In this era, the usage of electrical energy utilization is rapidly increasing due to the number of devices operating with electricity. Thermal power plants power generation has a huge portion in total power generation The increased demand may not match the production, the reliable power supplied to the consumer by load shedding or increased generation only. The cost plays a very important role, so power generation cost optimization is the solution. Economic load dispatch is a nonlinear challenge; it uses to optimize the generation cost by satisfying the various constraints. Plenty of classic and current hybrid optimization techniques had been evolved to deal with this challenge inside the terminal decay of the non-linear system. In economic load dispatch, valve point loading, convex, non-convex, equal and unequal constraints which include real power, reactive power, voltage, angle, and shunt capacitance. This paper aims to detract the energy era fee for forecasting steady load demand using the ant colony-based JAYA algorithm. The proposed algorithm's reliability and accuracy are examined by applying to benchmark the IEEE- 30 bus system.

Optimal Solution to Economic Load Dispatch by Modified Jaya Algorithm

Enhancing power generation to meet demand while simultaneously mitigating the operational expenses of power plants is a pivotal endeavor. The optimization of economic load dispatch (ELD) presents itself as a requisite process to curtail operational costs, including fuel expenses and incremental costs. In the context of this study, a comprehensive examination is conducted utilizing the Power World Simulator alongside the Lambda Iteration (LI) Method, the Particle Swarm Optimization (PSO) Method, and the Teaching Learning-Based Method, employing a standard IEEE 9 bus system as a benchmark. The execution of LI, PSO, and TLBO methodologies is facilitated through the MATLAB programming language. Comparative analysis reveals TLBO's superior efficacy in optimizing fuel costs, incremental costs, and power losses, outperforming both Power World Simulator and PSO, as well as the lambda iteration method. Noteworthy findings indicate a reduction in total fuel costs, incremental costs, and power losses with an increase in the number of generators, while the introduction of unconnected branch lines to the transformer escalates costs and losses. Furthermore, heightened load demand correlates with increased costs and losses within the system. Additionally, TLBO exhibits a smoother convergence curve in contrast to PSO, further accentuating its optimization capabilities.

A Comparative Study of Economic Load Dispatch Problem using Computer Simulation Tool, Conventional and Evolutionary Algorithms

Power generation must be increased in order to meet demand while minimizing operating costs. In order to reduce these operational costs, such as fuel costs and incremental costs, it is necessary to solve the economic load dispatch (ELD) problem, which requires an optimization process. The purpose of this research is to compare various parameters of a standard IEEE 30 bus system using metaheuristic algorithms, which include teaching learning-based optimization (TLBO), particle swarm optimization (PSO), and a hybrid algorithm that is a combination of PSO and TLBO. In comparison to PSO and TLBO, hybrid PSO-TLBO efficiently optimizes fuel cost, incremental cost, and power loss. Furthermore, an increase in load demand will result in an increase in the system's costs and losses. On the other hand, by increasing the step size of the TLBO method, the total fuel cost and power loss will decrease. However, it may occasionally cause the algorithm to overreach and miss the desired outcome if the step size is not within the standard value. Moreover, the hybrid PSO-TLBO algorithm is intelligent enough to reduce intervals compared to the PSO and TLBO algorithms.

Solving Economic Load Dispatch: A Hybrid Approach Integrating Particle Swarm Optimization and Teaching Learning based Optimization

Economic Load Dispatch (ELD) represents a challenging and nonlinear problem encountered in the power system's generation domain. This research introduces a new hybrid algorithm that merges the advantages of both Genetic Algorithms (GA) and TLBO algorithm. The hybrid approach introduces dynamic teaching factors and step size-based learning mechanisms, effectively addressing traditional TLBO limitations and mitigating the risk of getting trapped in local minima. The TLBO component enriches exploration within the search space, allowing for a broader examination of potential solutions, while the GA component enhances exploitation by fine-tuning the search within promising regions. Comprehensive testing of the proposed algorithm is conducted using the IEEE 30-bus system, comparing its performance against that of conventional TLBO algorithms as well as contemporary optimization techniques. The outcomes of this research reveal that the proposed algorithm surpasses its counterparts in terms of reliability, robustness, and accuracy. This innovative approach holds promise for optimizing economic load dispatch in power systems, offering a more efficient and dependable solution.

A Novel Hybrid GA-TLBO Algorithm for Minimization of Power Generation Cost

This research paper presents a novel approach to solving optimization problems using the BAT Algorithm with parameter tuning, local mechanism escaping, and adaptive control. The proposed algorithm is evaluated using the IEEE 30-bus system as a test system. The BAT Algorithm is a nature-inspired metaheuristic algorithm that mimics the echolocation behavior of bats. By incorporating parameter tuning, the algorithm's performance is enhanced by fine-tuning the algorithm's key parameters, such as loudness and pulse rate. The local mechanism escaping mechanism allows the algorithm to explore new regions of the search space, promoting diversification and avoiding local optima. The algorithm's performance is evaluated in terms of solution quality, convergence rate, and robustness. Comparative analysis is conducted by comparing the proposed algorithm with other well-known optimization algorithms.The superior performance of the proposed BAT algorithm with parameter tuning, local mechanism escaping, and adaptive control in results. The algorithm exhibits strong convergence and exploration abilities, leading to high-quality solutions for the optimization problem. Furthermore, the algorithm demonstrates its effectiveness in solving the IEEE 30-bus system optimization problem, showcasing its potential for real-world applications in power systems and related domains. The findings demonstrate the algorithm's capability to outperform other state-of-the-art optimization algorithms, including DE, ADE, Cuckoo Search, EADE, TLBO, among others. 

Achieving Cost Efficiency and Load Sharing in Power Systems: The Superiority of Adaptive BAT Algorithm

In modern power system the economic load dispatch is considered as a non-linear problem. Many conventional and modern optimization algorithms are proposed to define a solution for the economic load dispatch. Among them, Teacher and learner based optimization is considered as one of the modern searching algorithms. This paper aims to solve the non-linear problem that exists in economic load dispatch using TLBO. Generally, the non-linear problem of economic load dispatch deals with the number of constraints inequality available in nature. The constraints are mainly the voltage, real and reactive power, shunt capacitor, transformer tapping etc. TLBO leverages on optimal solution to the nonlinear problem with a good convergence ratio.

Optimal Solution of Economic Load Dispatch using Teaching Learning Algorithm

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chodagam Srinivas

Author Tools

Chat about Author

Papers

Optimal Solution of Economic Load Dispatch using Teaching Learning Algorithm