Integrated approach for dynamic economic/emission dispatch problem: multi-objective moth flame optimizer with transmission loss prediction using cascaded forward neural network

This paper presents improved single- and multi-objective algorithms based on the original moth flame optimizer (MFO) to tackle the dynamic economic emission dispatch (DEED) problem that affects power systems operations. The DEED problem is a multi-objective optimization problem that is strongly cons...

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Bibliographic Details
Published in:Electrical engineering Vol. 106; no. 3; pp. 3495 - 3528
Main Authors: Nalini, Nagulsamy, Kumar, Chandrasekaran, Vijayarajan, Periyasamy, Chidambararaj, Natarajan
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024
Springer Nature B.V
Subjects:
Artificial neural networks
Benchmarks
Butterflies & moths
Constraints
Convergence
Criteria
Deeds
Economics and Management
Electrical Engineering
Electrical Machines and Networks
Energy Policy
Engineering
Genetic algorithms
Machine learning
Methods
Multiple objective analysis
Optimization
Original Paper
Pareto optimum
Performance enhancement
Power Electronics
Search algorithms
Sorting algorithms
Transmission loss
Moth flame optimizer
Crowding distance
Economic load dispatch
Multi-objective problem
Non-dominated sorting
Emission dispatch
ISSN:0948-7921, 1432-0487
Online Access:Get full text
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Summary:This paper presents improved single- and multi-objective algorithms based on the original moth flame optimizer (MFO) to tackle the dynamic economic emission dispatch (DEED) problem that affects power systems operations. The DEED problem is a multi-objective optimization problem that is strongly constrained, multi-dimensional, nonlinear, and non-convex. It comprises several optimization criteria, many of which are in direct opposition to one another; therefore, no one solution is optimal with regard to all of those criteria. Firstly, an enhanced flame generation strategy is incorporated into the MFO algorithm to improve performance. Then, the improved MFO is combined with the crowding distance mechanism and non-dominated sorting framework to enhance the convergence rate and the quality of the results. This helps improve the convergence pace. Firstly, the proposed multi-objective moth flame optimizer (MOMFO) algorithm is validated using 15 ZDT and UF benchmark multi-objective test functions. Then, the nonlinear DEED problem is also solved by determining the feasible optimal solution using the MOMFO algorithm. The implementation of the MOMFO on 10-unit systems and the IEEE 30-bus test system is being done to display the ability to solve a nonlinear, non-convex, and constrained DEED optimization problem. The DEED problem is solved using the MOMFO algorithm and other state-of-the-art algorithms, such as the non-dominated sorting genetic algorithm-II (NSGA-II), the multi-objective teaching–learning-based optimization (MOTLBO) algorithm, and multi-objective reptile search algorithm (MORSA). The selection of the control parameters of the MOMFO can be decided from the algorithm’s findings on different IEEE bus systems. This study also introduces a new technique for incorporating loss predictions using artificial neural networks into the DEED model. During each phase of the dispatch time, the trained neural network can make only a single forecast of the transmission loss. The performance of MOMFO is compared with NSGA-II, MOTLBO, and MORSA, and the results obtained for both benchmarks and DEED proved the superiority of the proposed algorithm in solving the DEED of the power systems.
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ISSN:0948-7921
1432-0487
DOI:10.1007/s00202-023-02117-6