Journal ArticleDOI
Combined economic and emission power dispatch problems through multi-objective squirrel search algorithm
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The simulation results and comparisons with the other state-of-the-art heuristic algorithms confirm that the proposed multi-objective squirrel search algorithm for combined economic and environmental power dispatching can obtain a better trade-off between fuel cost and emission objectives.About:
This article is published in Applied Soft Computing.The article was published on 2021-03-01. It has received 49 citations till now. The article focuses on the topics: Search algorithm & Heuristic (computer science).read more
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Solving dynamic economic and emission dispatch in power system integrated electric vehicle and wind turbine using multi-objective virus colony search algorithm
TL;DR: A new multi-objective virus colony search (MOVCS) based on non-dominated sorting theory and fuzzy decision making for finding the best solution among Pareto fronts is suggested, which minimizes wind-thermal electrical energy cost and emissions obtained by fossil-fueled power generators at the same time.
Journal ArticleDOI
Solution of Combined Economic Emission Dispatch Problem Using Improved and Chaotic Population-Based Polar Bear Optimization Algorithm
Saqib Fayyaz,Muhammad Kashif Sattar,Muhammad Waseem,M. Usman Ashraf,Aftab Ahmad,Hafiz Ashiq Hussain,Khalid Alsubhi +6 more
TL;DR: In this paper, a novel improved polar bear optimization (IPBO) algorithm and chaotic population-based variants of PBO were employed to solve combined economic emission dispatch (CEED) problem.
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Economic and environmental operation of power systems including combined cooling, heating, power and energy storage resources using developed multi-objective grey wolf algorithm
TL;DR: The proposed multi-objective algorithm is a model developed based on non-dominated sorting theory, variable detection, memory-based strategy selection, and fuzzy theory to select the optimal Pareto from the set of solutions, which has powerful performance in solving the above problem.
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Distributed multi-objective grey wolf optimizer for distributed multi-objective economic dispatch of multi-area interconnected power systems
TL;DR: In this article , the authors proposed a distributed MOGWO for large-scale multi-area interconnected power systems (LMIPSs), where the sub-problems of each area are optimized independently, and the overall optimization can be realized by sharing only part of the boundary bus information between areas.
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Optimal operation of energy hubs integrated with electric vehicles, load management, combined heat and power unit and renewable energy sources
TL;DR: In this paper , an optimal load dispatch form for an energy hub to decrease the total costs of the energy hub, such as exploitation costs and CO 2 emission costs, was proposed, and three scheduling scenarios were discussed with different charge/discharge and DR settings.
References
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Multiobjective evolutionary algorithms for electric power dispatch problem
TL;DR: The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multi objective nonlinear optimization problem are comprehensively discussed and evaluated.
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A novel nature-inspired algorithm for optimization: Squirrel search algorithm
TL;DR: This optimizer imitates the dynamic foraging behaviour of southern flying squirrels and their efficient way of locomotion known as gliding and provides more accurate solutions with high convergence rate as compared to other existing optimizers.
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Economic environmental dispatch using multi-objective differential evolution
TL;DR: Results obtained from the proposed approach have been compared to those obtained from pareto differential evolution, nondominated sorting genetic algorithm-II and strength pare to evolutionary algorithm 2.
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Multiobjective particle swarm optimization for environmental/economic dispatch problem
TL;DR: A quality measure to Pareto-optimal solutions has been implemented where the results confirm the potential of the proposed MOPSO technique to solve the multiobjective EED problem and produce high quality nondominated solutions.
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Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques
TL;DR: Experimental results show that the hybrid model proposed for digital currency forecasting can capture nonlinear properties of digital currency time series.