Due to energy scarcity coupled with environment issues, it is likely to see the biggest shift in generation portfolio in the UK and world wide, stimulated by various governmental incentives policies for promoting renewable generation and reducing emission. The generation expansion in the future will be driven by not only peak demand growth but also emission reduction target. Thus, the traditional generation expansion planning (GEP) model has to be improved to reflect this change against the new environment. The policy makers need a better assessment tool to facilitate the new environment, so they can make appropriate policies for promoting renewable generation and emission reduction, and guide the generation mix to evolve appropriately over time. Since the expansion of new generation capacities is highly capital intensive, it makes the improvement of GEP quite urgent and important. The thesis proposes the GEP modelling improvement works from the following aspects: • Integrating short-term emission cost, unit commitment constraints in an emission target constrained GEP model. • Including the network transmission constraints and generation location optimization in an emission constrained GEP. • Investigating the impacts of multi-stage emission targets setting on an emission constrained GEP problem and its overall expansion cost. • Incorporating the uncertain renewable generation expansion and short-term DSR into the GEP problem and find out its potential contributions to the GEP problem. A real case study is made to determine the optimal generation mix of the Great Britain in 2020 in order to meet the 2020 emission reduction target. Different optimal generation mixes of the UK in 2020 are identified under a series of scenarios. The scenarios are constructed according to different GB network transmission capacity hypotheses and demand side response (DSR) level scenarios.

Nowadays modern electric power systems consist of large-scale and highly complex interconnected transmission systems, thus transmission expansion planning (TEP) is now a significant power system optimisation problem. The TEP problem is a large-scale, complex and nonlinear combinatorial problem of mixed integer nature where the number of candidate solutions to be evaluated increases exponentially with system size. The accurate solution of the TEP problem is essential in order to plan power systems in both an economic and efficient manner. Therefore, applied optimisation methods should be sufficiently efficient when solving such problems. In recent years a number of computational techniques have been proposed to solve this efficiency issue. Such methods include algorithms inspired by observations of natural phenomena for solving complex combinatorial optimisation problems. These algorithms have been successfully applied to a wide variety of electrical power system optimisation problems. In recent years differential evolution algorithm (DEA) procedures have been attracting significant attention from the researchers as such procedures have been found to be extremely effective in solving power system optimisation problems. The aim of this research is to develop and apply a novel DEA procedure directly to a DC power flow based model in order to efficiently solve the TEP problem. In this thesis, the TEP problem has been investigated in both static and dynamic form. In addition, two cases of the static TEP problem, with and without generation resizing, have also been investigated. The proposed method has achieved solutions with good accuracy, stable convergence characteristics, simple implementation and satisfactory computation time. The analyses have been performed within the mathematical programming environment of MATLAB using both DEA and conventional genetic algorithm (CGA) procedures and a detailed comparison has also been presented. Finally, the sensitivity of DEA control parameters has also been investigated.

Nowadays, electricity markets are becoming more deregulated, especially development of smart grid and introduction of renewable energy promote regulations of energy markets. On the other hand, the uncertainties of new energy sources and market participants’ bidding bring more challenges to power system operation and transmission system planning. These problems motivate us to study spot price (also called locational marginal pricing) of electricity markets, the strategic bidding of wind power producer as an independent power producer into power market, transmission expansion planning considering wind power investment, and analysis of the maximum loadability of a power grid. The work on probabilistic spot pricing for a utility grid includes renewable wind power generation in a deregulated environment, taking into account both the uncertainty of load forecasting and the randomness of wind speed. Based on the forecasted normal-distributed load and Weibull-distributed wind speed, probabilistic optimal power flow is formulated by including spinning reserve cost associated with wind power plants and emission cost in addition to conventional thermal power plant cost model. Simulations show that the integration of wind power can effectively decrease spot price, also increase the risk of overvoltage. Based on the concept of loacational marginal pricing which is determined by a marketclearing algorithm, further research is conducted on optimal offering strategies for wind power producers participating in a day-ahead market employing a stochastic market-clearing algoivrithm. The proposed procedure to drive strategic offers relies on a stochastic bilevel model: the upper level problem represents the profit maximization of the strategic wind power producer, while the lower level one represents the marketing clearing and the corresponding price formulation aiming to co-optimize both energy and reserve. Thirdly, to improve wind power integration, we propose a bilevel problem incorporating two-stage stochastic programming for transmission expansion planning to accommodate large-scale wind power investments in electricity markets. The model integrates cooptimizations of energy and reserve to deal with uncertainties of wind power production. In the upper level problem, the objective of independent system operator (ISO) modelling transmission investments under uncertain environments is to minimize the transmission and wind power investment cost, and the expected load shedding cost. The lower level problem is composed of a two stage stochastic programming problem for energy schedule and reserve dispatch simultaneously. Case studies are carried out for illustrating the effectiveness of the proposed model. The above market-clearing or power system operation is based on direct current optimal power flow (DC-OPF) model which is a linear problem without reactive power constraints. Power system maximum loadability is a crucial index to determine voltage stability. The fourth work in this thesis proposes a Lagrange semi-definite programming (SDP) method to solve the non-linear and non-convex optimization alternating current (AC) problem of the maximum loadability of security constrained power system. Simulation results from the IEEE three-bus system and IEEE 24-bus Reliability Test System (RTS) show that the proposed method is able to obtain the global optimal solution for the maximum loadability problem. Lastly, we summarize the conclusions from studies on the above mentioned optimization problems of electric power market under modern grid, as well as the influence of wind power integration on power system reliability, and transmission expansion planning, as well as the operations of electricity markets. Meanwhile, we also present some open questions on the related research, such as non-convex constraints in the lower-level problem of a bilevel problem, and integrating N-1 security criterion of transmission planning. / Graduate / lei.ming296@gmail.com

Two methodologies are presented for analyzing the choice between centralized and decentralized energy infrastructures from a least-cost perspective. The first of these develops a novel minimum spanning tree network algorithm to approximate the shortest-length network that connects a given fraction of total system population. This algorithm is used to identify high priority locations for decentralized electrification in 150 countries. The second methodology utilizes a mixed-integer programming framework to determine the least-cost combination of centralized and decentralized electricity infrastructure that is capable of serving demand throughout a given system. This methodology is demonstrated through a case study of Rwanda. The centralized-decentralized electrification paradigm is also approached from an energy security perspective, incorporating stochastic events and probabilistic parameters into a simulation model that is used to compare different development paths. The impact of explicitly modeling stochastic events as opposed to utilizing a conventional formulation is also considered Finally, a subsidy-free lighting cost curve is developed and a model is presented to compare the costs and benefits of three different financial mechanisms that can be employed to make capital intensive energy systems more accessible to rural populations. The optimal contract is determined on the basis of utility-maximization for a range of costs to the providing agency and a comprehensive single and multi-factor sensitivity analysis is performed.

Driven by a number of factors, the electric power industry is expected to undergo a paradigm shift with a considerably increased level of variable energy sources. A significant integration of such sources requires heavy transmission investments over geographically wide and large-scale networks. However, the stochastic nature of such sources, along with the sheer size of network systems, results in problems that may become intractable. Thus, the challenge addressed in this work is to design efficient and reasonably accurate models, strategies and tools that can solve large-scale TEP problems under uncertainty. A long-term stochastic network planning tool is developed, considering a multi-stage decision framework and a high level integration of renewables. Such a tool combines the need for short-term decisions with the evaluation of long-term scenarios, which is the practical essence of a real-world planning. Furthermore, in order to significantly reduce the combinatorial solution search space, a specific heuristic solution strategy is devised. This works by decomposing the original problem into successive optimization phases.One of the modeling challenges addressed in this work is to select the right network model for power flow and congestion evaluation: complex enough to capture the relevant features but simple enough to be computationally fast. Another relevant contribution is a domain-driven clustering process of snapshots which is based on a “moments” technique. Finally, the developed models, methods and solution strategies have been tested on standard and real-life systems. This thesis also presents numerical results of an aggregated 1060-node European network system considering multiple RES development scenarios. Generally, test results show the effectiveness of the proposed TEP model, since—as originally intended—it contributes to a significant reduction in computational effort while fairly maintaining optimality of the solutions. / Driven by several techno-economic, environmental and structural factors, the electric energy industry is expected to undergo a paradigm shift with a considerably increased level of renewables (mainly variable energy sources such as wind and solar), gradually replacing conventional power production sources. The scale and the speed of integrating such sources of energy are of paramount importance to effectively address a multitude of global and local concerns such as climate change, sustainability and energy security. In recent years, wind and solar power have been attracting large-scale investments in many countries, especially in Europe. The favorable agreements of states to curb greenhouse gas emissions and mitigate climate change, along with other driving factors, will further accelerate the renewable integration in power systems. Renewable energy sources (RESs), wind and solar in particular, are abundant almost everywhere, although their energy intensities differ very much from one place to another. Because of this, a significant integration of such energy sources requires heavy investments in transmission infrastructures. In other words, transmission expansion planning (TEP) has to be carried out in geographically wide and large-scale networks. This helps to effectively accommodate the RESs and optimally exploit their benefits while minimizing their side effects. However, the uncertain nature of most of the renewable sources, along with the size of the network systems, results in optimization problems that may become intractable in practice or require a huge computational effort. Thus, the challenge addressed in this work is to design models, strategies and tools that may solve large-scale and uncertain TEP problems, being computationally efficient and reasonably accurate. Of course, the specific definition of the term “reasonably accurate” is the key issue of the thesis work, since it requires a deep understanding of the main cost and technical drivers of adequate TEP investment decisions. A new formulation is proposed in this dissertation for a long-term planning of transmission investments under uncertainty, with a multi-stage decision framework and considering a high level of renewable sources integration. This multi-stage strategy combines the need for short-term decisions with the evaluation of long-term scenarios, which is the practical essence of a real-world planning. The TEP problem is defined as a stochastic mixed-integer linear programming (S-MILP) optimization, an exact solution method. This allows the use of effective off-the-shelf solvers to obtain solutions within a reasonable computational time, enhancing overall problem tractability. Furthermore, in order to significantly reduce the combinatorial solution search (CSS) space, a specific heuristic solution strategy is devised. In this global heuristic strategy, the problem is decomposed into successive optimization phases. Each phase uses more complex optimization models than the previous one, and uses the results of the previous phase so that the combinatorial solution search space is reduced after each phase. Moreover, each optimization phase is defined and solved as an independent problem; thus, allowing the use of specific decomposition techniques, or parallel computation when possible. A relevant feature of the solution strategy is that it combines deterministic and stochastic modeling techniques on a multi-stage modeling framework with a rolling-window planning concept. The planning horizon is divided into two sub-horizons: medium- and long-term, both having multiple decision stages. The first sub-horizon is characterized by a set of investments, which are good enough for all scenarios, in each stage while scenario-dependent decisions are made in the second sub-horizon. One of the first modeling challenges of this work is to select the right network model for power flow and congestion evaluation: complex enough to capture the relevant features but simple enough to be computationally fast. The thesis includes extensive analysis of existing and improved network models such as AC, linearized AC, “DC”, hybrid and pipeline models, both for the existing and the candidate lines. Finally, a DC network model is proposed as the most suitable option. This work also analyzes alternative losses models. Some of them are already available and others are proposed as original contributions of the thesis. These models are evaluated in the context of the target problem, i.e., in finding the right balance between accuracy and computational effort in a large-scale TEP problem subject to significant RES integration. It has to be pointed out that, although losses are usually neglected in TEP studies because of computational limitations, they are critical in network expansion decisions. In fact, using inadequate models may lead not only to cost-estimation errors, but also to technical errors such as the so-called “artificial losses”. Another relevant contribution of this work is a domain-driven clustering process to handle operational states. This allows a more compact and efficient representation of uncertainty with little loss of accuracy. This is relevant because, together with electricity demand and other traditional sources of uncertainty, the integration of variable energy sources introduces an additional operational variability and uncertainty. A substantial part of this uncertainty and variability is often handled by a set of operational states, here referred to as “snapshots”, which are generation-demand patterns of power systems that lead to optimal power flow (OPF) patterns in the transmission network. A large set of snapshots, each one with an estimated probability, is then used to evaluate and optimize the network expansion. In a long-term TEP problem of large networks, the number of operational states must be reduced. Hence, from a methodological perspective, this thesis shows how the snapshot reduction can be achieved by means of clustering, without relevant loss of accuracy, provided that a good selection of classification variables is used in the clustering process. The proposed method relies on two ideas. First, the snapshots are characterized by their OPF patterns (the effects) instead of the generation-demand patterns (the causes). This is simply because the network expansion is the target problem, and losses and congestions are the drivers to network investments. Second, the OPF patterns are classified using a “moments” technique, a well-known approach in Optical Pattern Recognition problems. The developed models, methods and solution strategies have been tested on small-, medium- and large-scale network systems. This thesis also presents numerical results of an aggregated 1060-node European network system obtained considering multiple RES development scenarios. Generally, test results show the effectiveness of the proposed TEP model, since—as originally intended—it contributes to a significant reduction in computational effort while fairly maintaining optimality of the solutions. / <p>QC 20160919</p>

Orientador: Rubén Augusto Romero Lázaro / Banca: José Roberto Sanches Mantovani / Banca: Antonio Padilha Feltrin / Banca: Luiz Carlos Pereira da Silva / Banca: Eduardo Nobuhiro Asada / Resumo: O Problema de Planejamento da Expansão de Sistemas de Transmissão de Energia Elétrica consiste em se escolher, entre um conjunto pré-definido de circuitos candidatos, aqueles que devem ser incorporados ao sistema de forma a minimizar os custos de investimento e operação ao e atender a demanda de energia futura ao longo de um horizonte de planejamento com confiabilidade, assumindo como conhecido o plano de geração. É considerado um problema muito complexo e difícil por se tratar de um problema não linear inteiro misto, não convexo, multimodal e altamente combinatório. Este problema tem sido solucionado usando técnicas clássicas como Decomposição ao de Benders e Branch and Bound, assim como também algoritmos heurísticos e metaheurísticas obtendo diversos resultados, mais com uma série de problemas como, por exemplo, alto esforço computacional e problemas de convergência. Neste trabalho apresentam-se duas novas técnicas de solução para o problema, a saber, as metaheurísticas Busca em Vizinhança Variável e a Busca Dispersa. A Busca em Vizinhança Variável é uma técnica baseada em trocas de estruturas de vizinhança dentro de um algoritmo de busca local, e a metaheurística Busca Dispersa, um método evolutivo que combina sistematicamente conjuntos de soluções para se obter solucões melhores. Essas técnicas de solução oferecem novas alternativas de solução que oferecem solução aos problemas encontrados com outros métodos, como é um baixo esforço computacional é uma melhor convergência, sendo este o principal aporte do trabalho. Os algoritmos são apresentados sistematicamente, explicando os seus algoritmos e a forma como são adaptados para resolver o problema do planejamento da expansão de sistemas de transmissão considerando-se a modelagem matemática conhecida com o modelo de transporte e o modelo DC. São realizados testes com os sistemas... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Electric Energy Transmission Network Expansion Problem consist in choose among a set of pre-defined circuits candidates, who must be incorporated into the system so as to minimize the investment costs and operation and meet the future energy demand over a planning horizon with reliability, assuming the generation plan is known. It is a very complex and difficult problem because it is non linear, non convex, multimodal and highly combinatorial. This problem has been solved using traditional techniques such as Benders decomposition and Branch and Bound, as well as heuristic algorithms and metaheuristics getting different results, but with a series of problems such as high computational effort and convergence problems. This paper tests out two new techniques for solving the problem as are the metaheuristics Variable Neighborhood Search and Scatter Search. The Variable Neighborhood Search is a technique based on trading structures within a neighborhood of a local search algorithm, and the Scatter Search metaheuristic is a method which combines systematically sets of solutions in an evolutionary way to achieve better solutions. These solution techniques offer new alternatives to solve the problems encountered with other methods, such as a low computational effort and better convergence, which is the main contribution of this work. The techniques are presented systematically, explaining their algorithms and the way they are adapted to solve the network expansion planning problem based on the mathematical model known as the transportation model and the DC model. They are tested with the systems Southern Brazilian with 46 buses and the IEEE 24 buses system, results are compared with those obtained with other metaheuristics, obtaining excellent results with a best performance both in processing speed as in computational effort. / Doutor

Thermal power plants dominate electric power generation in Taiwan, which causes high Green House gases (GHG) emissions. CO2 is the most important greenhouse gas that cause global warming and sea-level to rise. This paper faces the relationship between CO2 limitation and power generation expansion planning (GEP) for the utility. Modify Particle Swarm Optimization (MPSO) is presented to determine the generation expansion planning strategy of the utility with independent power providers (IPPs). The utility has to take both the IPPs¡¦ participation and environmental impact into account when a new generation unit is expanded. This problem also takes into account the CO2 reduction and reliability issues, while satisfying all electrical constraints simultaneously from the supply point of view. MPSO scheme was improved to avoid getting a premature answer. Testing results shows that MPSO can offer an efficient way in determining the generation expansion planning. Generation expansion planning is an important decision-making activity in a competitive market, all utilities including IPPs need to maximize the profit while meeting the load demand with a pre-specified reliability criterion. In order to achieve the objective, utilities will perform the generation expansion planning to determine the minimal-cost capacity power addition. For better economy and efficiency, they will consider options of either constructing new generating units or purchasing electricity from other utilities or IPPs.

The planning of distribution systems consists in determining the optimum site and size of new substations and feeders in order to satisfy the future power demand with minimum investment and operational costs and an acceptable level of reliability. This problem is a combinatorial, non-linear and constrained optimization problem. Several solution methods based on genetic algorithms have been reported in the literature; however, some of these methods have been reported with applications to small systems while others have long solution time. In addition, the vast majority of the developed methods handle planning problems simplifying them as single-objective problems but, there are some planning aspects that can not be combined into a single scalar objective; therefore, they require to be treated separately. The cause of these shortcomings is the poor representation of the potential solutions and their genetic operators This thesis presents the design of a genetic algorithm using a direct representation technique and specialized genetic operators for power distribution system expansion planning problems. These operators effectively preserve and exploit critical configurations that contribute to the optimization of the objective function. The constraints of the problems are efficiently handle with new strategies. The genetic algorithm was tested on several theoretical and real large-scale power distribution systems. Problems of network reconfiguration for loss reduction were also included in order to show the potential of the algorithm to resolve operational problems. Both single-objective and multi-objective formulations were considered in the tests. The results were compared with results from other heuristic methods such as ant colony system algorithms, evolutionary programming, differential evolution and other genetic algorithms reported in the literature. From these comparisons it was concluded that the proposed genetic algorithm is suitable to resolve problems of largescale power distribution system planning. Moreover, the algorithm proved to be effective, efficient and robust with better performance than other previous methods.

abstract: This research develops decision support mechanisms for power system operation and planning practices. Contemporary industry practices rely on deterministic approaches to approximate system conditions and handle growing uncertainties from renewable resources. The primary purpose of this research is to identify soft spots of the contemporary industry practices and propose innovative algorithms, methodologies, and tools to improve economics and reliability in power systems. First, this dissertation focuses on transmission thermal constraint relaxation practices. Most system operators employ constraint relaxation practices, which allow certain constraints to be relaxed for penalty prices, in their market models. A proper selection of penalty prices is imperative due to the influence that penalty prices have on generation scheduling and market settlements. However, penalty prices are primarily decided today based on stakeholder negotiations or system operator’s judgments. There is little to no methodology or engineered approach around the determination of these penalty prices. This work proposes new methods that determine the penalty prices for thermal constraint relaxations based on the impact overloading can have on the residual life of the line. This study evaluates the effectiveness of the proposed methods in the short-term operational planning and long-term transmission expansion planning studies. The second part of this dissertation investigates an advanced methodology to handle uncertainties associated with high penetration of renewable resources, which poses new challenges to power system reliability and calls attention to include stochastic modeling within resource scheduling applications. However, the inclusion of stochastic modeling within mathematical programs has been a challenge due to computational complexities. Moreover, market design issues due to the stochastic market environment make it more challenging. Given the importance of reliable and affordable electric power, such a challenge to advance existing deterministic resource scheduling applications is critical. This ongoing and joint research attempts to overcome these hurdles by developing a stochastic look-ahead commitment tool, which is a stand-alone advisory tool. This dissertation contributes to the derivation of a mathematical formulation for the extensive form two-stage stochastic programming model, the utilization of Progressive Hedging decomposition algorithm, and the initial implementation of the Progressive Hedging subproblem along with various heuristic strategies to enhance the computational performance. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

Orientador: Sergio Azevedo de Oliveira / Banca: Rubén Augusto Romero Lázaro / Banca: Antônio César Baleeiro Alves / Resumo: Neste trabalho propõe-se o desenvolvimento de uma interface gráfica voltada para a resolução do problema de planejamento da expansão das linhas de transmissão, que utiliza-se de programas para a obtenção das soluções ótimas ou de boas soluções. A grande dificuldade encontrada por usuários ao interagir com estes programas, motivou o desenvolvimento de uma aplicação com interface gráfica, a qual disponibiliza ao usuário diversas metodologias para a resolução do problema do planejamento da expansão das linhas de transmissão de energia elétrica. Espera-se que software como este, agregado ao sistema de ensino tradicional que normalmente 'e utilizado num curso de engenharia elétrica, possa fortalecer o processo de aprendizagem do aluno. Assim, o software educacional Transmission Expansion Planning (TEP 1.0) está sendo desenvolvido visando propiciar um ambiente agradável para a realização de simulações e testes e favorecer a aprendizagem dos conceitos que envolvem o problema do planejamento da expansão da transmissão. É possível através deste software fazer simulações para os sistemas: Garver (6 barras/ 15 ramos), Sul brasileiro (46 barras/79 ramos) e Norte-Nordeste brasileiro (87 barras/179 ramos). O software se beneficia de recursos oferecidos por programas que são executados em background, bem como da utilização de meta-heurísticas e do ambiente de processamento de máquinas paralelas virtuais, as quais podem ser selecionadas para realização dos testes em determinado sistema / Abstract: This work proposes the development of a computational tool aimed at solving the problem of the transmission expansion planning, which uses programs in the background to obtain optimal solutions or good solutions. The great difficulty for users to interact with these programs, motivated the development of an application with a graphical interface, which provides the user with various methodologies for solving the problem of expansion planning of transmission lines of electricity. It is expected that software like this, added to the traditional school system which is normally used in electrical engineering courses, can strengthen the process of student learning. Thus, the educational software Transmission Expansion Planning (TEP 1.0) is being developed to provide a pleasant environment for simulations and testing and promote the learning of concepts involving the issue of transmission expansion planning. It is possible using this software to do simulations for the systems: Garver (6 nodes / 15 branches), South Brazilian (46 nodes / 79 branches) and North-Northeast Brazilian (87 nodes / 179 branches). The software takes advantage of capabilities offered by programs that run on background, and using meta-heuristics and the processing environment, parallel virtual machine, which can be selected to test on a system / Mestre