An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects

Niu, Qun; Zhou, Z.; Zhang, Hongyun; Deng, Jiankang

doi:10.3390/en5093655

Cited by 33 publications

(19 citation statements)

References 32 publications

(40 reference statements)

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“…In particular, the C coefficients do not exist in Kron's loss formula. The new loss coefficients in this study are derived by the incremental loss model and solved using Equation (22); therefore, there is no constant coefficient B 0 in Equation (22). However, according to Equation (10), the constant coefficient B 0 can be calculated by power flow equations under the base case operating point, i.e., P 0 L .…”

Section: Numerical Results Of the New Loss Coefficientsmentioning

confidence: 99%

“…In this section, the proposed new loss formula and the corresponding B and C coefficients are obtained by the computing procedure using Equation (22) described in Section 3.3. The numerical resets of the IEEE 14-bus and 30-bus test systems are shown in Tables 1 and 2, respectively.…”

Section: Numerical Results Of the New Loss Coefficientsmentioning

confidence: 99%

“…Equation (21) can be solved using an inverse matrix as shown in Equation (22). This new TL formula and the corresponding coefficients can be used not only in real power dispatch but also in reactive power dispatch.…”

Section: Tl Formula Considering Real and Reactive Power Outputsmentioning

confidence: 99%

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Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch

et al. 2018

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Abstract:The expression and calculation of transmission loss (TL) play key roles for solving the power system economic dispatch (ED) problem. ED including TL must compute the total TL and incremental transmission loss (ITL) by executing power flow equations. However, solving the power flow equations is time-consuming and may result in divergence by the iteration procedure. This approach is unsuitable for real-time ED in practical power systems. To avoid solving nonlinear power flow equations, most power companies continue to adopt the TL formula in ED. Traditional loss formulas are composed of network parameters and in terms of the generator's real power outputs. These formulas are derived by several assumptions, but these basic assumptions sacrifice accuracy. In this study, a new expression for the loss formula is proposed to improve the shortcomings of traditional loss formulas. The coefficients in the new loss formula can be obtained by recording the power losses according to varying real and reactive power outputs without any assumptions. The simultaneous equations of the second-order expansion of the Taylor series are then established. Finally, the corresponding coefficients can be calculated by solving the simultaneous equations. These new coefficients can be used in optimal real and reactive power dispatch problems. The proposed approach is tested by IEEE 14-bus and 30-bus systems, and the results are compared with those obtained from the traditional B coefficient method and the load flow method. The numerical results show that the proposed new loss formula for ED can hold high accuracy for different loading conditions and is very suitable for real-time applications.

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Section: Numerical Results Of the New Loss Coefficientsmentioning

confidence: 99%

Section: Numerical Results Of the New Loss Coefficientsmentioning

confidence: 99%

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Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch

et al. 2018

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“…In this paper, we adopted the same constraints handling scheme in [16] to keep all particles within their feasible ranges.…”

Section: Constraint Handlingmentioning

confidence: 99%

Steam Generators Optimization Using A Modified Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm

Hao

Popa

2015

SPE Digital Energy Conference and Exhibition

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Steam injection is a common EOR method for the recovery of heavy crude. This type of oil, or bitumen, is extremely viscous at the reservoirs standard temperature and almost immobile. As a result, steam and other fluids are injected into these reservoirs to enable the transfer of latent heat to the oil-bearing formation and allow them to flow.The paper introduces a new and efficient algorithm for optimal steam allocation from a series of generators given a changing delivery target at certain time intervals. The solution is addressing the efficiency of the generators as well as the steam distribution to producing wells as a key component in the design of steam-based thermal operations.Particle Swarm Optimization (PSO), motivated by the social behavior of bird flocks or fish schooling, was first introduced by Kennedy and Eberhart as a population-based optimization technique. In 2004 Sun, Xu and Feng proposed a new version of PSO, Quantum-behaved Particle Swarm Optimization (QPSO), which was inspired by quantum mechanics and trajectory analysis of PSO mechanism. As a quantum system, characterized as an uncertain system when compared to the classical stochastic systems, every particle can appear at any position within a certain probability, thus enabling the swarm to search in the whole feasible region. Additionally, in the QPSO algorithm there are no velocity vectors for particles, thus it has fewer parameters to be adjusted, which makes it easier to implement.In this paper, a modified QPSO algorithm was employed to solve the steam generators optimization problem described above. First, efficiency curves are established for each one of the different steam generators. For the group of steam generators considered, the objective was to maximize the sum of their efficiencies by adjusting the generated steam from every generator. Additionally, there are operational and design constraints on the generators such as the minimum and maximum amount of steam generated from each generator, the sum of the steam output from all generators, steam quality, etc. Several experiments were generated and are described in the paper. The results show that the modified QPSO was able to produce an optimum realistic solution significant superior to current practices in the field.

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“…Some additional operators can be introduced for GA in order to get a better predictive power of ANNs selecting an optimal combination of input variables. Moreover, in recent years also the Particle Swarm Optimization (PSO) algorithm is gaining increasing attention for the integration in the training phase of ANNs [10,11].…”

Section: Hybrid Evolutionary Techniques Combined With Annmentioning

confidence: 99%

Hybrid Predictive Models for Accurate Forecasting in PV Systems

et al. 2013

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The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error.

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An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects

Cited by 33 publications

References 32 publications

Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch

Derivation and Application of a New Transmission Loss Formula for Power System Economic Dispatch

Steam Generators Optimization Using A Modified Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm

Hybrid Predictive Models for Accurate Forecasting in PV Systems

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