Design Framework for Privacy-Aware Demand-Side Management With Realistic Energy Storage Model

Abstract

This is the accepted version of a paper published in IEEE Transactions on Smart Grid. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.

Adversarial Inference Control in Cyber-Physical Systems: A Bayesian Approach With Application to Smart Meters

“…Several analytical techniques have been proposed in literature that quantify privacy using differential privacy measures [9], information-theoretic measures such as mutual information [10]- [13], conditional entropy [14], and others, providing axiomatic guarantees on the maximum possible information leakage. Detection-theoretic privacy-enhancing techniques, on the other hand, offer operational privacy guarantees, such as protection against hypothesis tests [3], [15]- [17]. Related to controller-aware adversarial hypothesis testing, few attempts have been made in the literature to develop control policies to worsen adversarial detection performance.…”

“…We model the storage system's state transitions using a first-order Markov model characterized by the conditional distribution P Zk+1|Zk ,Dk , where Z k represents the quantized value of the storage system state on a finite discrete alphabet Z. We estimate the conditional distribution P Zk+1|Zk ,Dk using Monte Carlo simulations and a sample-based density estimation approach [3]. In this work, we further simplify the storage system model by parametrizing the conditional distribution P Zk+1|Zk ,Dk for each (z k , d k ) ∈ Z × D using the beta distribution.…”

“…This situation presents a significant challenge for the user as the adversary is well-equipped to exploit any weaknesses in the control strategy, potentially leading to the exposure of private information about the user's habits, preferences, or lifestyle. In our previous work [3], we studied the problem of optimally controlling the sequential Bayesian hypothesis testing (SBHT) of an adversary who is unaware of the presence of a control system. In this work, we address an even stronger privacy question: How can a user protect their privacy against an adversary who has perfect knowledge about the control strategy employed by the user?…”

Local Energy Communities in Service of Sustainability and Grid Flexibility Provision: Hierarchical Management of Shared Energy Storage

“…The fundamental idea is to obfuscate the data in order to prevent inference of sensitive information. The most common strategies are either using data manipulation techniques to alter smart meter data [59] or user demand shaping through physical devices such as an energy storage system or flexible thermal devices [60], [61].…”

Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

“…A model was presented for a residential energy management system to dispatch battery energy storage in a market-based setting [8]. A privacy-aware framework was presented for utility-driven demand-side management with a realistic energy storage system model [9]. However, the economic viability of using BESSs to provide various services with a large scale is questionable due to their high investment costs [10].…”