Dynamic economic load-emission dispatch in power systems with renewable sources using an improved multi-objective particle swarm optimization algorithm

Mehrpour, Mahni; Seyedi, Iman; Askari, Milad

doi:10.1109/icecie50279.2020.9309606

Cited by 3 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the formulations of classic PSO indicate, the best approach to increase the efficiency of this algorithm is to adjust the coefficients so as to improve its local and global searches. In the improved algorithm, the inertia weight (w min � 0.4, w max � 0.9) and the acceleration coefficients and population number (c1 � 0.2 and c2 � 2.3, population � 50) are obtained by the parameters mentioned [16,30].…”

Section: The Simulation Resultsmentioning

confidence: 99%

Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

Sayedi

Fatehi

Simab

2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

The distributed energy management of interconnected microgrids, which is based on model predictive control (MPC), relies on the cooperation of all the agents (i.e., microgrids). Model predictive control or MPC is widely used in industrial applications as an effective tool for dealing with multivariable limited control problems. MPC uses an explicit system model to predict the future horizon of the system and its outputs. This predictability allows calculating the optimal order of inputs to minimize output errors over a limited horizon, which is effected by the limitations of the system. This study presents a distributed economic model predictive control method using the new state feedback controller to control the switching of interface converters and compensate for the unbalanced and nonlinear loads. In this model, the islanding mode and the reconnection of the grid are considered to improve the transient behavior of the system to achieve steady-state power distribution. It has been proposed that it could obtain better results in predictive control, utilizing similarity transform in the state matrix and its modification. First, this model is simulated on distributed generation sources with power-sharing and local loads using the state feedback controller in MATLAB Simpower. Then, the performance of the proposed method is evaluated, confirming that it is more reliable than the FS-MPC and DSVM-MPC methods.

show abstract

Section: The Simulation Resultsmentioning

confidence: 99%

Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

Sayedi

Fatehi

Simab

2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

show abstract

“…In 2020 16 integrated differential evolution with quantum particle swarm optimization (QPSO) to address the short-term EED challenge of microgrids. In another study, Mehrpour et al 17 focused on the dynamic load and emission dispatch in daily cycles, especially considering the potential impacts of renewable energy sources. In 2022 18 showcased the perfectly convergent Particle swarm optimization (PCPSO) for addressing combined economic and multiple emissions dispatch challenges.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Economic/Environmental Dispatch using NS-MJPSO(loc): A Multi-Objective Optimization Approach with Improved Pareto-Optimal Solutions

Rahman,

Khalil,

Zakarya

et al. 2023

Preprint

View full text Add to dashboard Cite

This work applies the recently developed n th state Markovian jumping particle swarm optimisation algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective Particle Swarm Optimisation with Local Best (NS-MJPSOloc), aims to enhance the performance of the Particle Swarm Optimisation (PSO) algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of the multi-objective optimisation. Economic dispatch refers to the systematic allocation of available power resources in order to fulfil all relevant limitations and effectively meet the demand for electricity at the lowest possible operating cost. As a result of heightened public consciousness regarding environmental pollution and the implementation of clean air amendments, nations worldwide have compelled utilities to adapt their operational practises in order to comply with environmental regulations. The (NS-MJPSOloc) approach has been utilised for the resolution of the EED problem, including cost and emission objectives that are not commensurable. Multiple optimisation iterations of the suggested methodology have been conducted on a conventional test setup. The objective of economic dispatch is to minimise the overall expenditure on fuel, without taking into account any limitations related to emissions. The emission dispatch prioritises the reduction of emissions without taking into account economic considerations. The findings illustrate the efficacy of the suggested (NS-MJPSOloc) approach in producing a collection of Pareto-optimal solutions that are evenly dispersed within a single iteration. The comparison of several approaches reveals the higher performance of the suggested (NS-MJPSOloc) in terms of the diversity of the Pareto-optimal solutions achieved. In addition, a measure of solution quality based on Pareto optimality has been incorporated. The findings validate the effectiveness of the proposed (NS-MJPSOloc) approach in addressing the multi-objective EED issue and generating a trade-off solution that is both optimal and of high quality.

show abstract

A multi-objective optimisation approach with improved pareto-optimal solutions to enhance economic and environmental dispatch in power systems

Khalil,

Rahman,

Zakarya

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This work implements the recently developed nth state Markovian jumping particle swarm optimisation (PSO) algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective PSO with Local Best (NS-MJPSOloc), aims to enhance the performance of the PSO algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of multi-objective optimisation. The NS-MJPSOloc algorithm uses an evolutionary factor-based mechanism to identify the optimum compromise solution, a Markov chain state jumping technique to control the Pareto-optimal set size, and a neighbourhood’s topology (such as a ring or a star) to determine its size. Economic dispatch refers to the systematic allocation of available power resources in order to fulfill all relevant limitations and effectively meet the demand for electricity at the lowest possible operating cost. As a result of heightened public consciousness regarding environmental pollution and the implementation of clean air amendments, nations worldwide have compelled utilities to adapt their operational practises in order to comply with environmental regulations. The (NS-MJPSOloc) approach has been utilised for resolving the EED problem, including cost and emission objectives that are not commensurable. The findings illustrate the efficacy of the suggested (NS-MJPSOloc) approach in producing a collection of Pareto-optimal solutions that are evenly dispersed within a single iteration. The comparison of several approaches reveals the higher performance of the suggested (NS-MJPSOloc) in terms of the diversity of the Pareto-optimal solutions achieved. In addition, a measure of solution quality based on Pareto optimality has been incorporated. The findings validate the effectiveness of the proposed (NS-MJPSOloc) approach in addressing the multi-objective EED issue and generating a trade-off solution that is both optimal and of high quality. We observed that our approach can reduce $$\sim $$ ∼ 6.4% of fuel costs and $$\sim $$ ∼ 9.1% of computational time in comparison to the classical PSO technique. Furthermore, our method can reduce $$\sim $$ ∼ 9.4% of the emissions measured in tons per hour as compared to the PSO approach.

show abstract

Dynamic economic load-emission dispatch in power systems with renewable sources using an improved multi-objective particle swarm optimization algorithm

Cited by 3 publications

References 33 publications

Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

Optimal Load Distribution in DG Sources Using Model Predictive Control and the State Feedback Controller for Switching Control

Enhancing Economic/Environmental Dispatch using NS-MJPSO(loc): A Multi-Objective Optimization Approach with Improved Pareto-Optimal Solutions

A multi-objective optimisation approach with improved pareto-optimal solutions to enhance economic and environmental dispatch in power systems

Contact Info

Product

Resources

About