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

A layered matrix encoding cascade genetic algorithm and particle swarm optimization approach (GA-PSO) for unit commitment and economic load dispatch problem in a thermal power system is presented in this paper. The tasks of determining and allocating power generation to different thermal units in a way that the total power production cost is at the minimum subject to equality and inequality constraints makes unit commitment and economic load dispatch challenging. A case study, based on the thermal power generation problem presented in [1], is used to demonstrate the effectiveness of the proposed method in generating a cost-effective power generation schedule. The schedule obtained is compared with that of Linear Programming (LP) as reported in [1]. The results show that the proposed GA-PSO approach outperforms LP in solving the unit commitment and economic load dispatch problem for thermal power generation system in this case study.

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.

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.

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

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 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.

The design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units. With the increasing variability in the operating conditions of power grids, the DC-OTS problem has lately sparked renewed interest, because operational strategies that include topological network changes have proved to be effective and efficient in helping maintain the balance between generation and demand. The DC-OTS problem includes a set of binaries that determine the on/off status of the switchable transmission lines. Therefore, it takes the form of a mixed-integer program, which is NP-hard in general. In this paper, we propose an approach to tackle the DC-OTS problem that leverages known solutions to past instances of the problem to speed up the mixed-integer optimization of a new unseen model. Although our approach does not offer optimality guarantees, a series of numerical experiments run on a real-life power system dataset show that it features a very high success rate in identifying the optimal grid topology (especially when compared to alternative competing heuristics), while rendering remarkable speed-up factors.

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.

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.

Firefly algorithm (FFA) optimization technique is a popular continuous optimization technique and also widely applied to tune controller gain values of proportional–integral–derivative (PID) controller. In the chaos-based firefly algorithm, the chaotic maps are included to improve the randomness when generating new solutions and thereby to increase the diversity of the population. It increases global search mobility for robust global optimization. The investigated power system incorporates two thermal power systems, and two areas are interconnected via tie-line. Also, this work system is equipped with a secondary (PID) controller. The gain value of the controller is optimized by implementing chaos-based firefly algorithm (CFFA) by applying step load in area 1. The effectiveness and supremacy of proposed optimization technique tuned controller performance are verified by comparing genetic algorithm (GA), particle swarm optimization (PSO) technique and firefly algorithm tuned PID controller performance in the same interconnected power system. The proposed optimization technique-based controller response is evaluated by considering time domain specifications parameters of the proposed optimization technique-based controller response. Further, effectiveness of recommended optimization technique is verified by adding nonlinearity’s [generation rate constraint (GRC) and governor dead band (GCB)] in the investigated power system with connecting unified power flow controller (UPFC) is applied, and it is a flexible alternating current transmission system (FACTS) controller family device connected parallel to the tie-line. Further, hydrogen aqua electrolyzer (HAE) energy storage unit with a fuel cell is connected in area 2 for improving the performance of the system during sudden load demand. Finally, simulation result proved that the proposed CFFA technique gives better controlled performance compared with GA, PSO and FFA technique tuned controller performance in terms of fast settled response with a minimum peak over and undershoot, damping oscillations during unexpected load demand conditions.

Chapter 28 - Optimal power generation and power flow control using artificial intelligence techniques