Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Copyright © 2013 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 09 June 2013, available online: http://www.tandfonline.com/10.1080/00207543.2013.784411Genetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I)

Economic and Emission Dispatch using Whale Optimization Algorithm (WOA)

This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using the above-mentioned algorithm have been compared with obtained results using other intelligent methods such as particle swarm Optimization. The overall performance of this algorithm collates with early proven optimization methodology, Particle Swarm Optimization (PSO). The minimum cost for the generation of units is obtained for the standard bus system

A cuckoo search optimization scheme for non-convex economic load dispatch

This paper presents a Cuckoo Search (CS) based algorithm to solve constrained economic load dispatch (ELD) problems. The proposed methodology easily deals with non-smoothness of cost function arising due to the use of valve point effects. The performance of the algorithm has been tested on systems possessing 13 and 40 generating units involving varying degrees of complexity. The findings affirm that the method outperforms the existing techniques, and can be a promising alternative approach for solving the ELD problems in practical power system

Intelligent Tuned Harmony Search for Solving Economic Dispatch Problem with Valve-point Effects and Prohibited Operating Zones

Economic dispatch with valve point effect and Prohibited Operating Zones (POZs) is a non-convex and discontinuous optimization problem. Harmony Search (HS) is one of the recently presented meta-heuristic algorithms for solving optimization problems, which has different variants. The performances of these variants are severely affected by selection of different parameters of the algorithm. Intelligent Tuned Harmony Search (ITHS) is a recently developed variant, which mitigates the drawbacks of parameter initializing by maintaining a proper balance between diversification and intensification throughout the search process. The proposed method is applied to five different cases of power systems and the effectiveness, feasibility, and robustness of method is explored through the comparison with reported results in recent literature. First three case studies are systems with 3, 13, and 40-units, considering valve- point effect. The fourth and fifth cases are six and 15-generation unit taking into account generator constraints including POZs, ramp rate limit and transmission line losses which is a challenging Economic Dispatch (ED) problem due to restriction in search space. Computation results imply the efficiency of the proposed method toward other optimization methods reported in recent literature, judged in terms of the objective function value and solution robustness

Multi-objective pareto ant colony system based algorithm for generator maintenance scheduling

Existing multi-objective Generator Maintenance Scheduling (GMS) models have considered unit commitment problem together with unit maintenance problem based on a periodic maintenance strategy. These models are inefficient because unit commitment does not undergo maintenance and periodic strategy cannot be applied on different types of generators. Present graph models cannot generate schedule for the multi-objective GMS models while existing Pareto Ant Colony System (PACS) algorithms were not able to consider the two problems separately. A multi-objective PACS algorithm based on sequential strategy which considers unit commitment and GMS problem separately is proposed to obtain solution for a proposed GMS model. A graph model is developed to generate the units’ maintenance schedule. The Taguchi and Grey Relational Analysis methods are proposed to tune the PACS’s parameters. The IEEE RTS 26, 32 and 36-unit dataset systems were used in the performance evaluation of the PACS algorithm. The performance of PACS algorithm was compared against four benchmark multi-objective algorithms including the Nondominated Sorting Genetic, Strength Pareto Evolutionary, Simulated Annealing, and Particle Swarm Optimization using the metrics grey relational grade (GRG), coverage, distance to Pareto front, Pareto spread, and number of non-dominated solutions. Friedman test was performed to determine the significance of the results. The multiobjective GMS model is superior than the benchmark model in producing the GMS schedule in terms of reliability, and violation objective functions with an average improvement between 2.68% and 92.44%. Friedman test using GRG metric shows significant better performance (p-values<0.05) for PACS algorithm compared to benchmark algorithms. The proposed models and algorithm can be used to solve the multi-objective GMS problem while the new parameters’ values can be used to obtain optimal or near optimal maintenance scheduling of generators. The proposed models and algorithm can be applied on different types of generating units to minimize the interruptions of energy and extend their lifespan

Optimal economic dispatch for carbon capture power plants using chaos-enhanced cuckoo search optimization algorithm

Accelerated global demand for low carbon operation of power systems have stimulated interest in Low Carbon Technologies (LCTs). The increased deployment of LCTs within power systems is fundamental to the emission abatement of power system. Carbon Capture Power Plant (CCPP) technology has a good potential for future low carbon emission. Existing Economic Dispatch (ED) formulations do not consider the flexibly-operated CCPPs. Flexible operation of Carbon Capture and Storage (CCS) units transforms conventional power plants in such a way that emission output and power output could be separately controlled. The resulting CCPPs have to be optimized in order to take advantage of the incentives available in both power and carbon markets. This thesis proposes an improved mathematical modelling for flexible operation of CCPPs. The developed work possesses simple and practical variables to appropriately model the flexible operation control of the CCPPs. Using this proposed model a new emission-oriented ED formulation is developed. With this new formulation, the thesis also proposes the concept of decoupling the emission and economic outputs and then quantifies its significance for power system operations. In addition to that, a new Metaheuristic Optimization Technique (MOT) named as Chaos-Enhanced Cuckoo Search Optimization Algorithm (CECSOA) has been developed to improve global optimum result for ED problem. The algorithm has been tested using standard test systems with varying degrees of complexity. The results proved that the CECSOA is superior to the existing techniques in terms of ability to obtain global optimal points and the stability of the solutions obtained. Simulation results also showed the possibility of $1.09 million of annual operational cost savings based on a practical power system located in the Greek island of Crete by applying this methodology in comparison with conventional techniques such as Genetic Algorithm. Further results showed that for a carbon price of 20$/tCO2 and a 60% of system capacity utilization, total emission of a power system is reduced by 10.90% as compared to a “business-as-usual” scenario. In terms of optimal ED for CCPPs, results showed that for carbon prices as low as (~ 8 – 10 $/tCO2), it is economically viable to operate a post-combustion CCS unit

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field

Particle swarm optimisation with applications in power system generation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 12/06/2007.Today the modern power system is more dynamic and its operation is a subject to a number of constraints that are reflected in various management and planning tools used by system operators. In the case of hourly generation planning, Economic Dispatch (ED) allocates the outputs of all committed generating units, which are previously identified by the solution of the Unit Commitment (UC) problem. Thus, the accurate solutions of the ED and UC problems are essential in order to operate the power system in an economic and efficient manner. A number of computation techniques have progressively been proposed to solve these critical issues. One of them is a Particle Swarm Optimisation (PSO), which belongs to the evolutionary computation techniques, and it has attracted a great attention of the research community since it has been found to be extremely effective in solving a wide range of engineering problems. The attractive characteristics of PSO include: ease of implementation, fast convergence compared with the traditional evolutionary computation techniques and stable convergence characteristic. Although the PSO algorithms can converge very quickly towards the optimal solutions for many optimisation problems, it has been observed that in problems with a large number of suboptimal areas (i.e. multi-modal problems), PSO could get trapped in those local minima, including ED and UC problems. Aiming at enhancing the diversity of the traditional PSO algorithms, this thesis proposes a method of combining the PSO algorithms with a real-valued natural mutation (RVM) operator to enhance the global search capability and investigate the performance of the proposed algorithm compared with the standard PSO algorithms and other algorithms. Prior to applying to ED and UC problems, the proposed method is tested with some selected mathematical functions where the results show that it can avoid being trapped in local minima. The proposed methodology is then applied to ED and UC problems, and the obtained results show that it can provide solutions with good accuracy and stable convergence characteristic with simple implementation and satisfactory calculation time. Furthermore, the sensitivity analysis of PSO parameters has been studied so as to investigate the response of the proposed method to the parameter variations, especially in both ED and UC problems. The outcome of this research shows that the proposed method succeeds in dealing with the PSO' s drawbacks and also shows the superiority over the traditional PSO algorithms and other methods in terms of high quality solutions, stable convergence characteristic, and robustness.Royal Thai Government; King Mongkut's Institute of Technology North Bangko

Mathematical framework for designing energy matching and trading within green building neighbourhood system

Nowadays, energy efficiency, energy matching and trading, power production based on renewable energyresources, improving reliability, increasing power quality and other concepts are providing the most important topics in the power systems analysis especially in green building in the neighbourhood systems (GBNS). To do so, the need to obtain the optimal and economical dispatch of energy matching and trading should be expressed at the same time. Although, there are some solutions in literature but there is still a lack of mathematical framework for energy matching and trading in GBNS. In this dissertation, a mathematical framework is developed with the aim of supporting an optimal energy matching and trading within a GBNS.This aim will be achieved through several optimization algorithms based on heuristic and realistic optimization techniques. The appearance of new methods based on optimization algorithms and the challenges of managing a system contain different type of energy resources was also replicating the challenges encountered in this thesis. As a result, these methods are needed to be applied in such a way to achieve maximum efficiency,enhance the economic dispatch as well as to provide the best performance in GBNS. In order to validate theproposed framework, several case studies are simulated in this thesis and optimized based on various optimization algorithms. The better performances of the proposed algorithms are shown in comparison with the realistic optimization algorithms, and its effectiveness is validated over several GBs. The obtained results show convergence speed increase and the remarkable improvement of efficiency and accuracy under different condition. The obtained results clearly show that the proposed framework is effective in achieving optimal dispatch of generation resources in systems with multiple GBs and minimizing the market clearing price for the consumers and providing the better utilization of renewable energy sources