IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm 2022)

Session BPA: Best Paper Award Ceremony and SmartGridComm 2023 Introduction
Session K3: Keynote Speech: AI-Enabled Smart Grid Communications and Transactive Energy Systems Session K4: Keynote Speech: Machine Learning Based Security Solutions for Smart Grids: Challenges and Solutions
Session CN2: Network Design and Resiliency for Smart Grids Session CO2: Electrical Vehicle Charging Session DAC2: Modeling and Learning in Electric Vehicle Charging Session SP2: Vulnerabilities and Attack Mitigations in Smart Grids
Session W1: Workshop: Data Sharing in Smart Grids

Session K3

Keynote Speech: AI-Enabled Smart Grid Communications and Transactive Energy Systems

Conference
9:00 AM — 10:00 AM +08
Local
Oct 26 Wed, 9:00 PM — 10:00 PM EDT

Keynote Speech: AI-Enabled Smart Grid Communications and Transactive Energy Systems

Prof. Melike Erol-Kantarci, University of Ottawa, Canada

0
In the past decade, Information and Communication Technologies (ICT) have enabled the modernization of the power grid and have led to many advances in smart grid technologies. Smart grid communications facilitate a large number of grid operations, including advanced metering, fault monitoring, microgrid control, transactive energy systems and so on. In parallel to advances in smart grids, communication technologies have been continuously evolving to provide better service to mobile users and vertical industries. Recently, machine learning has showed promising performance improvements in communication networks as well as smart grid operations. In this talk, we introduce novel AI-based tools that will allow a P2P energy trading platform, consisting of microgrids, to become a part of the future transactive energy systems. The energy trading platform relies on robust smart grid communications. We will show our recent results on low-latency communications that use reinforcement learning to support communication needs of such energy trading platforms.
Session Chair and Room

Daisuke Mashima (Advanced Digital Sciences Center, Singapore) — Room LT2

Session CN2

Network Design and Resiliency for Smart Grids

Conference
10:30 AM — 12:40 PM +08
Local
Oct 26 Wed, 10:30 PM — 12:40 AM EDT

A Framework to Evaluate PMU Networks for Resiliency Under Network Failure Conditions

Reuben Samson Raj and Dong Jin (University of Arkansas, USA)

0
Phasor Measurement Units (PMU), due to their capability for providing highly precise and time-synchronized measurements of synchrophasors, have now become indispensable in wide area monitoring of power-grid systems. Successful and reliable delivery of synchrophasor packets from the PMUs to the Phasor Data Concentrators (PDCs) and beyond, requires a backbone communication network that is robust and resilient to failures. These networks are vulnerable to a range of failures that include cyber-attacks, system or device level outages and link failures. In this paper, we present a framework to evaluate the resilience of a PMU network in the context of link failures. We model the PMU network as a connected graph and link failures as edges being removed from the graph. Our approach, inspired by model checking methods, involves exhaustively checking the reachability of PMU nodes to PDC nodes, for all possible combinations of link failures, given an expected number of links fail simultaneously. Using the IEEE 14-bus system, we illustrate the construction of the graph model and the solution design. Finally, a comparative evaluation on how adding redundant links to the network improves the Power System Observability, is performed on the IEEE 118 bus-system.

A Digital Twin integrated Cyber-physical systems for Community Energy Trading

Yakubu Tsado, Olamide Jogunola, Femi Olatunbosun Olatunji and Bamidele Adebisi (Manchester Metropolitan University, United Kingdom (Great Britain))

0
There is a need for an efficient methodology for modelling digital replicas for microgrid systems that can be used for multiple applications and ensure the reliable operation of the microgrid. In this paper, we present a whole system model-based design of peer-to-peer energy trading~(P2P-ET) among prosumers in a grid-connected community microgrid. The model consists of a power system component with distributed resources; an energy storage system~(ESS) with heuristic and optimised control policy; and two P2P market paradigms: bill sharing and mid-market rate, which estimates the financial benefit to the community. Simulated transaction facilitated through a case study of P2P-ET between prosumers illustrates the impact of transaction on grid voltage, ESS and individual household bills. Results show that the energy bought from grid by the community is reduced by 15.36\% when optimised ESS policy is used over the heuristic policy. Also, the P2P-ET reduces individual household bill in the community by at least 11.3\%.

OpenConduit: A Tool for Recreating Power System Communication Networks Automatically

Amarachi Umunnakwe (Texas AandM University, USA); Patrick Wlazlo (Vistra Corp, USA); Abhijeet Sahu, Julian Velasquez, Katherine Davis and Ana E Goulart (Texas A&M University, USA); Saman Zonouz (Rutgers University, USA)

1
The daily operations of critical infrastructures have long relied upon computer networks. Nevertheless, these net- works attract adversarial actions. To improve the security and resilience of electric power systems and other cyber-physical critical infrastructure, there is a crucial need to study their com- munication networks alongside their physical systems. However, there is a disconnect between network models used by research groups and the actual network topologies used in industry. These modeling differences lead to discrepancies between study results and what is attainable in the field. To address this, OpenConduit is introduced in this paper. OpenConduit is designed to achieve automated and realistic replication of electric power system networks in an emulation environment. OpenConduit interprets industrial networks' configuration data (real or synthetic) and rebuilds the network in the Common Open Research Emulator (CORE). OpenConduit's architecture, design, and integration into a large-scale cyber-physical testbed are the focus of the paper. Experiments with a sample synthetic electric utility network show its ability to efficiently enable detailed emulation studies for real utility networks in a safe environment. Finally, experiments on a range of cases demonstrate the OpenConduit tool to be effective for scalability in the emulation of larger networks, as well as achieving conformity with configuration files and system settings while maintaining functionality. Additionally, the emulation time which averages 59 seconds can be integrated with power systems operations, while upholding information security of system data.

Decentralized Load Management in HAN: An IoT-Assisted Approach

Jagnyashini Debadarshini and Sudipta Saha (Indian Institute of Technology Bhubaneswar, India); Subhransu Samantaray (Indian Institute of Technology, Bhubaneswar, India)

0
A Home Area Network (HAN) is considered to be a significant component of Advanced Metering Infrastructure (AMI) and has been studied well in many works. It binds all the electrical components installed in a defined premise together for their close monitoring and management. However, HAN has been realized so far mostly as a centralized system. Therefore, like any other centralized system, the traditional realization of HAN also suffers from various well-known problems, such as single-point-of-failure, susceptibility to attacks, requirement of specialized infrastructure, inflexibility to easy expansion, etc. To address these issues, in this work, we propose a decentralized design of HAN. In particular, we propose an IoT based design where instead of a central controller, the overall system operation is controlled and managed through decentralized coordination among the the electrical appliances. We leverage Synchronous-Transmission (ST) based data-sharing protocols in IoT to accomplish our goal. To demonstrate the efficacy of the proposed decentralized framework, we also design a real-time intra-HAN load-management strategy and implement it in real IoT-devices. Evaluation of the same over emulation platforms and IoT testbeds show upto 62% reduction of peak load over a wide variety of load profiles.

iCAD: information-Centric network Architecture for DDoS Protection in the Smart Grid

Sharad Shrestha, George Torres and Satyajayant Misra (New Mexico State University, USA)

1
With the proliferation of differently-abled and heterogeneous devices in the smart grid Denial of Service (DoS) is becoming an even more potent attack vector than it was before. This paper demonstrates the ease with which an adversary can orchestrate DoS and distributed DoS (DDoS) attacks on the grid. We then propose an extension to the iCAAP architecture from our prior work to propose iCAD -an information-centric architecture, complete with mitigation strategies built for DoS/DDoS resilience. We discuss our architecture in detail and show through simulations how effective it is in mitigating DoS/DDoS attacks.

Timing Analysis of GOOSE in a Real-World Substation

Juan C. Lozano (University of California, Santa Cruz); Keerthi Koneru (UC Santa Cruz, USA); John Castellanos (CISPA, Germany); Alvaro Cardenas (University of California, Santa Cruz, USA)

0
Substation Automation has transformed the power grid by providing solutions at the time, space, and quality levels. Despite the importance of GOOSE in power substations, there is very little exploration of the behavior of GOOSE in real-world deployments.

Due to the sensitivity of actual data, various analyses are performed only on small testbeds or emulated traffic with designed assumptions of how these systems behave. In this work, we provide a timing characterization of the GOOSE protocol in a real-world substation. We compare the results with a testbed that mimics a real-world power system. We also discuss the insights from the analysis regarding presumed differences between simulated traffic and real-world traffic to understand the actual behavior of the devices.
Session Chair and Room

Thanasis G. Papaioannou (Athens University of Economics and Business, Greece) — Room TT2-3

Session CO2

Electrical Vehicle Charging

Conference
10:30 AM — 12:00 PM +08
Local
Oct 26 Wed, 10:30 PM — 12:00 AM EDT

Time-of-Use-Aware Priority-Based Multi-Mode Online Charging Scheme for EV Charging Stations

Md Navid Bin Anwar (University of Victoria, Canada); Rukhsana Ruby (Shenzhen University, China); Yijun Cheng (Central South University, China); Jianping Pan (University of Victoria, Canada)

2
Electric vehicle charging stations (EVCS) play a vital role in providing charging support to EV users. In order to facilitate users in terms of charging speed, two different charging modes (L2 and L3) are currently available at public charging stations. L3 mode provides quick charging with higher power, whereas L2 mode offers moderate charging speed with low power. The integration of an EVCS into the power grid requires coordinated charging strategies in order to reduce the electricity bill for a profitable operation. However, the effective utilization of the multi-mode charging strategy to serve the maximum number of EVs for a small charging station with limited charging capacity and spots is an open issue. To this end, we propose a priority-based online charging scheme, namely PBOS, which is based on real-time information and does not depend on future knowledge. The objective is to serve as many vehicles as possible in a day while fulfilling their charging requirements under a multi-mode EVCS setting and reducing the charging costs by utilizing the time-of-use pricing based demand response strategy. Simulation results show that the proposed algorithm can increase profit for EVCS by up to 42% with a 20% lower rejection rate when compared with other schemes.

A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases

Nathaniel Tucker (UCSB, USA); Mahnoosh Alizadeh (University of California, Santa Barbara, USA)

2
We present a customizable online optimization framework for real-time EV smart charging to be readily implemented at real large-scale charging facilities. Notably, due to real-world constraints, we designed our framework around 3 main requirements. First, the smart charging strategy is readily deployable and customizable for a wide-array of facilities, infrastructure, objectives, and constraints. Second, the online optimization framework can be easily modified to operate with or without user input for energy request amounts and/or departure time estimates which allows our framework to be implemented on standard chargers with 1-way communication or newer chargers with 2-way communication. Third, our online optimization framework outperforms other real-time strategies (including first-come-first-serve, least-laxity-first, earliest-deadline-first, etc.) in multiple real-world test cases with various objectives. We showcase our framework with two real-world test cases with charging session data sourced from SLAC and Google campuses in the Bay Area.

Battery Charging Strategies Design for Battery Swapping Stations: A Game Theoretic Approach

Huanyu Yan (The Chinese University of Hong Kong, Shenzhen, China); Chenxi Sun (The Chinese University of Hong Kong (Shenzhen), China); Huanxin Liao (The Chinese University of Hongkong Shenzhen, China); Xiaoying Tang (The Chinese University of Hong Kong, Shenzhen, China)

0
Battery Swapping Stations (BSSs) are rapidly expanding infrastructures for electric vehicles. However, the inappropriate battery charging strategy of BSSs will lead to unnecessary charging costs. In this paper, we study the real-time optimal battery charging strategies for every BSSs in a system under a non-cooperative scenario and dynamic electricity pricing environment. We propose a non-cooperative game model to characterize the BSS charging competition. We prove the existence and uniqueness of Nash Equilibrium under arbitrary swapping demands and battery numbers, and an algorithm is proposed to solve the Equilibrium. Numerical results show that our proposed strategy outperforms the benchmark strategies in terms of overall profits.

Carbon-Aware EV Charging

Kai-Wen Cheng, Yuexin Bian and Yuanyuan Shi (University of California, San Diego, USA); Yize Chen (Lawrence Berkeley National Laboratory, USA)

0
This paper examines the problem of optimizing the charging pattern of electric vehicles (EV) by taking real-time electricity grid carbon intensity into consideration. The objective of proposed charging scheme is to minimize the carbon emissions contributed by EV charging events, while simultaneously satisfying constraints posed by EV charging schedules, charging station transformer limits, and battery physical constraints. Using real-world EV charging data and California electricity generation records, this paper shows that our carbon-aware real-time scheduling scheme saves an average of 3.81% of carbon emission while delivering the same amount of energy. Furthermore, by using an adaptive balanced factor, we can reduce 26.00% of carbon emission on average with a compromise of reducing around 12.61% of charging energy delivery.
Session Chair and Room

Shahab Bahrami (University of British Columbia, Canada) — Room LT2

Session K4

Keynote Speech: Machine Learning Based Security Solutions for Smart Grids: Challenges and Solutions

Conference
2:00 PM — 3:00 PM +08
Local
Oct 27 Thu, 2:00 AM — 3:00 AM EDT

Keynote Speech: Machine Learning Based Security Solutions for Smart Grids: Challenges and Solutions

Prof. Biplab Sikdar, National University of Singapore, Singapore

0
Smart grids take advantage of information and communication technologies to achieve energy efficiency, automation and reliability. Increasingly, smart grids are seeing a proliferation of dynamic new components and devices on the distribution edge of the grid. The integration of these components has led to new strategies for the planning and operational management of grids, particularly through two-way communications and power flow between the grid and consumers. However, these bidirectional communications and the convergence of the information technology and operational technology (IT/OT) networks introduce several security and privacy threats to the grid and the consumers. While machine learning based techniques have the potential to secure smart grids against various cyber threats, they are vulnerable to various attacks that can not only jeopardize the applications and their users, but also serve to expand the overall threat landscape. This talk will start by with examples of security vulnerabilities in machine learning applications in real-world smart grid environments and provide an overview of various types of attacks on machine learning algorithms. We will then provide a framework for security evaluation for machine learning in smart grid applications and present security solutions for such systems. The presentation will conclude with an overview of some promising approaches for future work in machine learning based security for smart grids.
Session Chair and Room

David Nicol (University of Illinois at Urbana-Champaign, United States) — Room LT2

Session W1

Workshop: Data Sharing in Smart Grids

Conference
3:00 PM — 6:00 PM +08
Local
Oct 27 Thu, 3:00 AM — 6:00 AM EDT

Keynote: Data Markets in Energy Forecasting

Pierre Pinson, Imperial College London, United Kingdom

0
This talk does not have an abstract.

Prediction Markets as a Data Aggregation Mechanism

Paul Cuffe, University College Dublin, Ireland

0
This talk does not have an abstract.

Market-oriented Data Valuation in Smart Grids

Jianxiao Wang, Peking University, China

0
This talk does not have an abstract.

Privacy-Preserving Probabilistic Forecasting in Smart Grids

Jean-François Toubeau, University of Mons, Belgium

0
This talk does not have an abstract.

Keynote: Data Sharing: Value, Method and Mechanism

Qinglai Guo, Tsinghua University, China

0
This talk does not have an abstract.

Panel: Discussion between the expert research panel and the audience about the current status quo of data sharing in smart grids and the challenges to their implementation, as well as a realistic assessment of their potential going forward

Moderator: Jean-François Toubeau, University of Mons, Belgium

0
This talk does not have an abstract.
Session Chair and Room

Room TT4-5

Session DAC2

Modeling and Learning in Electric Vehicle Charging

Conference
3:30 PM — 5:00 PM +08
Local
Oct 27 Thu, 3:30 AM — 5:00 AM EDT

Probabilistic Capacity Planning Framework for Electric Vehicle Charging Stations with Overstay

I Safak Bayram (University of Strathclyde, United Kingdom (Great Britain))

0
Public charging stations provide charging services as well as parking for the growing population of electric vehicles (EVs). Effective management of these facilities is becoming crucial, with a significant proportion of drivers remaining parked even after the services are completed. This phenomenon, known as overstay, results in an underutilization of station resources and becomes a barrier to other electric vehicle drivers seeking charging services. To that end, this article presents (i) stochastic modeling of charging stations with overstaying customers, and (ii) a methodology to calculate station capacities with respect to a performance metric probability of loss of load that represents the percentage of unsatisfied demand. The station model is constructed using a two-dimensional Markov chain reflecting interactions among the idle, charging, and overstaying customers. Initially, the generalized small-scale charging station model is studied to investigate the behavior of the station parameters. Then, the general model is extended using the statistical large deviation theory to cover the case of large-scale charging stations. Effective demand, a deterministic quantity, for each charger is calculated, and station capacity is calculated in terms of the above-mentioned performance metric. The case studies demonstrate that calculating effective demand-based capacity leads to substantial savings when provisioning station resources. However, a significant proportion of these savings diminish with increasing rate of overstay customers and durations.

Modelling Second-Life Batteries as the Energy Storage System for EV Charging Stations

Kiraseya Preusser, Wen Wei and Anke Schmeink (RWTH Aachen University, Germany)

0
This paper introduces a model for using second- life batteries (SLBs), retired from electric vehicles (EVs), as the energy storage system (ESS) in order to improve the profitability of a public charging station. Furthermore, the introduced model significantly flattens the peak loads to the grid introduced by the operation of charging stations. The reinforcement learning algorithm used here does not depend on forecast data, and learns to make optimal scheduling decisions for charging the ESS and the EVs online, either charging from the ESS or from the power grid. The implemented model is simulated using real data and compared to another charging scheduler. The resulting charging station system proves to be more profitable with the inclusion of the ESS and also helps flatten the electrical energy load on the power grid during on-peak times. Additionally, by modelling the battery degradation of the SLBs for each charge and discharge cycle, it is shown that the life time of these batteries can be extended. Therefore, it is a viable use for these batteries as they can fulfill the requirements of the ESS for a year before their charge capacity falls below the 50% mark that defines their end of life.

Feasibility of completely electrified two-way car sharing

Leo Strobel (University of Würzburg, Germany); Marco Pruckner (University of Erlangen-Nuremberg, Germany)

1
Car sharing is a more sustainable approach to personal mobility than vehicle ownership, especially if car sharing services electrify their fleets. However, due to the limited range and slow charging process of electric vehicles, car sharing providers might face problems keeping the vehicles adequately charged.

This paper analyzes 4.5 years of real booking data from a German two-way car sharing provider. Among other things, the dataset includes information on the booking time window, driven distance, and location. We use this data to study the customer behavior and simulate the past operation with a completely electrified fleet. Based on the simulation, we determine whether charging problems exist and how they can be solved by adapting the charging rate, battery capacity, and number of charging points.

Our results indicate that the operation of the service with modern electric vehicles is entirely feasible. Per car sharing station, two charging points are sufficient if vehicles do not have to be connected immediately upon arrival (by the customer), but can connect later once another vehicle has finished charging. Somewhat problematic is that 4% of the bookings have a longer driven distance than the vehicle's range. In these cases, the disutility to customers is unclear but, in all likelihood, manageable. Furthermore, we find that 50% of the charging events can be shifted by more than 10 h, indicating significant flexibility that could be utilized for smart charging and the provision of ancillary services in the future.

Pricing and Charging Scheduling for Cooperative Electric Vehicle Charging Stations via Deep Reinforcement Learning

Jie Liu and Xiaoying Tang (The Chinese University of Hong Kong, Shenzhen, China); Shuoyao Wang (Shenzhen University, China)

0
The rapid adoption of electric vehicles (EVs) stimulates the proliferation of charging stations (CSs), motivating the cooperative management of growing CSs. However, cooperative CS management still remains an open problem, due to the uncertain user behavior and heterogeneous service capabilities. To capture the CS dynamics caused by uncertain user behavior, we propose a deep reinforcement learning (DRL)-based cooperation method for multiple CSs, towards maximizing the total profit. The proposed method determines pricing and charging scheduling decisions for CSs, considering stochastic CSs selection and its impact on CSs energy supply. In order to reduce the computational burden of dimensions caused by the time-varying decisions, we design a discretization strategy for action space, based on the current market rule of tiered pricing and CS types. The simulations using real data demonstrate that our proposed method can obtain higher profit than the independent operation and benchmark cooperation algorithms such as Q-learning.
Session Chair and Room

Dragiša Mišković (University of Novi Sad, Serbia) — Room TT2-3

Session SP2

Vulnerabilities and Attack Mitigations in Smart Grids

Conference
3:30 PM — 5:20 PM +08
Local
Oct 27 Thu, 3:30 AM — 5:20 AM EDT

Online Attack-aware Risk Management for PMSG-based Wind Farm Depending on System Strength Evaluation

Hang Du, Jun Yan and Mohsen Ghafouri (Concordia University, Canada); Rawad Zgheib (Hydro-Quebec Research Institute, Canada); Mourad Debbabi (Concordia University, Canada)

0
The retirement of synchronous generators and the increasing capacity of renewable energy sources (RES) have contributed to the decline of system strength in the power grid, leading to weak grid issues such as subsynchronous oscillation (SSO) in permanent magnet synchronous generator (PMSG)-based wind farms. Cyberattacks aimed at reducing the system strength of the power grid may exacerbate the weak grid issues. However, existing solutions either are incomplete for system strength evaluation or exclude the consideration of cyberattacks. To this end, this paper presents a comprehensive system strength evaluation and a short-term system strength prediction process that takes into account the stealthy cyberattacks launched especially when the system strength provision is at its minimum level. In addition, proactive risk management is proposed to retain the grid's system strength for RES plants above the minimum required level even if a cyberattack takes place. The efficacy of the proposed system strength evaluation and risk management based on system strength prediction is demonstrated through case studies in the IEEE 9-bus benchmark.

Analysis of Message Authentication Solutions for IEC 61850 in Substation Automation Systems

Utku Tefek (Advanced Digital Sciences Center, Singapore & University of Illinois Urbana-Champaign, USA); Ertem Esiner (Advanced Digital Sciences Center, Singapore); Daisuke Mashima (Advanced Digital Sciences Center & National University of Singapore, Singapore); Yih-Chun Hu (University of Illinois at Urbana-Champaign, USA)

0
An inevitable consequence of automated control and communication in electric substations is the vulnerability against cyberattacks that compromise the integrity and authenticity of messages. IEC 62351 standard stipulates the use of message authentication solutions, although there is no firm guidance on the exact method to be adopted. The earlier IEC 62351-6:2007 standard recommended the use of digital signatures. However, digital signatures do not meet the timing requirements of IEC 61850 GOOSE and SV. Thus, the recent revisions to IEC 62351-6 backtracked from digital signatures in favor of message authentication code (MAC) algorithms, thereby sacrificing key properties, i.e., scaling well for multiple destinations, easy key distribution and management, public verifiability, and non-repudiation. Following these revisions, tailoring MAC-based algorithms for IEC 61850 message structure has gained traction. Additionally, new message authentication solutions that exploit the small or low entropy messages, such as those in GOOSE and SV, have been proposed to secure time-critical communication. These solutions retain certain key properties of digital signatures within the delay requirements of GOOSE and SV. This paper addresses the key trade-offs and discusses the feasibility of the promising message authentication solutions for IEC 61850 GOOSE and SV. Through their implementation on a low-cost hardware BeagleBoard-X15 we report on the real-world comparison of performance metrics.

Smart Grid Network Flows Best Practices Checker

David Nicol (University of Illinois, Champaign-Urbana, USA); Emily Belovich (University of Illinois Urbana-Champaign, USA); Atul Bohara (Network Perception, USA)

0
We describe BPC, an open source tool and a library of best-practice rules for configuration of smart grid communication networks and the flows they carry. We describe the kinds of rules BPC presently includes, the format of expressing best-practices rules, the way that BPC performs its evaluation and reporting, and application to a case study from a utility's network.

Mitigation of Cyberattacks through Battery Storage for Stable Microgrid Operation

Ioannis Zografopoulos and Panagiotis Karamichailidis (King Abdullah University of Science and Technology (KAUST), Saudi Arabia); Andreas T. Procopiou (Watts Battery Corp., USA); Fei Teng (Imperial College London, United Kingdom (Great Britain)); George C. Konstantopoulos (University of Patras, Greece); Charalambos Konstantinou (KAUST, Saudi Arabia)

0
In this paper, we present a mitigation methodology that leverages battery energy storage system (BESS) resources in coordination with microgrid (MG) ancillary services to maintain power system operations during cyberattacks. The control of MG agents is achieved in a distributed fashion, and once a misbehaving agent is detected, the MG's mode supervisory controller (MSC) isolates the compromised agent and initiates self-healing procedures to support the power demand and restore the compromised agent. Our results demonstrate the practicality of the proposed attack mitigation strategy and how grid resilience can be improved using BESS synergies. Simulations are performed on a modified version of the Canadian urban benchmark distribution model.

Connectivity Preserving Anonymization of Smart Grid Network Configurations

David Nicol (University of Illinois, Champaign-Urbana, USA)

0
We describe a method where a utility can anonymize network device configurations and upload them to a remote service provider who analyzes connectivity (oblivious to the anonymization) and returns the results in anonymized coordinates to the utility, where they are de-anonymized. The approach has application for sharing problematic configurations with vendors, for cloud-based services that analyze connectivity and detect problems, and for connectivity of the sort regulated by NERC-CIP requirements.
Session Chair and Room

Fei Teng (Imperial College London, United Kingdom) — Room LT2

Session BPA

Best Paper Award Ceremony and SmartGridComm 2023 Introduction

Conference
7:30 PM — 8:30 PM +08
Local
Oct 27 Thu, 7:30 AM — 8:30 AM EDT

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