Nonconvex Dynamic Economic Power Dispatch Problems Solution Using Hybrid Immune-Genetic Algorithm

Mohammadi‐Ivatloo, Behnam; Rabiee, Abbas; Soroudi, Alireza

doi:10.1109/jsyst.2013.2258747

Cited by 80 publications

(50 citation statements)

References 52 publications

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“…14, No. 5,2018Because the mining parameters are numerical, real numbers were adopted in the following parts, and the monitoring value was divided into different intervals. …”

Section: Data Preprocessingmentioning

confidence: 99%

“…combine immune algorithm and genetic algorithm into the immune genetic algorithm (IGA). Thanks to the unique concentration control function, the new algorithm outperforms immune algorithm and genetic algorithm in many aspects, such as the avoidance of local optimum trap, the maintenance of population diversity, and the accuracy of convergence results, as stated in [4][5][6]. So far, the IGA has been successfully applied in production system optimization, resource scheduling, allocation of water resources, etc.…”

Section: Introductionmentioning

confidence: 99%

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Method of Association Rules Mining and Its Application in Analysis of Seawater Samples

Sun

Liu

et al. 2018

Int. J. Onl. Eng.

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Abstract-This paper aims to set up new rules for processing seawater quality monitoring data collected by photoelectric sensor network, and mine out the useful information contained in the data. For this purpose, the immune algorithm was introduced to the classical genetic algorithm, the fitness function was designed, and the crossover and mutation probabilities were adjusted, thus creating the adaptive immune genetic algorithm (IIGA). The new algorithm was described in details and applied in an actual case. Through the comparison between the IIGA, IGA and apriori algorithms, the author concluded that the IIGA not only shortened the mining time, but also ensured the operation accuracy. The research findings are of great importance to the association rules mining in various fields.

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“…14, No. 5,2018Because the mining parameters are numerical, real numbers were adopted in the following parts, and the monitoring value was divided into different intervals. …”

Section: Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Method of Association Rules Mining and Its Application in Analysis of Seawater Samples

Sun

Liu

et al. 2018

Int. J. Onl. Eng.

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“…Considering multiple steams valves in conventional thermal power plants, it is essential to model the effect of valve-points on fuel cost. Valve-points effect can be modeled by a sinusoidal term, which will be added to the quadratic cost function [27]. P min i is minimum power generation of thermal unit i.…”

Section: Objective Functionmentioning

confidence: 99%

“…According to Table 5, the sum of power generation of four hydro units and three thermal plants meets the load demand during the scheduling time of the STHS problem. Proposed method provided the minimum fuel cost of $41,101.738, which is compared with simulated annealing (SA) [25], DE [11], chaotic artificial bee colony (CABC) [26], adaptive differential evolution (ADE) [23], RCGA [13], DE [10], SPPSO [12], RQEA [10], PSO [27], chaotic differential evolution (CDE) [23], clonal selection algorithm (CSA) [28], TLBO [29], TLBO [18], improved quantum-behaved particle swarm optimization (IQPSO) [30], quasi-oppositional teaching learning based optimization (QTLBO) [29], Improved differential evolution (IDE) [21], adaptive chaotic differential evolution (ACDE) [23], real coded chemical reaction based optimization (RCCRO) [22], differential real-coded quantum-inspired evolutionary algorithm (DRQEA) [10], and adaptive chaotic artificial bee colony algorithm (ACABC) [26], quasi-oppositional group search optimization (QOGSO), as shown in Table 6. Results show that proposed method is better than previous methods used in the test system 2, case 1.…”

Section: Test Systemmentioning

confidence: 99%

Application of Dynamic Non-Linear Programming Technique to Non-Convex Short-Term Hydrothermal Scheduling Problem

et al. 2017

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Short-term hydro-thermal scheduling aims to obtain optimal generation scheduling of hydro and thermal units for a one-day or a one-week scheduling time horizon. The main goal of the problem is to minimize total operational cost considering a series of equality and inequality constraints. The problem is considered as a non-linear and complex problem involving the valve-point loading effect of conventional thermal units, the water transport delay between connected reservoirs, and transmission loss with a set of equality and inequality constraints such as power balance, water dynamic balance, water discharge, initial and end reservoir storage volume, reservoir volume limits and the operation limits of hydro and thermal plants. A solution methodology to the short-term hydro-thermal scheduling problem with continuous and non-smooth/non-convex cost function is introduced in this research applying dynamic non-linear programming. In this study, the proposed approach is applied to two test systems with different characteristics. The simulation results obtained in this paper are compared with those reported in recent research studies, which show the effectiveness of the presented technique in terms of total operational cost. In addition, the obtained results ensure the capability of the proposed optimization procedure for solving short-term hydro-thermal scheduling problem with transmission losses and valve-point effects.

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“…Constraint (5) defines the CPPs operation cost. Constraint (6) defines the minimum and maximum outputs of the CPPs; and constraint (7) enforces the ramp-up and ramp-down limits [26,27]. Constraints (8), (9) and (10) preserve the logic of running, start up and shut down status changes.…”

Section: Model Formulationmentioning

confidence: 99%

Risk averse optimal operation of a virtual power plant using two stage stochastic programming

Tajeddini

Rahimi-Kian

Soroudi

2014

Energy

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152

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Publication informationEnergy, 73 (2014): 958-967Publisher Elsevier Item record/more information http://hdl.handle.net/10197/6116 Publisher's statementThis is the author's version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy (VOL 73, ISSUE 2014, (2014 AbstractVirtual Power Plant (VPP) is defined as a cluster of energy conversion/storage units which are centrally operated in order to improve the technical and economic performance. This paper addresses the optimal operation of a VPP considering the risk factors affecting its daily operation profits. The optimal operation is modelled in both day ahead and balancing markets as a two-stage stochastic mixed integer linear programming in order to maximize a generation companys (GenCo) expected profit. Furthermore, the Conditional Value at Risk (CVaR) is used as a risk measure technique in order to control the risk of low profit scenarios. The uncertain parameters, including the PV power output, wind power output and day-ahead market prices are modelled through scenarios. The proposed model is successfully applied to a real case study to show its applicability and the results are presented and thoroughly discussed.

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Nonconvex Dynamic Economic Power Dispatch Problems Solution Using Hybrid Immune-Genetic Algorithm

Cited by 80 publications

References 52 publications

Method of Association Rules Mining and Its Application in Analysis of Seawater Samples

Method of Association Rules Mining and Its Application in Analysis of Seawater Samples

Application of Dynamic Non-Linear Programming Technique to Non-Convex Short-Term Hydrothermal Scheduling Problem

Risk averse optimal operation of a virtual power plant using two stage stochastic programming

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