A hybrid dynamic economic environmental dispatch model for balancing operating costs and pollutant emissions in renewable energy: A novel improved mayfly algorithm

L, Li; Lou, Jiale; Tseng, Ming-Lang; Lim, Ming K.; Tan, Raymond R.

doi:10.1016/j.eswa.2022.117411

Cited by 22 publications

(4 citation statements)

References 54 publications

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“…The weighted-sum approach faces challenges in striking the right trade-offs between costs and emissions [31]. The specific value of the weight might have an insignificant impact when costs and emissions are quite different in size [52]. It also exhibits limited efficiency for non-convex Pareto-optimal fronts [53] and applies only to convex cost and emission functions [54].…”

Section: Mops Objective Functionmentioning

confidence: 99%

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Ebrie,

Kim

2024

Systems

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Multi-objective power scheduling (MOPS) aims to address the simultaneous minimization of economic costs and different types of environmental emissions during electricity generation. Recognizing it as an NP-hard problem, this article proposes a novel multi-agent deep reinforcement learning (MADRL)-based optimization algorithm. Within a custom multi-agent simulation environment, representing power-generating units as collaborative types of reinforcement learning (RL) agents, the MOPS problem is decomposed into sequential Markov decision processes (MDPs). The MDPs are then utilized for training an MADRL model, which subsequently offers the optimal solution to the optimization problem. The practical viability of the proposed method is evaluated across several experimental test systems consisting of up to 100 units featuring bi-objective and tri-objective problems. The results demonstrate that the proposed MADRL algorithm has better performance compared to established methods, such as teaching learning-based optimization (TLBO), real coded grey wolf optimization (RCGWO), evolutionary algorithm based on decomposition (EAD), non-dominated sorting algorithm II (NSGA-II), and non-dominated sorting algorithm III (NSGA-III).

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Section: Mops Objective Functionmentioning

confidence: 99%

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Ebrie,

Kim

2024

Systems

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“…Aiming at the problem of energy dispatching route cost, Ara et al [18] proposed a self-regulating PSO algorithm to reduce energy costs in energy dispatching under the premise of ensuring time. Li et al [19] proposed an improved new mayfy algorithm to reduce operating costs on the basis of ensuring the stability and balance of grid energy dispatch. Deng et al [20] proposed an improved krill swarm algorithm to reduce the overall cost in energy dispatch and improve energy efciency.…”

Section: Introductionmentioning

confidence: 99%

Energy Dispatching Based on an Improved PSO-ACO Algorithm

Song

et al. 2023

International Journal of Intelligent Systems

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In order to improve the comprehensive performance of energy dispatching between different sites, the optimization research of particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm is carried out. We proposed a new improved PSO-ACO algorithm based on the idea of hybrid algorithm to solve the problem of poor energy dispatching efficiency between sites. First, the multiobjective performance indicators were introduced to transform the sites’ energy dispatching problem into a multiobjective optimization problem. Second, the vitality factor was introduced into the PSO strategy to solve the local optimal problem, and in the PSO-ACO fusion strategy, the PSO routes were transformed into the ant colony enhancement pheromone to accelerate the accumulation speed of the ACO initial pheromone. Then, the angle guidance function was introduced into the state transition probability of the ACO strategy to improve the global search capability, and a high-quality pheromone update rule was proposed to improve the convergence speed of the algorithm. Finally, simulation experiments were carried out on the improved PSO-ACO algorithm, Min–Max Ant System (MMAS) algorithm, ACO algorithm, PSO algorithm, and PSO update algorithm in a variety of complex site scenarios. The simulation results show that the improved PSO-ACO algorithm can plan a site energy dispatching route with shorter route, less time-consuming, and higher security and realize the comprehensive and global optimization of energy dispatching.

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“…Zhou et al [ 5 ] used orthogonal learning as well as chaotic strategies to improve the diversity of the MOA. Li et al [ 6 ] proposed an IMA with chaotic initialization, the gravity coefficient, and a mutation strategy and applied it to the dynamic economic environment scheduling problem. Zhang et al [ 7 ] combined the Sparrow Search Algorithm (SSA) with the MOA and applied it to the RFID network planning problem.…”

Section: Introductionmentioning

confidence: 99%

AOBLMOA: A Hybrid Biomimetic Optimization Algorithm for Numerical Optimization and Engineering Design Problems

Zhao,

Huang,

Zhang

et al. 2023

Biomimetics

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The Mayfly Optimization Algorithm (MOA), as a new biomimetic metaheuristic algorithm with superior algorithm framework and optimization methods, plays a remarkable role in solving optimization problems. However, there are still shortcomings of convergence speed and local optimization in this algorithm. This paper proposes a metaheuristic algorithm for continuous and constrained global optimization problems, which combines the MOA, the Aquila Optimizer (AO), and the opposition-based learning (OBL) strategy, called AOBLMOA, to overcome the shortcomings of the MOA. The proposed algorithm first fuses the high soar with vertical stoop method and the low flight with slow descent attack method in the AO into the position movement process of the male mayfly population in the MOA. Then, it incorporates the contour flight with short glide attack and the walk and grab prey methods in the AO into the positional movement of female mayfly populations in the MOA. Finally, it replaces the gene mutation behavior of offspring mayfly populations in the MOA with the OBL strategy. To verify the optimization ability of the new algorithm, we conduct three sets of experiments. In the first experiment, we apply AOBLMOA to 19 benchmark functions to test whether it is the optimal strategy among multiple combined strategies. In the second experiment, we test AOBLMOA by using 30 CEC2017 numerical optimization problems and compare it with state-of-the-art metaheuristic algorithms. In the third experiment, 10 CEC2020 real-world constrained optimization problems are used to demonstrate the applicability of AOBLMOA to engineering design problems. The experimental results show that the proposed AOBLMOA is effective and superior and is feasible in numerical optimization problems and engineering design problems.

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A hybrid dynamic economic environmental dispatch model for balancing operating costs and pollutant emissions in renewable energy: A novel improved mayfly algorithm

Cited by 22 publications

References 54 publications

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Reinforcement Learning-Based Multi-Objective Optimization for Generation Scheduling in Power Systems

Energy Dispatching Based on an Improved PSO-ACO Algorithm

AOBLMOA: A Hybrid Biomimetic Optimization Algorithm for Numerical Optimization and Engineering Design Problems

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