Title: Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Abstract

Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This workmore » then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.« less

Authors:

Hatalis, Kostas  ^[1];

• Search DOE PAGES for author "Hatalis, Kostas"
• Search DOE PAGES for ORCID "0000-0001-8191-5728"

Zhao, Chengbo  ^[1];

• Search DOE PAGES for author "Zhao, Chengbo"
• Search DOE PAGES for ORCID "0000-0002-1797-7454"

Venkitasubramaniam, Parv ^[2]; Snyder, Larry  ^[3];

• Search DOE PAGES for author "Snyder, Larry"
• Search DOE PAGES for ORCID "0000-0002-2227-7030"

Kishore, Shalinee ^[2]; Blum, Rick S. ^[2]

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+ Show Author Affiliations

1. Lehigh Univ., Bethlehem, PA (United States). Dept. of Electrical and Computer Engineering
2. Lehigh Univ., Bethlehem, PA (United States). Faculty of Electrical and Computer Engineering
3. Lehigh Univ., Bethlehem, PA (United States). Faculty of Industrial and Systems Engineering

Publication Date:

Fri Aug 21 00:00:00 EDT 2020

Research Org.:

Univ. of Arkansas, Fayetteville, AR (United States)

Sponsoring Org.:

USDOE Office of Electricity (OE)

OSTI Identifier:

1801317

Grant/Contract Number:  

OE0000779

Resource Type:

Accepted Manuscript

Journal Name:

Energies

Additional Journal Information:

Journal Volume: 13; Journal Issue: 17; Journal ID: ISSN 1996-1073

Publisher:

MDPI

Country of Publication:

United States

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING; Energy & Fuels; demand side management; demand response; cyber-physical systems; dynamic pricing; load forecasting; attack detection