Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

The first-ever Ukraine cyberattack on power grid has proven its devastation by hacking into their critical cyber assets. With administrative privileges accessing substation networks/local control centers, one intelligent way of coordinated cyberattacks is to execute a series of disruptive switching executions on multiple substations using compromised supervisory control and data acquisition (SCADA) systems. These actions can cause significant impacts to an interconnected power grid. Unlike the previous power blackouts, such high-impact initiating events can aggravate operating conditions, initiating instability that may lead to system-wide cascading failure. A systemic evaluation of "nightmare" scenarios is highly desirable for asset owners to manage and prioritize the maintenance and investment in protecting their cyberinfrastructure. This survey paper is a conceptual expansion of real-time monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework that emphasizes on the resulting impacts, both on steady-state and dynamic aspects of power system stability. Hypothetically, we associate the combinatorial analyses of steady state on substations/components outages and dynamics of the sequential switching orders as part of the permutation. The expanded framework includes (1) critical/noncritical combination verification, (2) cascade confirmation, and (3) combination re-evaluation. This paper ends with a discussion of the open issues for metrics and future design pertaining the impact quantification of cyber-related contingencies

Distributed photovoltaic systems: Utility interface issues and their present status. Intermediate/three-phase systems

The interface issues between the intermediate-size Power Conditioning Subsystem (PCS) and the utility are considered. A literature review yielded facts about the status of identified issues

Energy-Driven Analysis of Electronically-Interfaced Resources for Improving Power System Dynamic Performance

This dissertation investigates the strengthening of power system dynamics with regard to electromechanical oscillations by using electronically-interfaced resources (EIR). The dissertation addresses (1) the modeling and control design of a flywheel energy storage system and a large-scale solar PV plant. The latest is enabled to participate in oscillation damping control without the need for power curtailment. (2) A new dynamic performance evaluation and coordination of damping controller is also developed to analyze systems with several critically low damping ratios. This is studied by using the system oscillation energy to define the total action and total action sensitivity, which allow the identification of control action that benefit exited modes, rather than fixed targeted modes. Finally, (3) this dissertation proposes a solution for the site selection of EIR-based damping controllers in a planning stage. The effect of wind power variability and correlation between geographically closed wind farms is modeled to analyze the system performance and determine the site selection that maximizes the probability of dynamic performance improvement. Mathematical description as well as simulations in different multi-machine power systems show the advantages of the methods described in this work. The findings of this thesis are expected to advance the state-of-the-art of power system control by effectively and efficiently utilizing the fast power capabilities of EIR in systems with high penetration of renewable energy

Electronic/electric technology benefits study

The benefits and payoffs of advanced electronic/electric technologies were investigated for three types of aircraft. The technologies, evaluated in each of the three airplanes, included advanced flight controls, advanced secondary power, advanced avionic complements, new cockpit displays, and advanced air traffic control techniques. For the advanced flight controls, the near term considered relaxed static stability (RSS) with mechanical backup. The far term considered an advanced fly by wire system for a longitudinally unstable airplane. In the case of the secondary power systems, trades were made in two steps: in the near term, engine bleed was eliminated; in the far term bleed air, air plus hydraulics were eliminated. Using three commercial aircraft, in the 150, 350, and 700 passenger range, the technology value and pay-offs were quantified, with emphasis on the fiscal benefits. Weight reductions deriving from fuel saving and other system improvements were identified and the weight savings were cycled for their impact on TOGW (takeoff gross weight) and upon the performance of the airframes/engines. Maintenance, reliability, and logistic support were the other criteria

Sequential Composition of Dynamically Dexterous Robot Behaviors

We report on our efforts to develop a sequential robot controller-composition technique in the context of dexterous “batting” maneuvers. A robot with a flat paddle is required to strike repeatedly at a thrown ball until the ball is brought to rest on the paddle at a specified location. The robot’s reachable workspace is blocked by an obstacle that disconnects the free space formed when the ball and paddle remain in contact, forcing the machine to “let go” for a time to bring the ball to the desired state. The controller compositions we create guarantee that a ball introduced in the “safe workspace” remains there and is ultimately brought to the goal. We report on experimental results from an implementation of these formal composition methods, and present descriptive statistics characterizing the experiments.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67990/2/10.1177_02783649922066385.pd

Reinforcement Learning and Planning for Preference Balancing Tasks

Robots are often highly non-linear dynamical systems with many degrees of freedom, making solving motion problems computationally challenging. One solution has been reinforcement learning (RL), which learns through experimentation to automatically perform the near-optimal motions that complete a task. However, high-dimensional problems and task formulation often prove challenging for RL. We address these problems with PrEference Appraisal Reinforcement Learning (PEARL), which solves Preference Balancing Tasks (PBTs). PBTs define a problem as a set of preferences that the system must balance to achieve a goal. The method is appropriate for acceleration-controlled systems with continuous state-space and either discrete or continuous action spaces with unknown system dynamics. We show that PEARL learns a sub-optimal policy on a subset of states and actions, and transfers the policy to the expanded domain to produce a more refined plan on a class of robotic problems. We establish convergence to task goal conditions, and even when preconditions are not verifiable, show that this is a valuable method to use before other more expensive approaches. Evaluation is done on several robotic problems, such as Aerial Cargo Delivery, Multi-Agent Pursuit, Rendezvous, and Inverted Flying Pendulum both in simulation and experimentally. Additionally, PEARL is leveraged outside of robotics as an array sorting agent. The results demonstrate high accuracy and fast learning times on a large set of practical applications

Computacional models for expansion planning of electric power distribution systems containing multiple microgrids and distributed energy resources under uncertainties

Orientador: Prof. Dr. Clodomiro Unsihuay VilaTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 05/12/2022Inclui referênciasResumo: Sistemas de distribuição incorporaram diversas tecnologias nos últimos anos, e a introdução de recursos energéticos distribuídos (DERs) proporcionam desafios para o planejamento da sua expansão e operação, como a inserção de geração distribuída (DG), sistemas de armazenamento de energia (ESSs), resposta da demanda (DR) e incertezas associadas com fontes de energia renováveis. Além disso, sistemas de distribuição modernos com múltiplas microrredes (MMG) e tecnologias para automação e monitoramento possibilitam a criação de um mercado local para comercialização de energia entre as microrredes. Devido às incertezas e ambientes competitivos gerados por microrredes que podem tomar suas próprias decisões e comercializar energia, a decisão para expansão do sistema é uma tarefa complexa feita pelo operador do sistema de distribuição (DSO). Dessa forma, essa tese propõe quatro modelos computacionais para o planejamento da expansão de sistemas elétricos de distribuição contendo MMGs e DERs sob incertezas. Cada modelo é projetado para um cenário específico: o primeiro soluciona o planejamento robusto para casos sem MMGs, não usa dados históricos e inclui confiabilidade; o segundo insere MMGs e contingências; o terceiro considera cenários com dados históricos disponíveis; e o quarto inclui o mercado de comercialização de energia para MMG em ambientes competitivos. Todos os modelos buscam minimizar os custos com investimento e operação na perspectiva do DSO, alocando novos componentes (DG, ESSs, capacitores e linhas) sob incertezas, considerando a coordenação das DERs, DR e confiabilidade/contingências. Eles são formulados como problemas multiníveis e solucionados através de decomposições multiestágio usando abordagens como adaptative robust optimization, distributionally robust optimization e column-andconstraint generation. Os métodos propostos são ilustrados usando uma versão modificada do sistema teste IEEE 123-bus. Os resultados mostram que os aspectos apresentados neste trabalho são importantes no planejamento da expansão de redes de distribuição pois reduzem o custo total ao mesmo tempo que melhor representam redes modernas. Incluir contingências melhora significativamente os índices de confiabilidade, reduzindo em 70% do índice EENS no caso de estudo. Cenários considerando mercado de energia tendem a aumentar o custo total do plano em ambientes de competição, 14% no caso de estudo. Ao mesmo tempo, esse caso tem a vantagem do compartilhamento de riscos entre o DSO e investidores. Ademais, os métodos propostos que usam dados históricos se comportam similarmente a métodos robustos ou estocásticos, dependendo do volume de dados disponível.Abstract: Power distribution systems have incorporated many new technologies in recent years, and the introduction of distributed energy resources (DERs) provides challenges for their operation and expansion planning, such as the inclusion of distributed generation (DG), energy storage systems (ESSs), demand response (DR), and uncertainties associated with renewable power sources. Moreover, modern distribution networks with multiple microgrids (MMG) and automation/monitoring technologies enable the creation of a market framework for local energy trading among the microgrids. Due to uncertainties and the competitive environment where microgrids can make their own decisions and trade energy, the expansion decision is a complex task performed by the distribution system operator (DSO). Thus, this dissertation proposes four computational models for the expansion planning of electric power distribution systems containing multiple microgrids and DERs under uncertainties. Each model is designed for specific scenarios: the first one solves a robust plan for cases without MMGs, has no historical data, and includes reliability; the second model inserts MMGs and contingencies; the third one considers cases having historical data; and the last model formulates the energy trading market for MMGs in competitive environments. All models aim to minimize the investment and operational costs from the DSO's perspective, placing new components (DG, ESSs, capacitors, and lines) under uncertainties, considering the DERs' optimal daily operation, DR, and reliability/contingency. They are formulated as multi-level problems and solved through multi-stage decompositions using robust adaptative optimization, distributionally robust optimization, and column-and-constraint approaches. The proposed methods are illustrated using a modified version of the IEEE 123-bus test system. The results show that the aspects presented in this work are important for the expansion planning of distribution networks since they reduce the total cost and better represent modern new tasks. Including contingencies significantly improves reliability indexes, reducing by 70% the EENS (Expected Energy Not Served) index in the study case. Scenarios considering energy trade markets tend to increase the total cost of the plan in competitive environments, 14% in the case study; at the same time, they have the benefit of sharing the investment risks among investors and the DSO. Furthermore, the proposed methods that use historical data behaved similarly to robust or stochastic approaches, depending on the amount of data available

Hybrid simulation-optimization methods: A taxonomy and discussion

The possibilities of combining simulation and optimization are vast and the appropriate design highly depends on the problem characteristics. Therefore, it is very important to have a good overview of the different approaches. The taxonomies and classifications proposed in the literature do not cover the complete range of methods and overlook some important criteria. We provide a taxonomy that aims at giving an overview of the full spectrum of current simulation-optimization approaches. Our study may guide researchers who want to use one of the existing methods, give insights into the cross-fertilization of the ideas applied in those methods and create a standard for a better communication in the scientific community. Future reviews can use the taxonomy here described to classify both general approaches and methods for specific application fields.The possibilities of combining simulation and optimization are vast and the appropriate design highly depends on the problem characteristics. Therefore, it is very important to have a good overview of the different approaches. The taxonomies and classifications proposed in the literature do not cover the complete range of methods and overlook some important criteria. We provide a taxonomy that aims at giving an overview of the full spectrum of current simulation-optimization approaches. Our study may guide researchers who want to use one of the existing methods, give insights into the cross-fertilization of the ideas applied in those methods and create a standard for a better communication in the scientific community. Future reviews can use the taxonomy here described to classify both general approaches and methods for specific application fields. (C) 2014 Elsevier B.V. All rights reserved