Cyber-physical simulation platform for security assessment of transactive energy systems

Zhang, Yue; Eisele, Scott; Dubey, Abhishek; Lászka, Áron; Srivastava, Anurag K.

doi:10.1109/mscpes.2019.8738802

Cited by 16 publications

(16 citation statements)

References 16 publications

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“…They showed that maximizing local energy transactions also maximizes the overall utility. Essentially, most community-based methods such as in [56], [61], [62] typically focus on developing effective ways to optimize the MCP to be used between participants to optimize the balance between the demand and supply of energy.…”

Section: E Transactive Mechanisms For the Different Architecturesmentioning

confidence: 99%

Transactive Energy: State-of-the-Art in Control Strategies, Architectures, and Simulators

Onumanyi¹,

Isaac

Kruger

et al. 2021

IEEE Access

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The concept of transactive energy (TE) in smart grid systems is gaining increased research attention for its potential to optimize distributed energy resources, improve system reliability, as well as provide a balanced ecosystem for fair economic transaction between prosumers. TE is defined by the GridWise Architecture Council as a system of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter. With control mechanisms being a key part of TE systems, in this article, we discuss the state-of-the-art in TE control strategies, architectures, and relevant simulators for designing, evaluating, and analysing TE systems. Most importantly, existing TE control strategies are examined and discussed via a hierarchical structure comprising four different levels wherein TE control strategies/controllers can be deployed. Architecture-wise, we highlight the different types of TE architectures including the centralized, decentralized, distributed, and hierarchical architecture. In terms of existing and potential simulators for designing and evaluating TE models, we discuss and compare notable software across different characteristics of interest. We conclude this article by highlighting the basic components of a typical TE controller and other future research directions spanning across security concerns, privacy issues, communication challenges, simulation and validation demands. As a main contribution, different from existing survey articles, this article presents a synthesis of existing works regarding TE control strategies, architectures, and TE-based simulators for the benefit of the budding researcher whose interest may lie in the study of TE systems.

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Section: E Transactive Mechanisms For the Different Architecturesmentioning

confidence: 99%

Transactive Energy: State-of-the-Art in Control Strategies, Architectures, and Simulators

Onumanyi¹,

Isaac

Kruger

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…[#11]: The paper describes the Transactive Energy Security Simulation Testbed (TESST) by Zhang et al, 72 a testbed for simulating cyber attacks in energy pricing. The authors have used PYPOWER, GridLAB‐D, ns‐3, and EnergyPlus for modeling transmission, distribution, telecommunication, and buildings, respectively.…”

Section: Co‐simulation Features For Modeling Cyber‐physical Securitymentioning

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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“…The collected security data can be utilized to create accurate virtual models representing different smart city applications/environments. These models can then be used to simulate different situations and security issues to identify problem areas [77]. For example, simulating a DDoS attack on the smart building management systems in a smart city and evaluating the impact on the buildings controls and residents.…”

Section: Security Modeling Simulation and Visualization Capabilimentioning

confidence: 99%

Data-Driven Security for Smart City Systems: Carving a Trail

et al. 2020

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Smart cities rely heavily on collecting and using data. Smart systems are implemented and deployed to provide intelligent features that help improve efficiency and quality of life. This creates a huge repository of data representing many aspects of smart city operations. Many data-driven applications can take advantage of this data to further improve the "smartness" of a smart city. At the same time, smart city systems, being very large-scale distributed systems and highly integrated with the physical infrastructure and residents of the city, pose immense security challenges as well. So why don't we take advantage of this data to improve security measures? In this paper we propose the use of data-driven security approaches to secure smart city systems. To illustrate the significance of this approach we first identify the different challenges for securing smart city systems given the unique characteristics of these systems. Then we discuss the benefits of using data-driven security. Furthermore, we categorize the different types of security applications (features) needed to help capitalize on the data needs and benefits. We also discuss the how these categories of applications can alleviate some of the challenges. In addition, we highlight possible future research directions to incorporate effective data-driven security in smart city systems.

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Cyber-physical simulation platform for security assessment of transactive energy systems

Cited by 16 publications

References 16 publications

Transactive Energy: State-of-the-Art in Control Strategies, Architectures, and Simulators

Transactive Energy: State-of-the-Art in Control Strategies, Architectures, and Simulators

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

Data-Driven Security for Smart City Systems: Carving a Trail

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