MOEA/D with many-stage dynamical resource allocation strategy to solution of many-objective OPF problems

Zhang, Jingrui; Zhu, Xiaoqing; Li, Po

doi:10.1016/j.ijepes.2020.106050

Cited by 26 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A novel hybrid salp swarm optimization and particle swarm optimization (SSOPSO) was proposed to solve MaOOPF problems by selecting cost minimization, emission reduction, transmission loss reduction, voltage profile improvement, and voltage stability enhancement as part of the objective functions in the IEEE 30-, 57-, and 118-bus systems [49]. J. Zhang, X. Zhu, and P. Li presented multiobjective evolutionary algorithm based on decomposition (MOEA/D) with many-stage dynamical resource allocation strategy to solve MaOOPF problems by using fuel cost, emission, voltage deviations, line losses and voltage stability index as part of the objective functions in the IEEE 30-, 57-, and 118-bus systems [50]. In [51], many-objective marine predators algorithm was introduced to solve MaOOPF problems by considering cost, emission, transmission loss, and voltage stability index as part of the objective functions in the IEEE 30-and 118-bus systems.…”

Section: Related Studiesmentioning

confidence: 99%

A Two-Archive Harris Hawk Optimization for Solving Many-Objective Optimal Power Flow Problems

Khunkitti,

Premrudeepreechacharn,

Siritaratiwat

2023

IEEE Access

View full text Add to dashboard Cite

To improve power system operation and management and accomplish modern power system requirements, a new algorithm named two-archive harris hawk optimization (TwoArchHHO) is proposed to solve many-objective optimal power flow (MaOOPF) problems in this work. For modern power systems, only single-objective and multiobjective (2-3 objectives) optimal power flow problems (MOOPF) are inadequate. So, the problems become many-objective (more than 3 objectives) optimal power flow problem which is more complicated to be solved. Although several metaheuristic algorithms have been proposed to solve MOOPF problems, very few algorithms have been introduced to solve MaOOPF problems and highperformance algorithms are still required to solve MaOOPF problems which are more complicated. To solve the complicated MaOOPF problems, TwoArchHHO is proposed by adding the two-archive concepts of the improved two-archive algorithm into the harris hawk optimization (HHO) in order to enhance the searchability and eventually provide superior solutions. The objective functions considered to be minimized include fuel cost, emission, transmission line loss, and voltage deviation to improve power systems in the economic, environmental, and secure aspects. Several sizes of IEEE standard systems, which are IEEE 30-, 57-, and 118-bus systems, are tested to evaluate the performance of the proposed TwoArchHHO. The simulation results comprise Pareto fronts, best-compromised solutions, and hypervolume analysis are generated and compared with results from several algorithms in the literature. The data provided by the experimental trials and the hypervolume performance metric were examined using statistical testing methods. The results indicate that the TwoArchHHO obtained better optimal solutions than those of the compared algorithms including its traditional algorithms, especially in large systems. Based on the best-compromised solutions, the TwoArchHHO provided one best objective aspect among the compared algorithm for most cases. Based on the hypervolume, the TwoArchHHO generated better hypervolume values than those of the compared algorithms around 33.96% to 99.59% in the tested systems.INDEX TERMS Harris Hawk Optimization, metaheuristic algorithms, many-objective optimal power flow, two-archive algorithm.

show abstract

Section: Related Studiesmentioning

confidence: 99%

A Two-Archive Harris Hawk Optimization for Solving Many-Objective Optimal Power Flow Problems

Khunkitti,

Premrudeepreechacharn,

Siritaratiwat

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Zhang et al [9] combined decomposition strategies with metaheuristic algorithms, using NSGA-II to fine-tune the parameters of sub-policies. Although several studies have applied metaheuristic algorithms to MO-FJSP [10][11][12], the application of DRL in MOFJSP is still relatively limited. Addressing the shortcomings of metaheuristic algorithms, this paper applies DRL to MOFJSP of minimizing the maximum makespan and total machine load.…”

Section: Introductionmentioning

confidence: 99%

A multiobjective flexible job shop scheduling method based on deep reinforcement learning

Liu,

Chen,

Zhang

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

View full text Add to dashboard Cite

To address the multi-objective flexible job shop scheduling problem, this paper establishes a Markov Decision Process for Multi-objective Flexible Job-Shop Scheduling Problem, aiming to minimize makespan and total machine load. Additionally, a multi-objective optimization algorithm base deep reinforcement learning is proposed. Firstly, a multi-objective decomposition strategy is introduced to decompose the problem into a set of subproblems, and graph neural networks with identical structures are employed to represent the priority dispatching rules for each subproblem. Experimental results demonstrate that this approach exhibits better generalization, higher solution quality, and faster convergence compared to previous methods.

show abstract

“…In the past, the OPF problem in its classical formulation was solved by deterministic methods [7,8]. After that, many classical optimization methods were introduced to solve the classical OPF problem [9]. However, the classical type of OPF problem did not include RES in the problem formulation [10].…”

Section: Introductionmentioning

confidence: 99%

Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm

Shaheen

Ullah

Qais³

et al. 2022

Energies

View full text Add to dashboard Cite

Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.

show abstract

MOEA/D with many-stage dynamical resource allocation strategy to solution of many-objective OPF problems

Cited by 26 publications

References 43 publications

A Two-Archive Harris Hawk Optimization for Solving Many-Objective Optimal Power Flow Problems

A Two-Archive Harris Hawk Optimization for Solving Many-Objective Optimal Power Flow Problems

A multiobjective flexible job shop scheduling method based on deep reinforcement learning

Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm

Contact Info

Product

Resources

About