Σκοπός αυτής της Διπλωματικής είναι η μελέτη του προβλήματος οικονομικής κατανομής φορτίου και η μελέτη του προβλήματος ένταξης μονάδων ενός καθαρά θερμικού συστήματος. Το πρόβλημα της οικονομικής κατανομής φορτίου και της ένταξης μονάδων εξετάζονται αρχικά θεωρητικά. Στα πλαίσια αυτής της Διπλωματικής γίνεται και εφαρμογή της οικονομικής κατανομής φορτίου και της ένταξης μονάδων σ' ένα ενδεικτικό δίκτυο δοκιμών με χρήση Η/Υ. Για την οικονομική κατανομή φορτίου χρησιμοποιείται το πρόγραμμα Economic Dispatch Program και για την ένταξη μονάδων, τα προγράμματα Unit Commitment και Unitcom. Επίσης εξετάζεται συνοπτικά το Ελληνικό σύστημα ηλεκτρικής ενέργειας και δίνονται τα βασικά σημεία της Απελευθέρωσης Αγοράς Ηλεκτρικής Ενέργειας στην Ελλάδα. / This diploma essay's purpose is to study the economic dispatch problem and the unit commitment problem of a simple thermal power system. Initially, the economic dispatch problem and the unit commitment problem are examined theoretically. In this essay takes place an application of the economic dispatch problem and an application of the unit commitment problem, in an indicative test network, with the use of a PC. For the economic dispatch problem is used the Economic Dispatch Program and for the unit commitment problem are used the Unit Commitment and Unitcom programs. Furthermore, in this essay are concisely examined the Hellenic power system and the release of the Hellenic electric market.

Economic dispatch is an important optimization challenge in power systems. It helps to find the optimal output power of a number of generating units that satisfy the system load demand at the cheapest cost, considering equality and inequality constraints. Many nature inspired algorithms have been broadly applied to tackle it such as particle swarm optimization. In this dissertation, two improved particle swarm optimization techniques are proposed to solve economic dispatch problems. The first is a hybrid technique with Bat algorithm. Particle swarm optimization as the main optimizer integrates bat algorithm in order to boost its velocity and to adjust the improved solution. The second proposed approach is based on Cuckoo operations. Cuckoo search algorithm is a robust and powerful technique to solve optimization problems. The study investigates the effect of levy flight and random search operation in Cuckoo search in order to ameliorate the performance of the particle swarm optimization algorithm. The two improved particle swarm algorithms are firstly tested on a range of 10 standard benchmark functions and then applied to five different cases of economic dispatch problems comprising 6, 13, 15, 40 and 140 generating units. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-08-23Bitstream added on 2014-06-13T20:48:01Z : No. of bitstreams: 1 souza_mas_me_bauru.pdf: 1718716 bytes, checksum: 06558a2073d16192fb7eaf1e9f95ca28 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Este trabalho visa a investigação e implementação de métodos Primal - Dual Previsor-Corretor de Pontos Interiores com a estratégia de busca unidimensional, e a aplicação destes em problemas de Despacho Econômico e Ambiental. Objetiva-se utilizar estes métodos para determinar soluções aproximadas e consistentes dos problemas causados citados, que forneçam a solução de minimização dos custos dos combustíveis empregados na geração termoelétrica de energia, otimizando um processo de alocação da demanda de energia elétrica entre as unidades geradoras disponíveis, de tal forma que as restrições operacionais sejam atendidas e que o custo de geração é minimizado. Pretende-se também, analisar o problema de Despacho Ambiental com um objetivo único quando se acopla a este o Problema de Despacho Econômico e busca-se, simultaneamente, a minimização dos custos de geração e a redução da emissão de poluentes na natureza. Os métodos foram implementados, testados em Problemas de Despacho Econômico e Ambiental, e o seu desempenho foi comparado com outros métodos já utilizados, cujos resultados são encontrados na literatura / This work aims the investigation and implementation of Primal-Dual Predictor-Corrector interior points methods, with the strategy of one-dimensional search, and its application in Economic and Environmental Dispatch Problems. It pretends to use these methods to determine approximate and consistent solutions of the mentioned problems, that provide the solution to minimize the fuel costs used in thermoelectric power generation, optimizing an allocations process of eletric power demand among available generation units, such that the operational constraints are attended and that generation cost is minimized. It too pretends to analyze the Environmental Dispatch Problem with the one objective when it is joined with the Dispatch Problems and it searchs, simultaneously, the minimization of the generation costs and the reduction of emission of the polluants in the nature. The methods were implemented, tested on the Economic and Environemental Dispatch Problems and its performance was compared with others method currently used, whose results are found in the literature

Submitted by Patrícia Cerveira (pcerveira1@gmail.com) on 2017-06-13T15:56:56Z No. of bitstreams: 1 Rene_Cruz_Freire.pdf: 5170376 bytes, checksum: 8c6b6dd8986d23b53ae99ba90dd69ef5 (MD5) / Approved for entry into archive by Biblioteca da Escola de Engenharia (bee@ndc.uff.br) on 2017-06-29T16:38:47Z (GMT) No. of bitstreams: 1 Rene_Cruz_Freire.pdf: 5170376 bytes, checksum: 8c6b6dd8986d23b53ae99ba90dd69ef5 (MD5) / Made available in DSpace on 2017-06-29T16:38:47Z (GMT). No. of bitstreams: 1 Rene_Cruz_Freire.pdf: 5170376 bytes, checksum: 8c6b6dd8986d23b53ae99ba90dd69ef5 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Um dos temas de alta relevância para a sociedade atual é a qualidade do suprimento de energia elétrica, que deve ser ininterrupto, seguro e econômico. Para tal, é primordial que o sistema de potência esteja preparado para um possível defeito de algum equipamento da rede, mantendo a operação dentro dos patamares seguros, evitando os blecautes e todas as suas consequências para a sociedade. Isso pode ser feito através do redespacho das unidades geradoras, de modo a encontrar um ponto de operação que concilie segurança e economicidade, dois objetivos conflitantes, enquanto busca se afastar o mínimo possível do ponto de operação previamente estabelecido, via planejamento eletroenergético, para o sistema de potência em questão. Trata-se de uma abordagem multiobjetiva do Fluxo de Potência Ótimo com Restrições de Segurança (FPORS) que pode ser solucionada com uma abordagem de Computação Evolucionária (CE) com viés multiobjetivo. Neste trabalho, foram implementadas e comparadas duas meta-heurísticas evolutivas multiobjetivo: Nondominated Sorting Genetic Algorithm II (NSGA-II) e o Multi-objective Evolutionary Particle Swarm Optimization (MOEPSO). Os resultados dessas heurísticas também foram comparados com a abordagem mono-objetivo do mesmo problema. Os algoritmos foram implementados no MATLAB® e testados em um sistema-teste que simula as condições do Sistema Interligado Nacional (SIN). As heurísticas multiobjetivo foram comparadas através da metodologia de análise da Fronteira de Pareto (FP), onde é analisado qual método concilia melhor os objetivos de economia e segurança. Na primeira análise o NSGA-II saiu-se melhor, entretanto após a implementação de melhorias no algoritmo, o MOEPSO mostrou desempenho superior na segunda análise. Nas duas análises, o viés multiobjetivo mostrou-se superior ao mono-objetivo, na comparação através do critério de agregação de objetivos. Em relação ao tempo de simulação de cada método, o MOEPSO foi superior na primeira análise, já na segunda análise foi implementado um refinamento baseado no Fluxo de Potência Linearizado no FPORS, que baixou o tempo de simulação das duas heurísticas multiobjetivas em comparação com a primeira análise, e o MOEPSO teve o menor tempo de simulação. Na comparação com o viés mono-objetivo, apenas o NSGA-II teve tempo médio de simulação maior que o método mono-objetivo na primeira análise. Na segunda análise, todas as heurísticas multiobjetivo possuíam tempo de simulação menores que o método mono-objetivo. / One of the topics of high relevance to the today’s society is the quality of electric power supply, which must be uninterrupted, safe and economical. To this end, it is essential that the power system be prepared for a possible defect of some equipment from the network while maintaining operation within safe levels, avoiding blackouts and all its consequences for society. This can be done by redispatch of generating units, in order to find an operation point which conciliate security and economy, two conflicting objectives, while seeking to depart as little as possible of the operation point previously established in the energy planning for the power system in question. This is a multi-objective approach to Security Constrained Optimal Power Flow (SCOPF) that can be solved with an approach of Evolutionary Computation with multi-objective bias. In this work we were implemented and compared two multi-objective evolutionary meta-heuristics: Nondominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Evolutionary Particle Swarm Optimization (MOEPSO). The results of these heuristics were also compared with mono-objective approach to the same problem. The algorithms were implemented in MATLAB® and tested in a test-case that simulates the conditions of the Brazilian Sistema Interligado Nacional (National Interconnected System). The multi-objective heuristics were compared using the analysis methodology of the Pareto Frontier, where is analyzed which method is better to conciliate the economy and security objectives. In the first analysis the NSGA-II fared better, but after the implementation of improvements in the algorithm, the MOEPSO showed superior performance in the second analisys. In both analyzes, the multi-objective bias was superior to the mono-objective bias, in the comparison through objectives aggregation criteria. Concerning the simulation time of each method, the MOEPSO was superior in the first analysis, but in the second analysis was implemented a refinement based on DC Load Flow, which lowered the simulation time of the two multi-objective heuristics compared with the first analysis, and the MOEPSO had the shortest time simulation. Compared to the mono-objective bias, only the NSGA-II had an average time simulation greater than the mono-objective method in the first analysis. In the second analysis, all multi-objectives heuristics had simulation time smaller than the mono-objective method.

In modern days electric power systems, the penetration of renewable resources and the introduction of free market principles have led to new challenges facing the power producers and regulators. Renewable production is intermittent which leads to fluctuations in the grid and requires more control for regulators, and the free market principle raises the challenge for power plant producers to operate their plants in the most profitable way given the fluctuating prices. Those problems are addressed in the literature as the Economic Dispatch, and they have been discussed from both regulator and producer view points. Combined Cycle Power plants have the privileges of being dispatchable very fast and with low cost which put them as a primary solution to power disturbance in grid, this fast dispatch-ability also allows them to exploit price changes very efficiently to maximize their profit, and this sheds the light on the importance of prices forecasting as an input for the profit optimization of power plants. In this project, an integrated solution is introduced to optimize the dispatch of combined cycle power plants that are bidding for electricity markets, the solution is composed of two models, the forecasting model and the optimization model. The forecasting model is flexible enough to forecast electricity and fuel prices for different markets and with different forecasting horizons. Machine learning algorithms were used to build and validate the model, and data from different countries were used to test the model. The optimization model incorporates the forecasting model outputs as inputs parameters, and uses other parameters and constraints from the operating conditions of the power plant as well as the market in which the plant is selling. The power plant in this mode is assumed to satisfy different demands, each of these demands have corresponding electricity price and cost of energy not served. The model decides which units to be dispatched at each time stamp to give out the maximum profit given all these constraints, it also decides whether to satisfy all the demands or not producing part of each of them.

<p>In the US, seven regional transmission organizations (RTOs) operate wholesale electricity markets within three largely independent transmission systems, the largest of which includes five RTO regions and many vertically integrated utilities.</p> <p>RTOs operate a day-ahead and a real-time market. In the day-ahead market, generation and demand-side resources are optimally scheduled based on bids and offers for the next day. Those schedules are adjusted according to actual operating conditions in the real-time market. Both markets involve a unit commitment calculation, a mixed integer program that determines which generators will be online, and an economic dispatch calculation, an optimization determines the output of each online generator for every interval and calculates locational marginal prices (LMPs).</p> <p>The use of LMPs for the management of congestion in RTO transmission systems has brought efficiency and transparency to the operation of electric power systems and provides price signals that highlight the need for investment in transmission and generation. Through this work, we aim to extend these efficiency and transparency gains to the coordination across RTOs. Existing market-based inter-regional coordination schemes are limited to incremental changes in real-time markets. </p> <p>We propose a multi-regional unit-commitment that enables coordination in the day-ahead timeframe by applying a distributed approach to approximate a system-wide optimal commitment and dispatch while allowing each region to largely maintain their own rules, model only internal transmission up to the boundary, and keep sensitive financial information confidential. A heuristic algorithm based on an extension of the alternating directions method of multipliers (ADMM) for the mixed integer program is applied to the unit commitment. </p> The proposed coordinated solution was simulated and compared to the ideal single-market scenario and to a representation of the current uncoordinated solution, achieving at least 58% of the maximum potential savings, which, in terms of the annual cost of electric generation in the US, could add up to nearly $7 billion per year. In addition to the coordinated day-ahead solution, we develop a distributed solution for financial transmission rights (FTR) auctions with minimal information sharing across RTOs that constitutes the first known work to provide a viable option for market participants to seamlessly hedge price variability exposure on cross-border transactions.

[pt] Um dos algoritmos mais eficientes para resolver problemas de planejamento de operações hidrotérmicas, que são modelos estocásticos multiestágio de larga escala, é o chamado algoritmo de programação dinâmica dupla estocástica (SDDP). O planejamento da operação dos sistemas de energia visa avaliar o valor dos recursos escassos (por exemplo, água) para alimentar os modelos de despacho de curto prazo usados na implementação real das decisões. Quando o modelo de planejamento se desvia significativamente da realidade da operação implementada, as políticas de decisão são consideradas inconsistentes no tempo. A literatura recente explorou diferentes fontes de inconsistência, como medidas de risco dinâmico inconsistentes no tempo, representação imprecisa do processo de informação e simplificações no modelo de planejamento de rede. Este trabalho aborda a inconsistência no tempo devido a simplificações na representação da rede no modelo de planejamento que estende a literatura existente. O objetivo deste trabalho é propor uma estrutura, composta por uma metodologia e um pacote computacional de código aberto, para testar o impacto operacional e econômico das simplificações da modelagem sobre o fluxo de energia da rede em sistemas de energia hidrotérmica. Entre as inúmeras formulações disponíveis no pacote, nos concentramos em avaliar o custo e o desempenho operacional das seguintes aproximações de modelos: o modelo de rede de transporte (NFA), atualmente em uso pelo operador de sistema brasileiro; o relaxamento de cone de segunda ordem (SOC); o relaxamento de programação semidefinida (SDP); a aproximação do fluxo de energia de corente continua (DC); e o DC com aproximação de fluxo de potência com perda de linha (DCLL). Todas as formulações mencionadas anteriormente são testadas como aproximações para o modelo de rede na fase de planejamento, onde é construída a função de custo futuro. Em seguida, avaliamos cada aproximação simulando a operação do sistema usando um modelo de implementação que minimiza o custo imediato sob as restrições de fluxo de energia AC e a respectiva função de custo futuro. A comparação é feita para dois sistemas, um composto por um ciclo e o outro aproximadamente radial. / [en] One of the most efficient algorithms for solving hydrothermal operation planning problems, which are large-scale multi-stage stochastic models, is the so-called stochastic dual dynamic programming (SDDP) algorithm. Operation planning of power systems aims to assess the value of the scarce resources (e.g. water) to feed short-term dispatch models used in the actual implementation of the decisions. When the planning model significantly deviates from the reality of the implemented operation, decision policies are said to be time-inconsistent. Recent literature has explored different sources of inconsistency such as time-inconsistent dynamic risk measures, inaccurate representation of the information process and simplifications in the network planning model. This work addresses the time-inconsistency due to simplifications in the network representation in the planning model extending the existing literature. The objective of this work is to propose a framework, comprised of a methodology and an open-source computational package, for testing the operative and economic impact of modeling simplifications over the network power-flow in hydrothermal power systems. Among the myriad of formulations available in the package, we focused on assessing the cost and operative performance of the following model approximations: the transportation network-flow model (NFA), currently in use by the Brazilian system operator; the second-order cone relaxation (SOC); the semidefinite programming relaxation (SDP); the DC power-flow approximation (DC); and the DC with line-loss power-flow approximation (DCLL). All the previously mentioned formulations are tested as approximations for the network model in the planning stage, where the cost-to-go function is built. Then, we evaluate each approximation by simulating the system s operation using an implementation model, which minimizes the immediate cost under AC power-flow constraints and the respective cost-to-go function. The comparison is made for two systems, one composed of a cycle and the other approximately radial.

[pt] Transmission switching já foi apresentado anteriormente como uma ferramenta capaz de prover benefícios significativos na operação de sistemas de potência, como redução de custos e aumento de confiabilidade. Dentro do contexto de mercados co-otimizados para energia e reservas, este trabalho endereça a co-otimização de transmission switching pós-contingência no planejamento da operação de sistemas elétricos. Os modelos propostos para programação diária e despacho econômico diferem de formulações existentes devido à consideração conjunta de três fatores complicadores. Primeiro, ações de transmission switching são consideradas nos estados pré e pós-contingência, portanto requerendo variáveis binárias pós-contingência. Adicionalmente, a programação de geradores e as ações de transmission switching são co-otimizadas. Além disso, a operação de geradores é caracterizada temporalmente em um contexto multi-período. Os modelos propostos são formulados como programas inteiros-mistos desafiadores para os quais os softwares comerciais comumente utilizados para modelos mais simples podem levar à intratabilidade até para instâncias de tamanho moderado. Como metodologia de solução, nós apresentamos uma versão aperfeiçoada de um algoritmo de geração de colunas e restrições aninhado, com a adição de restrições válidas para melhorar o desempenho computacional. Simulações numéricas demonstram o desempenho efetivo da abordagem proposta, assim como suas vantagens econômicas e operacionais sobre modelos existentes que desconsideram o transmission switching pós-contingência. / [en] Transmission switching has been previously shown to offer significant benefits to power system operation, such as cost savings and reliability enhancements. Within the context of co-optimized electricity markets for energy and reserves, this work addresses the co-optimization of post contingency transmission switching in power system operation planning. The proposed models for unit commitment and economic dispatch differ from existing formulations due to the joint consideration of three major complicating factors. First, transmission switching actions are considered both in the preand post-contingency states, thereby requiring binary post-contingency variables. Secondly, generation scheduling and transmission switching actions are co-optimized. In addition, the time coupled operation of generating units is precisely characterized. The proposed models are formulated as challenging mixed-integer programs for which the off-the-shelf software customarily used for simpler models may lead to intractability even for moderatelysized instances. As a solution methodology, we present enhanced versions of an exact nested column-and-constraint generation algorithm featuring the inclusion of valid constraints to improve the overall computational performance. Numerical simulations demonstrate the effective performance of the proposed approach as well as its economic and operational advantages over existing models disregarding post-contingency transmission switching.

This dissertation focuses on selected issues in regard to the mathematical modeling of electricity markets. In a first step the interrelations of electric power market modeling are highlighted a crossroad between operations research, applied economics, and engineering. In a second step the development of a large-scale continental European economic engineering model named ELMOD is described and the model is applied to the issue of wind integration. It is concluded that enabling the integration of low-carbon technologies appears feasible for wind energy. In a third step algorithmic work is carried out regarding a game theoretic model. Two approaches in order to solve a discretely-constrained mathematical program with equilibrium constraints using disjunctive constraints are presented. The first one reformulates the problem as a mixed-integer linear program and the second one applies the Benders decomposition technique. Selected numerical results are reported.

Fundação de Apoio a Pesquisa e à Inovação Tecnológica do Estado de Sergipe - FAPITEC/SE / The growth of the electric energy consumption in the last years has generated the need of the increase in the amount of power sources, making the electricity sector undergo some large changes. This has provided the search for tools that promotes a better efficiency and security to the electrical power systems. A planning problem that is considered important in the daily operation of the power systems is the Unit Commitment, where the time schedule of the operation is defined, determining which machines will be online or offline, and which are the operating points. Those units must operate by load variation, respecting the operative and security constraints. This research proposes the resolution of the problem for the short-term planning, taking a set of constraints associated with the thermal generation and the power system. Among them, we can highlight the output power variation constraints of the machines and the security restrictions of the transmission system, avoided in most Unit Commitment studies. This problem is nonlinear, mixed-integer and has a large scale. The methodology used involves the utilization of an Adaptive Genetic Algorithm, for the Unit Commitment problem, and the Interior-Point Primal- Dual Predictor–Corrector Method, for DC power flow resolution in economic dispatch problem. Furthemore, this research proposes the implementation of cross-over and mutation operators of Genetic Algorithm based on a ring methodology applied in Unit Commitment matrix. The results were obtained through simulations in a mathematical simulation software, using the IEEE test systems with 30 bus and 9 generators, and another with 24 bus and 26 generators. The validation of the algorithm was done by comparing the results with other works in the literature. / O crescimento do consumo de energia elétrica nos últimos anos vem gerando a necessidade de um aumento na quantidade de fontes geradoras, fazendo com que o setor elétrico passe por grandes mudanças. Isso tem proporcionado a busca por ferramentas que ofereçam maior eficiência e segurança aos sistemas de potência. Um problema considerado de extrema importância na operação diária dos sistemas elétricos é o planejamento da Alocação das Unidades Geradoras, onde define-se a programação horária das unidades do sistema, determinando quais máquinas deverão estar ligadas ou desligadas, e quais serão seus respectivos pontos de operação. Essas unidades geradoras devem operar de forma eficaz, mediante a variação da carga, respeitando restrições operativas e de segurança do sistema. Este trabalho propõe a resolução do problema para o planejamento de curto prazo, levando em consideração uma série de restrições relacionadas a geração térmica e ao sistema elétrico. Entre elas, podemos destacar as restrições de variação de potência de saída das máquinas e as restrições de segurança do sistema de transmissão, evitadas na maioria dos estudos de Alocação de Unidades Geradoras. Este problema tem característica não-linear, inteiro-misto e de grande escala. A metodologia utilizada para resolução do problema envolve a utilização de um Algoritmo Genético Adaptativo, para Alocação das Unidades, e o Método de Pontos Interiores Primal-Dual Preditor-Corretor, para a resolução do Fluxo de Potência Ótimo DC no problema do Despacho Econômico. Além disso, este trabalho propõe a implementação dos operadores de cross-over e mutação do Algoritmo Genético com base em uma metodologia anelar aplicada na matriz de alocação de unidades. Os resultados foram obtidos através de simulações em um software de simulação matemática, utilizando os sistemas testes do IEEE de 30 barras com 9 geradores e 24 barras com 26 geradores, e a validação do algoritmo foi feita comparando os resultados obtidos com os outros trabalhos da literatura.