Abstract
Communication through social media can help engage end users to improve the efficiency of demand-side management in smart power grids. However, this opens a channel between the social network and the power grid through which malicious attackers can publish false information that can actually cause problems to the power grid. In this paper, we analyze this new problem by modeling a social network-coupled smart grid and investigating its vulnerability to false pricing attacks in the social network. The energy consumption profile based on social information is modeled as a consumption rescheduling problem, which aims to maximize the benefit of demand-side management. The false price spreading process is described by a multi-level influence propagation model, which takes into account the personalities of the end users. Different attack strategies are considered and the power operator's response is modeled. The residual ampacity of distribution lines and the expected energy not supplied are adopted to quantify the impacts of the attacks on the power system. To account for the stochastic characteristics of the influence propagation process, Monte Carlo simulation is utilized. The proposed modeling and analysis framework is applied on a modified IEEE 13 nodes test feeder and a notional social network. The vulnerability to attack is analyzed at both component and system levels.
Highlights
Smart grids (SGs) are systems of systems, which integrate power grids with information and communication networks
We propose a stochastic multi-level influence propagation model by taking into account the users psychological characteristics and the anticipated benefit of scheduling consumption based on future prices
SOCIAL SMART GRID MODEL we describe the social smart grids (SSGs) model, including the power grid model, the social network model, the interdependence between power grid and social network, and the consumption rescheduling model
Summary
Smart grids (SGs) are systems of systems, which integrate power grids with information and communication networks. In the past few decades, various methods are investigated to maximize the influence of social networks [14], which can help actively improve the influence level of consumers In this regard, integration of the SNs provides a potential solution for consumers to learn from others, get easy access to information such as future electricity prices and change their decision making on consumption [15]. For electricity users having social media accounts, when they receive (false) future prices on SN, they may spread the information and reschedule their consumption according to the extent they are influenced. We propose a stochastic multi-level influence propagation model by taking into account the users psychological characteristics and the anticipated benefit of scheduling consumption based on future prices.
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