496 research outputs found
Load Hiding of Household's Power Demand
With the development and introduction of smart metering, the energy
information for costumers will change from infrequent manual meter readings to
fine-grained energy consumption data. On the one hand these fine-grained
measurements will lead to an improvement in costumers' energy habits, but on
the other hand the fined-grained data produces information about a household
and also households' inhabitants, which are the basis for many future privacy
issues. To ensure household privacy and smart meter information owned by the
household inhabitants, load hiding techniques were introduced to obfuscate the
load demand visible at the household energy meter. In this work, a
state-of-the-art battery-based load hiding (BLH) technique, which uses a
controllable battery to disguise the power consumption and a novel load hiding
technique called load-based load hiding (LLH) are presented. An LLH system uses
an controllable household appliance to obfuscate the household's power demand.
We evaluate and compare both load hiding techniques on real household data and
show that both techniques can strengthen household privacy but only LLH can
increase appliance level privacy
Smart Meter Privacy: A Utility-Privacy Framework
End-user privacy in smart meter measurements is a well-known challenge in the
smart grid. The solutions offered thus far have been tied to specific
technologies such as batteries or assumptions on data usage. Existing solutions
have also not quantified the loss of benefit (utility) that results from any
such privacy-preserving approach. Using tools from information theory, a new
framework is presented that abstracts both the privacy and the utility
requirements of smart meter data. This leads to a novel privacy-utility
tradeoff problem with minimal assumptions that is tractable. Specifically for a
stationary Gaussian Markov model of the electricity load, it is shown that the
optimal utility-and-privacy preserving solution requires filtering out
frequency components that are low in power, and this approach appears to
encompass most of the proposed privacy approaches.Comment: Accepted for publication and presentation at the IEEE SmartGridComm.
201
A Lightweight Privacy-Preserved Spatial and Temporal Aggregation of Energy Data
Smart grid provides fine-grained real time energy consumption, and it is able to improve the efficiency of energy management. It enables the collection of energy consumption data from consumer and hence has raised serious privacy concerns. Energy consumption data, a form of personal information that reveals behavioral patterns can be used to identify electrical appliances being used by the user through the electricity load signature, thus making it possible to further reveal the residency pattern of a consumer’s household or appliances usage habit. This paper proposes to enhance the privacy of energy con- sumption data by enabling the utility to retrieve the aggregated spatial and temporal consumption without revealing individual energy consumption. We use a lightweight cryptographic mech- anism to mask the energy consumption data by adding random noises to each energy reading and use Paillier’s additive homo- morphic encryption to protect the noises. When summing up the masked energy consumption data for both Spatial and Temporal aggregation, the noises cancel out each other, hence resulting in either the total sum of energy consumed in a neighbourhood at a particular time, or the total sum of energy consumed by a household in a day. No third party is able to derive the energy consumption pattern of a household in real time. A proof-of- concept was implemented to demonstrate the feasibility of the system, and the results show that the system can be efficiently deployed on a low-cost computing platform
Smart Meter Privacy with Renewable Energy and a Finite Capacity Battery
We address the smart meter (SM) privacy problem by considering the
availability of a renewable energy source (RES) and a battery which can be
exploited by a consumer to partially hide the consumption pattern from the
utility provider (UP). Privacy is measured by the mutual information rate
between the consumer's energy consumption and the renewable energy generation
process, and the energy received from the grid, where the latter is known by
the UP through the SM readings, and the former two are to be kept private. By
expressing the information leakage as an additive quantity, we cast the problem
as a stochastic control problem, and formulate the corresponding Bellman
equations.Comment: To appear in IEEE SPAWC 201
Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning
Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns. Recent scholarly efforts have focused on developing battery-aided load-shaping techniques to protect user privacy while balancing costs. This paper proposes a novel deep reinforcement learning-based load-shaping algorithm (PLS-DQN) designed to protect user privacy by proactively creating artificial load signatures that mislead potential attackers. We evaluate our proposed algorithm against a non-intrusive load monitoring (NILM) adversary. The results demonstrate that our approach not only effectively conceals real energy usage patterns but also outperforms state-of-the-art methods in enhancing user privacy while maintaining cost efficiency. PLS-DQN reduces the F1 score for the NILM adversary’s classification results by 95% and 92% for the on/off status of two common appliances: kettle and toaster, respectively. When compared to the state-of-the-art DDQL-MI model, PLS-DQN not only lowers the F1 score by 84% and 79% respectively but also achieves a 42% reduction in household electricity costs
Privacy-Protecting Energy Management Unit through Model-Distribution Predictive Control
The roll-out of smart meters in electricity networks introduces risks for
consumer privacy due to increased measurement frequency and granularity.
Through various Non-Intrusive Load Monitoring techniques, consumer behavior may
be inferred from their metering data. In this paper, we propose an energy
management method that reduces energy cost and protects privacy through the
minimization of information leakage. The method is based on a Model Predictive
Controller that utilizes energy storage and local generation, and that predicts
the effects of its actions on the statistics of the actual energy consumption
of a consumer and that seen by the grid. Computationally, the method requires
solving a Mixed-Integer Quadratic Program of manageable size whenever new meter
readings are available. We simulate the controller on generated residential
load profiles with different privacy costs in a two-tier time-of-use energy
pricing environment. Results show that information leakage is effectively
reduced at the expense of increased energy cost. The results also show that
with the proposed controller the consumer load profile seen by the grid
resembles a mixture between that obtained with Non-Intrusive Load Leveling and
Lazy Stepping.Comment: Accepted for publication in IEEE Transactions on Smart Grid 2017,
special issue on Distributed Control and Efficient Optimization Methods for
Smart Gri
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