Combined Economic Emission Load Dispatch Research Articles

Improved Pelican optimization algorithm for solving load dispatch problems

The Pelican Optimization Algorithm (POA) is a newly developed algorithm inspired by the hunting behavior of pelicans. Despite its fast convergence rate, it suffers from premature convergence, the imbalance between exploration and exploitation, and lack of population diversity. In this work, an improved POA is proposed to attenuate these shortcomings. IPOA benefits from three motion strategies and predefined knowledge-sharing factors that better describe the stochastic hunting behavior of pelicans, as well as a modified dimension-learning-based hunting (DHL) behavior to retain diversity. To test the effectiveness of these improvements, it was used to solve 23 benchmark functions, including unimodal, multimodal, and 6 composite functions (CEC 2017). To evaluate performance, it was applied to solve economic and combined economic emission load dispatch problems that play a critical role in real-world power system planning and operation while considering environmental impacts. This experiment includes 6, 10, 11, 40, 140, 160, and 320 generating units with nonconvex and non-smooth objective functions. The comparison is performed for benchmark functions and the optimal dispatch problems. The results confirm the competitive and almost superior performance of the IPOA in several cases, which proves the applicability and efficiency of the proposed approach in solving real-world problems.

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

This paper deals with the combined economic emission load dispatch (CEELD) problem with and without the integration of renewable energy sources (RESs), in some more rational test scenarios of single CEELD and multi-objective CEELD (MO-CEELD) optimization. Hence, an efficient and coherent approach is presented to minimize the generation and emission cost using one of the bio-inspired metaheuristic algorithms named flower pollination algorithm (FPA). The evolution of a power system along with the integration of RESs demands equal advancement in the operation and control algorithms of the power grid. Therefore, the proposed approach in this paper offers an evolutionary single and multi-objective optimization process based on a bio-inspired FPA. Further, it has been validated by achieving the best compromise solution (BCS) using the Pareto categorizing process and fuzzy membership function. Moreover, different study cases comprising eleven and fifteen thermal units with and without considering RESs are tested with the proposed technique. Finally, the effectiveness of the proposed approach is tested by comparing the simulation results with some already existing techniques in terms of overall fuel and emission cost. Significantly, it has been noticed from the results that it outperforms all the previously presented approaches like PSO, DE, GSA, AEO, BA, and dBA, thus justifying its applicability.

A Modified Artificial Bee Colony Algorithm Based on a Non-Dominated Sorting Genetic Approach for Combined Economic-Emission Load Dispatch Problem

A Modified Artificial Bee Colony Algorithm Based on a Non-Dominated Sorting Genetic Approach for Combined Economic-Emission Load Dispatch Problem

Combined economic-emission load dispatch solution using firefly algorithm and fuzzy approach

This paper proposes Firefly Algorithm (FA) with fuzzy approach for solving combined economic-emission load dispatch (CEELD) problem in power generation. The main objective of the CEELD problem is to determine the optimal cost and emission level of power generation and fulfill the load demand and operational constraints. The CEELD problem used weighted sum method to combine two objective functions (cost and emission) into single objective. Then, the Fuzzy-based mechanism approach is integrated in FA to find the best compromise solution for both conflicting cost and emission level. The FA has been tested on IEEE 118-bus 14-unit test system for minimization both cost of power generation and emission level. The performance of FA for solving CEELD problem has been investigated in terms of optimal solution, convergence characteristics and Pareto front solution. From the comparison of the CEELD solutions, it found that FA can provide significant cost and emission reduction compared to other methods. Thus, it can be used as alternative approach for solving any optimization problems.

Combined Economic and Emission Load Dispatch Problem Solving by ITLBO Algorithm

Recent trends in economic and emission dispatch problem which turn has to promote the rise in the number of power generating stations and their capacity of generation. The Sulphur Dioxide (S02), Nitrogen Oxides (N2O) and Carbon Dioxide (CO2) are yields the CEED problem and they are created from generation of electricity from fossil fuel. Combined economic and emission load dispatch problem is a procedure to determine the generation of electrical power isdevoted generating units in a power system so that the both total generation costand total emission of the system is minimized, while fulfilling the load demand in directly. Improved teaching learning based optimization algorithm is used to solve combined economic emission load dispatch problem with constraints in generation of power system. This method was verified by thirteen generating bus and use with different load demand and compared with other existing techniques display the advantage of the proposed algorithm. The simulation has been done in MATLAB/Simulink with required formulation and the result is gotten in graph and numerical.

Optimal Solution of Combined Economic Emission Load Dispatch using Genetic Algorithm

paper, a genetic algorithm (GA) approach is presented for optimal solution of combined economic emission load dispatch (CEELD) problem. Fuel cost and emission are considered to formulate the multi-objective optimization problem. An optimal trade-off between fuel cost and emission is obtained using genetic algorithm. Two test systems are considered to show the effectiveness of the GA approach. An extensive analysis is done by presenting a short term thermal generation scheduling for the Test system-1.

Interval-based differential evolution approach for combined economic emission load dispatch

In this paper we propose an Interval based Differential Evolution (IDE) algorithm, an improved version of Differential Evolution (DE) algorithm using interval arithmetic. After estimating the global minimum roughly, the IDE algorithm constructs a mechanism which updates the upper bound of global minimum at each generation and defines an efficient termination criterion. Also at each generation it modifies the new population using the subset of current population and thus reduces the computational effort. We apply the proposed algorithm to the Combined Economic Emission Load Dispatch (CEELD) problem which obtains the optimal amount of generated power for the thermal generating units in the system by simultaneously minimising the fuel and emission costs. We find the proposed algorithm to be more efficient than the conventional DE when applied to IEEE 6 generators system. We also find that these results using the IDE algorithm are better than some recently reported evolutionary algorithms.