Analysis of optimal wind power integration capacity based on the scenario tree construction method

Određivanje optimalnog integrativnog kapaciteta energije vjetra velike vjetroelektrane postalo je važno pitanje kod planiranja i rada energetskog postrojenja. Razumni integrativni kapacitet energije vjetra može poboljšati korištenje energije vjetra i uštedjeti na ulaganju za izgradnju vjetroelektrane. U ovom je radu prikazan matematički model za određivanje optimalnog integrativnog kapaciteta vjetroelektrane uzimajući maksimalizaciju čiste dobiti godišnje proizvodnje vjetroelektrane kao objektivnu funkciju, a potrebe osiguranja kao ograničenja modela. Metoda konstrukcije stabla scenarija predložena je zbog bavljenja nesistematskim karakteristikama energije vjetra tijekom jedne godine na temelju teorije vjerojatnosti. Predloženi su principi diskretizacije vjerojatnoće raspodjele brzine vjetra i postupci konstrukcije stabla scenarija energije vjetra. Primijenjen je algoritam hijerarhijske optimalizacije koji kombinira algoritam optimalizacije roja w-čestica i algoritam društveno emocionalne optimalizacije za rješenje modela optimalizacije u ovom radu. Predloženi se model primijenio u istraživanju učinaka izazvanih varijacijom faktora povezanih s rezultatima optimalnog integrativnog kapaciteta energije vjetra. Daju se detaljni postupci u dobivanju rješenja. Točnost modela i valjanost algoritama verificirani su prema simulaciji na sustavu IEEE-30 čvorova.The determination of the optimal wind power integration capacity of a large-scale wind farm has become an important issue in power system operation and planning. Reasonable wind power integration capacity can improve the wind energy utilization and save investment for wind farm construction. This paper builds a mathematical model for determining wind farm optimal integration capacity by taking the maximization of the wind farm annual generation net benefits the objective function and the security operation requirements as the model constraints. The scenario tree construction method is proposed to deal with the random characteristics of wind power in one year based on the probability theory. The discretization principles of wind speed probability distribution and the scenario tree construction processes of wind power are proposed. Hierarchical optimization algorithm combining w-particle swarm optimization algorithm and social emotional optimization algorithm is used to solve the optimization model in this paper. The effects caused by the variation of factors correlated with the optimal wind power integration capacity results are investigated using the proposed model. The detailed solution steps are given. The correctness of the model and the validity of the algorithms for solving are verified according to the simulation on the IEEE-30 nodes system

On the Diameter of a Graph Related to p-Regular Conjugacy Classes of Finite Groups

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Analysis of optimal wind power integration capacity based on the scenario tree construction method

Određivanje optimalnog integrativnog kapaciteta energije vjetra velike vjetroelektrane postalo je važno pitanje kod planiranja i rada energetskog postrojenja. Razumni integrativni kapacitet energije vjetra može poboljšati korištenje energije vjetra i uštedjeti na ulaganju za izgradnju vjetroelektrane. U ovom je radu prikazan matematički model za određivanje optimalnog integrativnog kapaciteta vjetroelektrane uzimajući maksimalizaciju čiste dobiti godišnje proizvodnje vjetroelektrane kao objektivnu funkciju, a potrebe osiguranja kao ograničenja modela. Metoda konstrukcije stabla scenarija predložena je zbog bavljenja nesistematskim karakteristikama energije vjetra tijekom jedne godine na temelju teorije vjerojatnosti. Predloženi su principi diskretizacije vjerojatnoće raspodjele brzine vjetra i postupci konstrukcije stabla scenarija energije vjetra. Primijenjen je algoritam hijerarhijske optimalizacije koji kombinira algoritam optimalizacije roja w-čestica i algoritam društveno emocionalne optimalizacije za rješenje modela optimalizacije u ovom radu. Predloženi se model primijenio u istraživanju učinaka izazvanih varijacijom faktora povezanih s rezultatima optimalnog integrativnog kapaciteta energije vjetra. Daju se detaljni postupci u dobivanju rješenja. Točnost modela i valjanost algoritama verificirani su prema simulaciji na sustavu IEEE-30 čvorova.The determination of the optimal wind power integration capacity of a large-scale wind farm has become an important issue in power system operation and planning. Reasonable wind power integration capacity can improve the wind energy utilization and save investment for wind farm construction. This paper builds a mathematical model for determining wind farm optimal integration capacity by taking the maximization of the wind farm annual generation net benefits the objective function and the security operation requirements as the model constraints. The scenario tree construction method is proposed to deal with the random characteristics of wind power in one year based on the probability theory. The discretization principles of wind speed probability distribution and the scenario tree construction processes of wind power are proposed. Hierarchical optimization algorithm combining w-particle swarm optimization algorithm and social emotional optimization algorithm is used to solve the optimization model in this paper. The effects caused by the variation of factors correlated with the optimal wind power integration capacity results are investigated using the proposed model. The detailed solution steps are given. The correctness of the model and the validity of the algorithms for solving are verified according to the simulation on the IEEE-30 nodes system

Fuzzy logic system for frequency stability analysis of wind farm integrated power systems

U radu se uvodi sustav fuzzy logike u analizi frekvencijskog odziva vjetroelektrana sastavljenih od generatora s turbinama na vjetar promjenljive brzine - variable speed wind turbine generators (WTGs). WTGs mogu pružiti frekvencijsku podršku koristeći svoje zalihe energije. Međutim, frekvencijski odziv pojedinačnog WTG razlikuje se od odziva drugih WTGs zbog njihovih različitih brzina vjetra. Istražuje se i koristi model frekvencijskog odziva utemeljen na protoku opterećenja istosmjerne struje kako bi se poboljšale funkcije članice sustava logike. Kad se pojave smetnje na mreži, sustav fuzzy logike može odrediti frekvencijski odziv vjetroelektrane prema ulaznim varijablama realnog vremena. Rezultati simulacije pokazuju da je projektirani sustav učinkovit i može doprinijeti analizi frekvencijske stabilnosti.This paper introduces a fuzzy logic system to analyse the frequency response of wind farms composed of variable speed wind turbine generators (WTGs). WTGs can provide frequency support by using their energy reserves. However, the frequency response of an individual WTG is different from that of other WTGs because of their respective actual wind speeds. The frequency response model based on the direct-current load flow is investigated and used to amend the membership functions of the logic system. When a network disturbance occurs, the fuzzy logic system can determine the frequency response of the wind farm according to real-time input variables. The simulation results indicate that the designed system is effective and can contribute to frequency stability analysis

A Novel Idea for Optimizing Condition-Based Maintenance Using Genetic Algorithms and Continuous Event Simulation Techniques

Effective maintenance strategies are of utmost significance for system engineering due to their direct linkage with financial aspects and safety of the plants’ operation. At a point where the state of a system, for instance, level of its deterioration, can be constantly observed, a strategy based on condition-based maintenance (CBM) may be affected; wherein upkeep of the system is done progressively on the premise of monitored state of the system. In this article, a multicomponent framework is considered that is continuously kept under observation. In order to decide an optimal deterioration stage for the said system, Genetic Algorithm (GA) technique has been utilized that figures out when its preventive maintenance should be carried out. The system is configured into a multiobjective problem that is aimed at optimizing the two desired objectives, namely, profitability and accessibility. For the sake of reality, a prognostic model portraying the advancements of deteriorating system has been employed that will be based on utilization of continuous event simulation techniques. In this regard, Monte Carlo (MC) simulation has been shortlisted as it can take into account a wide range of probable options that can help in reducing uncertainty. The inherent benefits proffered by the said simulation technique are fully utilized to display various elements of a deteriorating system working under stressed environment. The proposed synergic model (GA and MC) is considered to be more effective due to the employment of “drop-by-drop approach” that permits successful drive of the related search process with regard to the best optimal solutions

Dual cloud point extraction coupled with hydrodynamic-electrokinetic two-step injection followed by micellar electrokinetic chromatography for simultaneous determination of trace phenolic estrogens in water samples

A dual cloud point extraction (dCPE) off-line enrichment procedure coupled with a hydrodynamic-electrokinetic two-step injection online enrichment technique was successfully developed for simultaneous preconcentration of trace phenolic estrogens (hexestrol, dienestrol, and diethylstilbestrol) in water samples followed by micellar electrokinetic chromatography (MEKC) analysis. Several parameters affecting the extraction and online injection conditions were optimized. Under optimal dCPE-two-step injection-MEKC conditions, detection limits of 7.9-8.9 ng/mL and good linearity in the range from 0.05 to 5 mu g/mL with correlation coefficients R (2) a parts per thousand yenaEuro parts per thousand 0.9990 were achieved. Satisfactory recoveries ranging from 83 to 108 % were obtained with lake and tap water spiked at 0.1 and 0.5 mu g/mL, respectively, with relative standard deviations (n = 6) of 1.3-3.1 %. This method was demonstrated to be convenient, rapid, cost-effective, and environmentally benign, and could be used as an alternative to existing methods for analyzing trace residues of phenolic estrogens in water samples.A dual cloud point extraction (dCPE) off-line enrichment procedure coupled with a hydrodynamic-electrokinetic two-step injection online enrichment technique was successfully developed for simultaneous preconcentration of trace phenolic estrogens (hexestrol, dienestrol, and diethylstilbestrol) in water samples followed by micellar electrokinetic chromatography (MEKC) analysis. Several parameters affecting the extraction and online injection conditions were optimized. Under optimal dCPE-two-step injection-MEKC conditions, detection limits of 7.9-8.9 ng/mL and good linearity in the range from 0.05 to 5 mu g/mL with correlation coefficients R (2) a parts per thousand yenaEuro parts per thousand 0.9990 were achieved. Satisfactory recoveries ranging from 83 to 108 % were obtained with lake and tap water spiked at 0.1 and 0.5 mu g/mL, respectively, with relative standard deviations (n = 6) of 1.3-3.1 %. This method was demonstrated to be convenient, rapid, cost-effective, and environmentally benign, and could be used as an alternative to existing methods for analyzing trace residues of phenolic estrogens in water samples

Redistribution of the astrocyte phenotypes in the medial vestibular nuclei after unilateral labyrinthectomy

Astrocytes are highly heterogeneous and involved in different aspects of fundamental functions in the central nervous system (CNS). However, whether and how this heterogeneous population of cells reacts to the pathophysiological challenge is not well understood. To investigate the response status of astrocytes in the medial vestibular nucleus (MVN) after vestibular loss, we examined the subtypes of astrocytes in MVN using single-cell sequencing technology in a unilateral labyrinthectomy mouse model. We discovered four subtypes of astrocytes in the MVN with each displaying unique gene expression profiles. After unilateral labyrinthectomy, the proportion of the astrocytic subtypes and their transcriptional features on the ipsilateral side of the MVN differ significantly from those on the contralateral side. With new markers to detect and classify the subtypes of astrocytes in the MVN, our findings implicate potential roles of the adaptive changes of astrocyte subtypes in the early vestibular compensation following peripheral vestibular damage to reverse behavioral deficits