User-Centric Multiobjective Approach to Privacy Preservation and Energy Cost Minimization in Smart Home

Abstract

This paper investigates smart home energy management in consideration of tradeoffs between residential privacy and energy costs. A multiobjective approach that minimizes energy costs and maximizes privacy protection is proposed. The approach leads to a multiobjective optimization problem in which the two objectives are addressed in separate dimensions. A hybrid algorithm that employs stochastic search for power scheduling of home appliances and uses deterministic battery control is developed accordingly. The proposed approach can avoid some drawbacks faced by conventional weighted-sum methods for multiobjective optimization: the combination of objectives in different units, heuristic assignment of weighting coefficients, and possible misrepresentation of user preference.In contrast with existing studies on residential user privacy that assume limited controllability of appliances to facilitate algorithm development, the approach addresses the use of flexible appliances in smart homes. Simulations reveal that the proposed approach can maintain a reasonable energy cost while robustly preserving user privacy at a sensible level; its convergence rate is comparable to existing multiobjective evolutionary algorithms while the proposed approach yields a better level of convergence; the proposed approach is scalable to a group of smart houses, achieving a superior peak-to-average ratio that is beneficial to the stability of the underlying power grid.

Privacy-Preserving Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids Using Functional Encryption

“…However, these methods are adding noise to the measurements; thus, they affect the billing and control functionalities [68]. Therefore, many studies have proposed the use of batteries to mask the energy-consumption statistics [68][69][70]. The advantage of using batteries is that they not only improve privacy but also reduce energy costs when charging and discharging are cleverly controlled.…”

“…The advantage of using batteries is that they not only improve privacy but also reduce energy costs when charging and discharging are cleverly controlled. For this purpose, multiobjective optimization methods have been proposed to simultaneously minimize privacy leakage and energy cost [69,70]. However, these proposals assume that energy prices are set up by the utility company, and the prosumer's active pricing mechanism with P2P trading has not been considered.…”

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

“…Stochastic optimization such as genetic algorithms has been applied to attain a satisfactory load factor as well [25], yielding single-objective optimization problems. Some drawbacks may inherit from such single-objective formulations, such as the need of prior decision making for the tradeoff between objectives [26].…”

A Comparative Study of Deep Neural Network and Meta-Model Techniques in Behavior Learning of Microgrids

“…The main difficulty is that utilities generally have limited access to real-time asset behaviors and models behind the Point of Common Coupling (PCC) with MGs. This problem becomes more severe with the increasing demand for user privacy [2]. Hence, how to deal with this challenge is an urgent issue that needs to be taken in distributed network operation and energy market design.…”