Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-02-23Bitstream added on 2014-06-13T20:09:48Z : No. of bitstreams: 1 martins_lfb_me_bauru.pdf: 963421 bytes, checksum: 7c1f9175f040c64a63fdf8db9f7a10a1 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho foi feita uma análise comparativa entre diferentes escolhas dos pontos utilizados pelo método look ahead na estimação do ponto de máximo carregamento de um sistema elétrico de potência. O Fluxo de Cargo Continuado é utilizado na geração dos pontos de operação utilizados pelo método look ahead e para servir como referência na comparação entre os resultados previstos e o ponto de máximo carregamento real. Uma vez que a exatidão dessa estimativa é fortemente afetada pela escolha desses pontos, o FCC é modificado para fornecer pontos mais adequados para o bom funcionamento do método look ahead. A metodologia proposta é aplicada ao sistema IEEE de 300 barras, os resultados obtidos mostram o seu bom funcionamento / Here we did a comparative analysis between different choices of the points used by the look ahead method for estimating maximum loading point of a power system. The Continued Power Flow (CPF) is used in the generation of operating points used by the look ahead method and to serve as a reference in comparison between the predicted results and the real maximum loading point. Since the accurancy of this estimative is strongly affected by choicen of these points, the CPF is modified to provide the most appropriate for the proper functioning of the method look ahead. The proposed methodology system is applied to IEEE 300 buses, the results have shown its good functioning

In deregulated electricity markets, profit driven electricity retailers compete to supply cheap reliable electricity to electricity consumers, and the electricity consumers have free will to switch between the electricity retailers. The need to maximize the profits of the electricity retailers while minimizing the electricity costs of the electricity consumers has therefore seen a drastic increase in the research of electricity markets. One of the factors that affect the profits of the electricity retailers and the energy cost of the consumers in electricity retail markets is the supply and demand. During high-supply and low-demand periods, the excess electricity if not managed, is wasted. During low-supply high-demand periods, the deficit supply can lead to electricity blackouts or costly electricity because of the volatile electricity wholesale spot market prices. Research studies have shown that electricity retailers can achieve significant profits and reduced electricity costs for their electricity consumers by minimizing the excess electricity and deficit electricity. Existing studies developed load forecasting models that aimed to match electricity supply and electricity demand. These models reached excellent accuracy levels, however due to the high volatility character of load demand and the rise of new electricity consumers, load forecasting alone is unable to mitigate excess and deficit electricity. In other studies, researchers proposed charging the electricity consumers’ batteries with excess electricity during high-supply low-demand periods and supplying their deficit electricity during low-supply high-demand periods. Electricity consumers’ incorporating batteries resulted in minimized excess and deficit electricity, in turn, maximizing the profits for the electricity retailers and minimizing the electricity costs for the electricity consumers. However, the batteries are consumer centric and only provide battery energy for the battery-owned consumer. Electricity consumers without battery energy during low-supply highdemand periods have electricity blackouts or require costly electricity from the electricity wholesale spot market. The peer-to-peer (P2P) energy sharing framework which allows electricity consumers to share their energy resources with one another is a viable solution to allow electricity consumers to share their battery energy. P2P energy sharing is a hot topic in research because of its potential to maximize the electricity retailers’ profits and minimize the electricity consumers’ electricity costs. Due to the increased profits for the electricity retailer and reduced electricity costs for the electricity consumers from implementing battery charging and P2P energy sharing, this dissertation proposes a day-ahead electricity retail market structure in which the electricity retailer supplies consumers’ batteries with excess electricity during high-supply low-demand periods, and during low-supply highdemand periods the electricity retailer discharges the consumers’ batteries to supply their deficit supply or supply their peers’ deficit supply. The electricity retailer aims to maximize its profits and minimize the electricity cost of the electricity consumers in its electricity retail market, by minimizing the excess and deficit electricity. The problem is formulated as a non-linear optimization model and solved using game theory. This dissertation compares the profits of the electricity retailer and electricity costs of the consumers that charge their batteries with excess electricity, discharge their batteries and purchase electricity from their peers to supply their deficit supply, with consumers that only charge their batteries with excess electricity but do not share their battery energy with their peers, consumers that only purchase electricity from their peers to supply their deficit supply but do not employ a battery, and consumers that neither employ a battery nor purchase electricity from their peers to supply their deficit supply. The results show that the consumers that charge their batteries with excess electricity, discharge their batteries and purchase electricity from their peers to supply their deficit supply achieved the lowest electricity cost and highest profits for the electricity retailer. / Dissertation (MEng)--University of Pretoria, 2020. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted

Within forecasting electricity pricing, we analyse whether adding various vari- ables improves the predictions, and if shorter time intervals between observa- tions enhance accuracy of the forecasting. Next, we focus on proper selection of lagged observations, which has not been thoroughly covered in the past litera- ture. In addition, many papers studied electricity prices in larger markets (e.g. United States, Australia, Nord Pool, etc.) on datasets limited in scope, with 2-3 years timespan. To address these gaps in literature, we obtain one daily and one hourly dataset, both spanning 6 years (January 1, 2015 - December 31, 2020), from four Central and Eastern European countries - the Czech Repub- lic, the Slovak Republic, Hungary, and Romania. These contain information on the electricity prices, and information on our observed added variables - temperature and cross-border electricity flows. For the forecasting, we use two different methods - Autoregression (AR) and Seemingly Unrelated Regression (SUR). The thorough selection of lagged observations, which we accustom to the closing time of the auction-based electricity market system, serves further studies as a guidance on how to avoid possible errors and inconsistencies in their predictions. In our analyses, both AR and SUR models show that...

In a stopping rule problem, a real-time player decides to stop or continue at stage n based on the observations up to that stage, but in a k-step look-ahead stopping rule problem, we suppose the player knows k steps ahead. The aim of this Ph.D. dissertation is to study this type of prophet problems for simple random walk, determine the optimal stopping rule and calculate the expected return for them. The optimal one-step look-ahead stopping rule for a finite simple random walk is determined in this work. We also study two infinite horizon stopping rule problems, sum with negative drift problems and discounted sum problems. The optimal one, two and three-step look-ahead stopping rules are introduced for the sum with negative drift problem for simple random walk. We also compare the maximum expected returns and calculate the upper bound for the advantage of the prophet over the decision maker. The last chapter of this dissertation concentrates on the discounted sum problem for simple random walk. Optimal one-step look-ahead stopping rule is defined and lastly we compare the optimal expected return for one-step look-ahead prophet with a real-time decision maker.

Predicting and characterizing the state of the ocean is needed for various scientific, industrial, social, management, and recreational activities. Despite the tremendous progress in ocean modeling and simulation capabilities, the ocean models still suffer from different sources of uncertainties. To obtain accurate ocean state predictions, data assimilation (DA) is widely used to constrain the ocean model outputs with available observations. Ensemble Kalman filtering (EnKF) is a sequential DA approach that represents the distribution of the system state through an ensemble of ocean state samples. Different factors may limit the performance of an EnKF in realistic ocean applications, particularly the use of small ensembles and poorly known model error statistics, and also to a lesser extent the strongly nonlinear variations and abrupt regime changes, and unsatisfied underlying assumptions such as the commonly used white observation noise assumption. The objective of this PhD thesis is to develop, implement and test efficient ensemble filtering schemes to enhance the performances of EnKFs in such challenging settings. We resort to the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to introduce EnKFs involving a new update step with future observations (smoothing) between two successive analyses, thereby conditioning the ensemble sampling with more information. We show that this approach enhances the EnKFs performances by providing improved ensemble background statistics, and showcase its performance with realistic ocean DA and forecasting applications, namely a storm surge EnKF forecasting system and the Red Sea ensemble DA and forecasting system. We then derive new EnKF-based schemes accounting for time-correlated observation errors for efficient DA into the class of large dimensional DA problems where observation errors statistics are correlated in time, and further propose a new approach for online estimation of the parameters of the observation error time-correlations model concurrently with the state. We also exploit the OSA-smoothing formulation to propose a new joint EnKF with OSA-smoothing which mitigates for the reported inconsistencies in the joint EnKF update for efficient DA into one-way-coupled systems.

L’objectif de cette thèse est d’étudier les nouvelles dynamiques et leurs impacts dans le secteur de l'électricité. Elle discute des sujets critiques d’après les perspectives de la macroéconomie, de la configuration structurelle, et de la transition vers des sources d'énergie renouvelables. Plus précisément, trois sujets se dégagent: le lien entre la consommation d'électricité et la croissance économique, les impacts de l'intégration verticale entre les producteurs et les détaillants, et les impacts d'intégration de production d'énergie renouvelable intermittente. En mettant en jeu ces trois sujets, elle tente d’apporter des réponses aux défis principaux de la sécurité d'approvisionnement, de la compétitivité, et de la durabilité du développement énergétique. En donnant de nouvelles orientations dans la recherche sur l’économie de l’énergie, elle servira à éclairer des débats politiques. / The objective of this thesis is to study the new dynamics and their impacts in the electricity sector. It discusses the critical issues from the perspectives of macroeconomics, structural configuration, and a transition to renewable energy sources. More precisely, three topics emerge: the nexus between electricity consumption and economic growth, the impacts of vertical integration between power generators and retailers, and the market impacts and integration issues of intermittent renewable generation. By studying these three topics, it provides answers to the key challenges of supply security, competitiveness and sustainable development in the energy sector. By giving new research directions of energy economics, it serves to inspire related policy debates.

The unpredictability and variability of wind power generation can pose an economical risk to the wind power producer when participating in the day-ahead market and delivering the committed generation. These risks come from the creation of imbalances due to a mismatch between the sold and real generation fed to the grid. Energy Storage System (ESS) are a good solution for the wind power producer to plan the operation of the wind farm once the day-ahead market prices are cleared. However, depending on the price forecasts and wind generation, one type of storage technology might be more optimal than others. This is due to the fact that lithium-ion batteries have costs, power and energy ratings and limits that differ from other ESS (vanadium redox flow batteries, supercapacitors, pumped hydro or even other lithium-ion batteries with different chemistries). Hence, a multi-energy storage system technology solution can be proposed to be combined with a wind farm in order to both optimise the bids in the day-ahead market and to take part in current and emerging electricity markets. For this purpose, a mathematical model has been developed, and it provides the optimal bidding strategy to the day-ahead market and the most convenient operational planning for the energy storage systems. Based on the expected daily profits, a yearly stream of revenues is obtained and an overall techno-economical assessment is provided. The results show that, with the current capital costs of energy storage systems, the multi-ESS hybrid wind farm would recover the initial investment after 2-5 years depending on the ESS combinations. Moreover, the wind power producer would need an extra stream of revenues in order for it to be more profitable than the wind farm operating without storage blocks. / Den oförutsägbara och varierande vindkraftsproduktionen kan utgöra en teknisk och ekonomisk risk för vindkraftsproducenten när denne deltar i dayahead-marknaden och levererar den sålda energin. Dessa risker beror på att det uppstår obalanser på grund av bristande överensstämmelse mellan den sålda och den verkliga produktionen som matas in i nätet. Energilagringssystem (ESS på engelska) är en bra lösning för vindkraftsproducenten för att planera driften av vindkraftparken när priserna på dagen före marknaden är klara. Beroende på prisprognoserna och vindkraftsproduktionen kan dock en typ av lagringsteknik vara mer optimal än andra. Detta beror på att litiumjonbatterier har kostnader, effekt- och energimärkningar och gränser som skiljer sig från dem som gäller för vanadiumredoxflödesbatterier, superkondensatorer, pumpad vattenkraft eller till och med andra litiumjonbatterier med olika kemiska sammansättningar. Därför kan man använda en teknisk lösning med olika typer av energilager som kombineras för att både optimera budgivningen på day-ahead-marknaden och för att delta i nuvarande och nya elmarknader. För detta ändamål har en matematisk modell utvecklats som ger den optimala budstrategin för day-ahead-marknadenochdenmestpraktiskadriftsplaneringen för energilagringssystemen. På grundval av de förväntade dagliga vinsterna erhålls en årlig intäktsström och en övergripande teknisk-ekonomisk bedömning görs. Resultaten visar att med de nuvarande kapitalkostnaderna för energilagringssystem skulle återbetalningstiden för en vindkraftpark med flera olika energilager vara 2-5 år beroende på vilka energilager som kombinerats. Dessutom skulle vindkraftsproducenten behöva en extra intäktsström för att bli mer lönsam än en vindkraftpark som drivs utan lagringsblock.

This thesis investigates the relation between the day-ahead electricity market prices and the electricity production in the Nordic synchronous area of the European electric power system by looking into the market data ranging from 2015 to the current time. The increasing penetration of variable renewable energy sources, coupled with the deeper electrification in various sectors of the economy, has led to a higher volatility in the market, e.g. in the market prices. Since all power plant owners plan their production depending on prices, price forecasts, availability, it is necessary to better understand the relation between price signals and the production variations. Firstly, balancing contributions, considered as a suitable tool, are found unfit to apply to this research. Secondly, spectral analysis is used to highlight frequencies in the signals, leading to the determination of three time scales of variations: daily, two-weekly and yearly. It is used to define three timehorizons which are used to apply different mathematical tools on the market data. Thirdly, the correlation coefficient between a type of production and the day-ahead prices assesses the linearity of their respective variations. Lastly, the relative market value of the production gives insight into the , e.g. with regard to flexibility. Results of this research show that the correlation between the day-ahead prices and the production depends on the time-horizon, the production type, as well as the area. E.g., if SE2’s and SE1’s hydro production are highly correlated to prices in the daily and two-weekly patterns, the former has not enough storage to follow prices on the yearly horizon, while the latter can, due to its bigger reservoirs. Results also show that wind power is one of the drivers of the day-ahead prices, especially in the two-weekly time horizon. The increasing share of wind power will hence lead in more price variations as well as lower average price levels.. This study aims to provide insights to the plant owners with flexibility possibility on how to face those future challenges. / Detta examensarbete undersöker sambandet mellan day-ahead-priser på elmarknaden och elproduktion i den nordiska synkrona delen av det europeiska elsystemet. Analysen bygger på data mellan 2015 och maj 2021. I och med att andelen väderberoende förnybara energikällor ökar samtidigt som olika sektorer elektrifieras har elmarknaden och elpriserna blivit mer volatila. Den volatiliteten behöver elproducenter förhålla sig till när de planerar sin produktion beroende på elpriser, elprisprognoser, tillgänglighet m.m. och därför är det avgörande att belysa förhållandet mellan prissignaler och anpassning av produktionsnivåer. Arbetet inkluderar olika steg: Först undersöktes balansbidrag och det kunde visas att detta mått inte lämpar sig för att beskriva sambandet mellan produktion och elpriser. Sedan gjordes en spektralanalys för att identifiera de grundläggande variationerna: dygns-, tvåveckors- och årsvariationer. Baserad på dem specificerades tre tidshorisonter som olika matematiska mått utvärderas på. Ett sådant mått är korrelationskoefficienter som användes för att beskriva sambandet mellan produktionsdata per kraftslag och elpriser på ett linjärt sätt. Slutligen användes relativa marknadsvärden på samma sätt för att kunna undersöka olika kraftslags karakteristik och få inblick i deras flexibilitet samt körsättet. Resultaten visar att korrelationen mellan day-ahead-priser och produktion beror påtidshorisonten, kraftslag och prisområde. Till exempel är vattenkraften i norra Sverige (SE1 och SE2) starkt korrelerad med elpriserna på dyngs- och tvåveckorsnivå medan korrelationen på årsnivå är starkare för SE1 eftersom vattenmagasin i SE1 har större reglerutrymme på den långa tidshorisonten än magasinen i SE2. Resultaten visar dessutom att vindkraften är den drivande faktorn för elprisvariationerna, särskilt när det gäller tvåveckorshorisonten. Vindkraften kommer öka prisvariationerna och samtidigt sänka prisnivån. Målet med examensarbetet är att få mer inblick i kraftslagens flexibilitet och därmed öka förståelsen för framtida utmaningar.

Predictive control relies on predictions of the future behaviour of the system to be controlled. These predictions are calculated from a model of this system, thus making the model the cornerstone of the predictive controller. Furthermore predictive control is the only advanced control methodology that has managed to become widely used in the industry. The necessity of good models in the predictive control context can thus be motivated both from the very nature of predictive control and from its widespread use in industry. This thesis is concerned with examining the use of multiple models in the predictive controller. In order to do this the standard predictive control formulation has been extended to incorporate the use of multiple models. The most general case of this new formulation allows the use of an individual model for each prediction horizon. The models are estimated using measurements of the input and output sequences from the true system. When using this data to find a good model of the system it is important to remember the intended purpose of the model. In this case the model is going to be used in a predictive controller and the most important feature of the models is to deliver good k-step ahead predictions. The identification algorithms used to estimate the models thus strives for estimating models good at calculating these predictions. Finally this thesis presents some complete simulations of these ideas showing the potential of using multiple models in the predictive control framework.

<p>Predictive control relies on predictions of the future behaviour of the system to be controlled. These predictions are calculated from a model of this system, thus making the model the cornerstone of the predictive controller. Furthermore predictive control is the only advanced control methodology that has managed to become widely used in the industry. The necessity of good models in the predictive control context can thus be motivated both from the very nature of predictive control and from its widespread use in industry. </p><p>This thesis is concerned with examining the use of multiple models in the predictive controller. In order to do this the standard predictive control formulation has been extended to incorporate the use of multiple models. The most general case of this new formulation allows the use of an individual model for each prediction horizon. </p><p>The models are estimated using measurements of the input and output sequences from the true system. When using this data to find a good model of the system it is important to remember the intended purpose of the model. In this case the model is going to be used in a predictive controller and the most important feature of the models is to deliver good k-step ahead predictions. The identification algorithms used to estimate the models thus strives for estimating models good at calculating these predictions. </p><p>Finally this thesis presents some complete simulations of these ideas showing the potential of using multiple models in the predictive control framework.</p>