Simulated Annealing Technique for Dynamic Economic Dispatch

Panigrahi, Chinmoy Kumar; Chattopadhyay, Pabitra; Chakrabarti, R.; Basu, Madhumita

doi:10.1080/15325000500360843

Cited by 203 publications

(131 citation statements)

References 13 publications

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“…The FES is set as 60000, and the population number for the SL-TLBO method is 50. The 5-unit economic dispatch benchmark data is taken from [28]. According to the experimental tests, x is set as 0.05, which is much smaller than the previous setting in benchmark functions tests.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Yang

Niu

et al. 2014

J. Mod. Power Syst. Clean Energy

108

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Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Yang

Niu

et al. 2014

J. Mod. Power Syst. Clean Energy

108

View full text Add to dashboard Cite

show abstract

“…It is applied on a 33-units threeareas system [17]. Area 1 consists of 10 thermal generation units, and the total wind generation capacity is assumed to be 250MW in this area.…”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal multi‐area generation schedule considering renewable resources mix: a real‐time approach

Soroudi

Rabiee²

2013

IET Generation, Transmission & Distribution

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This paper proposes a new model for multi-area dynamic economic dispatch (MA-DED) problem, taking into account hydro-thermal generating units, wind power generation and power pool market, for supplying the overall demand of the system for a given horizon. The uncertainties in wind power generations, energy prices and demand of the system are also modeled to make the proposed approach more practical in case of real-time operation of practical power systems. Scenario based approach is adopted for uncertainty modeling. In order to make the proposed MA-DED applicable in real-time operation of power systems, optimality condition decomposition technique is employed along with parallel computation ability. The proposed approach is examined on a three-area interconnected power network, to demonstrate its applicability for real-time scheduling of joint thermal and undispatchable renewable energy resources.

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“…The transmissions B-loss Matrix are indicated in [24]. In the proposed method effectiveness is acquired by simulating the QBSO technique for 40 trials and the corresponding optimum results are presented in Fig.…”

Section: Nominal Test Case (10 Thermal Generating System)mentioning

confidence: 99%

Scheduling practical generating system using an improved bacterial swarm optimization

Raviprabakaran

Ravichandran

2016

Teh. vjesn.

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Original scientific paper This paper offers an improved technique namely, Quorum sensing based Bacterial Swarm Optimization (QBSO) technique to solve Practical Dynamic Economic Dispatch (PDED) problem. Quorum sensing is the communication signalling mechanism that permits the bacteria to organize the collective behaviour. The enhanced QBSO method improves the exploration capability of bacterial swarm. The practicability of the proposed method is tested on standard 10 generating unit system and the public practical south Indian 20 thermal generating system with recorded load demand profiles. The enhanced technique increases the operational speed of scheduling and reduces the generating cost of standard system and the thermal generating units owned by Tamilnadu Generation and Distribution Corporation Limited (TANGEDCO). The presented technique outperforms when compared with conventional bacterial swarm optimization technique and some recently published methods. Keywords: Dynamic Economic Dispatch; Practical Load Demand Profiles; Quorum Sensing based Bacterial Swarm Optimization; 20 South Indian Thermal UnitsProgramiranje praktičnog sustava generiranja uporabom poboljšane optimizacije bakterijskog roja Izvorni znanstveni članak Ovaj rad nudi poboljšanu metodu, naime, Quorum sensingbased Bacterial Swarm Optimization (QBSO) metodu za rješavanje problema praktične dinamične ekonomične isporuke -Practical Dynamic Economic Dispatch (PDED). Prepoznavanje kvoruma je mehanizam za signaliziranje komunikacije koji omogućuje organiziranje kolektivnog ponašanja bakterija. Poboljšana QBSO metoda povećava istraživačku sposobnost roja bakterija. Praktičnost predložene metode ispitana je na standardnomsustavu od 10 jedinicageneriranja energije i javnom praktičnom južno indijskom sustavu od 20 jedinica za generiranjetoplinske energije sa snimljenim profilima potrebnogopterećenja. Ta poboljšana metoda povećava operativnu brzinu programiranjaoperacija i smanjuje troškove generiranja standardnog sustava i jedinicaza dobivanje toplinske energije u vlasništvu Tamilnadu Generation and Distribution Corporation Limited (TANGEDCO). Prikazana metodaje bolja u usporedbi s konvencionalnom metodom optimizacije roja bakterija inekim nedavno objavljenim metodama.Ključne riječi: dinamična ekonomična isporuka; 20 South Indian Thermal Units; optimizacija roja bakterija temeljena naprepoznavanju kvoruma; praktični profili potreba opterećenja

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Simulated Annealing Technique for Dynamic Economic Dispatch

Cited by 203 publications

References 13 publications

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Optimal multi‐area generation schedule considering renewable resources mix: a real‐time approach

Scheduling practical generating system using an improved bacterial swarm optimization

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