Cooperation and Storage Tradeoffs in Power-Grids with Renewable Energy Resources

One of the most important challenges in smart grid systems is the integration of renewable energy resources into its design. In this work, two different techniques to mitigate the time varying and intermittent nature of renewable energy generation are considered. The first one is the use of storage, which smooths out the fluctuations in the renewable energy generation across time. The second technique is the concept of distributed generation combined with cooperation by exchanging energy among the distributed sources. This technique averages out the variation in energy production across space. This paper analyzes the trade-off between these two techniques. The problem is formulated as a stochastic optimization problem with the objective of minimizing the time average cost of energy exchange within the grid. First, an analytical model of the optimal cost is provided by investigating the steady state of the system for some specific scenarios. Then, an algorithm to solve the cost minimization problem using the technique of Lyapunov optimization is developed and results for the performance of the algorithm are provided. These results show that in the presence of limited storage devices, the grid can benefit greatly from cooperation, whereas in the presence of large storage capacity, cooperation does not yield much benefit. Further, it is observed that most of the gains from cooperation can be obtained by exchanging energy only among a few energy harvesting sources

Short term load forecasting with Markovian switching distributed deep belief networks

In modern power systems, centralised short term load forecasting (STLF) methods raise concern on high communication requirements and reliability when a central controller undertakes the processing of massive load data solely. As an alternative, distributed methods avoid the problems mentioned above, whilst the possible issues of cyberattacks and uncertain forecasting accuracy still exist. To address the two issues, a novel distributed deep belief networks (DDBN) with Markovian switching topology is proposed for an accurate STLF, based on a completely distributed framework. Without the central governor, the load dataset is separated and the model is trained locally, while obtaining the updates through communication with stochastic neighbours under a designed consensus procedure, and therefore significantly reduced the training time. The overall network reliability against cyberattacks is enhanced by continually switching communication topologies. In the meanwhile, to ensure that the distributed structure is still stable under such a varying topology, the consensus controller gain is delicately designed, and the convergence of the proposed algorithm is theoretically analysed via the Lyapunov function. Besides, restricted Boltzmann machines (RBM) based unsupervised learning is employed for DDBN initialisation and thereby guaranteeing the success rate of STLF model training. GEFCom 2017 competition and ISO New England load datasets are applied to validate the accuracy and effectiveness of the proposed method. Experiment results demonstrate that the proposed DDBN algorithm can enhance around 19% better forecasting accuracy than centralised DBN algorithm.</p

The Resilience Of Smart Energy Systems Against Adversarial Attacks, Operational Degradation And Variabilities

The presented research investigates selected topics concerning resilience of critical energy infrastructures against certain types of operational disturbances and/or failures whether natural or man-made. A system is made resilient through the deployment of physical devices enabling real-time monitoring, strong feedback control system, advanced system security and protection strategies or through prompt and accurate man-made actions or both. Our work seeks to develop well-planned strategies that act as a foundation for such resiliency enabling techniques.The research conducted thus far addresses three attributes of a resilient system, namely security, efficiency, and robustness, for three types of systems associated with current or future energy infrastructures. First (chapter 1), we study the security aspect of cyber-physical systems which integrate physical system dynamics with digital cyberinfrastructure. The smart electricity grid is a common example of this system type. In this work, an abstract theoretical framework is proposed to study data injection/modification attacks on Markov modeled dynamical systems from the perspective of an adversary. The adversary is capable of modifying a temporal sequence of data and the physical controller is equipped with prior statistical knowledge about the data arrival process to detect the presence of an adversary. The goal of the adversary is to modify the arrivals to minimize a utility function of the controller while minimizing the detectability of his presence as measured by the K-L divergence between the prior and posterior distribution of the arriving data. The trade-off between these two metrics– controller utility and the detectability cost is studied analytically for different underlying dynamics.Our second study (chapter 2) reviews the state of the art ocean wave generation technologies along with system level modeling while providing an initial study of the impacts of integration on a typical electrical grid network as compared to the closest related technology, wind energy extraction. In particular, wave power is computed from high resolution measured raw wave data to evaluate the effects of integrating wave generation into a small power network model. The system with no renewable energy sources and the system with comparable wind generation have been used as a reference for evaluation. Simulations show that wave power integration has good prospects in reducing the requirements of capacity and ramp reserves, thus bringing the overall cost of generation down.Our third study(chapter 3) addresses the robustness of resilient ocean wave generation systems. As an early-stage but rapidly developing technology, wave power extraction systems must have strong resilience requirements in harsh, corrosive ocean environments while enabling economic operation throughput their lifetime. Such systems are comprised of Wave Energy Converters (WECs) that are deployed offshore and that derive power from rolling ocean waves. The Levelized Cost of Electricity (LCOE) for WECs is high and one important way to reduce this cost is to employ strategies that minimize the cost of maintenance of WECs in a wave farm. In this work, an optimal maintenance strategy is proposed for a group of WECs, resulting in an adaptive scheduling of the time of repair, based on the state of the entire farm. The state-based maintenance strategy seeks to find an optimal trade-off between the moderate revenue generated from a farm with some devices being in a deteriorated or failed state and the high repair cost that typifies ocean wave farm maintenance practices. The formulation uses a Markov Decision Process (MDP) approach to devise an optimal policy which is based on the count of WECs in different operational states.Our fourth study (chapter 4) focuses on enabling resilient electricity grids with Grid Scale Storage (GSS). GSS offers resilient operations to power grids where the generation, transmission, distribution and consumption of electricity has traditionally been ``just in time . GSS offers the ability to buffer generated energy and dispatch it for consumption later, e.g., during generation outage and shortages. Our research addresses how to operate GSS to generate revenue efficiency in frequency regulation markets. Operation of GSS in frequency regulation markets is desirable due to its fast response capabilities and the corresponding revenues. However, GSS health is strongly dependent on its operation and understanding the trade-offs between revenues and degradation factors is essential. This study answers whether or not operating GSS at high efficiency regularly reduces its long-term performance (and thereby its offered resilience to the power grid).Our fifth study (chapter 5) focuses on the resilience of Wide Area Measurement Systems (WAMS) which is an integral part of modern electrical grid infrastructure. The problem of the global positioning system (GPS) spoofing attacks on smart grid endowed with phasor measurement units (PMUs) is addressed, taking into account the dynamical behavior of the states of the system. It is shown how GPS spoofing introduces a timing synchronization error in the phasor readings recorded by the PMU and alters the measurement matrix of the dynamical model. A generalized likelihood ratio-based hypotheses testing procedure is devised to detect changes in the measurement matrix when the system is subjected to a spoofing attack. Monte Carlo simulations are performed on the 9-bus, 3-machine test grid to demonstrate the implication of the spoofing attack on dynamic state estimation and to analyze the performance of the proposed hypotheses test. Asymptotic performance analysis of the proposed test, which can be used for large-scale smart grid networks, is also presented

Stochastic timeseries analysis in electric power systems and paleo-climate data

In this thesis a data science study of elementary stochastic processes is laid, aided with the development of two numerical software programmes, applied to power-grid frequency studies and Dansgaard--Oeschger events in paleo-climate data. Power-grid frequency is a key measure in power grid studies. It comprises the balance of power in a power grid at any instance. In this thesis an elementary Markovian Langevin-like stochastic process is employed, extending from existent literature, to show the basic elements of power-grid frequency dynamics can be modelled in such manner. Through a data science study of power-grid frequency data, it is shown that fluctuations scale in an inverse square-root relation with their size, alike any other stochastic process, confirming previous theoretical results. A simple Ornstein--Uhlenbeck is offered as a surrogate model for power-grid frequency dynamics, with a versatile input of driving deterministic functions, showing not surprisingly that driven stochastic processes with Gaussian noise do not necessarily show a Gaussian distribution. A study of the correlations between recordings of power-grid frequency in the same power-grid system reveals they are correlated, but a theoretical understanding is yet to be developed. A super-diffusive relaxation of amplitude synchronisation is shown to exist in space in coupled power-grid systems, whereas a linear relation is evidenced for the emergence of phase synchronisation. Two Python software packages are designed, offering the possibility to extract conditional moments for Markovian stochastic processes of any dimension, with a particular application for Markovian jump-diffusion processes for one-dimensional timeseries. Lastly, a study of Dansgaard--Oeschger events in recordings of paleoclimate data under the purview of bivariate Markovian jump-diffusion processes is proposed, augmented by a semi-theoretical study of bivariate stochastic processes, offering an explanation for the discontinuous transitions in these events and showing the existence of deterministic couplings between the recordings of the dust concentration and a proxy for the atmospheric temperature

Power systems automation, communication, and information technologies for smart grid: A technical aspects review

Smart grid (SG) introduced proven power system, based on modernized power delivery system with introduction of advanced data-information and communication technologies (ICT). SGs include improved quality of power transmission/distribution from power generation to end-users with optimized power flow and efficiency. In addition to above modern automation, two-way communications, advanced monitoring, and control to optimize power quality issues are the classic features of SGs. This ensures the efficiency and reliability of all its interconnected power system elements against potential threats and life time cycle. By integrating ICT into the power system SGs improved the working capabilities of the utility companies. Resultant of ICT with SG leads to better management of assets and ensure energy management for end users. This review article presents the different areas of communication and information technology areas involved in SG automation

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