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

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>

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

Electricity transmission network investments are playing a key role in the integration process of power systems in the European Union. Given the magnitude of investment costs, their irreversibility, and their impact in the overall development of a region, accounting for the role of uncertainties as well as the involvement of multiple parties in the decision process allows for improved and more robust investment decisions. Even though the creation of this internal energy market requires attention to flexibility and strategic decision-making, existing literature and practitioners have not given proper attention to these topics. Using portfolios of real options, we present two stochastic mixed integer linear programming models for transmission network expansion planning. We study the importance of explicitly addressing uncertainties, the option to postpone decisions and other sources of flexibility in the design of transmission networks. In a case study based on the Azores archipelago we show how renewables penetration can increase by introducing contingency planning into the decision process considering generation capacity uncertainty. We also present a two-party Nash-Coase bargaining transmission capacity investment model. We illustrate optimal fair share cost allocation policies with a case study based on the Iberian market. Lastly, we develop a new model that considers both interconnection expansion planning under uncertainty and cross-border cost allocation based on portfolios of real options and Nash-Coase bargaining. The model is illustrated using Iberian transmission and market data.

No description available.

Tese de Programa Doutoral. Sistemas Sustentáveis de Energia. Universidade do Porto. Faculdade de Engenharia. 2011

Changes in the electric power industry have brought great challenges and uncertainties in transmission planning area. More effective planning of transmission grids with the appropriate development of advanced planning technologies is badly-needed. The aim of this research is to develop an advanced probabilistic transmission expansion planning (TEP) methodology in a continually changing market environment. The methodology should be able to strengthen and increase the robustness of existing transmission network. By using the proposed probabilistic TEP methodology, it can reduce the risks of major outages and identify weak buses in the system. The significance of this research is shown by its comprehensiveness and powerful practicability. Results from this research are able to improve the planning efficiency and reliability with consideration of financial risks in an electricity market. In order to achieve the target, this research methodologies focused on two main important issues, (1) probability based technical assessment and (2) financial investment evaluation. During the first stage study, probabilistic congestion management, probabilistic reliability evaluation and probabilistic load flow for TEP under uncertainties have been investigated and improved. The developed methodologies and indices, which truly represent the composite impact from both critical state and probability, have linked with financial terms. At financial investment evaluation part, Monte Carlo market simulation is performed to assist economic analysis. The overall planning process has been treated as a constrained multi-objective optimisation task. Comprehensive investigations are conducted on several test systems and testified by real power systems using the available reliability data and economic information from the Australian National Electricity Market (NEM). Overall, this research developed probabilistic transmission planning methodologies that can reflect modern market structures more accurately and it enable a greater utilization of current generation and transmission resources to increase potential operation efficiencies.

Made available in DSpace on 2014-06-11T19:22:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-11-20Bitstream added on 2014-06-13T18:08:26Z : No. of bitstreams: 1 proto_ab_me_ilha.pdf: 2777482 bytes, checksum: ac3152da7c340dcf6ab04c187cabe418 (MD5) / 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 / 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