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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 research focuses on improving the efficiency of power market operations by providing system operators additional tools for managing the costs of supplying and delivering electricity. A transmission topology control (TC) framework for production cost reduction based on a shift factor (SF) representation of branch and breaker flows is proposed. The framework models topology changes endogenously while maintaining linearity in the overall Mixed Integer Linear Programming (MILP) formulation. This work develops the DC lossless, and loss-adjusted TC formulations that can be used in a Day Ahead or intra-day market framework as well as an AC-based model that can be used in operational settings. Practical implementation choices for the Shift Factor formulation are discussed as well as the locational marginal prices (LMPs) under the TC MIP setting and their relation to LMPs without TC. Compared to the standard B-theta alternative used so far in TC research, the shift factor framework has significant computational complexity advantages, particularly when a tractably small switchable set is optimized under a representative set of contingency constraints. These claims are supported and elaborated by numerical results.

abstract: This thesis addresses the issue of making an economic case for bulk energy storage in the Arizona bulk power system. Pumped hydro energy storage (PHES) is used in this study. Bulk energy storage has often been suggested for large scale electric power systems in order to levelize load (store energy when it is inexpensive [energy demand is low] and discharge energy when it is expensive [energy demand is high]). It also has the potential to provide opportunities to avoid transmission and generation expansion, and provide for generation reserve margins. As the level of renewable energy resources increases, the uncertainty and variability of wind and solar resources may be improved by bulk energy storage technologies. For this study, the MATLab software platform is used, a mathematical based modeling language, optimization solvers (specifically Gurobi), and a power flow solver (PowerWorld) are used to simulate an economic dispatch problem that includes energy storage and transmission losses. A program is created which utilizes quadratic programming to analyze various cases using a 2010 summer peak load from the Arizona portion of the Western Electricity Coordinating Council (WECC) system. Actual data from industry are used in this test bed. In this thesis, the full capabilities of Gurobi are not utilized (e.g., integer variables, binary variables). However, the formulation shown here does create a platform such that future, more sophisticated modeling may readily be incorporated. The developed software is used to assess the Arizona test bed with a low level of energy storage to study how the storage power limit effects several optimization outputs such as the system wide operating costs. Large levels of energy storage are then added to see how high level energy storage affects peak shaving, load factor, and other system applications. Finally, various constraint relaxations are made to analyze why the applications tested eventually approach a constant value. This research illustrates the use of energy storage which helps minimize the system wide generator operating cost by "shaving" energy off of the peak demand. The thesis builds on the work of another recent researcher with the objectives of strengthening the assumptions used, checking the solutions obtained, utilizing higher level simulation languages to affirm results, and expanding the results and conclusions. One important point not fully discussed in the present thesis is the impact of efficiency in the pumped hydro cycle. The efficiency of the cycle for modern units is estimated at higher than 90%. Inclusion of pumped hydro losses is relegated to future work. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2014

abstract: This thesis addresses the issue of making an economic case for energy storage in power systems. Bulk energy storage has often been suggested for large scale electric power systems in order to levelize load; store energy when it is inexpensive and discharge energy when it is expensive; potentially defer transmission and generation expansion; and provide for generation reserve margins. As renewable energy resource penetration increases, the uncertainty and variability of wind and solar may be alleviated by bulk energy storage technologies. The quadratic programming function in MATLAB is used to simulate an economic dispatch that includes energy storage. A program is created that utilizes quadratic programming to analyze various cases using a 2010 summer peak load from the Arizona transmission system, part of the Western Electricity Coordinating Council (WECC). The MATLAB program is used first to test the Arizona test bed with a low level of energy storage to study how the storage power limit effects several optimization out-puts such as the system wide operating costs. Very high levels of energy storage are then added to see how high level energy storage affects peak shaving, load factor, and other system applications. Finally, various constraint relaxations are made to analyze why the applications tested eventually approach a constant value. This research illustrates the use of energy storage which helps minimize the system wide generator operating cost by "shaving" energy off of the peak demand. / Dissertation/Thesis / M.S. Electrical Engineering 2013

Submitted by Renata Lopes (renatasil82@gmail.com) on 2015-12-16T14:48:47Z No. of bitstreams: 1 ezequieldasilvaoliveira.pdf: 3924049 bytes, checksum: 2e25250cd277eeed6b8f5625283b8b69 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2015-12-16T15:15:57Z (GMT) No. of bitstreams: 1 ezequieldasilvaoliveira.pdf: 3924049 bytes, checksum: 2e25250cd277eeed6b8f5625283b8b69 (MD5) / Made available in DSpace on 2015-12-16T15:15:57Z (GMT). No. of bitstreams: 1 ezequieldasilvaoliveira.pdf: 3924049 bytes, checksum: 2e25250cd277eeed6b8f5625283b8b69 (MD5) Previous issue date: 2015-08-07 / O atendimento à demanda requer um uso eficiente do sistema geração sem afetar sua confiabilidade. Para o sistema termoelétrico o uso eficiente está diretamente relacionado com a queima de combustível e, consequentemente, com o custo de operação. Portanto, a minimização deste custo é obtida a partir da alocação da potência ativa a ser gerada para cada termoelétrica, que configura um problema de otimização denominado de Despacho Econômico (DE). Este trabalho aborda de forma real o problema do Despacho Econômico, devido a consideração das características que ocorrem durante a geração de energia elétrica. Estas características são as restrições de Zonas de Operação Proibidas (ZOP), Múltiplo Combustível (MC) e o efeito de ponto de válvula, que torna o problema do Despacho Econômico num problema não convexo e descontínuo. A proposta deste trabalho é a adoção de duas metaheurísticas bioinspiradas para resolver o problema do Despacho Econômico com características reais de operação. As técnicas bioinspiradas que são utilizadas consistem na: (i) Otimização via Enxame de Partículas e (ii) otimização baseada no fenômeno da ecolocalização do morcego, denominado Algoritmo do Morcego. Ambas as metaheurísticas são implementadas noMATLAB® e para a otimização do problema não linear e não convexo do Despacho Econômico é utilizado o modelo LINGO. Os resultados obtidos através das técnicas bioinspiradas aplicadas ao estudos de casos, são comparados comos encontrados na literatura especializada e, por fim, é feito a análise da eficiência das metaheurísticas utilizadas, cujo Algoritmo do Morcego apresenta o melhor desempenho. / The demand supply requires efficient use of generation system without affecting its reliability. For the thermoelectric systemits efficient use is directly related with fuel burn and, consequently, with cost operation. Therefore, to minimize this cost is obtained from the allocation of active power to be generated for each thermoelectric, which sets up an optimization problem called Economic Dispatch (DE). Thiswork considers a realway of the Economic Dispatch problemdue consideration of the characteristics that occur during eletricity generation. These features are restrictions Prohibited Operating Zones (POZ), multiple fuel and the valve-point effect, which makes the Economic Dispatch problem in non-convex and discontinuous problem. The proposal this work is adopting two bioinspired metaheuristics to solve the EconomicDispatch problemwith real operating characteristics. The bioinspired techniques that are used consist of: (i) Particle Swarm Optimization and (ii) Optimization based on bat echolocation phenomenon, called Bat Algorithm. Both metaheuristics are implemented in MATLAB® and for optimization of non-linear and non-convex problem is used LINGO model. The results obtained through the bioinspired techniques applied the study cases, are compared with those found in literature and, finally, is made the analysis of the efficiency of metaheuristics used, which Bat Algorithm has the best performance.

Multi-energy systems offer an opportunity to leverage energy conversion processes and temporary energy storage mechanisms to reduce costs and emissions during operation of campuses, cities, and buildings. With increasing options for flexibility in demand-side resources it is possible to meet demand without sacrificing comfort and convenience of MES occupants. This Thesis develops a multi-period, linear optimization model of an MES with flexible buildings that captures nonlinearities in the efficiency of energy conversion processes. The flexible buildings are parametrized, in part, based on historical data from a college campus in Vermont, USA. The idea of the MES model is to investigate the role that flexibility plays in reducing costs and emissions for a small campus relative to that of a possible carbon tax. The operation of the MES is optimized to reduce costs based on representative seasons. Interestingly, it is found that when utilized optimally, flexible buildings allows for a more cost and energy effective method of not only meeting demand but also reducing carbon emissions in the process.

No / A genetic algorithm (GA) augmented with knowledge-based methods has been developed for solving the unit commitment economic dispatch problem. The GA evolves a population of binary strings which represent commitment schedules. The initial population of schedules is chosen using a method based on elicited scheduling knowledge. A fast rule-based dispatch method is then used to evaluate candidate solutions. The knowledge-based genetic algorithm is applied to a test system of ten thermal units over 24-hour time intervals, including minimum on/off times and ramp rates, and achieves lower cost solutions than Lagrangian relaxation in comparable computational time.

Variable generator output levels from renewable energies is an important technical obstacle to the transition from fossil fuels to renewable resources. Super grids and smart grids are among the most effective solutions to mitigate generation variability. In a super grid, electric utilities within an interconnected system can share generation and reserve units so that they can produce electricity at a lower overall cost. Smart grids, in particular demand response programs, enable flexible loads such as plug-in electric vehicles and HVAC systems to consume electricity preferntially in a grid-friendly way that assists the grid operator to maintain the power balance. These solutions, in conjunction with energy storage systems, can facilitate renewable integration. This study aims to provide an understanding of the achievable benefits from integrating demand response into wholesale and retail electricity markets, in particular in the presence of significant amounts of variable generation. Among the options for control methods for demand response, market-based approaches provide a relatively efficient use of load flexibility, without restricting consumers' autonomy or invading their privacy. In this regard, a model of demand response integration into bulk electric grids is presented to study the interaction between variable renewables and demand response in the double auction environment, on an hourly basis. The cost benefit analysis shows that there exists an upper limit of renewable integration, and that additional solutions such as super grids and/or energy storage systems are required to go beyond this threshold. The idea of operating an interconnection in an unified (centralized) manner is also explored. The traditional approach to the unit commitment problem is to determine the dispatch schedule of generation units to minimize the operation cost. However, in the presence of price-sensitive loads (market-based demand response), the maximization of economic surplus is a preferred objective to the minimization of cost. Accordingly, a surplus-maximizing hour-ahead scheduling problem is formulated, and is then tested on a system that represents a 20-area reduced model of the North America Western Interconnection for the planning year 2024. The simulation results show that the proposed scheduling method reduces the total operational costs substantially, taking advantage of renewable generation diversity. The value of demand response is more pronounced when ancillary services (e.g. real-time power balancing and voltage/frequency regulation) are also included along with basic temporal load shifting. Relating to this, a smart charging strategy for plug-in electric vehicles is developed that enables them to participate in a 5-minute retail electricity market. The cost reduction associated with implementation of this charging strategy is compared to uncontrolled charging. In addition, an optimal operation method for thermostatically controlled loads is developed that reduces energy costs and prevents grid congestion, while maintaining the room temperature in the comfort range set by the consumer. The proposed model also includes loads in the energy imbalance market. The simulation results show that market-based demand response can contribute to a significant cost saving at the sub-hourly level (e.g. HVAC optimal operation), but not at the super-hourly level. Therefore, we conclude that demand response programs and super grids are complementary approaches to overcoming renewable generation variation across a range of temporal and spatial scales. / Graduate / 0791 / sahandbehboodi@gmail.com

The purpose of this research is to identify the need of Smart Grid Technologies in communication between industrial plants with co-generation capability and the electric utilities in providing the most optimum scheme for buying and selling of electricity in such a way that the fuel consumption is minimized, reliability is increased, and time to restore the system is reduced. A typical industrial plant load profile based on statistical mean and variance of industrial plants' load requirement is developed, and used in determining the minimum cost of producing the next megawatt-hours by a typical electric utility. The 24-hour load profile and optimal power flow program are used to simulate the IEEE 39 Bus Test System. The methodology for the use of smart grid technology in fuel saving is documented in the thesis. The results obtained from this research shall be extended to include several industrial plants served by electric utilities in future work by the UNO research team.