A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

A regulatory framework for short-term stochastic maintenance outage scheduling in smart grids

The current regulatory framework for maintenance outage scheduling in distribution systems needs revision to face the challenges of future smart grids. In the smart grid context, generation units and the system operator perform new roles with different objectives, and an efficient coordination between them becomes necessary. In this paper, the distribution system operator (DSO) of a microgrid receives the proposals for shortterm (ST) planned outages from the generation and transmission side, and has to decide the final outage plans, which is mandatory for the members to follow. The framework is based on a coordination procedure between the DSO and other market players. This paper undertakes the challenge of optimization problem in a smart grid where the operator faces with uncertainty. The results show the effectiveness and applicability of the proposed regulatory framework in the modified IEEE 34- bus test system.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

Utilisation of Short Term Electricity Derivatives in Industrial Energy Management

Työn päätavoite on selvittää kuinka erityisesti sähkön markkinahinnan ennustamiseen ja johdannaismarkkinoiden tietämykseen perustuva lyhyen tähtäimen sähköjohdannaisten hyödyntäminen tapahtuu teollisessa energianhallinnassa. Tätä aihetta lähestytään luomalla prosessi lyhyen tähtäimen sähköjohdannaisten hyödyntämiselle. Prosessi esitellään ja selvitetään aina lähtökohdista todelliseen kaupankäyntiin asti erillisen esimerkkitehtaan avulla.Lyhyen tähtäimen sähköjohdannaisten hyödyntäminen teollisessa energianhallinnassa perustuu pääosin tulevaisuuden odotuksiin sähkön markkinahinnan kehittymisestä sekä tehtaiden operatiiviseen tilanteeseen. Operatiiviseen tilanteeseen perustuva lyhyen tähtäimen sähköjohdannaisten kaupankäynti on pääasiassa pitkän tähtäimen suojausten sopeuttamista lyhyelle tähtäimelle sopivaksi.Hinnan ennustamisella on suuri rooli lyhyen tähtäimen sähköjohdannaisten hyödyntämisprosessissa. Työssä esitelty hinnan ennustamismalli on sopiva päivä- ja viikkotason Nord Poolin Elspot -systeemihinnan ennustamiseen. Elspot -systeemihinnan ennustamismalli on suunniteltu käytännönläheiseksi ja sen perustana ovat todelliset fysikaaliset ja mitattavat suureet. Futuurimarkkinatietämys on tarpeen lyhyen tähtäimen johdannaisia käytettäessä. Työssä tutkitaan yleisiä markkinoiden odotuksia ja futuurimarkkinoiden tietoisuuden kehittymistä koskien tulevaa vallitsevaa tilannetta. Työssä luodaan myös työkalu, mikä auttaa kaupan laatijaa muodostamaan suuntaa-antavat todennäköisyydet eri hintanäkemyksille ja paikallistamaan mahdolliset markkinoiden epätodennäköiset hintaodotukset.Kokemukset Elspot -systeemihinnan ennustamismallin soveltamisesta ovat lupaavia. Lisäksi havainnot futuurimarkkinoiden käyttäytymisestä Nord Poolissa ja muodostettu työkalu suuntaa-antavien todennäköisyyksien selvittämiseksi auttavat kaupan laatijaa päätöksenteossa. Lyhyen tähtäimen sähköjohdannaisten hyödyntäminen teollisessa energianhallinnassa on periaatteessa mahdollista esitellyn prosessin avulla, vaikka täydellinen käyttöönotto vaatisi vielä joitakin järjestelyjä. Keskittymällä tilanteisiin jotka työssä kuvatulla prosessilla ovat hoidettavissa, työssä määritellyllä menettelyllä on mahdollisuudet saavuttaa epäedullisen hintakehityksen riskin väheneminen ja parempi taloudellinen tulos teollisen energianhallinnan sähkökaupankäynnissä.

A Method for Estimating Short-term Profitability, Case Stora Enso Packaging Boards

Tutkimuksen tavoitteena oli rakentaa case yritykselle malli lyhyen aikavälin kannattavuuden estimointia varten. Tutkimusmetodi on konstruktiivinen, ja malli kehitettiin laskentaihmisten avustuksella. Teoriaosassa käytiin kirjallisuuskatsauksen avulla läpi kannattavuutta, budjetointia sekä itse ennustamista. Teoriaosassa pyrittiin löytämään sellaisia menetelmiä, joita voitaisiin käyttää lyhyen aikavälin kannattavuuden estimoinnissa. Rakennettavalle mallille asetettujen vaatimusten mukaan menetelmäksi valittiin harkintaan perustuva menetelmä (judgmental). Tutkimuksen mukaan kannattavuuteen vaikuttaa myyntihinta ja –määrä, tuotanto, raaka-aineiden hinnat ja varaston muutos. Rakennettu malli toimii kohdeyrityksessä kohtalaisen hyvin ja huomattavaa on se, että eri tehtaiden ja eri koneiden väliset erot saattavat olla kohtuullisen suuret. Nämä erot johtuvat pääasiassa tehtaan koosta ja mallien erilaisuudesta. Mallin käytännön toimivuus tulee kuitenkin parhaiten selville silloin, kun se on laskentaihmisten käytössä. Ennustamiseen liittyy kuitenkin aina omat ongelmansa ja uudetkaan menetelmät eivät välttämättä poista näitä ongelmia.

Optimal trading of wind power in the short term market

The energy reform, which is happening all over the world, is caused by the common concern of the future of the humankind in our shared planet. In order to keep the effects of the global warming inside of a certain limit, the use of fossil fuels must be reduced. The marginal costs of the renewable sources, RES are quite high, since they are new technology. In order to induce the implementation of RES to the power grid and lower the marginal costs, subsidies were developed in order to make the use of RES more profitable. From the RES perspective the current market is developed to favor conventional generation, which mainly uses fossil fuels. Intermittent generation, like wind power, is penalized in the electricity market since it is intermittent and thus diffi-cult to control. Therefore, the need of regulation and thus the regulation costs to the producer differ, depending on what kind of generation market participant owns. In this thesis it is studied if there is a way for market participant, who has wind power to use the special characteristics of electricity market Nord Pool and thus reach the gap between conventional generation and the intermittent generation only by placing bids to the market. Thus, an optimal bid is introduced, which purpose is to minimize the regulation costs and thus lower the marginal costs of wind power. In order to make real life simulations in Nord Pool, a wind power forecast model was created. The simulations were done in years 2009 and 2010 by using a real wind power data provided by Hyötytuuli, market data from Nord Pool and wind forecast data provided by Finnish Meteorological Institute. The optimal bid needs probability intervals and therefore the methodology to create probability distributions is introduced in this thesis. In the end of the thesis it is shown that the optimal bidding improves the position of wind power producer in the electricity market.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

Quantification of uncertainties associated with space-time estimates of short-term soil CO2 emissions in a sugar cane area

The characterization of soil CO2 emissions (FCO2) is important for the study of the global carbon cycle. This phenomenon presents great variability in space and time, a characteristic that makes attempts at modeling and forecasting FCO2 challenging. Although spatial estimates have been performed in several studies, the association of these estimates with the uncertainties inherent in the estimation procedures is not considered. This study aimed to evaluate the local, spatial, local-temporal and spatial-temporal uncertainties of short-term FCO2 after harvest period in a sugar cane area. The FCO2 was featured in a sampling grid of 60m×60m containing 127 points with minimum separation distances from 0.5 to 10m between points. The FCO2 was evaluated 7 times within a total period of 10 days. The variability of FCO2 was described by descriptive statistics and variogram modeling. To calculate the uncertainties, 300 realizations made by sequential Gaussian simulation were considered. Local uncertainties were evaluated using the probability values exceeding certain critical thresholds, while the spatial uncertainties considering the probability of regions with high probability values together exceed the adopted limits. Using the daily uncertainties, the local-spatial and spatial-temporal uncertainty (Ftemp) was obtained. The daily and mean emissions showed a variability structure that was described by spherical and Gaussian models. The differences between the daily maps were related to variations in the magnitude of FCO2, covering mean values ranging from 1.28±0.11μmolm-2s-1 (F197) to 1.82±0.07μmolm-2s-1 (F195). The Ftemp showed low spatial uncertainty coupled with high local uncertainty estimates. The average emission showed great spatial uncertainty of the simulated values. The evaluation of uncertainties associated with the knowledge of temporal and spatial variability is an important tool for understanding many phenomena over time, such as the quantification of greenhouse gases or the identification of areas with high crop productivity. © 2013 Elsevier B.V.

Are there non-linearities in short-term interest rates?

The present paper investigates the characteristics of short-term interest rates in several countries. We examine the importance of nonlinearities in the mean reversion and volatility of short-term interest rates. We examine various models that allow the conditional mean (drift) and conditional variance (diffusion) to be functions of the current short rate.We find that different markets require different models. In particular, we find evidence of nonlinear mean reversion in some of the countries that we examine, linear mean reversion in others and no mean reversion in some countries. For all countries we examine, there is strong evidence of the need for the volatility of interest rate changes to be highly sensitive to the level of the short-term interest rate. Out-of-sample forecasting performance of one-factor short rate models is poor, stemming from the inability of the models to accommodate jumps and discontinuities in the time series data.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

Short-term Physiological Changes In Roots And Leaves Of Sugarcane Varieties Exposed To H2o2 In Root Medium.

The aim of this study was to evaluate the differential sensitivity of sugarcane genotypes to H2O2 in root medium. As a hypothesis, the drought tolerant genotype would be able to minimize the oxidative damage and maintain the water transport from roots to shoots, reducing the negative effects on photosynthesis. The sugarcane genotypes IACSP94-2094 (drought tolerant) and IACSP94-2101 (drought sensitive) were grown in a growth chamber and exposed to three levels of H2O2 in nutrient solution: control; 3mmolL(-1) and 80mmolL(-1). Leaf gas exchange, photochemical activity, root hydraulic conductance (Lr) and antioxidant metabolism in both roots and leaves were evaluated after 15min of treatment with H2O2. Although, root hydraulic conductance, stomatal aperture, apparent electron transport rate and instantaneous carboxylation efficiency have been reduced by H2O2 in both genotypes, IACSP94-2094 presented higher values of those variables as compared to IACSP94-2101. There was a significant genotypic variation in relation to the physiological responses of sugarcane to increasing H2O2 in root tissues, being root changes associated with modifications in plant shoots. IACSP94-2094 presented a root antioxidant system more effective against H2O2 in root medium, regardless H2O2 concentration. Under low H2O2 concentration, water transport and leaf gas exchange of IACSP94-2094 were less affected as compared to IACSP94-2101. Under high H2O2 concentration, the lower sensitivity of IACSP94-2094 was associated with increases in superoxide dismutase activity in roots and leaves and increases in catalase activity in roots. In conclusion, we propose a general model of sugarcane reaction to H2O2, linking root and shoot physiological responses.

Flicker perimetry in healthy subjects: influence of age and gender, learning effect and short-term fluctuation

PURPOSE: To determine the mean critical fusion frequency and the short-term fluctuation, to analyze the influence of age, gender, and the learning effect in healthy subjects undergoing flicker perimetry. METHODS: Study 1 - 95 healthy subjects underwent flicker perimetry once in one eye. Mean critical fusion frequency values were compared between genders, and the influence of age was evaluated using linear regression analysis. Study 2 - 20 healthy subjects underwent flicker perimetry 5 times in one eye. The first 3 sessions were separated by an interval of 1 to 30 days, whereas the last 3 sessions were performed within the same day. The first 3 sessions were used to investigate the presence of a learning effect, whereas the last 3 tests were used to calculate short-term fluctuation. RESULTS: Study 1 - Linear regression analysis demonstrated that mean global, foveal, central, and critical fusion frequency per quadrant significantly decreased with age (p<0.05).There were no statistically significant differences in mean critical fusion frequency values between males and females (p>0.05), with the exception of the central area and inferonasal quadrant (p=0.049 and p=0.011, respectively), where the values were lower in females. Study 2 - Mean global (p=0.014), central (p=0.008), and peripheral (p=0.03) critical fusion frequency were significantly lower in the first session compared to the second and third sessions. The mean global short-term fluctuation was 5.06±1.13 Hz, the mean interindividual and intraindividual variabilities were 11.2±2.8% and 6.4±1.5%, respectively. CONCLUSION: This study suggests that, in healthy subjects, critical fusion frequency decreases with age, that flicker perimetry is associated with a learning effect, and that a moderately high short-term fluctuation is expected.

Comparasion of two minimally invasive methods on the longevity of glass ionomer cement restorations: short-term results of a pilot study

The purpose of this study was to evaluate the clinical performance of glass ionomer cement (GIC) restorations comparing two minimally invasive methods in permanent teeth after 12 months. Fifty pregnant women (second trimester of pregnancy), mean age 22 ± 5.30 years, were treated by two previously trained operators. The treatment approaches tested were: chemomechanical method (CarisolvTM; MediTeam) and atraumatic restorative treatment (ART). A split-mouth study design was used in which the two treatments were randomly placed in 50 matched pairs of permanent teeth. The chemomechanical method (CM) was the test group and the ART was the control group. The treatments were performed in Public Health Centers. The tested restorative material was a high-strength GIC (Ketac Molar; 3M/ESPE). The restorations were placed according to the ART guidelines. Two calibrated independent examiners evaluated the restorations in accordance with ART criteria. The inter-examiner kappa was 0.97. Data were analyzed using 95% confidence interval on the binomial distribution and Fisher's exact test at 5% significance level. In a 12-month follow-up, 86% of the restorations were evaluated. In the test group (CM), 100% (CI=93.3-100%) of the restorations were considered successful. In the control group (ART) 97.6% (CI=87.4-99.9%) of the restorations were considered successful and 2.4% unsuccessful (marginal defect >0.5 mm). There was no statistically significant difference between the 12-mounth success rate for both groups (Fisher's exact test: P=0.49) and between the two operators (Fisher's exact test: P=1.00). Both minimally invasive methods, chemomechanical method and ART, showed a similar clinical performance after 12 months of follow up.

Short-Term effect of COX-2 selective inhibitor as an adjunct for the treatment of periodontal disease: a clinical double-blind study in humans

Adjunctive therapeutic strategies that modulate the inflammatory mediators can play a significant role in periodontal therapy. In this double-blind, placebo-controlled study, 60 subjects diagnosed as periodontitis patients were evaluated for 28 days after periodontal treatment combined with selective cyclooxygenase-2 (COX-2) inhibitor. The experimental group received scaling and root planning (SRP) combined with the Loxoprofen antiinflammatory drug (SRP+Loxoprofen). The control group received SRP combined with placebo (SRP+placebo). Plaque index (PI), probing pocket depth (PD) and bleeding on probing (BOP) were monitored with an electronic probe at baseline and after 14 and 28 days. Both groups displayed clinical improvement in PD, PI and BOP. They also showed statistically similar values (p>0.05) of PD reduction on day 14 (0.4 mm) and on day 28 (0.6 mm). At the baseline, few deeper sites (>7 mm) from SRP+Loxoprofen group were responsible and most PD reduction was observed after 14 days (p<0.05). The percentage of remaining deep pockets (>7 mm) after 14 days in the SRP+Loxoprofen group was significantly lower (p<0.05) than in the SRP+placebo group. Loxoprofen presents potential effect as an adjunct of periodontal disease treatment, but long-term clinical trials are necessary to confirm its efficacy.