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.

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.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

A Comparative Study of Improved Teaching Learning Based Optimization Technique on Economic Load Dispatch Problem with Generator Constraints

2016 ◽  
Vol 5 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Sumit Banerjee ◽  
Chandan Chanda ◽  
Deblina Maity
Keyword(s):  
Optimization Technique ◽  
Inequality Constraints ◽  
Test System ◽  
Economic Load Dispatch ◽  
Thermal Plant ◽  
Equality And Inequality Constraints ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Operational Constraints ◽  
Rate Limit

This article presents a novel improved teaching learning based optimization (I-TLBO) technique to solve economic load dispatch (ELD) problem of the thermal plant without considering transmission losses. The proposed methodology can take care of ELD problems considering practical nonlinearities such as ramp rate limit, prohibited operating zone and valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. I-TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. The effectiveness of the proposed algorithm has been verified on test system with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm.

Economic Dispatch Problems Using Backtracking Search Optimization

2018 ◽  
Vol 7 (2) ◽  
pp. 39-60
Author(s):  
Kuntal Bhattacharjee
Keyword(s):  
Large Scale ◽  
Transmission Loss ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Inequality Constraints ◽  
Optimization Techniques ◽  
Backtracking Search ◽  
Search Optimization ◽  
Set Up ◽  
Optimum Solution

The purpose of this article is to present a backtracking search optimization technique (BSA) to determine the feasible optimum solution of the economic load dispatch (ELD) problems involving different realistic equality and inequality constraints, such as power balance, ramp rate limits, and prohibited operating zone constraints. Effects of valve-point loading, multi-fuel option of large-scale thermal plants, system transmission loss are also taken into consideration for more realistic application. Two effective operations, mutation and crossover, help BSA algorithms to find the global solution for different optimization problems. BSA has the capability to deal with multimodal problems due to its powerful exploration and exploitation capability. BSA is free from sensitive parameter control operations. Simulation results set up the proposed approach in a better stage compared to several other existing optimization techniques in terms quality of solution and computational efficiency. Results also reveal the robustness of the proposed methodology.

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 An Improved Electromagnetic Field Optimization for the Global Optimization Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Alkin Yurtkuran
Keyword(s):  
Electromagnetic Field ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Golden Ratio ◽  
New Physics ◽  
Population Based ◽  
Statistical Testing ◽  
Optimal Point ◽  
Bee Colony ◽  
Solution Generation

Electromagnetic field optimization (EFO) is a relatively new physics-inspired population-based metaheuristic algorithm, which simulates the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, the population consists of electromagnetic particles made of electromagnets corresponding to variables of an optimization problem and is divided into three fields: positive, negative, and neutral. In each iteration, a new electromagnetic particle is generated based on the attraction-repulsion forces among these electromagnetic fields, where the repulsion force helps particle to avoid the local optimal point, and the attraction force leads to find global optimal. This paper introduces an improved version of the EFO called improved electromagnetic field optimization (iEFO). Distinct from the EFO, the iEFO has two novel modifications: new solution generation function for the electromagnets and adaptive control of algorithmic parameters. In addition to these major improvements, the boundary control and randomization procedures for the newly generated electromagnets are modified. In the computational studies, the performance of the proposed iEFO is tested against original EFO, existing physics-inspired algorithms, and state-of-the-art meta-heuristic algorithms as artificial bee colony algorithm, particle swarm optimization, and differential evolution. Obtained results are verified with statistical testing, and results reveal that proposed iEFO outperforms the EFO and other considered competitor algorithms by providing better results.

scholarly journals Economic Load Dispatch

2021 ◽  
Vol 9 (VI) ◽  
pp. 1646-1651
Author(s):  
Prof. Kanika Lamba
Keyword(s):  
Particle Swarm Optimization ◽  
Convex Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Ant Lion Optimization ◽  
Ant Lion

ELD or Economic load dispatch is an online process of allocating generating among the available generating units to minimize the total generating cost and satisfy the equality and inequality constraint. ELD means the real and reactive power of the generator vary within the certain limits and fulfils theload demand with less fuel cost. There are some traditional methods for = 1; 2; :::;N) isgiven as Vi=[Vi;1; Vi;2; :::; Vi;D]. The index ivaries from solving ELD include lambda irritation method, Newton-Raphson method, Gradient method, etc. All these traditional algorithms need the incremental fuel cost curves of the generators to be increasing monotonically or piece-wise linear. But in practice the input-output characteristics of a generator are highly non-linear leading to a challenging non-convex optimization problem. Methods like artificial intelligence, DP (dynamic programming), GA (genetic algorithms), and PSO (particle swarm optimization), ALO ( ant-lion optimization), solve non convex optimization problems in an efficient manner and obtain a fast and near global and optimum solution. In this project ELD problem has been solved using Lambda-Iterative technique, ALO (ant-lion Optimization) and PSO (Particle Swarm Optimization) and the results have been compared. All the analyses have been made in MATLAB environment

scholarly journals Intelligent Heuristic Techniques for the Optimization of the Transshipment and Storage Operations at Maritime Container Terminals

2017 ◽  
Vol 20 (60) ◽  
pp. 20
Author(s):  
Christopher Expósito-Izquierdo
Keyword(s):  
Computational Efficiency ◽  
Scientific Literature ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Container Terminals ◽  
Maritime Logistics ◽  
Point Of Interest ◽  
And Storage

This paper summarizes the main contributions of the Ph.D. thesis of Christopher Exp\'osito-Izquierdo. This thesis seeks to develop a wide set of intelligent heuristic and meta-heuristic algorithms aimed at solving some of the most highlighted optimization problems associated with the transshipment and storage of containers at conventional maritime container terminals. Under the premise that no optimization technique can have a better performance than any other technique under all possible assumptions, the main point of interest in the domain of maritime logistics is to propose optimization techniques superior in terms of effectiveness and computational efficiency to previous proposals found in the scientific literature when solving individual optimization problems under realistic scenarios. Simultaneously, these optimization techniques should be enough competitive to be potentially implemented in practice. }}

Transient Stability Constrained Optimal Power Flow Using Teaching Learning-Based Optimization

2014 ◽  
Vol 3 (4) ◽  
pp. 55-71 ◽  
Author(s):  
Aparajita Mukherjee ◽  
Sourav Paul ◽  
Provas Kumar Roy
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Transient Stability ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Nonlinear Problems ◽  
Optimal Power ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Bus System

Transient stability constrained optimal power flow (TSC-OPF) is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. In order to solve the TSC-OPF problem efficiently, a relatively new optimization technique named teaching learning based optimization (TLBO) is proposed in this paper. TLBO algorithm simulates the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The authors have explained in detail, the basic philosophy of this method. In this paper, the authors deal with the comparison of other optimization problems with TLBO in solving TSC-OPF problem. Case studies on IEEE 30-bus system WSCC 3-generator, 9-bus system and New England 10-generator, 39-bus system indicate that the proposed TLBO approach is much more computationally efficient than the other popular methods and is promising to solve TSC-OPF problem.

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