A Parsimonious Bootstrap Method to Model Natural Inflow Energy Series

The Brazilian energy generation and transmission system is quite peculiar in its dimension and characteristics. As such, it can be considered unique in the world. It is a high dimension hydrothermal system with huge participation of hydro plants. Such strong dependency on hydrological regimes implies uncertainties related to the energetic planning, requiring adequate modeling of the hydrological time series. This is carried out via stochastic simulations of monthly inflow series using the family of Periodic Autoregressive models, PAR(p), one for each period (month) of the year. In this paper it is shown the problems in fitting these models by the current system, particularly the identification of the autoregressive order “p” and the corresponding parameter estimation. It is followed by a proposal of a new approach to set both the model order and the parameters estimation of the PAR(p) models, using a nonparametric computational technique, known as Bootstrap. This technique allows the estimation of reliable confidence intervals for the model parameters. The obtained results using the Parsimonious Bootstrap Method of Moments (PBMOM) produced not only more parsimonious model orders but also adherent stochastic scenarios and, in the long range, lead to a better use of water resources in the energy operation planning

Long Memory Models to Generate Synthetic Hydrological Series

In Brazil, much of the energy production comes from hydroelectric plants whose planning is not trivial due to the strong dependence on rainfall regimes. This planning is accomplished through optimization models that use inputs such as synthetic hydrologic series generated from the statistical model PAR(p) (periodic autoregressive). Recently, Brazil began the search for alternative models able to capture the effects that the traditional model PAR(p) does not incorporate, such as long memory effects. Long memory in a time series can be defined as a significant dependence between lags separated by a long period of time. Thus, this research develops a study of the effects of long dependence in the series of streamflow natural energy in the South subsystem, in order to estimate a long memory model capable of generating synthetic hydrologic series

Optimal trade-offs between energy efficiency improvements and additional renewable energy supply: A review of international experiences

Energy efficiency is a key priority also from a climate perspective, but efforts to increase efficiency should be balanced with the effort to increase the share of renewable sources in order to reduce fossil emissions. The climate impact of various energy efficiency measures are quite different depending on the type of fuel used and the impact from the efficiency increase on energy costs and thereby the demand for that particular energy use. Therefore it is important to address the energy efficiency options together with the alternative to switch the energy supply towards renewable sources. This calls for models and analysis that incorporate both types of options and thereby address the trade-off in a consistent way. The literature dealing with the trade-off in a direct or less explicit way is categorized and reviewed here. The aim of this paper is to review and evaluate international experiences that include the trade-off between efficiency improvements and additional renewable energy supply whether in a partial analysis of a sector or in an energy system optimization model. A critical review of the approach, focusing on purpose, methodology and outcome, is provided along with a review of modelling tools adopted for the analyses. Models are categorized and presented according to their main characteristics (e.g. bottom- up/top-down model, regional/national analysis, partial/general equilibrium, static/dynamic model). This paper intends, to provide future modelers and policy evaluators with an overview of approaches and methodologies suitable for analyzing energy efficiency policies and options with a focus on the optimal trade-off between renewables and energy efficiency measures in energy-systems under different objectives

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Development and application of decision support systems for improved planning and operation of large dams along the White Nile.

Doctor of Philosophy in Agricultural Engineering. University of KwaZulu-Natal, Pietermaritzburg 2015.In this study the regulation of Lakes Victoria, Kyoga and Albert in East Africa are investigated with the objective of maximising hydropower production subject to system constraints for existing and future planned dams along the Upper White Nile in Uganda. A Decision Support System (DSS) has been assembled and applied to search for efficient lake-reservoir operating rules for this basin. Elements of the DSS include power plant functions, a simulation model of the Upper Nile Equatorial Lake Basin, the Stochastic Analysis Modelling and Simulation (SAMS) computer software package for analysing hydrologic time series and the Colorado State University Dynamic Programming (CSUDP) model for solution of the optimisation problem. A concurrent record of observed lake levels and outflows for the three lakes during the reference period 1899 – 2008 has been constructed from various long term monitoring stations and utilised to derive net basin supply or net inflow time series at a monthly and annual time scale. Statistical tests confirmed the non-stationarity of the annual lake net basin supply time series. A justification to model the stochastic process of the monthly inflows as a Markov process was also reached. A Univariate Shifting Mean model was fitted to the annual historical data in tandem with a model for temporal disaggregation of annual to monthly net basin supplies for the purposes of generating synthetic flow series. The model performed well in terms of preserving the statistical characteristics of the historical reference set for each lake. The synthetic time series are considered to be a useful reference data set for future research in generating reservoir operating rules. Two Dynamic Programming (DP) models that may be used to generate reservoir operating rules were investigated. The desired scope of optimization was however curtailed by the well-known dimensionality problem of DP. Application of the deterministic method of Incremental Dynamic Programming (IDP) to the optimisation problem could only be carried out on a monthly time step and for single years separately. Annual time step optimization could only be carried out for the historic net inflows. The 1000 stochastically generated time series of net basin supplies could not be utilized within the implicit framework of deriving operating rules due to impractical computational requirements. The IDP however, yielded a realistic set of optimal operating policies at an annual time scale for the historical reference period (1898 – 2008). The beginning of year lake levels and annual release magnitudes obtained were compared against similar data for natural unregulated lake conditions. It is concluded that, in general, lake regulation would yield desirable benefits in terms of hydropower generation but would lead to marked deviation from natural lake levels and more variable outflows. The Stochastic Dynamic Programing (SDP) model was only applied to Lake Victoria in single reservoir optimization scheme due to limitations imposed by the large dimensionality of the problem and difficulty of simultaneously incorporating multiple lake reservoir transition probability matrices in the model. Application of the model for Lake Victoria showed that, it was feasible to define final storage levels for discretized initial storage and previous period inflow class combinations. The results from the study indicate that realistic heuristic operation rules can be inferred from the results of applying the IDP models and SDP algorithm

Medium-term power planning in electricity markets with renewable generation sources

The problem addressed in this thesis is the medium-term generation planning over a yearly horizon of a generation company participating in a liberalized electricity market with pool auction of generation and consumption and with bilateral contracts between generation companies and distributions companies or big consumers. It is assumed that the generation portfolio of several generation companies includes a significant proportion of dispatchable renewables (hydro generation with storage reservoirs and pumping schemes) and non-dispatchable renewables as wind power and solar photovoltaic generation. It is also assumed than more than one generation company are able to influence market-price levels through their bidding in the auction so that the market could be oligopolistic. The results obtained are of interest to price-maker generation companies, but also to price-taker generators, and to the market operator to check whether the participants in the market behave as a cartel or seeking an equilibrium. The stochasticity of parameters in the medium-term planning is modeled in two ways. Regarding consumers load and generation unit outages, through the use of the probabilistic method of load matching: by representing the load through predicted load-duration curves of each period into which the yearly horizon is subdivided, by considering the capacity and an outage probability of each generation unit and by using the existing convolution techniques and the linear-inequality load-matching constraints. Regarding renewable energy sources, stochastic programming is used. Hydro-generation scenarios of inflows are developed for each period. As for non-dispatchable renewables (wind power and solar photo-voltaic generation), a novel model of representing them through two pseudo-units: one base unit with small outage probability and a crest unit with large outage probability is proposed, and scenarios are developed for the relevant parameters of the pseudo-units. The solar photo-voltaic generation model requires splitting each period into three subperiods with the dark hours, with the medium-light hours and with the bright hours. Quasi-Monte Carlo techniques have been employed to create a large scenario fan later reduced to a scenario tree with a reduced number of scenarios. Market prices are taken into account through an endogenous linear market-price function of load duration whose intercept depends on total hydro generation level and on wind power and solar photovoltaic level in each node of the scenario tree. With such market price function, the endogenous cartel solution and the equilibrium solutions to the medium-term planning can be obtained. To avoid having to consider the total exponential number of load-matching constraints, a load matching heuristic has been employed where small batches of new load matching constraints are generated after successive optimizations considering only the generated load matching constraints. For equilibrium solutions, the Nikaido-Isoda relaxation algorithm of successive solutions is employed using the successive optimizations of the load-matching heuristic. In mixed-market systems with auction and bilateral contracts, a time-share hypothesis is formulated and the profits function for generation companies with the generation left after honoring their bilateral contracts is formulated. The profit function obtained is non-convex, and a direct global optimization solver was tried, but proved not to be practical for the size of problem to be solved. A non-linear interior-point constrained optimization solver, also employed for problems in pure pool markets, was tried with several special techniques to circumvent the troubles caused by the non-convexity of the objective function and satisfactory results were obtained. A novel model of multi-period medium-term pumping was presented and employed. Results for several realistic test cases having different generation settings have been presented and analyzed.El problema adreçat en aquesta tesi és el de la planificació a mig termini de la generació elèctrica d'una companyia que participa dins d'un mercat elèctric. S'ha assumit que aquestes companyies generadores disposen d'una proporció significant d'energies renovables despatxables (com la generació hidràulica amb embassaments d'emmagatzematge i sistemes de bombeig) i d'energies renovables no-despatxables tals com l'energia eòlica i la generació d'energia solar fotovoltaica. També s’ha tingut en compte que més d'una companyia generadora és capaç d'influir en els nivells de preu de mercat a través de les seves ofertes dins de la subhasta tractant-se, doncs, d'un mercat oligopolístic. Els resultats obtinguts són interessants per a companyies generadores del tipus 'price-maker', però alhora també ho són per companyies 'price-taker' i, finalment, també ho són per a l'operador del mercat per tal de comprovar si els participants en el mercat es comporten com si hi hagués 'cartel' o si bé busquen l'equilibri. L'estocasticitat de la càrrega i les panes de les unitats de generació dins de la planificació a mig termini es modela mitjançant l'ús del mètode probabilístic de recobriment de la càrrega: tot representant la càrrega a través de corbes predites de durada de la càrrega per a cada període tractat, utilitzant tècniques existents de convolucions i les anomenades constriccions de recobriment de la càrrega que són de desigualtat i lineals. Pel que fa a les energies renovables s'ha emprat programació estocàstica. Per a cada període s'han desenvolupat escenaris d'hidràulica per a les aportacions naturals d'aigua. Pel que fa a les renovables no-despatxables (eòlica i solar), es presenta un nou model per a representar-les a través de dues pseudo-unitats: una unitat de base amb una probabilitat de pana molt petita i una unitat de cresta amb una probabilitat de pana gran. La generació solar requereix un model més complex ja que s'han dividit les hores solars en tres subperíodes: sense sol, sol mig i sol. També s'han creat escenaris per als paràmetres més rellevants d'aquestes pseudo-unitats. S'han emprat mètodes de Quasi-Monte Carlo per a crear un gran arbre d'escenaris de tipus FAN que, posteriorment, s'ha reduït a un arbre d'escenaris d'una determinada mida. La funció de preu de mercat respecte a la durada de la càrrega és una funció endògena on es té en compte la variació observada del nivell dels preus amb la generació hidràulica, la generació eòlica i la solar a cada node dins de l'arbre d'escenaris. Amb aquest tipus de funció de preu de mercat, les solucions de cartel i les d'equilibri poden ser obtingudes. Per tal de no haver de considerar un nombre exponencial de constriccions de recobriment, s'utilitza una heurística on petits subconjunts de restriccions es van generant després de successives optimitzacions considerant només les constriccions de recobriment generades. Per a les solucions d'equilibri, s'ha utilitzat l'algoritme de relaxació de Nikaido-Isoda en les successives optimitzacions de l'heurística. Pels mercats mixtos (amb subhasta i contractes bilaterals) s'ha formulat una hipòtesi de 'time-share' i s’ha presentat una funció de beneficis de les empreses generadores on només es té en compte la generació que resta després d'haver satisfet els contractes bilaterals. La funció obtinguda és no convexa i s'ha utilitzat un resolutor d'optimització global, però s'ha vist que no era pràctic per a la mida del problema que s'estava solucionant. Per aquest motiu, s'ha utilitzat un resolutor no lineal de punt interior (Ipopt) amb diverses tècniques especials per tal d'eludir els problemes causats per la no convexitat de la funció objectiu tot obtenint resultats satisfactoris. Finalment, s'ha presentat i s'ha utilitzat un nou mètode per introduir els esquemes de bombeig multi-període i a mig termini. Es mostren i s'analitzen els resultats obtinguts per a diversos casos de prova (realistes) amb diferents configuracions de generació

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications