MOPF solution methodology

Shaheen, Abdullah M.; Farrag, Sobhy M.; El‐Sehiemy, Ragab A.

doi:10.1049/iet-gtd.2016.1379

Cited by 78 publications

(47 citation statements)

References 62 publications

(71 reference statements)

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“…where , is the average value of the successful numbers ( 1,2,3,4) in the k-th generation. The factor, min , 0 , , 1 , acts as the standard deviation.…”

Section: Self-adaptive Strategy For the Control Parametersmentioning

confidence: 99%

“…n recent decades, there are increasing needs to obtain fast solutions [1 ] of the MOPF problems [2 ]- [ 3 ] for a more effective operation and planning in modern power systems [ 4 ]. The MOPF can be understood as an extension of the constrained single-objective optimal power flow (OPF) [5] that aims to minimize the total generation cost of the power system while meeting the need of a power balance between the supply and the demand, subject to the constraints [6] of the operating limits of the generator active and reactive powers, and the security of the overall system.…”

Section: Introductionmentioning

confidence: 99%

“…Ref [21] use the non-dominated sorting genetic algorithm (NSGA-II) [22] to obtain the entire PF on which the fuzzy theory [3] is applied for the final 'best compromise solution'. Such design is not efficient because it is not necessary to obtain the entire PF of the MOPFs for a compromise solution.…”

Section: Introductionmentioning

confidence: 99%

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A Judicious Decision-Making Approach for Power Dispatch in Smart Grid Using a Multiobjective Evolutionary Algorithm Based on Decomposition

Xiao

Sutanto

Muttaqi

et al. 2020

IEEE Trans. on Ind. Applicat.

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Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is an efficient mathematical strategy for solving multiobjective optimization problems. However, the MOEA/D algorithm has not yet been widely used on the multiobjective optimal power flow (MOPF) problems, which consider several conflicting objectives with varying tradeoff levels. This article proposes a novel differential evolution (DE) strategy based on the MOEA/D framework to quickly determine a set of optimal solutions of MOPFs, in the objective space formed from the different objectives, such as the most optimal economic dispatch, the least environmental emission objectives, and the minimum transmission losses, while considering the power system constraints. A judicious decision can be made by the user from the set of optimal solutions of the MOPF associated with the weight vectors representing the tradeoff levels of the different objectives. For improved performance, two aggregate objective functions, a load flow operator and a selfadaptive DE strategy work cooperatively: 1) to improve the weak convergence of the MOEA/D and to achieve a better decision speed; 2) to obtain more accurate optimal solutions even under non-convex conditions; 3) to ensure that the power system constraints are taken into account; 4) to integrate the above features into a fast and efficient algorithm. The proposed algorithm has been validated using the IEEE 30-bus system and a revised 33-bus radial system added to one node of the 30-bus system. The simulation results show that the proposed algorithm can provide a good accuracy and can converge to a set of optimal solutions of the MOPFs. Disciplines Disciplines Engineering | Science and Technology StudiesAbstract-Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an efficient mathematical strategy for solving multi-objective optimization problems. However, the MOEA/D algorithm has not yet been widely used on the multiobjective optimal power flow (MOPF) problems, which considers several conflicting objectives with varying trade-off levels. This paper proposes a novel differential evolution (DE) strategy based on the MOEA/D framework to quickly determine a set of optimal solutions of MOPFs, in the objective space formed from the different objectives, such as the most optimal economic dispatch, the least environmental emission objectives, and the minimum transmission losses, while considering the power system constraints. A judicious decision can be made by the user from the set of optimal solutions of the MOPF associated with the weight vectors representing the trade-off levels of the different objectives. For improved performance, two aggregate objective functions, a load flow operator and a self-adaptive DE strategy work cooperatively: 1) to improve the weak convergence of the MOEA/D and to achieve a better decision speed; 2) to obtain more accurate optimal solutions even under non-convex conditions; 3) to ensure that the power system constraints are taken into account; 4) to integrate the ab...

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“…where , is the average value of the successful numbers ( 1,2,3,4) in the k-th generation. The factor, min , 0 , , 1 , acts as the standard deviation.…”

Section: Self-adaptive Strategy For the Control Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Judicious Decision-Making Approach for Power Dispatch in Smart Grid Using a Multiobjective Evolutionary Algorithm Based on Decomposition

Xiao

Sutanto

Muttaqi

et al. 2020

IEEE Trans. on Ind. Applicat.

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show abstract

“…Hybrid methods are developed for solving the economic dispatch (ED) problem such as fuzzy linear programming as in El‐Ela et al for predicted and emergency operation of power systems. The chaotic krill herd optimizer that was developed for solving the ED problem in Bentouati et al The continuous development of search‐based methods is also integrated for different power systems problem such as optimal power flow . A comprehensive review on this topic for optimal combined heat and power dispatch from economic and environmental perspective was presented in Nazari‐Heris et al The fruit fly was developed to solve engineering design problem called multi‐objective framework for synchronous motor design .…”

Section: Introductionmentioning

confidence: 99%

“…The chaotic krill herd optimizer that was developed for solving the ED problem in Bentouati et al 19 The continuous development of search-based methods is also integrated for different power systems problem such as optimal power flow. [20][21][22][23][24] A comprehensive review on this topic for optimal combined heat and power dispatch from economic and environmental perspective was presented in Nazari-Heris et al 24 The fruit fly was developed to solve engineering design problem called multiobjective framework for synchronous motor design. 25 The heuristic algorithms were used for short term hydro-based power plants scheduling 26 and for phasor measurements units' placement of in electrical networks in Nazari-Heris and Mohammadi-Ivatloo.…”

Section: Introductionmentioning

confidence: 99%

Assessment of hurricane versus sine-cosine optimization algorithms for economic/ecological emissions load dispatch problem

El‐Sehiemy

Rizk‐Allah

Attia

2018

Int Trans Electr Energ Syst

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Summary In the current research, a comparative study of two modern optimization algorithms is carried out for finding the solution of non‐smooth economic/ecological emission load dispatch (EELD) problem. These optimization methods are hurricane optimization algorithm (HOA) and sine‐cosine algorithm (SCA). A multi‐objective optimization module is successively developed for the competitive algorithms. In the competitive algorithms, random initial populations of the search agents are created in the search space with optimizing the conflicted objectives, economic and emission, simultaneously of the EELD problem. The multi‐objective optimal solutions are achieved based on Pareto concepts. The competitive algorithms are tested on six test function and two general standard engineering problems called tension/compression design and welded beam design problem. Then, the optimization algorithms are validated for an important operation issue of power systems by solving the non‐smooth EELD on the standard six generators, IEEE 30‐bus standard test system. Single and multiobjective frameworks are considered to reduce the generation fuel costs as well as minimizing the corresponding ecological emissions. Simulation results are assessed with previous famous optimizers. Also, these results prove the reasonable performances of the proposed two competitive algorithms compared with previous optimization techniques. In addition, HOA has more competitive performance compared with SCA according to convergence rate and statistical analysis of the studied real engineering problems as well as for benchmarking and design engineering problems. Therefore, these algorithms are considered as efficient and capable algorithms for other non‐smooth large‐scale complex problems in real power networks.

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Performance Analysis of Multiobjective Particle Swarm Optimization Based Optimal Power Flow

Chauhan

Chakravorty

2021

Algorithms for Intelligent Systems

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MOPF solution methodology

Cited by 78 publications

References 62 publications

A Judicious Decision-Making Approach for Power Dispatch in Smart Grid Using a Multiobjective Evolutionary Algorithm Based on Decomposition

A Judicious Decision-Making Approach for Power Dispatch in Smart Grid Using a Multiobjective Evolutionary Algorithm Based on Decomposition

Assessment of hurricane versus sine-cosine optimization algorithms for economic/ecological emissions load dispatch problem

Performance Analysis of Multiobjective Particle Swarm Optimization Based Optimal Power Flow

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