Optimal Strategic Pricing Attacks in Smart Grids: A Dynamic Programming Approach

The demand-response (DR) technology in smart grids is designed to improve power system stability by reducing the concurrent peak power demand. The DR is based on dynamic prices where grid operators provide guideline prices that help consumers adapt their consumption. The guideline prices are not usually used in billing, they are provided as an indication of the real-time prices. The savings in the power consumption are driven by the flexibility in electricity demand. Smart homes using the DR technology require support from communication systems associated with the smart grid and is provided by advanced metering infrastructure (AMI). This infrastructure is vulnerable to cyberattacks that can manipulate the guideline prices to shift the demand to periods of the day that cause instability of the grid or high costs on the consumers. Understanding the effects of these vulnerabilities is the key to enabling a resilient and reliable DR system. In this paper we study optimal strategies to shift the demand for two types of attacks: the first is grid instability where the attacker aims at maximizing the time that the peak power demand is above a stability threshold, and the second is an economic attack where the attacker shifts the demand to increase the costs on the consumers. In both cases we model the system as a Markov decision process with nonstationary costs, and use dynamic programming to compute optimal demand-shifting attack policies.