Management and Control of Water Supply Systems

The fast increase in the energy’s price has brought a growing concern about the highly expensive task of transporting water. By creating an hydraulic model of the Water Supply System’s (WSS) network and predicting its behaviour, it is possible to take advantage of the energy’s tariffs, reducing the total cost on pumping activities. This thesis was developed, in association with a technology transfer project called the E-Pumping. It focuses on finding a flexible supervision and control strategy, adaptable to any existent Water Supply System (WSS), as well as forecasting the water demand on a time period chosen by the end user, so that the pumping actions could be planned to an optimum schedule, that minimizes the total operational cost. The OPC protocol, associated to a MySQL database were used to develop a flexible tool of supervision and control, due to their adaptability to function with equipments from various manufacturers, being another integrated modular part of the E-Pumping project. Furthermore, in this thesis, through the study and performance tests of several statistical models based on time series, specifically applied to this problem, a forecasting tool adaptable to any station, and whose model parameters are automatically refreshed at runtime, was developed and added to the project as another module. Both the aforementioned modules were later integrated with an Graphical User Interface (GUI) and installed in a pilot application at the ADDP’s network. The implementation of this software on WSSs across the country will reduce the water supply companies’ running costs, improving their market competition and, ultimately, lowering the water price to the end costumer.

Projection de la mortalité aux âges avancées au Canada : comparaison de trois modèles

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Integración técnica y económica de energías renovables, en un sistema eléctrico liberalizado, a partir de vectores energéticos

El objetivo de la tesis es estudiar la bondad del almacenamiento de energía en hidrógeno para minorar los desvíos de energía respecto a su previsión de parques eólicos y huertas solares. Para ello se ha partido de datos de energías horarias previstas con 24 h de antelación y la energía real generada. Se ha procedido a dimensionar la planta de hidrógeno, a partir de una modelización de la operación de la misma, teniendo siempre como objetivo la limitación de los desvíos. Posteriormente, se ha procedido a simular la operación de la planta con dos objetivos en mente, uno limitar los desvíos y por otro lado operar la planta como una central de bombeo, generando hidrógeno en horas valle y generando electricidad en horas punta. Las dos simulaciones se han aplicado a tres parques eólicos de diferentes potencias, y a una huerta solar fotovoltaica. Se ha realizado un estudio económico para determinar la viabilidad de las plantas dimensionadas, obteniendo como resultado que no son viables a día de hoy y con la estimación de precios considerada, necesitando disminuir considerablemente los costes, dependiendo fuertemente de la bondad de los métodos de previsión de viento. Por último se ha estudiado la influencia de la disminución de los desvíos generados sobre una red tipo de 30 nudos, obteniendo como resultado, que si bien no disminuyen sensiblemente los extra costes generados en regulación, sí que mejora la penetración de las energías renovables no despachables en la red. Se observa disminuyen los vertidos eólicos cuando se usa la planta de hidrógeno. ABSTRACT The aim of this thesis is to study the benefit of hydrogen energy storage to minimize energy deviations of Wind Power and Solar Photovoltaic (PV) Power Plants compared to its forecast. To achieve this goal, first of all we have started with hourly energy data provided 24 h in advance (scheduled energy), and real generation (measured energy). Secondly, It has been sized the hydrogen plant, from a modeling of its working mode, always keeping the goal in mind of limiting energy imbalances. Subsequently, It have been simulated the plant working mode following two goals, one, to limit energy imbalances and secondly to operate the plant as a pumping power plant, generating hydrogen-in valley hours and generating electricity at peak hours. The two simulations have been applied to three wind power plants with different installed power capacities, and a photovoltaic solar power plant. It has been done an economic analysis in order to determine the viability of this sized plants, turning out not viable plants today with the estimated prices considered, requiring significantly lower costs, depending heavily on the reliability of the Wind Power forecast methods. Finally, It has been studied the influence of decreasing measured imbalances (of energy) in a 30 grid node, resulting that, while it not reduces significantly the extra costs generated by reserve power, it does improve the penetration of non-manageable renewable energy on the grid, by reducing the curtailments of power of these plants.

Effektiv lagerstyrning med AHP och tvådimensionell artikelklassificering : En fallstudie på Permobil AB, Timrå

Costs related to inventory are usually a significant amount of the company’s total assets. Despite this, companies in general don’t pay a lot of interest in it, even if the benefits from effective inventory are obvious when it comes to less tied up capital, increased customer satisfaction and better working environment. Permobil AB, Timrå is in an intense period when it comes to revenue and growth. The production unit is aiming for an increased output of 30 % in the next two years. To make this possible the company has to improve their way to distribute and handle material,The purpose of the study is to provide useful information and concrete proposals for action, so that the company can build a strategy for an effective and sustainable solution when it comes to inventory management. Alternative methods for making forecasts are suggested, in order to reach a more nuanced perception of different articles, and how they should be managed. Analytic Hierarchy Process (AHP) was used in order to give specially selected persons the chance to decide criteria for how the article should be valued. The criteria they agreed about were annual volume value, lead time, frequency rate and purchase price. The other method that was proposed was a two-dimensional model where annual volume value and frequency was the criteria that specified in which class an article should be placed. Both methods resulted in significant changes in comparison to the current solution. For the spare part inventory different forecast methods were tested and compared with the current solution. It turned out that the current forecast method performed worse than both moving average and exponential smoothing with trend. The small sample of ten random articles is not big enough to reject the current solution, but still the result is a reason enough, for the company to control the quality of the forecasts.

Report of the Working Group for the Bay of Biscay and the Iberian waters Ecoregion (WGBIE). 13-19 May 2016, ICES HQ, Copenhagen, Denmark

The ICES Working Group for the Bay of Biscay and the Iberic waters Ecoregion (WGBIE) met in Copenhagen, Denmark during 13–14 May 2016. There were 22 stocks in its remit distributed from ICES Divisions 3.a–4.a though mostly distributed in Sub Areas 7, 8 and 9. There were 21 participants, some of whom joined the meeting re-motely. The group was tasked with conducting assessments of stock status for 22 stocks using analytical, forecast methods or trends indicators to provide catch forecasts for eight stocks and provide a first draft of the ICES advice for 2016 for fourteen stocks. For the remaining stocks, the group had to update catch information and indices of abundance where needed. Depending on the result of this update, namely if it would change the perception of the stock, the working group drafted new advice. Analytical assessments using age-structured models were conducted for the northern and southern stocks of megrim and the Bay of Biscay sole. The two hake stocks and one southern stock of anglerfish were assessed using models that allow the use of only length-structured data (no age data). A surplus-production model, without age or length structure, was used to assess the second southern stocks of anglerfish. No ana-lytical assessments have been provided for the northern stocks of anglerfish after 2006. This is mostly due to ageing problems and to an increase in discards in recent years, for which there is no reliable data at the stock level. The state of stocks for which no analytical assessment could be performed was inferred from examination of commer-cial LPUE or CPUE data and from survey information. Three nephrops stocks from the Bay of Biscay and the Iberian waters are scheduled for benchmark assessments in October 2016. The WGBIE meeting spent some time review-ing the progress towards the benchmark (see Annex 6) together with longer term benchmarks (2017 and after, see section 1.) for sea bass in the Bay of Biscay, all an-glerfish and hake stocks assessed by the WG. For the northern megrim stock, the sched-ule an inter-benchmark meeting was completed successfully and the group reviewed the outcome and accepted the category 1 update assessment. A recurrent issue significantly constrained the group’s ability to address the terms of reference this year. Despite an ICES data call with a deadline of six weeks before the meeting, data for several stocks were resubmitted during the meeting which lead to increased workloads during the working group, as in that case, the assessments could not be carried out in National Laboratories prior to the meeting as mentioned in the ToRs. This is an important matter of concerns for the group members. Section 1 of the report presents a summary by stock and discusses general issues. Sec-tion 2 provides descriptions of the relevant fishing fleets and surveys used in the as-sessment of the stocks. Sections 3–18 contains the single stock assessments.

Forecast comparison with nonlinear methods for brazilian industrial production

Este trabalho avalia as previsões de três métodos não lineares — Markov Switching Autoregressive Model, Logistic Smooth Transition Autoregressive Model e Autometrics com Dummy Saturation — para a produção industrial mensal brasileira e testa se elas são mais precisas que aquelas de preditores naive, como o modelo autorregressivo de ordem p e o mecanismo de double differencing. Os resultados mostram que a saturação com dummies de degrau e o Logistic Smooth Transition Autoregressive Model podem ser superiores ao mecanismo de double differencing, mas o modelo linear autoregressivo é mais preciso que todos os outros métodos analisados.

Forecast comparison with nonlinear methods for Brazilian industrial production

This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.

Volatility timing : how best to forecast portfolio exposures

This paper investigates how best to forecast optimal portfolio weights in the context of a volatility timing strategy. It measures the economic value of a number of methods for forming optimal portfolios on the basis of realized volatility. These include the traditional econometric approach of forming portfolios from forecasts of the covariance matrix, and a novel method, where a time series of optimal portfolio weights are constructed from observed realized volatility and directly forecast. The approach proposed here of directly forecasting portfolio weights shows a great deal of merit. Resulting portfolios are of equivalent economic benefit to a number of competing approaches and are more stable across time. These findings have obvious implications for the manner in which volatility timing is undertaken in a portfolio allocation context.

Polarization of forecast densities : a new approach to time series classification

Time series classification has been extensively explored in many fields of study. Most methods are based on the historical or current information extracted from data. However, if interest is in a specific future time period, methods that directly relate to forecasts of time series are much more appropriate. An approach to time series classification is proposed based on a polarization measure of forecast densities of time series. By fitting autoregressive models, forecast replicates of each time series are obtained via the bias-corrected bootstrap, and a stationarity correction is considered when necessary. Kernel estimators are then employed to approximate forecast densities, and discrepancies of forecast densities of pairs of time series are estimated by a polarization measure, which evaluates the extent to which two densities overlap. Following the distributional properties of the polarization measure, a discriminant rule and a clustering method are proposed to conduct the supervised and unsupervised classification, respectively. The proposed methodology is applied to both simulated and real data sets, and the results show desirable properties.

Inferential, non-parametric statistics to assess quality of probabilistic forecast systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.

Development of an operational tool for oil spill forecast: application to oil exposed regions

Tese de doutoramento, Ciências do Mar, da Terra e do Ambiente (Modelação), Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.