Economic dispatch with valve point effect using iteration particle swarm optimization

Abdullah, Mohd Noor; Bakar, Ab Halim Abu; Rahim, N.A.; Jamian, Jasrul Jamani; Aman, M.M.

doi:10.1109/upec.2012.6398693

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…𝑃 𝑖𝑚𝑖𝑛 ≤ 𝑃 𝑖 ≤ 𝑃 𝑖𝑚𝑎𝑥 (11) where, Pimin : Minimum power limits Pimax : Maximum power limits Equations (12) show the power generated by photovoltaic system must be less than or equal to power demand required for the photovoltaic that must not be exceed than 30% of power demand [24]. This regulation is to balance the total power generation between thermal and PV generation to cover the power demand as a power balance constraint.…”

Section: Equality Constraintmentioning

confidence: 99%

“…In solving Economic emission dispatch problem on thermal and photovoltaic, objective function is defines using equation ( 2), (6), and (7). The random initial possible solution with respect to the constraint are generated in (10), (11), and (12). Then, evaluate the new solution of FA with respect to the objective function which are to minimize cost, minimize emission and minimize the combined economic and emission.…”

Section: Imentioning

confidence: 99%

“…Optimization techniques have been employed to solve the generation scheduling problem [10]. The classic method such as lambda iteration [11], gradient method, integer programming, quadratic programming, and linear programming [12]. Modern heuristic optimization methods are proposed by researcher based on artificial intelligence concepts such as Evolutionary Programming (EP), Genetic Algorithm (GA), Hopfield Neural Network, Particle Swarm Optimization (PSO) [13], Ant Colony Algorithm (ACO), Ant-Lion Algorithm (ALO), Krill Herd Algorithm (KHA), and Firefly Algorithm (FA) [14].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Economic and Emission Dispatch of Photovoltaic Integrated Power System Using Firefly Algorithm

Abdullah

Salim

2022

IJIE

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The main purpose of Economic Load Dispatch (ELD) is to determine the optimal output of generating units to meet the power demand at lowest possible cost and subjected to the operational constraints. Various ELD optimization methods have been developed in order to deal with the challenges of continuous and sustainable power at optimal cost. The deficiency of fossil fuel reserve and rapid increase of fuel prices to generate electricity have encouraged the use of Renewable Energy (RE). Furthermore, the concerns over environmental pollution also become a factor to incorporate the RE and fossil fuel in generating electricity. This paper proposes the Firefly Algorithm (FA) to solve Economic and Emission Load Dispatch (EELD) problems by considering the photovoltaic integration power systems. The FA algorithm is used to determine the optimal cost and emission level of power generation. The test case considered in this paper is Static Combined Economic and Emission Dispatch (SCEED) that been simulated for each hour. The test system with 6 units of thermal generator and 13 units of PV generator are used to optimize SCEED problem by using FA. The Weight Sum Method (WSM) approach is used to determine the best compromise solution among the cost and emission. It found that FA can provide the fast convergence in finding the global minima value. Itcan be concluded that FA can solve the problem of economic and emission dispatch accurately.

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Section: Equality Constraintmentioning

confidence: 99%

Section: Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Economic and Emission Dispatch of Photovoltaic Integrated Power System Using Firefly Algorithm

Abdullah

Salim

2022

IJIE

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“…In IPSO, the best iteration, I j best , d is employed to improve the basic PSO performance in term of accuracy. The equation for the velocity in Eq (1) is modified as follows [ 32 ]: where…”

Section: Introductionmentioning

confidence: 99%

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

et al. 2015

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It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

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“…However, the structure of distribution networks currently is becoming more complex and cannot be modeled as a passive node because of the demand response and distributed generators (DGs) [12]. Furthermore, distribution networks transport electricity from DGs instead of transmission systems to end customers, which not only reduce distribution network usage cost but also potentially avoid congestion in transmission systems [13][14][15][16][17][18][19][20][21][22]. In [15], the optimal objectives are maximizing the power usage from DGs and the power usage costs of distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Economic optimization of smart distribution networks considering real-time pricing

Zhang

Zhao

et al. 2014

J. Mod. Power Syst. Clean Energy

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With the development of smart meters, a realtime pricing (RTP) demand response is becoming possible for households in distribution networks. The power flow can be bidirectional in distribution networks which become smarter with distributed generators (DGs). It is expensive to import electricity from the generation far from load centers because of the cost of power loss and network use, so that it is more economical to use electricity generated by local distributed generators. Therefore, in order to curtail operating costs of distribution networks, this paper proposes a model of economic optimization conducted by distribution network operators. The electricity purchasing costs for distribution network operators are minimized by optimizing electric power from transmission systems and local distributed generators. Further, based on price elasticity, the formulations of load demand considering RTP are proposed with economic optimization of distribution networks. The economic optimization problems are resolved by an interior point method. The case study shows that peak load demand can be reduced about 3.5% because the household RTP and electricity purchasing costs of distribution network operators can save 28.86 £ every hour.

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Economic dispatch with valve point effect using iteration particle swarm optimization

Cited by 14 publications

References 21 publications

Optimal Economic and Emission Dispatch of Photovoltaic Integrated Power System Using Firefly Algorithm

Optimal Economic and Emission Dispatch of Photovoltaic Integrated Power System Using Firefly Algorithm

Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

Economic optimization of smart distribution networks considering real-time pricing

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