AN ABSTRACT OF THE THESIS OF Mohamed A. Abuella, for the Master of Science degree in Electrical and Computer Engineering, presented on May 10, 2012, at Southern Illinois University Carbondale. TITLE:STUDY OF PARTICLE SWARM FOR OPTIMAL POWER FLOW IN IEEE BENCHMARK SYSTEMS INCLUDING WIND POWER GENERATORS MAJOR PROFESSOR: Dr. C. Hatziadoniu, The aim of this thesis is the optimal economic dispatch of real power in systems that include wind power. The economic dispatch of wind power units is quite different of conventional thermal units. In addition, the consideration should take the intermittency nature of wind speed and operating constraints as well. Therefore, this thesis uses a model that considers the aforementioned considerations in addition to whether the utility owns wind turbines or not. The optimal power flow (OPF) is solved by using one of the modern optimization algorithms: the particle swarm optimization algorithm (PSO). IEEE 30-bus test system has been adapted to study the implementation PSO algorithm in OPF of conventional-thermal generators. A small and simple 6-bus system has been used to study OPF of a system that includes wind-powered generators besides to thermal generators. The analysis of investigations on power systems is presented in tabulated and illustrative methods to lead to clear conclusions.

[pt] O sistema elétrico é planejado para operar na condição mais confiável possível de atendimento, sem que sejam violadas as restrições operativas a que está sujeito. Para a busca da região segura de operação utilizam-se vários programas de otimização e simulação. O Fluxo de Potência Ótimo (FPO) é uma ferramenta muito útil para a busca do ponto de operação seguro. Mas, devido a sua complexidade computacional para solução das restrições não-lineares, possui resposta relativamente lenta, impossibilitando a sua utilização em diversos casos práticos, tanto em tempo real, onde existe a necessidade de rapidez de resposta, como no planejamento da operação, quando é necessário um grande número de execuções do FPO. Em contrapartida, o Fluxo de Potência Ótimo Linearizado (FPO DC), devido a sua simplicidade computacional, possui grande rapidez de resposta. Mas, nas suas aproximações não são consideradas as restrições reativas, o que pode levar a uma solução em que estas sejam violadas. Este trabalho apresenta uma metodologia que incorpora ao Fluxo de Potência Ótimo Linearizado restrições reativas em função da potência ativa do geradores, de tal forma que a solução fornecida por este modelo leve em consideração as restrições de tensão e potência reativa. / [en] The electrical network is planned to stay in the most reliable operation conditions, without any violated operation constraints. Many optimization and simulation models are used to define the safe operation region. The Optimal Power Flow (OPF) is a useful tool for the search of the best safe operation point. However, the computational complexity associated to its non-linear constraints implies in a heavy computation time and makes it difficult to be used whenever a fast response is required, as in on-line oporation or even most operation planning problems. On the other hand, the Linearized Optimal Power Flow (LOPF), due to its simplicity, is a very fast tool. However, it does not consider reactive (voltage) constraints, and is not able to detect any violation. This work presents a model to incorporate voltage constraints in the Linearized Power Flow.

Orientador: Prof. Dr. Edmarcio Antônio Belati / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia Elétrica, 2014. / Esse trabalho apresenta uma análise da introdução de controladores FACTS no sistema elétrico através do fluxo de potência ótimo. O fluxo de potência ótimo foi modelado utilizando as ferramentas AMPL e o solver Knitro. Nos estudos feitos buscouse determinar a quantidade de reativos e também o ponto ótimo de instalação desses dispositivos. Para realizar a análise, os estudos foram feitos em três cenários de carga diferentes e com três funções objetivos diferentes. Com análises feitas nos sistemas IEEE 14 e 118 barras, foi possível notar uma melhora no sistema quando temos os controladores FACTS conectados. Observou-se que o ponto ideal de instalação de fonte de reativos depende da função objetivo e da carga do sistema. Com isso, demonstra-se que a introdução desse tipo de dispositivo pode aumentar a capacidade do sistema elétrico, suprindo o aumento da demanda de potência reativa. / This study presents an analysis of the introduction of different FACTS controllers in the electrical system by optimal power flow. For the analysis we used an optimal power flow in the system using the tools AMPL and solver Knitro. In studies of the electrical system were made seeking not only determine the amount of reactive, but also determine the optimum installation location of these devices. To perform the analysis studies were performed in three different load scenarios and with three different objectives functions. With analyzes the IEEE systems 14 and 118 bars, it was possible to notice an improvement in the system when we have the FACTS controllers connected. It was observed that the ideal location of reactive depends of the objective function and system load. Thus it is shown that the introduction of this type of device can be made to meet the increased demand of reactive power.

Submitted by ELAYNNE XAVIER SOUZA ARAÚJO null (elaynnearaujo@hotmail.com) on 2018-03-13T18:51:38Z No. of bitstreams: 1 Tese_Final.pdf: 5331631 bytes, checksum: 60e1011da397d7e88cc9d80319169d76 (MD5) / Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2018-03-14T12:06:56Z (GMT) No. of bitstreams: 1 araujo_exs_dr_ilha.pdf: 5331631 bytes, checksum: 60e1011da397d7e88cc9d80319169d76 (MD5) / Made available in DSpace on 2018-03-14T12:06:56Z (GMT). No. of bitstreams: 1 araujo_exs_dr_ilha.pdf: 5331631 bytes, checksum: 60e1011da397d7e88cc9d80319169d76 (MD5) Previous issue date: 2018-02-23 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Neste trabalho é proposto o desenvolvimento de uma ferramenta computacional para o planeja-mento e despacho ótimo de fontes de potência ativa, considerando as incertezas das cargas (le-ve, nominal e pesada) e fontes de energia renováveis não despacháveis através de uma aborda-gem probabilística. O modelo matemático é um problema de programação não linear inteiro misto, multiobjetivo, não convexo e probabilístico na sua forma original sem a necessidade de realizar qualquer tipo de simplificação ou linearização tanto das funções objetivo como das res-trições. Um algoritmo baseado na meta-heurística Non-dominated Sorting Genetic Algorithm (NSGA-II) é proposto para resolver o problema de maneira eficaz. Os resultados obtidos com as simulações realizadas usando a implementação computacional nos sistemas de testes IEEE30 barras e IEEE118 barras mostram a eficiência e robustez da metodologia proposta. / This work proposes the development of a computational tool for the planning and optimal dispatch of active power sources, considering the uncertainties of the loads (light, nominal and heavy) and non-dispatchable renewable energy sources through a probabilistic approach. The mathematical model is a multi-objective mixed-integer nonlinear programing problem, that is nonconvex and probabilistic in its original form, without the need to perform any kind of simplification or linearization of both objective functions and constraints. An algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) meta-heuristic is pro-posed to solve the problem effectively. The results obtained with the simulations performed using the computational implementation in the IEEE30 bus and IEEE118 bus test systems show the efficiency and robustness of the proposed methodology. / 167761/2014-5

abstract: Due to great challenges from aggressive environmental regulations, increased demand due to new technologies and the integration of renewable energy sources, the energy industry may radically change the way the power system is operated and designed. With the motivation of studying and planning the future power system under these new challenges, the development of the new tools is required. A network equivalent that can be used in such planning tools needs to be generated based on an accurate power flow model and an equivalencing procedure that preserves the key characteristics of the original system. Considering the pervasive use of the dc power flow models, their accuracy is of great concern. The industry seems to be sanguine about the performance of dc power flow models, but recent research has shown that the performance of different formulations is highly variable. In this thesis, several dc power-flow models are analyzed theoretically and evaluated numerically in IEEE 118-bus system and Eastern Interconnection 62,000-bus system. As shown in the numerical example, the alpha-matching dc power flow model performs best in matching the original ac power flow solution. Also, the possibility of applying these dc models in the various applications has been explored and demonstrated. Furthermore, a novel hot-start optimal dc power-flow model based on ac power transfer distribution factors (PTDFs) is proposed, implemented and tested. This optimal-reactance-only dc model not only matches the original ac PF solution well, but also preserves the congestion pattern obtain from the OPF results of the original ac model. Three improved strategies were proposed for applying the bus-aggregation technique to the large-scale systems, like EI and ERCOT, to improve the execution time, and memory requirements when building a reduced equivalent model. Speed improvements of up to a factor of 200 were observed. / Dissertation/Thesis / M.S. Engineering 2013

abstract: This thesis focuses on developing an integrated transmission and distribution framework that couples the two sub-systems together with due consideration to conventional demand flexibility. The proposed framework ensures accurate representation of the system resources and the network conditions when modeling the distribution system in the transmission OPF and vice-versa. It is further used to develop an accurate pricing mechanism (Distribution-based Location Marginal Pricing), which is reflective of the moment-to-moment costs of generating and delivering electrical energy, for the distribution system. By accurately modeling the two sub-systems, we can improve the economic efficiency and the system reliability, as the price sensitive resources can be controlled to behave in a way that benefits the power system as a whole. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2014

To integrate large volumes of renewables and use electricity more efficiently, many industrial trials are on-going around the world that aim to realize decentralized or hierarchical control of renewable and distributed energy resources, flexible loads and monitoring devices. As the cost and complexity involved in the centralized communications and control infrastructure may be prohibitive in controlling millions of these distributed energy resources and devices, distributed optimization methods are expected to become much more prevalent in the operation of future electric power systems, as they have the potential to address this challenge and can be applied to various applications such as optimal power ow, state estimation, voltage control, and many others. While many distributed optimization algorithms are developed mathematically, little effort has been reported so far on how these methods should actually be implemented in real-world large-scale systems. The challenges associated with this include identifying how to decompose the overall optimization problem, what communication infrastructures can support the information exchange among subproblems, and whether to coordinate the updates of the subproblems in a synchronous or asynchronous manner. This research is dedicated to developing mathematical tools to address these issues, particularly for solving the non-convex optimal power flow problem. As the first part of this thesis, we develop a partitioning method that defines the boundaries of regions when applying distributed algorithms to a power system. This partitioning method quantifies the computational couplings among the buses and groups the buses with large couplings into one region. Through numerical experiments, we show that the developed spectral partitioning approach is the key to achieving fast convergence of distributed optimization algorithms on large-scale systems. After the partitioning of the system is defined, one needs to determine whether the communications among neighboring regions are supported. Therefore, as the second part of this thesis, we propose models for centralized and distributed communications infrastructures and study the impact of communication delays on the efficiency of distributed optimization algorithms through network simulations. Our findings suggest that the centralized communications infrastructure can be prohibitive for distributed optimization and cost-effective migration paths to a more distributed communications infrastructure are necessary. As the sizes and complexities of subproblems and communication delays are generally heterogeneous, synchronous distributed algorithms can be inefficient as they require waiting for the slowest region in the system. Hence, as the third part of this thesis, we develop an asynchronous distributed optimization method and show its convergence for the considered optimal power flow problem. We further study the impact of parameter tuning, system partitioning and communication delays on the proposed asynchronous method and compare its practical performance with its synchronous counterpart. Simulation results indicate that the asynchronous approach can be more efficient with proper partitioning and parameter settings on large-scale systems. The outcome of this research provides important insights into how existing hardware and software solutions for Energy Management Systems in the power grid can be used or need to be extended for deploying distributed optimization methods, which establishes the interconnection between theoretical studies of distributed algorithms and their practical implementation. As the evolution towards a more distributed control architecture is already taking place in many utility networks, the approaches proposed in this thesis provide important tools and a methodology for adopting distributed optimization in power systems.

The co-optimization of energy and reserves has become a standard requirement in integrated markets. This is due to the inverse relationship that exists between energy and reserves. The provision of reserves generally reduces the amount of primary energy a generating unit can produce and vice versa. This suggests that these products should be procured through a simultaneous auction to ensure optimal procurement and pricing. Furthermore, forward markets dictate that this co-optimization of energy and reserves be done over a multi-period planning horizon. This dissertation addresses the problem of optimal scheduling and pricing of energy and reserves over a multi-period planning horizon using an optimal power flow formulation. The extension of the problem from a static optimization problem to a dynamic optimization problem is presented. Price definitions for energy and reserves in terms of shadow prices emanating from the optimization algorithm are provided. It is shown that the proposed formulation of prices leads to the cascading of reserve prices and eliminates the problem of “price reversal” where lower quality reserves are priced higher than higher ii quality reserves. Pricing conditions are also established for the downward substitution of higher quality reserves for lower quality reserves. The proposed pricing formulations are tested on the IEEE 24 Bus Reliability Test System and on the South African power network. The simulated results show that cascading of reserve prices does occur and that prices of different types of reserves are equal when downward substitution of reserves occurs. Zonal reserve requirements result in higher energy and reserve prices, which in term result in higher procurement costs to the system operator and higher profits to market participants. Congestion on the network also results in higher procurement costs to the system operator and higher profits to market participants in the case of zonal pricing of reserves. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

Transition towards low-carbon energy systems requires an increase in the volume of renewable Distributed Generation (DG), particularly wind and photovoltaic, connected to distribution networks. To facilitate the connection of renewable DG without the need for expensive and time-consuming network reinforcements, distribution networks should move from passive to active methods of operation, whereby technical network constraints are actively managed in real time. This requires the deployment of control solutions that manage network constraints and, crucially, ensure adequate levels of energy curtailment from DG plants by using other controllable elements to solve network issues rather than resorting to generation curtailment only. This thesis proposes a deterministic distribution Network Management System (NMS) to facilitate the connections of renewable DG plants (specifically wind) by actively managing network voltages and congestion in real time through the optimal control of on-load tap changers (OLTCs), DG power factor and, then, generation curtailment as a last resort. The set points for the controllable elements are found using an AC Optimal Power Flow (OPF). The proposed NMS considers the realistic modelling of control by adopting one-minute resolution time-series data. To decrease the volumes of control actions from DG plants and OLTCs, the proposed approach departs from multi-second control cycles to multi-minute control cycles. To achieve this, the decision-making algorithm is further improved into a risk-based one to handle the uncertainties in wind power throughout the multi-minute control cycles. The performance of the deterministic and the risk-based NMS are compared using a 33 kV UK distribution network for different control cycles. The results show that the risk-based approach can effectively manage network constraints better than the deterministic approach, particularly for multi-minute control cycles, reducing also the number of control actions but at the expense of higher levels of curtailment. This thesis also proposes energy storage sizing framework to find the minimum power rating and energy capacity of multiple storage facilities to reduce curtailment from DG plants. A two-stage iterative process is adopted in this framework. The first stage uses a multi-period AC OPF across the studied horizon to obtain initial storage sizes considering hourly wind and load profiles. The second stage adopts a high granularity minute-by-minute control driven by a mono-period bi-level AC OPF to tune the first-stage storage sizes according to the actual curtailment. The application of the proposed planning framework to a 33 kV UK distribution network demonstrates the importance of embedding real-time control aspects into the planning framework so as to accurately size storage facilities. By using reactive power capabilities of storage facilities it is possible to reduce storage sizes. The combined active management of OLTCs and power factor of DG plants resulted in the most significant benefits in terms of the required storage sizes.

No / Due to high penetration of single-phase Photovoltaic (PV) cells into low voltage (LV) distribution networks, several impacts such as voltage unbalance, voltage rise, power losses, reverse power flow arise which leads to operational constraints violation in the network. In this paper, a time series Three Phase Optimal Power Flow (TPOPF) method is proposed to minimize the voltage unbalance in LV distribution networks with high penetration of residential PVs. TPOPF problem is formulated using the current injection method in which the PVs are modelled via a time-varying PV power profile with active and reactive power control. The proposed method is validated on a real LV distribution feeder. The results show that the reactive power management of the PVs helps mitigate the voltage unbalance significantly. Moreover, the voltage unbalance index reduced significantly compared to the case without voltage unbalance minimisation. / Innovate UK GCRF Energy Catalyst Pi-CREST project under Grant number 41358; British Academy GCRF COMPENSE project under Grant GCRFNGR3\1541; Mut’ah University, Jordan