Privacy-preserving spectral estimation in smart grid

Abstract

Inter-area Oscillations pose serious threats to the stability of power transmission grids. Failure to notice or to handle inter-area oscillations may lead to widespread catastrophic blackouts. It is, therefore, critically important to monitor the oscillation conditions such that timely mitigation controls can be applied at the early stage. Recent research shows that it is an effective approach to continuously track the frequency domain of ambient phasor measurement units (PMU) data for the monitoring purpose. However, since inter-area oscillations usually involve multiple utility companies, tracking them requires PMU data from some different trust domains, which raises data privacy concerns. To address these concerns, we proposed privacy-preserving multiparty spectral estimation, which enables power grid operators to obtain the power spectrum of PMU measurement data, while the data remain encrypted. A case study was also carried out, which showed that the proposed scheme is secure, efficient, and practical.

Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid

“…In practice, as discussed in Section IV-B, the Paillier cryptosystem involves only additions and multiplications. Therefore, in [27], the authors use a solution by multiplying each floating number with a constant K. However, the selection of K must guarantee that we do not overflow the Paillier cryptosystem plaintext space during the entire operation. To guarantee this, the selection of K must satisfy:…”

“…[26] tries to securely outsource widely applicable linear programming (LP) computations by applying affine mapping on decision variables, which will transform the original vector space to a different one. [27] proposes a novel scheme that enables privacy-preserving multi-party spectral estimations, which conduct spectral estimation directly over the encrypted synchrophasors to limit privacy breaches. However, both works only deal with the minimal scenario which cannot be applied to solve more complicated problems such as SCOPF.…”