An Improved DA-PSO Optimization Approach for Unit Commitment Problem

Khunkitti, Sirote; Watson, N.R.; Chatthaworn, Rongrit; Premrudeepreechacharn, S.; Siritaratiwat, Apirat

doi:10.3390/en12122335

Cited by 33 publications

(20 citation statements)

References 48 publications

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“…the optimal binary combination of committed units. proposed AGA gives better results compared to the algorithms proposed in [38] and [39]. This is based on the fact that the transmission line constraint has a big impact on operating costs as it affects the power flow.…”

Section: G Particle Swarm Optimizationmentioning

confidence: 93%

“…As it was pointed out, the total costs are € 10048.36, compared to [38] and [39], where they amount to € 13292.28 and € 13517.00. Although in these two references it is a classic UC (for a system which consists only thermal power plants), if we take into account that in this paper a non-convex criterion function is considered, but also the transmission line constraint (DC optimal power flow), and on the other hand there is a big difference in total costs, so it can be concluded that the newly…”

Section: International Journal Of Electrical Engineering and Computingmentioning

confidence: 98%

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Adaptive Genetic Algorithm for Hydro-thermal Unit Commitment Considering the Security Constraints

Postolov

Iliev²

2020

IJEEC

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This paper proposes a new approach with two efficient metaheuristic algorithms, in combination with quadratic programming, to solve the nonlinear optimization problem Unit Commitment in a complex hydro-thermal power system i.e. Hydrothermal Unit Commitment (HTUC). The main goal is to minimize the total costs (which are a very non-linear and non-convex problem), while satisfying the many hydro-thermal constraints. Such constraints, together with the nonlinear non-convex and mixed-integer objective function, make the search space extremely complex. To solve such a complicated system, the paper proposes a hybridization of a developed binary-coded genetic algorithm (in which quadratic programming is integrated), with a particle swarm optimization (PSO) algorithm. PSO is applied to the final economic load dispatch (ELD), based on the optimal binary combination obtained from the genetic algorithm. A new approach has been proposed through the application of a repair mechanism, which is based on a priority list, in order to maintain the diversity of the population and prevent premature convergence. The entire algorithm was developed and tested in MATLAB and then applied to the IEEE 30 BUS test system. The experimental results show better performance of the proposed algorithm compared to the recently published algorithms, in terms of convergence, constraint handling, as well as better solution quality.

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Section: G Particle Swarm Optimizationmentioning

confidence: 93%

Section: International Journal Of Electrical Engineering and Computingmentioning

confidence: 98%

Adaptive Genetic Algorithm for Hydro-thermal Unit Commitment Considering the Security Constraints

Postolov

Iliev²

2020

IJEEC

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“…In other words, an artificial dragonfly moves within a search space by only assuming values of -1 or 1, where each velocity represents the probability of a bit of position taking the value 1. To be within the range [0,1], the velocity, which is a probability, must be limited [39]. A function used to do this is called the sigmoid function, and is formulated mathematically as follows:…”

Section: Cohesion Strategy   F R Cmentioning

confidence: 99%

Discrete Artificial Dragonflies Algorithm in Agent Based Modelling for Exact Boolean kSatisfiability Problem

Abubakar

Sagir²,

Yusuf³

et al. 2020

JAMCS

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The development of metaheuristics and Boolean Satisfiability representation plays an important part in a neural network (NN) and Artificial Intelligence (AI) communities. In this paper, a new hybrid discrete version of the artificial dragonfly algorithm (DADA) applying a minimum objective function in agent-based modelling (ABM) obeying a specified procedure to optimize the states of neurons, for optimal Boolean Exact Satisfiability representation on NETLOGO as a dynamic platform. We combined the artificial dragonfly algorithm for its random searching ability that encourages diverse solutions and formation of static swarm’s mechanism to stimulus computational problems to converge to the best global optimal search space. The global performance of the proposed DADA was compared with genetic algorithm (GA) that are available in the literature based on the global minimum ratio (gM), Local Minimum Ratio (yM), Computational time (CPU) and Hamming distance (HD). The final results showed good agreement between the proposed DADA and discrete version of GA to efficiently optimize the Exact-kSAT problem. It found that DADA-ABM has high potentiality for optimizing or modelling a network that is very hard or often impossible to capture by exact or traditional optimization modelling techniques such as Boolean satisfiability problem is better than existing methods in the literature.

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“…There are several papers on UC for minimizing system costs, but each of them is distinct from each author's viewpoint. Each manuscript solves UC problem by considering different conditions and constraints [36][37][38][39]. Reference [40] represents a UC problem formulation for minimizing cost as follows:…”

Section: Cost Minimization Ucmentioning

confidence: 99%

Energy Storage System Analysis Review for Optimal Unit Commitment

et al. 2019

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Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

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An Improved DA-PSO Optimization Approach for Unit Commitment Problem

Cited by 33 publications

References 48 publications

Adaptive Genetic Algorithm for Hydro-thermal Unit Commitment Considering the Security Constraints

Adaptive Genetic Algorithm for Hydro-thermal Unit Commitment Considering the Security Constraints

Discrete Artificial Dragonflies Algorithm in Agent Based Modelling for Exact Boolean kSatisfiability Problem

Energy Storage System Analysis Review for Optimal Unit Commitment

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