the most powerful software systems which has been widely used for the simulation of power systems. In order to solve various non‐linear optimization problems arising in power systems, an optimization algorithm based on the Quasi‐Newton Technique is presented in this paper. The algorithm that works as a special mode of the simulation program can be flexibly used to most describable non‐linear optimization problems either static or dynamic, and provides acceptable solutions under the consideration of all possible operational and security constraints. The algorithm offers substantial improvements in practical applications mainly due to adaptive scaling by heuristic considerations of the variable, handling the bound constraints directly with an efficient quadratic programming approach and using a modified augmented Lagrange multiplier method to deal with the functional constraints. The paper concludes with the three application examples for solving different optimization problems of power systems.

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 30-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

Alternating current optimal power flow (AC OPF) is one of the most fundamental optimization problems in electrical power systems. It can be formulated as a semidefinite program (SDP) with rank constraints. Solving AC OPF, that is, obtaining near optimal primal solutions as well as high quality dual bounds for this non-convex program, presents a major computational challenge to today's power industry for the real-time operation of large-scale power grids. In this paper, we propose a new technique for reformulation of the rank constraints using both principal and non-principal 2-by-2 minors of the involved Hermitian matrix variable and characterize all such minors into three types. We show the equivalence of these minor constraints to the physical constraints of voltage angle differences summing to zero over three- and four-cycles in the power network. We study second-order conic programming (SOCP) relaxations of this minor reformulation and propose strong cutting planes, convex envelopes, and bound tightening techniques to strengthen the resulting SOCP relaxations. We then propose an SOCP-based spatial branch-and-cut method to obtain the global optimum of AC OPF. Extensive computational experiments show that the proposed algorithm significantly outperforms the state-of-the-art SDP-based OPF solver and on a simple personal computer is able to obtain on average a 0.71% optimality gap in no more than 720 seconds for the most challenging power system instances in the literature.