Dynamic Distribution System Reconfiguration to Improve System Reliability Considering Renewables and Energy Storage

O desenvolvimento económico e o crescente uso de novas tecnologias por parte dos consumidores fazem com que o fornecimento de energia, bem como a sua qualidade, se tornem uma séria preocupação. Uma forma de abordar essa preocupação é implementando sistemas de distribuição automatizados com tecnologias inteligentes para melhorar a fiabilidade e a eficiência da operação de sistema. Os sistema eléctricos actuais estão em evolução devido às novas funcionalidades que o sistema eléctrico deverá ter, nomeadamente a integração de fontes de energia renováveis em grande escala, a integração de veículos eléctricos, a implementação de tecnologias de redes inteligentes, entre outras. Neste cenário, os Sistemas de Distribuição Inteligentes (SDI) devem operar e restaurar o serviço interrompido aos consumidores. Para que o sistema ganhe esta capacidade, é necessário substituir os interruptores manuais por interruptores controlados de forma remota, melhorando a capacidade de restauração do sistema tendo em vista a implementação de redes inteligentes. Este trabalho tem como objectivo desenvolver um novo modelo, determinando o conjunto mínimo de interruptores a substituir para automatizar o sistema, juntamente com uma análise de sensibilidade sobre a posição dos novos interruptores, que podem ser colocados no mesmo local dos substituídos ou num novo local. A optimização do sistema é feita considerando a integração de fontes de energia renováveis na rede e sistemas de armazenamento de energia, simultaneamente com os requisitos económicos e funcionais do sistema. A ferramenta computacional é testada usando o sistema de teste IEEE 119 Bus, onde são considerados vários tipos de carga (residencial, comercial e industrial).The economic development and the use of more and more technologies by the consumers make the constant energy supply and quality become a critical concern. One way to address this concern is through the implementation of automated distribution systems with intelligent technologies to improve the systems reliability and efficiency in operation. The present electrical systems are evolving due to the new functionalities that the electrical system are expected to have, namely the integration of renewable energy sources in large-scale, the integration of electric vehicles, enable smart grid technologies, among others. In this scenario, Distributed Smart Systems (DSS) should operate and restore discontinued service to consumers. In order to the system gain theses ability is necessary replace the manual switches for remotely controlled switches, improving the system restoration capability having in view the Smart Grids implementation. This paper aims to develop a new model, determining the minimal set of switches to replace in order to automate the system, along with a senility analysis on the position of the new switches, whether it should be placed in the same place as the manual switch or in a new location. The optimization of the system is made considering the renewable energy sources integration in the grid, energy storage systems simultaneously with the economic and functional requirements of the system, in order to improve the system reliability. The computational tool is tested using the IEEE 119 Bus test system to validate the new tool, where different types of load are considered (residential, commercial and industrial)

Active congestion quantification and reliability improvement considering aging failure in modern distribution networks

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfiguration and the utilization of the proposed indices by utility operators in CM procedures is also explained. The numerical studies also verify that the improved Arrhenius-Weibull can better indicate the aging process and demonstrate the superior accuracy of the proposed method in identifying residual lives and end-of-life failure probabilities of transformers and conductors. The integration of EV has a great impact on equipment aging failure probability and loss-of-life, thus resulting in lower network reliability and higher cost for managing aging failure. Finally, the proposed piecewise linear optimization model of the EV smart charging framework can significantly improve network reliability by 90% and reduce the total cost by 83.8% for customers and power utilities

Progress on protection strategies to mitigate the impact of renewable distributed generation on distribution systems

The benefits of distributed generation (DG) based on renewable energy sources leads to its high integration in the distribution network (DN). Despite its well-known benefits, mainly in improving the distribution system reliability and security, there are challenges encountered from a protection system perspective. Traditionally, the design and operation of the protection system are based on a unidirectional power flow in the distribution network. However, the integration of distributed generation causes multidirectional power flows in the system. Therefore, the existing protection systems require some improvement or modification to address this new feature. Various protection strategies for distribution system have been proposed so that the benefits of distributed generation can be fully utilized. This paper reviews the current progress in protection strategies to mitigate the impact of distributed generation in the distribution network. In general, the reviewed strategies in this paper are divided into: (1) conventional protection systems and (2) modifications of the protection systems. A comparative study is presented in terms of the respective benefits, shortcomings and implementation cost. Future directions for research in this area are also presented

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

Operational Planning and Optimisation in Active Distribution Systems for Flexible and Resilient Power

The electricity network is undergoing significant changes to cater to environmental-deterioration and fuel-depletion issues. Consequently, an increasing number of renewable resources in the form of distributed generation (DG) are being integrated into medium-voltage distribution networks. The DG integration has created several technical and economic challenges for distribution network operators. The main challenge is basically the problem of managing network voltage profile and congestion which is caused by increasing demand and intermittent DG operations. The result of all of these changes is a paradigm shift in the way distribution networks operate (from passive to active) and are managed that is not limited only to the distribution network operator but actively engages with network users such as demand aggregators, DG owners, and transmission-system operators. This thesis expands knowledge on the active distribution system in three specific areas and attempts to fill the gaps in existing approaches. A comprehensive active network management framework in active distribution systems is developed to allow studies on (i) the flexibility of network topology using modern power flow controllers, (ii) the benefits of centralised thermal electricity storage in achieving the required levels of flexibility and resiliency in an active distribution system, and (iii) system resiliency toward fault occurrence in hybrid AC/DC distribution systems. These works are implemented within the Advanced Interactive Multidimensional Modelling Systems (AIMMS) software to carry out optimisation procedure. Results demonstrate the benefit provided by a range of active distribution system solutions and can guide future distribution-system operators in making practical decisions to operate active distribution systems in cost-effective ways

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

A planning scheme for penetrating embedded generation in power distribution grids

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis. Page 238 blank.Includes bibliographical references (pages 229-237).Penetrating Embedded Generation, or Distributed Generation (DG), in power distribution grids presents great benefits and substantial positive social impacts to utilities, system operators and electricity consumers. Existing research and practices on DG penetration planning have a few deficiencies: (1) limited to specific system configurations and capacities; (2) inaccurate and tending to lose its optimality in application to specific scenario; (3) computationally expensive in time and space; and (4) in need of considerable investment in sensors, communication assets, and retrofitting equipment with control functionalities. This thesis proposes a planning scheme for DG penetration in distribution systems that maximizes DG penetration's benefit, in terms of power delivery loss reduction, and restricts its adverse impact of steady-state voltage rise. A unique approach is taken to simplify the DG penetration problem with two sets of rules that describes the interaction of DG penetration and power delivery loss and voltage profiles in distribution systems. The proposed planning scheme is generally applicable to any distribution system regardless of its configuration and load capacity. More importantly, it is a theoretical toolkit that can provide users an intuition how DG penetration affects the performance of a distribution system. The policy makers, regulators, industries and utilities will be able to use this toolkit, without going through complicated computations, as guidelines to make policies, standards and decisions in DG penetration and related business.by Jiankang Wang.Ph.D

Fault Location, Isolation and Network Restoration as a Self-Healing function

One of the main emphasis of the smart grid is the interaction of power supply and power customer in order to provide a reliable supply of power as well as to improve the flexibility of the network. Along with this, the increased energy demand, coupled with strict regulations on the quality and reliability of supply intensifies the pressure on distribution network operators to maintain the integrity of the network in its faultless operation mode. Additionally, regardless of the huge investments already made in replacing aging infrastructure and translating “the old-fashioned grid” in a “Smart Grid” to minimize the probability for equipment failure, the chances of failure cannot be completely eliminated. In accordance, in the event of faults in the network, apart from the high penalty costs in which network operators may incur, certain safety factors must be taken into consideration for particular customers (for example, hospitals). In view of that, there is a necessity to minimize the impact on customers without supply and maintain outages times as brief as possible. Within this scenario comes the concept of self-healing grid as one of the key-technologies in the smart grid environment which is partly due to the rapid development of distribution automation. Self-healing refers to the capacity of the smart grid to restore efficiently and automatically power after an outage. Self-healing main goals comprise supply maximum load affected by the fault, take the shortest time period possible for restoration of the load, minimizing the number of switching operations and keeping the network capacity within its operating limits. This research has explored insights into the smart grid in terms of the self-healing functionality within the distribution network with main emphasis on self-healing implementation types and its applicability. Initially a detailed review of the conception of the smart grid in order to integrate the self-healing and thus fault location, isolation and service restoration capabilities was conducted. This was complemented with a detailed discussion about the electricity distribution system automatic fault management in order to create a framework around which the aim of the research is based. Finally the self-healing problem coupled with current practical implementation cases was addressed with the objective of exploring the means of improvement and evolution in the automation level in the distribution network using Fault Location Isolation and Service Restoration (FLISR) applicability as a medium