Stability of Transactive Energy Market-Based Power Distribution System Under Data Integrity Attack

Future smart power distribution system with multiple active consumers and aggregators exchanging real-time electricity pricing and electricity consumption information over a communication network is prone to cyberattack. The goal of this paper is to understand the impact of attacks on pricing/load signals on the physical grid within a transactive energy market framework. First, this paper models the interaction between real-time electricity price and total energy demand in the form of a discrete time nonlinear autonomous dynamical system. Second, equilibrium electricity price and energy demand associated with this coupled dynamical system is derived and conditions for bounded input bounded output (BIBO) stability are identified. Third, a BIBO stable algorithm to design real-time electricity pricing scheme from a techno-economic perspective is developed. Finally, the impact of various levels of false data injection (FDI) attack on price of electricity, demand, and distribution system voltage is investigated. The proposed model is analyzed using simulations on the IEEE 69-bus test system and the impact of FDI attack on both electricity price/demand and distribution grid voltage is quantified. This paper shows that impact of FDI attack on electricity prices is more severe than an attack on electricity demand.

[1]  Pierluigi Mancarella,et al.  Automated Demand Response From Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints , 2015, IEEE Transactions on Smart Grid.

[2]  Linqiang Ge,et al.  A novel architecture against false data injection attacks in smart grid , 2012, 2012 IEEE International Conference on Communications (ICC).

[3]  Zuyi Li,et al.  Quantitative Analysis of Load Redistribution Attacks in Power Systems , 2012, IEEE Transactions on Parallel and Distributed Systems.

[4]  S. Shankar Sastry,et al.  Secure Control: Towards Survivable Cyber-Physical Systems , 2008, 2008 The 28th International Conference on Distributed Computing Systems Workshops.

[5]  Balasubramaniam Natarajan,et al.  Probabilistic voltage sensitivity analysis (PVSA) for random spatial distribution of active consumers , 2018, 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[6]  Coordinated electric vehicle charging solutions using renewable energy sources , 2014, 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG).

[7]  Balasubramaniam Natarajan,et al.  Prospect Theory-Based Active Consumer Behavior Under Variable Electricity Pricing , 2019, IEEE Transactions on Smart Grid.

[8]  Zhao Yang Dong,et al.  A Review of False Data Injection Attacks Against Modern Power Systems , 2017, IEEE Transactions on Smart Grid.

[9]  A. Faruqui,et al.  Household response to dynamic pricing of electricity: a survey of 15 experiments , 2010 .

[10]  Pan Li,et al.  Multi-Objective Optimal Energy Consumption Scheduling in Smart Grids , 2013, IEEE Transactions on Smart Grid.

[11]  Lang Tong,et al.  Malicious Data Attacks on the Smart Grid , 2011, IEEE Transactions on Smart Grid.

[12]  Peng Ning,et al.  False data injection attacks against state estimation in electric power grids , 2009, CCS.

[13]  Zhihua Qu,et al.  Enhanced protection against false data injection by dynamically changing information structure of microgrids , 2012, 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[14]  Hao Liang,et al.  Multi-agent transactive energy management system considering high levels of renewable energy source and electric vehicles , 2017 .

[15]  Abhishek Somani,et al.  AEP Ohio gridSMART Demonstration Project Real-Time Pricing Demonstration Analysis , 2014 .

[16]  Balasubramaniam Natarajan,et al.  Coordinated Electric Vehicle Charging for Commercial Parking Lot with Renewable Energy Sources , 2017 .

[17]  Balasubramaniam Natarajan,et al.  Real-Time Differential Pricing Scheme for Active Consumers with Electric Vehicles , 2017 .

[18]  Balasubramaniam Natarajan,et al.  Data-Driven Preemptive Voltage Monitoring and Control Using Probabilistic Voltage Sensitivities , 2019, 2019 IEEE Power & Energy Society General Meeting (PESGM).

[19]  Balasubramaniam Natarajan,et al.  Probabilistic Voltage Sensitivity Analysis (PVSA)—A Novel Approach to Quantify Impact of Active Consumers , 2018, IEEE Transactions on Power Systems.

[20]  Balasubramaniam Natarajan,et al.  Impact of Real-Time Pricing Attack on Demand Dynamics in Smart Distribution Systems , 2018, 2018 North American Power Symposium (NAPS).

[21]  B. K. Panigrahi,et al.  Joint-Transformation-Based Detection of False Data Injection Attacks in Smart Grid , 2018, IEEE Transactions on Industrial Informatics.

[22]  Kumarsinh Jhala Coordinated electric vehicle charging with renewable energy sources , 2015 .

[23]  Wei Xing Zheng,et al.  Distributed Load Sharing Under False Data Injection Attack in an Inverter-Based Microgrid , 2019, IEEE Transactions on Industrial Electronics.