Random Perturbation Scheme Research Articles

Perturbation-Based Schemes with Ultra-Lightweight Computation to Protect User Privacy in Smart Grid

In smart grid, smart meters are deployed to collect power consumption data periodically, and the data are analyzed to improve the efficiency of power transmission and distribution. The collected consumption data may leak the usage patterns of domestic appliances, so that it may damage the behavior privacy of customers. Most related work to protect data privacy in smart grid relies on cryptographic primitives, for example, encryption, which induces a large amount of power consumption overhead. In this paper, we make the first attempt to propose solutions without any cryptographic computation to protect user privacy. The privacy in smart grid is formally defined in the paper. Three schemes are proposed: random perturbation scheme (RPS), random walk scheme (RWS), and distance-bounded random walk with perturbation scheme (DBS). Three algorithms are also proposed in each scheme, respectively. All schemes are ultra-lightweight in terms of computation without relying any cryptographic primitive. The privacy, soundness, and accuracy of proposed schemes are guaranteed and justified by strict analysis.

Simultaneous estimation and variable selection in median regression using Lasso-type penalty

We consider the median regression with a LASSO-type penalty term for variable selection. With the fixed number of variables in regression model, a two-stage method is proposed for simultaneous estimation and variable selection where the degree of penalty is adaptively chosen. A Bayesian information criterion type approach is proposed and used to obtain a data-driven procedure which is proved to automatically select asymptotically optimal tuning parameters. It is shown that the resultant estimator achieves the so-called oracle property. The combination of the median regression and LASSO penalty is computationally easy to implement via the standard linear programming. A random perturbation scheme can be made use of to get simple estimator of the standard error. Simulation studies are conducted to assess the finite-sample performance of the proposed method. We illustrate the methodology with a real example.

A tabu search based hybrid optimization approach for a fuzzy modelled unit commitment problem

This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid tabu search (TS), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid TS–PSO–SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid TS–PSO–SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. TS are used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the TS method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO–SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system.

A Simulated Annealing Based Hybrid Optimization Approach for a Fuzzy Modelled Unit Commitment Problem

This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid simulated annealing (SA), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid SA-PSO-SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid SA-PSO-SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. SA is used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the SA method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO-SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system.