Implementation of Hour-by-Hour Restoration Plan for Electrical Distribution Networks to improve Network Resiliency

After extreme natural events like earthquakes, storms, and floods, the electrical distribution network may be isolated from the upstream grid. In such a situation, the electrical power of the upstream grid is lost and the available microgrids (MGs) are the only power sources. Since the power outputs of MGs are restricted, restoring all de-energized loads is impossible and restoring critical loads (CLs) becomes the most important concern of the distribution network operators. Therefore, in this paper, a new hour-by-hour restoration strategy for CLs restoration after a blackout is proposed. The desired objective functions include restored energy and switching operations. The proposed restoration strategy presents a restoration plan for each hour of the outage period. The efficiency of the proposed strategy is carried out on an IEEE 123-bus distribution network and the simulation results confirm the superiority of the proposed hour-by-hour restoration strategy over the other conventional restoration methods

Microgrid Formation-based Service Restoration Using Deep Reinforcement Learning and Optimal Switch Placement in Distribution Networks

A power distribution network that demonstrates resilience has the ability to minimize the duration and severity of power outages, ensure uninterrupted service delivery, and enhance overall reliability. Resilience in this context refers to the network's capacity to withstand and quickly recover from disruptive events, such as equipment failures, natural disasters, or cyber attacks. By effectively mitigating the effects of such incidents, a resilient power distribution network can contribute to enhanced operational performance, customer satisfaction, and economic productivity. The implementation of microgrids as a response to power outages constitutes a viable approach for enhancing the resilience of the system. In this work, a novel method for service restoration based on dynamic microgrid formation and deep reinforcement learning is proposed. To this end, microgrid formation-based service restoration is formulated as a Markov decision process. Then, by utilizing the node cell and route model concept, every distributed generation unit equipped with the black-start capability traverses the power system, thereby restoring power to the lines and nodes it visits. The deep Q-network is employed as a means to achieve optimal policy control, which guides agents in the selection of node cells that result in maximum load pick-up while adhering to operational constraints. In the next step, a solution has been proposed for the switch placement problem in distribution networks, which results in a substantial improvement in service restoration. Accordingly, an effective algorithm, utilizing binary particle swarm optimization, is employed to optimize the placement of switches in distribution networks. The input data necessary for the proposed algorithm comprises information related to the power system topology and load point data. The fitness of the solution is assessed by minimizing the unsupplied loads and the number of switches placed in distribution networks. The proposed methods are validated using a large-scale unbalanced distribution system consisting of 404 nodes, which is operated by Saskatoon Light and Power, a local utility in Saskatoon, Canada. Additionally, a balanced IEEE 33-node test system is also utilized for validation purposes

Estudo e implementação de um esquema de self-healing em sistemas modernos de distribuição de energia elétrica

Orientador: Marcos Julio Rider FloresTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O objetivo deste trabalho é projetar e implementar um software eficiente de self-healing em sistemas de distribuição de energia elétrica trifásicos, usando as leituras dos medidores inteligentes instalados na rede e considerando a operação dos geradores distribuídos (GD). Self-healing é a capacidade de um sistema de distribuição para se restaurar automaticamente após a identificação e isolamento de uma falta permanente na rede. Em função dos parâmetros do sistema e das medidas, o esquema de self-healing proposto deve: a) estimar as demandas dos nós, no estado de pré e pós-falta, através de um estimador de estado trifásico e um modelo de previsão da demanda ao curto prazo, b) identificar a zona da rede onde existe uma falta permanente, e c) gerar a sequência de operações das chaves instaladas ao longo do sistema para isolar a zona com falta e restaurar o serviço de energia do maior número de usuários desenergizados, no menor tempo possível, e com mínima intervenção humana. Foi desenvolvida uma ferramenta computacional com um entorno gráfico amigável capaz de ler e processar os dados elétricos e geográficos das redes trifásicas, os parâmetros das fontes de GD, as leituras dos medidores inteligentes, e o estado de operação das chaves remotamente controláveis. Um algoritmo de estimação de estado trifásico determinará continuamente as injeções de potência nos nós em função das medidas registradas pelos medidores. Em caso de falta permanente, um modelo de localização de faltas, baseado nas leituras dos medidores e dos indicadores de falta instalados na rede, fornecerá o local aproximado da falta. Após localizar a falta e, segundo o valor das demandas estabelecidas pelo estimador de estado, o esquema de restauração fornecerá a sequência de operações das chaves para levar o sistema até um estado restaurativo eficiente. Modelos de otimização matemática serão desenvolvidos para representar o estimador de estado e o problema de restauração trifásica, respectivamente. O método de localização de falta utilizado será uma versão melhorada do método basedo em medida esparsas e a matriz de impedância das barras. A meta-heurística Tabu Search será utilizada para resolver os modelos de otimização propostos. O modelo ARIMA será utilizado para a previsão da demanda. O software de self-healing será testado utilizando sistemas reaisAbstract: The main objective of this theses is to design and to implement a centralized self-healing software for unbalanced three-phase electrical distribution systems (EDS), using the information provided by smart meters and considering distributed generation (DG). Self-healing is the ability of the EDS to automatically restore themselves in case of a permanent fault. According to the data gathered by smart meters and the EDS's parameters, the proposed self-healing software is able to: a) estimate the nodal demands during the pre and post-fault status, using a three-phase state estimator and a short-term load forecasting method, b) identify the zone wherein a permanent fault is located, and c) generate the sequence of operations that must be deployed by the remote-controlled switches installed along the system. Ultimately, the self-healing scheme will isolate the faulty section of the network and restore the service of as many customers as possible, in the least amount of time and with minimal human intervention. The proposed self-healing software will have a friendly graphical user interface to simplify the data acquisition process and to present the results, considering geographic data, dispatchable DG units, smart meters and remote-controlled switching devices. A three-phase state estimator will continuously calculate the power demands at the nodes. In case of a permanent fault, the fault location algorithm will use the smart meters' data and the fault indicators signals to establish the zone where a permanent fault is most probably located. After finding the faulty section of the system and the estimated post-fault demands, an optimal service restoration will be deployed in order to determine the sequence of switch operations. Mathematical optimization models will be used to represent the three-phase state estimator and the service restoration process. An enhanced bus-impedance-matrix-based fault-location method will be also implemented. The methodology used to solve the optimization models will be the metaheuristic \emph{Tabu {Search}}. The short-term load forecasting method will be an adaptation of the seasonal ARIMA models. The proposed self-healing scheme will be tested using real EDSDoutoradoEnergia EletricaDoutor em Engenharia Elétrica2015/12564-1FAPES

Black Start Restoration for Electric Distribution Systems and Microgrids

Modern power systems face increased risk of wide area blackouts caused by extreme weather events, man-made errors, cyber-attacks, and other threats. In order to promptly restore power after a blackout, an efficient black start restoration methodology should be developed. Emerging smart grid technologies such as remote control switches (RCSs) and distributed energy resources (DERs) present significant potential that can be leveraged for developing advanced black start restoration methodologies. In this dissertation research, a new black start restoration (BSR) method, which could be used in distribution management system (DMS) or microgrid control center (MGCC), was presented. The BSR problem was formulated as a dynamic optimization problem in order to coordinate the dispatching actions of DERs and the switching actions of RCSs over multiple decision time steps. Several linearization techniques were presented to reformulate the dynamic optimization model as a mixed-integer linear programming (MILP) model. The rolling-horizon functionality was used to reduce the computation time. The MILP mode was simulated in MATLAB® using YALMIP Toolbox and solved in the IBM CPLEXTM solver. Several case studies were conducted to illustrate how the new BSR method works on the modified IEEE 13 node and 123 node systems installed with dispatchable distributed generations (DGs), renewable DGs, energy storage systems (ESSs), and RCSs. OpenDSS was used to simulate power flow, and PSCAD™/EMTDC™ was used to simulate frequency response. The new BSR method was able to generate black start sequences in response to varying operating conditions such as balanced and unbalanced conditions, cold load pick up (CLPU) conditions, and different fault scenarios. The performance of the new BSR method was analyzed through extensive case studies. It was observed that using properly selected rolling-horizon parameters could reduce the computation time and achieve near-optimal solutions. However, some operating conditions resulted in infeasible solutions, such as limited DG ramp rate and capacity, heavy loading conditions, and excessive fluctuation of renewable DG outputs and load demands. The new BSR method can be further improved by incorporating a method for determining the rolling-horizon parameters without conducting exhaustive case studies and developing an integrated structure to coordinate with DG primary controls

Estrutura multiagentes para autorrecuperação de redes elétricas inteligentes

Electricity distribution systems represent the final link between energy-generating companies and consumers. Having radial characteristics, they have lower reliability, so a failure in some components causes the interruption of the power supply. Even with the development of new technologies to improve the operation and maintenance of these systems, losses and interruptions are inevitable. When the power supply is out of the supply due to a failure, the system must be re-established to meet the largest number of loads safely and in the shortest possible time interval. The current distribution network is at a stage of development known as the smart grid, using advances in information and communication technologies. This thesis presents a contribution to implementing self-healing strategies of distribution systems in a decentralized way, which brings as main advantages the good cost-benefit ratio, the improvement in reliability, and the possibility of incremental expansion of the system. The proposed system is based on an intelligent multiagent structure, in which each agent has a logical structure that is classified as hierarchical and hybrid. Hierarchical, because the actions are divided into four logical levels: instinct, normal operation, abnormal operation, and optimization and prediction. And hybrid, because in addition to the logical part, the rules can trigger numerical routines that help in the agents' decision-making process. In addition to self healing, several demand response functions are part of the agent, such as overload, load cutting, load prioritization, and load evolution. These functions allow the self- healing squealing solution found to consider the current system load and its evolution in the coming hours. In the proposed multiagent structure, agents only communicate with agents adjacent to it, reducing the need for a more robust communication system covering long distances. It is also proposed a structure of communication and exchange of messages with redundancy and effectiveness, mitigating the risks of erroneous communication between agents. Even with this type of communication, agent action occurs locally but is based on data received from multiple (even non-adjacent) system agents. The agent also implements functions that allow it to interact with the traditional protection systems of urban distribution networks, monitoring the operation of fuses, reclosers, and sectionalizers, allowing the advantages of an intelligent electrical network, even in a still incomplete system. The proposed multiagent system is validated through several examples using energy distribution systems and the computational implementation of the proposed hybrid hierarchical intelligent agents working together.Os sistemas de distribuição de energia elétrica representam o elo final entre as empresas geradoras de energia e os consumidores. Tendo características radiais, eles apresentam uma menor confiabilidade, de forma que a falha em algum componente causa a interrupção do fornecimento de energia. Mesmo com o desenvolvimento de novas tecnologias para melhoria na operação e manutenção destes sistemas, falhas e interrupções são inevitáveis. Quando ocorre a interrupção no suprimento de energia devido a uma falha, é necessário que seja feito o restabelecimento do sistema para atender a maior quantidade de cargas, com segurança e no menor intervalo de tempo possível. A rede de distribuição atual está em um estágio de desenvolvimento conhecido como rede elétrica inteligente, utilizando os avanços das tecnologias de informação e comunicação. Esta tese apresenta uma contribuição na implementação de estratégias de autorrecuperação dos sistemas de distribuição de forma descentralizada, que traz como principais vantagens a boa relação custo-benefício, a melhoria na confiabilidade e a possibilidade de expansão incremental do sistema. O sistema proposto é baseado em uma estrutura multiagente inteligente, no qual cada agente possui uma estrutura lógica que é classificada como hierárquica e híbrida. Hierárquica, pois as ações estão divididas em quatro níveis lógicos, a saber: de instinto, de operação normal, de operação anormal e de otimização e predição. E híbrida, pois além da parte lógica, as regras podem acionar rotinas numéricas que auxiliam no processo de tomada de decisão dos agentes. Várias funções de resposta à demanda, além das de autorrecuperação, fazem parte do agente, tais como: de sobrecarga, de corte de carga, de priorização de carga e de evolução da carga. Estas funções permitem que a solução de chaveamento da autorrecuperação encontrada leve em consideração não somente a carga atual do sistema, mas a sua evolução nas próximas horas. Na estrutura multiagente proposta, os agentes só se comunicam com os agentes adjacentes a ele, reduzindo a necessidade de um sistema de comunicação mais robusto e que cubra longas distâncias. Também é proposta uma estrutura de comunicação e troca de mensagens com redundância e eficácia, mitigando os riscos de uma comunicação errônea entre agentes. Mesmo com este tipo de comunicação, a ação do agente ocorre localmente, mas baseado em dados recebido de vários agentes (mesmo não adjacentes) do sistema. Existem também funções implementadas no agente que permitem que ele interaja com os sistemas de proteção tradicionais das redes urbanas de distribuição, monitorando o funcionamento de fusíveis, religadores e seccionalizadores, permitindo que se tenha as vantagens de uma rede elétrica inteligente, mesmo em um sistema ainda incompleto. O sistema multiagente proposto é validado através de vários exemplos com a utilização de sistemas de distribuição de energia e também com a implementação computacional dos agentes inteligentes hierárquicos híbridos propostos trabalhando em conjunto