Privacy-preserving smart meter control strategy including energy storage losses

Avula, Ramana R.; Oechtering, Tobias J.; Månsson, Daniel

doi:10.1109/isgteurope.2018.8571537

Cited by 12 publications

(14 citation statements)

References 24 publications

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“…Similar to [5] and [7], we use an operational privacy metric corresponding to an unauthorized adversarial hypothesis test. Let C h,ĥ be the normalized cost of the energy management unit (EMU) when the adversary guesses the hypothesis asĥ while h is true.…”

Section: A Privacy Loss Measurementioning

confidence: 99%

“…where F is the Faraday's constant. The energy loss, denoted by L k , and the battery energy state Z k+1 , assuming a constant internal resistance are given by the three-circuit energy storage system model from [7] as…”

Section: B Energy Storage Degradation Modelmentioning

confidence: 99%

“…To evaluate the privacy-cost trade-off, we assume that the three-circuit model [7] along with the generated degradation maps constitute a real ESS, thereby neglecting the error due to the ESS model approximations. We select the Bayesian cost C h,ĥ defined in (1) as C 11 = C 22 = 1 and C 12 = C 21 = 0 such that the Bayesian privacy risk corresponds to the average adversarial detection probability.…”

Section: B Evaluation Of Privacy-cost Trade-offmentioning

confidence: 99%

“…Energy storage systems (ESSs) are key components in sustainable smart grids that act as a buffer for fluctuations in the grid. ESSs are also considered as a promising physical layer solution to the smart meter (SM) privacy problem [1]- [7]. This privacy-by-design approach, also known as load signature moderation, uses ESSs to alter the consumers' energy consumption profile in order to hide the appliances' usage pattern.…”

Section: Introductionmentioning

confidence: 99%

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Smart Meter Privacy Control Strategy Including Energy Storage Degradation

Avula

Oechtering

Chin

et al. 2019

2019 IEEE Milan PowerTech

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In this paper, we present a degradation-aware privacy control strategy for smart meters by taking into account the capacity fade and energy loss of the battery, which has not been included previously. The energy management strategy is designed by minimizing the weighted sum of both privacy loss and total energy storage losses, where the weightage is set using a trade-off parameter. The privacy loss is measured in terms of Bayesian risk of an unauthorized hypothesis test. By making firstorder Markov assumptions, the stochastic parameters of energy loss and capacity fade of the energy storage system are modelled using degradation maps. Using household power consumption data from the ECO dataset, the proposed control strategy is numerically evaluated for different trade-off parameters. Results show that, by including the degradation losses in the design of the privacy-enhancing control strategy, significant improvement in battery life can be achieved, in general, at the expense of some privacy loss.

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Section: A Privacy Loss Measurementioning

confidence: 99%

Section: B Energy Storage Degradation Modelmentioning

confidence: 99%

Section: B Evaluation Of Privacy-cost Trade-offmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smart Meter Privacy Control Strategy Including Energy Storage Degradation

Avula

Oechtering

Chin

et al. 2019

2019 IEEE Milan PowerTech

Self Cite

View full text Add to dashboard Cite

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“…The RB charging and discharging process is assumed ideal without any losses (see [6] for a model with energy losses). E t ∈ E:={0, B max } units of energy is generated by the RES at the beginning of each time slot t; that is, when the renewable energy arrives, it completely recharges the RB, and it can be used by the appliances only through the RB.…”

Section: Introductionmentioning

confidence: 99%

Privacy-cost Trade-off in a Smart Meter System with a Renewable Energy Source and a Rechargeable Battery

Erdemir

Dragotti

Gündüz

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We study the privacy-cost trade-off in a smart meter (SM) system with a renewable energy source (RES) and a finitecapacity rechargeable battery (RB). Privacy is measured by the mutual information rate between the energy demand and the energy received from the grid, where the latter also determines the cost, and hence, reported by the SM to the utility provider (UP). We consider a renewable energy generation process that fully charges the RB at random time instants, and its realization is assumed to be known also by the UP. We reformulate the problem as a Markov decision process (MDP), and solve it by dynamic programming (DP) to design battery charging and discharging policies that minimize a linear combination of the privacy leakage and energy cost. We also propose a lower bound and two alternative low-complexity energy management policies, one of which is shown numerically to perform close to the MDP solution.Index Terms-Home energy management, Markov decision processes, privacy, smart meter.

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