On the Control of Microgrids Against Cyber-Attacks: A Review of Methods and Applications

Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified

Energy efficiency control of direct expansion air conditioning systems

The dynamic mathematical models for direct expansion air conditioning (DX A/C) systems with respect to indoor carbon dioxide (CO2) concentration, relative humidity and air temperature and the coupling effects among them have been built in this thesis. To reduce the energy cost and improve the energy efficiency for DX A/C systems while maintaining both indoor air quality (IAQ) and thermal comfort at acceptable levels, a hierarchical control structure is proposed in this thesis. This control structure includes two levels. The upper level is an open loop optimal controller to generate the optimal setpoints of indoor CO2 concentration, relative humidity and air temperature for the lower level controller. The lower level designs a closed-loop model predictive control (MPC) controller to optimize the transient processes reaching the setpoints where the energy efficiency improvement and energy cost savings are achieved. In Chapter 2, the control objective is to improve both IAQ and thermal comfort as well as energy efficiency for a DX A/C system. The details of a hierarchical control structure in this chapter are as follows: In the upper layer, an energy-optimised open loop controller is proposed based on an optimization of energy consumption of the DX A/C system and given reference points of indoor CO2 concentration, relative humidity and air temperature to generate a unique and optimised steady state for the lower layer controller. In the lower layer, the closed-loop MPC controller is proposed such that the indoor CO2 concentration, relative humidity and air temperature follow the steady state computed by the upper layer, whereas the energy efficiency is improved. To facilitate the MPC design, the nonlinear DX A/C control system is linearized around the optimised steady state. In Chapter 3, the control objective is to lower the energy cost and consumption of a DX A/C system while maintaining both IAQ and thermal comfort at comfort levels. To achieve this purpose, an autonomous hierarchical control (AHC) structure is designed and described below. The upper level is an open loop nonlinear optimal controller, which optimizes the predicted mean vote (PMV) index and the energy cost for the DX A/C system under a time-of-use (TOU) price structure of electricity according to the changing environment over a 24-hour period, to generate the tradeoff setpoints of indoor CO2 concentration, relative humidity and air temperature for the lower level controller. The lower layer is formed as a closed-loop MPC to track the trajectory reference points calculated by the optimization layer. This AHC strategy means the upper controller can adaptively and automatically set the setpoints and the lower layer adaptively and optimally tracks them, minimizing energy consumption and costs. In addition, in this chapter, the volumes of outside air allowed to enter the DX A/C system are regarded as varying with the changing circumstance over a day and are optimized by the AHC. Moreover, a supply fan to steer the pressure swing absorption with a built-in proportional-integral (PI) controller is proposed to lower the indoor CO2 concentration such that it would reduce the complexity of computation for the AHC and the cost of hardware. In Chapter 4, the control objective is to reduce energy cost, improve energy efficiency, and reduce communication resources, computational complexity and conservativeness, as well as peak demand for a multi-zone building multi-evaporator air conditioning (ME A/C) system while maintaining multi-zones’ thermal comfort and IAQ at comfort levels. To realize this objective and to consider the interaction effects between rooms, we present an autonomous hierarchical distributed control (AHDC) method. The upper level is an open loop nonlinear optimizer, which only collects measurement information and solves a distributed steady state optimization problem to adaptively and automatically generate time-varying and optimised reference points of indoor CO2 concentration, relative humidity and air temperature for the lower-layer controllers, by minimizing the demand and energy costs of a multi-zone building ME A/C system under the TOU price structure of electricity according to the changing circumstance during the day. The lower level also uses local information to track the trajectory references calculated by the upper-layer distributed controller, via distributed MPC controllers. The proposed hierarchical control strategy is distributed in two layers since they use only local information from the working zone and its neighbours. To validate the performance of these hierarchical control strategies for DX A/C systems, simulation tests are performed in this thesis. In Chapter 2, simulations are provided to show that the closed-loop regulation of the MPC controller and the energy-optimised open loop controller can maintain indoor CO2 concentration, relative humidity and air temperature at their desired setpoints with small deviations and reduce the effect of indoor cooling and pollutant loads. The simulations also demonstrate that the controllers are superior to conventional controllers in terms of energy efficiency. In Chapter 3, the simulation tests show that the AHC strategy can reduce more energy consumption and cost than the baseline strategy. In addition, the tests demonstrate that the AHC scheme is not sensitive to the physical parameters of the DX A/C system. In Chapter 4, to show the performance of the two-layer distributed control strategies, a case study is given. The simulation tests demonstrate that the AHDC strategy is capable of shifting demand from peak hours to off-peak hours and reducing the energy cost for a multi-zone building ME A/C system while maintaining multi-zones’ IAQ and thermal comfort at comfort levels.Electrical, Electronic and Computer EngineeringPhDUnrestricte

Real-time Adaptive Detection and Recovery against Sensor Attacks in Cyber-physical Systems

Cyber-physical systems (CPSs) utilize computation to control physical objects in real-world environments, and an increasing number of CPS-based applications have been designed for life-critical purposes. Sensor attacks, which manipulate sensor readings to deceive CPSs into performing dangerous actions, can result in severe consequences. This urgent need has motivated significant research into reactive defense. In this dissertation, we present an adaptive detection method capable of identifying sensor attacks before the system reaches unsafe states. Once the attacks are detected, a recovery approach that we propose can guide the physical plant to a desired safe state before a safety deadline.Existing detection approaches tend to minimize detection delay and false alarms simultaneously, despite a clear trade-off between these two metrics. We argue that attack detection should dynamically balance these metrics according to the physical system\u27s current state. In line with this argument, we propose an adaptive sensor attack detection system comprising three components: an adaptive detector, a detection deadline estimator, and a data logger. This system can adapt the detection delay and thus false alarms in real-time to meet a varying detection deadline, thereby improving usability. We implement our detection system and validate it using multiple CPS simulators and a reduced-scale autonomous vehicle testbed. After identifying sensor attacks, it is essential to extend the benefits of attack detection. In this dissertation, we investigate how to eliminate the impact of these attacks and propose novel real-time recovery methods for securing CPSs. Initially, we target sensor attack recovery in linear CPSs. By employing formal methods, we are able to reconstruct state estimates and calculate a conservative safety deadline. With these constraints, we formulate the recovery problem as either a linear programming or a quadratic programming problem. By solving this problem, we obtain a recovery control sequence that can smoothly steer a physical system back to a target state set before a safe deadline and maintain the system state within the set once reached. Subsequently, to make recovery practical for complex CPSs, we adapt our recovery method for nonlinear systems and explore the use of uncorrupted sensors to alleviate uncertainty accumulation. Ultimately, we implement our approach and showcase its effectiveness and efficiency through an extensive set of experiments. For linear CPSs, we evaluate the approach using 5 CPS simulators and 3 types of sensor attacks. For nonlinear CPSs, we assess our method on 3 nonlinear benchmarks

Learning, technologies, and time in the age of global neoliberal capitalism

Though diverse in nature, the articles in this collection discuss both socio-cultural and temporal transformations linked to technology and learning and can be classified into three broad themes. The first theme is interested in temporal experiences within time and learning; the second theme is about practical implementations of these concerns, and the third theme inquires into relationships between our understanding of time and human nature. In many articles, the boundaries between these themes are blurred and fluid. Yet, this general classification does indicate the present state of the art in studies of time, technology and education

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

Abstract There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.</jats:p

Consumer behavior modeling for electrical energy systems : a complex systems approach

Orientador: Alexandre Rasi AokiCoorientador: Germano Lambert-TorresTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 27/02/2019Inclui referências: p. 141-154Resumo: Um sistema complexo é um sistema composto de muitas partes que interagem entre si, de modo que o comportamento coletivo emergente dessas partes é mais do que a soma de seus comportamentos individuais. O sistema elétrico de potência pode ser considerado um sistema complexo devido à sua diversidade de agentes heterogêneos inter-relacionados e a emergência de comportamento complexo. Sistemas de potência estão aumentando em complexidade com novos avanços relacionados à redes elétricas inteligentes tais como tecnologia de informação e comunicação, geração distribuída, veículos elétricos, armazenamento de energia e, especialmente, uma crescente interação e participação de um grande número de consumidores heterogêneos dispersos geograficamente. O sistema elétrico de potência pode ser estudado como um sistema técnico-socioeconômico complexo com múltiplas facetas, e a teoria de sistemas complexos pode fornecer uma base teórica sólida para seus desafios de modelagem e análise. O presente trabalho trata da aplicação da teoria de sistemas complexos em sistemas de potência, focando a análise no consumidor e no seu comportamento relacionado ao consumo de eletricidade, utilizando técnicas do campo da economia comportamental. Comportamentos complexos e emergentes sobre o consumo de eletricidade, bem como seu impacto nas redes elétricas, são analisados através da modelagem do comportamento dos cliente em uma simulação baseada em agentes, considerando quatro categorias de consumidores. A análise da simulação, aplicada a um estudo de caso em uma rede de distribuição de média tensão radial com dados reais, mostrou que premissas ligeiramente diferentes sobre o comportamento do consumidor no nível micro levam a resultados macro muito distintos e com comportamento não linear. Entender e modelar adequadamente o comportamento dos consumidores é de grande importância para o planejamento e operação de redes de energia, e a economia comportamental serve como uma base teórica promissora para modelar o comportamento no consumo de eletricidade. Os resultados deste trabalho mostraram que a teorias de sistemas complexos fornece ferramentas adequadas para lidar com sistemas de potência cada vez mais complexos, considerando-os não mais como um sistema independente agregado, mas como um sistema complexo integrado. Palavras-chave: distribuição de energia; consumo de eletricidade; teoria de sistemas complexos; simulação baseada em agentes; economia comportamental.Abstract: A complex system is a system composed of many interacting parts, such that the collective emergent behavior of those parts is more than the sum of their individual behaviors. Electrical energy systems may be considered a complex system due to its diversity of interrelated heterogeneous agents and emergent complex behavior. Energy systems are increasing in complexity with new advances related to the smart grid such as information and communication technology, distributed generation, electric vehicles, energy storage, and, especially, increasing interaction and participation of a large number of geographically distributed heterogeneous consumers. Power systems can be studied as a complex techno-socio-economical system with multiple facets, and Complex System Theory (CST) may provide a solid theoretical background for these modeling and analysis challenges. The present work deals with the application of CST into electrical energy systems, focusing the analysis on the consumer and their behavior on electricity consumption, using insights from the field of behavioral economics. Emergent complex behaviors on electricity consumption as well as its impact on power grids are analyzed by modeling customer behavior on an agent-based simulation, considering four different consumer categories. The analysis of the simulation, applied on a case study on a radial medium voltage distribution grid with real-world data, showed that slightly different assumptions on consumer behavior at the micro-level lead to very different and non-linear macro outcomes. To properly understand and model consumer behavior is of great importance to the planning and operation of electrical grids, and behavioral economics serves as a promising theoretical background to model behavior on electricity consumption. The results of this work showed that CST provides suitable tools to tackle electrical energy systems' increasing complexity, by considering the electrical power systems not as an aggregated independent system anymore, but as an integrated complex system. Keywords: power distribution; electricity consumption; complex systems theory; agent-based simulation; behavioral economics

Selected Papers from the 8th Annual Conference of Energy Economics and Management

This collection represents successful invited submissions from the papers presented at the 8th Annual Conference of Energy Economics and Management held in Beijing, China, 22–24 September 2017. With over 500 participants, the conference was co-hosted by the Management Science Department of National Natural Science Foundation of China, the Chinese Society of Energy Economics and Management, and Renmin University of China on the subject area of “Energy Transition of China: Opportunities and Challenges”. The major strategies to transform the energy system of China to a sustainable model include energy/economic structure adjustment, resource conservation, and technology innovation. Accordingly, the conference and its associated publications encourage research to address the major issues faced in supporting the energy transition of China. Papers published in this collection cover the broad spectrum of energy economics issues, including building energy efficiency, industrial energy demand, public policies to promote new energy technologies, power system control technology, emission reduction policies in energy-intensive industries, emission measurements of cities, energy price movement, and the impact of new energy vehicle