Large-scale economic load dispatch using squirrel search algorithm

2020 ◽  
Vol 14 (6) ◽  
pp. 1351-1380
Author(s):  
Sakthivel V.P. ◽  
Suman M. ◽  
Sathya P.D.
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Research Work ◽  
Superior Performance ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Content Type

Purpose Economic load dispatch (ELD) is one of the crucial optimization problems in power system planning and operation. The ELD problem with valve point loading (VPL) and multi-fuel options (MFO) is defined as a non-smooth and non-convex optimization problem with equality and inequality constraints, which obliges an efficient heuristic strategy to be addressed. The purpose of this study is to present a new and powerful heuristic optimization technique (HOT) named as squirrel search algorithm (SSA) to solve non-convex ELD problems of large-scale power plants. Design/methodology/approach The suggested SSA approach is aimed to minimize the total fuel cost consumption of power plant considering their generation values as decision variables while satisfying the problem constraints. It confers a solution to the ELD issue by anchoring with foraging behavior of squirrels based on the dynamic jumping and gliding strategies. Furthermore, a heuristic approach and selection rules are used in SSA to handle the constraints appropriately. Findings Empirical results authenticate the superior performance of SSA technique by validating on four different large-scale systems. Comparing SSA with other HOTs, numerical results depict its proficiencies with high-qualitative solution and by its excellent computational efficiency to solve the ELD problems with non-smooth fuel cost function addressing the VPL and MFO. Moreover, the non-parametric tests prove the robustness and efficacy of the suggested SSA and demonstrate that it can be used as a competent optimizer for solving the real-world large-scale non-convex ELD problems. Practical implications This study has compared various HOTs to determine optimal generation scheduling for large-scale ELD problems. Consequently, its comparative analysis will be beneficial to power engineers for accurate generation planning. Originality/value To the best of the authors’ knowledge, this manuscript is the first research work of using SSA approach for solving ELD problems. Consequently, the solution to this problem configures the key contribution of this paper.

Implementation of Smooth and Non-Smooth Fuel Cost Function for Economic Load Dispatch using Cuckoo Search Method

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Apurva Gautam ◽  
Anupam Masih
Keyword(s):  
Cost Function ◽  
Power Plants ◽  
Search Algorithm ◽  
Inequality Constraints ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Practical Applications ◽  
Equality And Inequality Constraints

Economic Load Dispatch (ELD) is an important topic in the operation of power plants which can help to build up effecting generating management plans. The ELD problem has no smooth cost function with equality and inequality constraints which make it difficult to be effectively solved. The paper presents an application of Cuckoo Search Algorithm (CSA) to solve Economic Load Dispatch (ELD) problems with smooth and non-smooth fuel cost objective functions. Main objective of ELD is to determine the most economic generating dispatch required to satisfy the predicted load demands including line losses over a certain period of time while relaxing various equality and inequality constraints. The unit Min/Max operational constraints, effects of valve-point loading ripples and line losses are considered for the practical applications. This paper describes the implementation of smooth and non smooth fuel cost function by CSA Method and its comparison with BAT method. We have used 6 and 12 bus system for calculating their total fuel cost.

scholarly journals Application of Firefly Optimization Technique for Solving Convex Economic Load Dispatch of Generation On Nigerian 330 kV, 24-Bus Grid System

2018 ◽  
Vol 3 (5) ◽  
pp. 77 ◽  
Author(s):  
Ganiyu Adedayo Ajenikoko ◽  
O. E. Olabode ◽  
A. E. Lawal
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Cost Model ◽  
Optimization Technique ◽  
Inequality Constraints ◽  
Population Based ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Firefly Optimization ◽  
Bus System

Firefly optimization is a population based technique in which the attractiveness of a firefly is determined by its attractiveness which is then encoded as the objective function of the optimization problems. Firefly algorithm is one of the newest meta-heuristic algorithms based on the mating or flashing behavior of fireflies. Economic load dispatch of generation allocates power generation to match load demand at minimal possible cost without violating power units and system constraints. This paper presents application of firefly optimization technique (FFOT) for solving convex economic load dispatch of generation. The economic load dispatch problem was formulated to minimize the total fuel cost for the heat optimal combination of thermal generators without violating any of the system constraints using quadratic fuel cost model of Sapele, Delta, Afam and Egbin power stations as case studies. The equality and inequality constraints used on the system were the power balance equation and the transmission line constraints respectively. Firefly optimization technique was then developed using appropriate control parameters for a faster convergence of the technique. The optimization technique was tested and validated on the IEEE 30-bus system and Nigerian 24-bus system. The results obtained from the IEEE 30-bus system were compared to published results obtained via Differential Evolution (DE), Ant Colony Optimization (ACO) and Genetic Algorithm (GA). The comparison confirms the superiority, fast convergence and proficiency of the algorithm.

scholarly journals Economic Load Dispatch using Modified Vortex Search Algorithm

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3395-3401
Keyword(s):  
Power Systems ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Test System ◽  
Economic Load Dispatch ◽  
Step Size ◽  
Parameters Tuning ◽  
Generation Costs

This paper presents an efficacy optimization technique for solving Economic Load Dispatch (ELD) problem in power systems. Economic load dispatch has become more complicated with added ramp rate limits constraints to the problem. Latterly their were many traditional approach’s applied to ELD problem like Merit order loading, particle swarm optimation technique. This approach can be efficient for ELD problems. It cannot be applied due to high elaboration of their solutions. The Vortex Search Algorithm (VSA) is a newly advanced algorithm influenced by a vortex arrangement which can be designed as a number of nested circles. VSA approach was advanced from state of stirring liquids. The expediency and effectiveness of this approach is determined in different cases. It has no additional problem-specific parameters and it can be applicable to the optimization problems without control parameters tuning. VS Algorithm basically adjusts its step size automatically for the changing values of radius (circles) to improve the solution. In this proposed work, modified vortex search (MVSA) algorithm is applied to solve the ELD problem in some 6-unit test system by considering the system constraints and also the performance of this algorithm can be analyzed in terms of total generation costs and power losses. Obtained results of the test systems will be compared with particle swarm optimization (PSO), Merit order loading, VSA literature. The obtained results will demonstrate the MVSA algorithm is efficient way of solving the ELD problem and finding the output power of all the generation units accurately.

scholarly journals Comparative study of the price penalty factors approaches for Bi-objective dispatch problem via PSO

2020 ◽  
Vol 10 (4) ◽  
pp. 3343
Author(s):  
Mohammed Amine Meziane ◽  
Youssef Mouloudi ◽  
Abdelghani Draoui
Keyword(s):  
Power Systems ◽  
Power Plants ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Operating Cost ◽  
Fuel Cost ◽  
Load Demand ◽  
Penalty Factor ◽  
The Right ◽  
The Impact

One of the main objectives of electricity dispatch centers is to schedule the operation of available generating units to meet the required load demand at minimum operating cost with minimum emission level caused by fossil-based power plants. Finding the right balance between the fuel cost the green gasemissionsis reffered as Combined Economic and Emission Dispatch (CEED) problem which is one of the important optimization problems related the operationmodern power systems. The Particle Swarm Optimization algorithm (PSO) is a stochastic optimization technique which is inspired from the social learning of birds or fishes. It is exploited to solve CEED problem. This paper examines the impact of six penalty factors like "Min-Max", "Max-Max", "Min-Min", "Max-Min", "Average" and "Common" price penalty factors for solving CEED problem. The Price Penalty Factor for the CEED is the ratio of fuel cost to emission value. This bi-objective dispatch problem is investigated in the Real West Algeria power network consisting of 22 buses with 7 generators. Results prove capability of PSO in solving CEED problem with various penalty factors and it proves that Min-Max price penalty factor provides the best compromise solution in comparison to the other penalty factors.

Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm

2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Feedforward Neural Networks ◽  
Training Algorithms ◽  
Local Optima ◽  
Local Solutions

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.

Application of eco-friendly natural dye obtained from Spathodea campanulata on silk using combination of mordants

2016 ◽  
Vol 27 (1) ◽  
pp. 15-21 ◽  
Author(s):  
M Kumaresan
Keyword(s):  
Large Scale ◽  
Potassium Dichromate ◽  
Ferrous Sulphate ◽  
Research Work ◽  
Natural Dye ◽  
Alkaline Media ◽  
Silk Fabric ◽  
Content Type ◽  
Wide Range ◽  
New Research

Purpose – The purpose of this paper is to extract the eco-friendly natural dye obtained from the flower of Spathodea campanulata and apply on silk fabric using combination of mordants. The fastness properties of the flower of Spathodea campanulata dyed silk fabric have been studied using different combination (1:3, 1:1 and 3:1) of various mordants, such as myrobolan: nickel sulphate, myrobolan: aluminium sulphate, myrobolan: potassium dichromate, myrobolan: ferrous sulphate and myrobolan: stannous chloride. The wash, rub, light and perspiration fastness of the dyed samples have been evaluated. Design/methodology/approach – For dyeing there are three methods are used. They are Pre mordanting, Simultaneous mordanting and Post mordanting methods. Dyed silk materials are tested by using wash fastness, rub fastness, light and perspiration fastness methods. Findings – It is found that Spathodea campanulata dye can be successfully used for the dyeing of silk to obtain a wide range colours by using various combinations of mordants. With regards to colour fastness, test samples exhibit excellent fastness to washing, rubbing, except for pre-mordanting using myrobolan: potassium dichromate combination; and good to excellent fastness to perspiration in both acidic and alkaline media. Originality/value – Availability of literature related to this work is not available. The study of combination of mordants of this natural dye on silk is a new research work and the large scale preparation is definitely very useful to the society.

scholarly journals Ls-II: An Improved Locust Search Algorithm for Solving Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Octavio Camarena ◽  
Erik Cuevas ◽  
Marco Pérez-Cisneros ◽  
Fernando Fausto ◽  
Adrián González ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Optimization Method ◽  
Superior Performance ◽  
Solution Quality ◽  
Original Algorithm ◽  
Motion Models ◽  
Search Mechanism ◽  
Solitary Phase

The Locust Search (LS) algorithm is a swarm-based optimization method inspired in the natural behavior of the desert locust. LS considers the inclusion of two distinctive nature-inspired search mechanism, namely, their solitary phase and social phase operators. These interesting search schemes allow LS to overcome some of the difficulties that commonly affect other similar methods, such as premature convergence and the lack of diversity on solutions. Recently, computer vision experiments in insect tracking methods have conducted to the development of more accurate locust motion models than those produced by simple behavior observations. The most distinctive characteristic of such new models is the use of probabilities to emulate the locust decision process. In this paper, a modification to the original LS algorithm, referred to as LS-II, is proposed to better handle global optimization problems. In LS-II, the locust motion model of the original algorithm is modified incorporating the main characteristics of the new biological formulations. As a result, LS-II improves its original capacities of exploration and exploitation of the search space. In order to test its performance, the proposed LS-II method is compared against several the state-of-the-art evolutionary methods considering a set of benchmark functions and engineering problems. Experimental results demonstrate the superior performance of the proposed approach in terms of solution quality and robustness.

Economic Load Dispatch With Multiple Fuel Options and Valve Point Effect Using Cuckoo Search Algorithm With Different Distributions

2020 ◽  
Vol 9 (3) ◽  
pp. 24-38
Author(s):  
Cuong Dinh Tran ◽  
Tam Thanh Dao ◽  
Ve Song Vo
Keyword(s):  
Random Walk ◽  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cauchy Distribution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Natural Phenomenon ◽  
Economic Load Dispatch ◽  
Valve Point Effect

The cuckoo search algorithm (CSA), a new meta-heuristic algorithm based on natural phenomenon of the cuckoo species and Lévy flights random walk has been widely and successfully applied to several optimization problems so far. In the article, two modified versions of CSA, where new solutions are generated using two distributions including Gaussian and Cauchy distributions in addition to imposing bound by best solutions mechanisms are proposed for solving economic load dispatch (ELD) problems with multiple fuel options. The advantages of CSA with Gaussian distribution (CSA-Gauss) and CSA with Cauchy distribution (CSA-Cauchy) over CSA with Lévy distribution and other meta-heuristic are fewer parameters. The proposed CSA methods are tested on two systems with several load cases and obtained results are compared to other methods. The result comparisons have shown that the proposed methods are highly effective for solving ELD problem with multiple fuel options and/nor valve point effect.

scholarly journals Symbiotic Organism Search Algorithm for Power Loss Minimization in Radial Distribution Systems by Network Reconfiguration and Distributed Generation Placement

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Tung Tran The ◽  
Sy Nguyen Quoc ◽  
Dieu Vo Ngoc
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Power Loss ◽  
Distribution Systems ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Distribution Networks ◽  
Network Reconfiguration ◽  
Symbiotic Organism Search

This paper proposes the Symbiotic Organism Search (SOS) algorithm to find the optimal network configuration and the placement of distributed generation (DG) units that minimize the real power loss in radial distribution networks. The proposed algorithm simulates symbiotic relationships such as mutualism, commensalism, and parasitism for solving the optimization problems. In the optimization process, the reconfiguration problem produces a large number of infeasible network configurations. To reduce these infeasible individuals and ensure the radial topology of the network, the graph theory was applied during the power flow. The implementation of the proposed SOS algorithm was carried out on 33-bus, 69-bus, 84-bus, and 119-bus distribution networks considering seven different scenarios. Simulation results and performance comparison with other optimization methods showed that the SOS-based approach was very effective in solving the network reconfiguration and DG placement problems, especially for complex and large-scale distribution networks.

Strategic impact of SCM and SCQM practices on competitive dimensions of Indian manufacturing industries

2019 ◽  
Vol 31 (5) ◽  
pp. 696-721 ◽  
Author(s):  
Mandeep Kaur Sidhu ◽  
Kanwarpreet Singh ◽  
Doordarshi Singh
Keyword(s):  
Business Performance ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Discriminant Validity ◽  
Research Work ◽  
Manufacturing Industries ◽  
Combined Approach ◽  
Total Quality ◽  
Content Type ◽  
Manufacturing Organizations

Purpose The purpose of this paper is to evaluate the capabilities of total quality management (TQM) and supply chain management (SCM) and extract various significant factors which influence the implementation of SCM alone and synergy of both TQM–SCM in terms of business performance of Indian medium and large scale manufacturing industry. Design/methodology/approach In the present study, 116 Indian manufacturing organizations have been extensively surveyed to ascertain the inter-relationships between various success factors and competitive dimensions of SCM alone and for combined approach (TQM–SCM), through different statistical techniques. Further, to evaluate the significance of time period on competitive dimensions, two-tailed t-test has been deployed. Finally the discriminant validity test has been applied to extract highly successful and moderately successful organizations for both approaches. Findings The study compares the contributions played by only SCM initiatives and combined approach (TQM–SCM) initiatives toward realization of significant improvements of various competitive dimensions of Indian manufacturing organizations. Finally, this study reveals that synergistic relationship of TQM and SCM paradigms can be more helpful as compared to only SCM initiatives for Indian manufacturing industries to enhance overall business performance. Originality/value TQM and SCM are considered as performance improvement techniques by the manufacturing organizations. The present research work establishes that combined (TQM–SCM) initiatives have effectively contributed for realization of significant competitive dimensions, progressively from introduction to maturity phases. So, the study stresses upon the need for improving coordination between various manufacturing parameters as well as competitive dimensions of TQM and SCM paradigms to enjoy higher potential of business performance.

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