Smart Grid: Overview, Issues and Opportunities. Advances and Challenges in Sensing, Modeling, Simulation, Optimization and Control

Amin, Saurabh

doi:10.3166/ejc.17.547-567

Cited by 114 publications

(35 citation statements)

References 25 publications

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“…A considerable body of work exists on broad aspects of cyber‐physical security tackling different dimensions, 43,44,45,46,8,6 as well as a survey of surveys about these issues 47 . Research gaps highlighted in these surveys include: (1) modeling incident responses and impact in co‐simulation frameworks; (2) easy to read and interchangeable format for representing security incidents; (3) simulation of cascading attacks (or chain failures) and their effects on the infrastructure; (4) data obtained from security based testbeds (used as parameters to models) from real‐world architectures addressing attacks or instabilities; (5) studying the system‐wide effects of incidents over wide‐areas impacting energy GT&D as well as telecommunication networks; and (6) corrupted data transmission across the ICT infrastructure (from different sources with replay or data injection attacks) and impacts on system‐level decisions to balance energy supply and demand.…”

Section: Power Grids and Co‐simulationmentioning

confidence: 99%

Systematic review of features for co‐simulating security incidents in Cyber‐Physical Systems

Czekster

Morisset

Clark

et al. 2021

Security and Privacy

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Cyber‐Physical Systems (CPS) and Internet‐of‐Things (IoT) plus energy are the enabling technology of modern power systems also known as the Smart Grid (SG). A SG may consist of thousands of interconnected components communicating and exchanging data across layers that stretch beyond technical capabilities, for instance, markets and customer interactions. Cyber‐physical security is a major source of concern due to the high reliance of the SG on Information and Communication Technologies (ICT) and their widespread use. Addressing security requires developing modeling and simulation tools that approximate and replicate adversarial behavior in the SG. These tools have in fact two simulators, one handling continuous power flows and another for capturing the discrete behavior when communicating across CPS or IoT components. The technique of composing two models of computation in a global simulation of these coupled systems is called co‐simulation. Although there are many frameworks and tools for co‐simulation, the set of features for modeling cyber‐physical security incidents in the SG lacks thorough understanding. We present a systematic review of features and tools for co‐simulating these concerns in CPS. We also highlight and discuss research gaps with respect to the most used tools in industry and academia and comment on their relevant features.

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Section: Power Grids and Co‐simulationmentioning

confidence: 99%

Systematic review of features for co‐simulating security incidents in Cyber‐Physical Systems

Czekster

Morisset

Clark

et al. 2021

Security and Privacy

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“…At present, some methods have been proposed for the frequency control of power systems [12][13][14][15][16]. As a high-dimensional nonlinear complex large-scale system, the control strategy design of modern power grids (the control output of ADES controllers) involves large-scale analytical modeling [17][18][19]. Data-driven methods are often used to solve the analytical modeling issues [20,21].…”

Section: Introductionmentioning

confidence: 99%

Convex Neural Networks Based Reinforcement Learning for Load Frequency Control under Denial of Service Attacks

Zeng

Zhu

et al. 2022

Algorithms

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With the increase in the complexity and informatization of power grids, new challenges, such as access to a large number of distributed energy sources and cyber attacks on power grid control systems, are brought to load-frequency control. As load-frequency control methods, both aggregated distributed energy sources (ADES) and artificial intelligence techniques provide flexible solution strategies to mitigate the frequency deviation of power grids. This paper proposes a load-frequency control strategy of ADES-based reinforcement learning under the consideration of reducing the impact of denial of service (DoS) attacks. Reinforcement learning is used to evaluate the pros and cons of the proposed frequency control strategy. The entire evaluation process is realized by the approximation of convex neural networks. Convex neural networks are used to convert the nonlinear optimization problems of reinforcement learning for long-term performance into the corresponding convex optimization problems. Thus, the local optimum is avoided, the optimization process of the strategy utility function is accelerated, and the response ability of controllers is improved. The stability of power grids and the convergence of convex neural networks under the proposed frequency control strategy are studied by constructing Lyapunov functions to obtain the sufficient conditions for the steady states of ADES and the weight convergence of actor–critic networks. The article uses the IEEE14, IEEE57, and IEEE118 bus testing systems to verify the proposed strategy. Our experimental results confirm that the proposed frequency control strategy can effectively reduce the frequency deviation of power grids under DoS attacks.

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“…Generally, microgrids are not opposite but complementary to the interconnected power system. They can be as efficient and self-sufficient as possible, and they can island rapidly during emergencies [1]. However, when operating in islanded mode, they become weak grids and are thus less stable and more vulnerable to variable sources and loads.…”

Section: Introductionmentioning

confidence: 99%

Gain Scheduling Control of an Islanded Microgrid Voltage

et al. 2014

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Abstract:The aim of this research study has been to design a gain scheduling (GS) digital controller in order to control the voltage of an islanded microgrid in the presence of fast varying loads (FVLs), and to compare it to a robust controller. The inverter which feeds the microgrid is connected to it through an inductance-capacitor-inductance (LCL) filter. The oscillatory and nonlinear behaviour of the plant is analyzed in the whole operating zone. Afterwards, the design of the controllers which contain two loops in cascade are described. The first loop concerns the current control, while the second is linked to the voltage regulation. Two controllers, one defined as Robust and another one as GS controller, are designed for the two loops, emphasizing in their robustness and their ability to damp the oscillatory plant behaviour. To finish, some simulations are carried out to study and compare the two kinds of controllers in different operating points. The results show that both controllers damp the oscillatory behaviour of the plant in closed loop (CL), and that the GS controller ensures a better rejection of current disturbances from FVLs.

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Smart Grid: Overview, Issues and Opportunities. Advances and Challenges in Sensing, Modeling, Simulation, Optimization and Control

Cited by 114 publications

References 25 publications

Systematic review of features for co‐simulating security incidents in Cyber‐Physical Systems

Systematic review of features for co‐simulating security incidents in Cyber‐Physical Systems

Convex Neural Networks Based Reinforcement Learning for Load Frequency Control under Denial of Service Attacks

Gain Scheduling Control of an Islanded Microgrid Voltage

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