Made available in DSpace on 2014-06-11T19:30:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-07-21Bitstream added on 2014-06-13T19:00:51Z : No. of bitstreams: 1 amorim_ea_dr_ilha.pdf: 1200042 bytes, checksum: 598a8d060889d642964ac8c022c167e1 (MD5) / Esta pesquisa tem por objetivo o desenvolvimento de uma ferramenta computacional para a solução do problema de Fluxo de Potência Ótimo Multimercado (FPOM). O problema de fluxo de potência ótimo mutimercado é decomposto em vários subproblemas, uma para cada, submercado que compõe o sistema de potência interconectado. O modelo de decomposição utilizado permite resolver o problema de FPO considerando-se os modelos de mercado desverticalizados e centralizados e os desverticalizados e descentralizados. Neste contexto, a pesquisa desenvolvida considera o novo esquema de funcionamento dos mercados de energia elétrica, no qual é vi freqüentemente desejável preservar a autonomia de cada um dos submercados que compõem o sistema de potência multimercado. O problema de FPO proposto é modelado como um problema de otimização não-linear inteiro misto, com variáveis de controle contínuas e discretas e têm ênfase no despacho econômico da geração de potência ativa e nos ajustes dos controles de tensão. Além disso, este modelo de FPO trata os subproblemas ativo e reativo simultaneamente. Para a sua solução é apresentado um algoritmo evolutivo multiobjetivo, baseado no NSGA (Nondominated Sorting Genetic Algorithm), pois características do problema abordado dificultam a sua solução através das técnicas baseadas em programação matemática e justificam a escolha da metaheurística multiobjetivo. / This research is aimed at developing a computational tool for the solution of the Multimarket Optimal Power Flow (MOPF) problem. The multimarket optimal power flow problem is decomposed in various subproblems, one for each submarket that is part of the interconnected power system. The decomposition model used here allows solving the OPF problem considering the deregulated and centralized, and the deregulated and decentralized market models. In this context, the developed research takes into account the new functioning scheme of the electric power markets, viii where it is frequently desirable to preserve the autonomy of each one of those submarkets that compose the multimarket power system. The proposed OPF problem is modeled as a mixed integer non-linear optimization problem with continuous and discrete control variables, emphasizing the economic dispatch of the active power generation and the voltage control adjustments. In addition, this model of OPF deals simultaneously with the active and reactive subproblems. For its solution, a multiobjective evolutionary algorithm based on the NSGA (Nondominated Sorting Genetic Algorithm) is presented. The characteristics of the problem make difficult the utilization of techniques based on mathematical programming, justifying the adoption of a multiobjective metaheuristic.

This thesis develops a robust methodology for on-line identification of power system dynamic signature based on incoming system responses from Phasor Measurement Units (PMUs) in Wide Area Measurement Systems (WAMS). Data mining techniques are used in the methodology to convert real-time monitoring data into transient stability information and the pattern of system dynamic behaviour in the event of instability. The future power system may operate closer to its stability limit in order to improve its efficiency and economic value. The changing types and patterns of load and generation are resulting in highly variable operating conditions. Corrective control and stabilisation is becoming a potentially viable option to enable safer system operation. In the meantime, the number of WAMS projects and PMUs is rising, which will significantly improve the system situational awareness. The combination of all these factors means that it is of vital importance to exploit a new and efficient Transient Stability Assessment (TSA) tool in order to use real-time PMU data to support decisions for corrective control actions. Data mining has been studied as the innovative solution and considered as promising. This work contributes to a number of areas of power systems stability research, specifically around the data driven approach for real-time emergency mode TSA. A review of past research on on-line TSA using PMU measurements and data mining is completed, from which the Decision Tree (DT) method is found to be the most suitable. This method is implemented on the test network. A DT model is trained and the sensitivity of its prediction accuracy is assessed according to a list of network uncertainties. Results showed that DT is a useful tool for on-line TSA for corrective control approach. Following the implementation, a generic probabilistic framework for the assessment of the prediction accuracy of data mining models is developed. This framework is independent of the data mining technique. It performs an exhaustive search of possible contingencies in the testing process and weighs the accuracies according to the realistic probability distribution of uncertain system factors, and provides the system operators with the confidence level of the decisions made under emergency conditions. After that, since the TSA for corrective control usually focuses on transient stability status without dealing with the generator grouping in the event of instability, a two-stage methodology is proposed to address this gap and to identify power system dynamic signature. In this methodology, traditional binary classification is used to identify transient stability in the first stage; Hierarchical Clustering is used to pre-define patterns of unstable dynamic behaviour; and different multiclass classification techniques are investigated to identify the patterns in the second stage. Finally, the effects of practical issues related to WAMS on the data mining methodologies are investigated. Five categories of issues are discussed, including measurement error, communication noise, wide area signal delays, missing measurements, and a limited number of PMUs.

Environmental concerns and long term energy security are the key drivers behind most current electric energy policies whose primary aim is to achieve a sustainable, reliable and affordable energy system. In a bid to achieve these aims many changes have been taking place in most power systems such as emergence of new low carbon generation technologies, structural changes of power system and introduction of competition and choice in electricity supply. As a result of these changes, the level of uncertainties is growing especially on generation side where the locations and available capacities of the future generators are not quite clear-cut. The transmission network needs to be flexibly and economically robust against all these uncertainties. The traditional operation of the network under preventive control mode is an inflexible practice which increases the total system cost. Corrective control operation strategy, however, can be alternatively used to boost the flexibility, to expedite the integration of the new generators and to decrease the overall cost. In this thesis, the main focus is on development of new techniques and methodologies that can be used for modelling and solving a transmission planning problem under the assumption that post-contingency corrective actions are plausible. Three different corrective actions, namely substation switching, demand response and generation re-dispatch are investigated in this thesis. An innovative multi-layer procedure deploying a genetic algorithm is proposed to calculate the required transmission capacity while substation switching is deployed correctively to eradicate the post-fault network violations. By using the proposed approach, a numerical study shows that the network investment reduces by 6.36% in the IEEE 24 bus test system. In another original study, generation re-dispatch corrective action is incorporated into the transmission planning problem. The ramp-rate constraints of generators are taken into account so that the network may be overloaded up to its short-term thermal rating while the generation re-dispatch action is undertaken. The results show that the required network investment for the modified IEEE 24 bus test system can be reduced by 23.8% if post-fault generation re-dispatch is deployed. Furthermore, a new recursive algorithm is proposed to study the effect of price responsive demands and peak-shifting on transmission planning. The results of a study case show that 7.8% of total investment can be deferred. In an additional study on demand response, a new probabilistic approach is introduced for transmission planning in a system where direct load curtailment can be used for either balancing mechanism or alleviating the network violations. In addition, the effect of uncertainties such as wind power fluctuation and CO2 emission price volatility are taken into account by using Monte Carlo simulation and Hypercube sampling techniques. Last but not least, a probabilistic model for dynamic thermal ratings of transmission lines is proposed, using past meteorological data. The seasonal correlations between wind power and thermal ratings are also calculated. £26.7 M is the expected annual benefit by using dynamic thermal ratings of part of National Grid's transmission network.

Orientador: José Roberto Sanches Mantovani / Banca: Rubén Augusto Romero Lázaro / Banca: Carlos Roberto Minussi / Banca: Geraldo Roberto Martins da Costa / Banca: Antônio César Baleeiro Alves / Resumo: Esta pesquisa tem por objetivo o desenvolvimento de uma ferramenta computacional para a solução do problema de Fluxo de Potência Ótimo Multimercado (FPOM). O problema de fluxo de potência ótimo mutimercado é decomposto em vários subproblemas, uma para cada, submercado que compõe o sistema de potência interconectado. O modelo de decomposição utilizado permite resolver o problema de FPO considerando-se os modelos de mercado desverticalizados e centralizados e os desverticalizados e descentralizados. Neste contexto, a pesquisa desenvolvida considera o novo esquema de funcionamento dos mercados de energia elétrica, no qual é vi freqüentemente desejável preservar a autonomia de cada um dos submercados que compõem o sistema de potência multimercado. O problema de FPO proposto é modelado como um problema de otimização não-linear inteiro misto, com variáveis de controle contínuas e discretas e têm ênfase no despacho econômico da geração de potência ativa e nos ajustes dos controles de tensão. Além disso, este modelo de FPO trata os subproblemas ativo e reativo simultaneamente. Para a sua solução é apresentado um algoritmo evolutivo multiobjetivo, baseado no NSGA (Nondominated Sorting Genetic Algorithm), pois características do problema abordado dificultam a sua solução através das técnicas baseadas em programação matemática e justificam a escolha da metaheurística multiobjetivo. / Abstract: This research is aimed at developing a computational tool for the solution of the Multimarket Optimal Power Flow (MOPF) problem. The multimarket optimal power flow problem is decomposed in various subproblems, one for each submarket that is part of the interconnected power system. The decomposition model used here allows solving the OPF problem considering the deregulated and centralized, and the deregulated and decentralized market models. In this context, the developed research takes into account the new functioning scheme of the electric power markets, viii where it is frequently desirable to preserve the autonomy of each one of those submarkets that compose the multimarket power system. The proposed OPF problem is modeled as a mixed integer non-linear optimization problem with continuous and discrete control variables, emphasizing the economic dispatch of the active power generation and the voltage control adjustments. In addition, this model of OPF deals simultaneously with the active and reactive subproblems. For its solution, a multiobjective evolutionary algorithm based on the NSGA (Nondominated Sorting Genetic Algorithm) is presented. The characteristics of the problem make difficult the utilization of techniques based on mathematical programming, justifying the adoption of a multiobjective metaheuristic. / Doutor

Le développement de la production éolienne permet de satisfaire les objectifs de lutte contre le réchauffement climatique. Cependant, dans certaines zones du réseau électrique, l’intégration d’un volume important de production peut créer des congestions qui traduisent l'incapacité du réseau à évacuer cette production. Les méthodes actuelles pour gérer les congestions sont basées sur des calculs prévisionnels de restrictions de production qui peuvent entrainer des pertes de production importantes pour le renouvelable. Cependant, dans le cadre d’un développement important du renouvelable, il est nécessaire de définir une méthodologie de gestion des congestions fiable, optimale du point de vue économique et non discriminatoire pour la production renouvelable.Dans le cadre de cette thèse, la méthodologie de gestion des congestions locales proposée repose sur l’usage d’un contrôle correctif. Le contrôle correctif est basé sur une boucle de régulation et un algorithme utilisant les réseaux de Petri. Une étude de stabilité de la boucle de régulation a montré que les marges de stabilité dépendantes des gains composant la boucle sont suffisantes. L’algorithme permet de définir les groupes de production à choisir pour la gestion des congestions en considérant leur coût d’utilisation et leur impact sur la congestion. Les essais, effectués sous le logiciel EUROSTAG, ont montré la pertinence de la méthodologie proposée et sa capacité à s’adapter à l’insertion des moyens de production. De plus, des conclusions générales sur les différents coûts associés à la gestion des congestions en fonction des différentes règlementations régissant la production renouvelable ont été obtenues / Development of wind generation is a mean towards global warming reduction. However, in some parts of the electrical grid, the massive integration of renewable generation can lead to congestion problems. These congestions are related to the impossibility for the power grid to transport the generation. Nowadays, congestion management methods are based on day(s)-ahead computation of generation restriction which leads to important production losses for renewables. Based on this context, it’s therefore important to develop a methodology which is optimal, reliable and non-discriminatory for renewable.In this work, the proposed congestion management method is based on corrective actions. These actions are computed in real-time using regulation loops and Petri net-based algorithms. A stability study proved that gain margins are sufficient to assure the stability of the corrective actions. The algorithm allows an optimal selection of the generators than will participate in the congestion management. This selection is based on their cost and efficiency for congestion alleviation. Simulation results using the software EUROSTAG have shown the efficiency of the method and its adaptability to different generator types. Furthermore, general conclusions on congestions costs according to different regulations on the renewable generation were obtained