Differentially Private Local Electricity Markets

Privacy-preserving electricity markets have a key role in steering customers towards participation in local electricity markets by guarantying to protect their sensitive information. Moreover, these markets make it possible to statically release and share the market outputs for social good. This paper aims to design a market for local energy communities by implementing Differential Privacy (DP) that provably guarantees the privacy of market participants. Besides achieving a near-optimal solution and preserving the privacy of market participants, the proposed model maintains the utility of the market data for statistical releases. The required randomization for satisfying DP is embedded as Gaussian noise in each iteration of the gradient ascent algorithm underlying the optimization process of the market-clearing problem. In numerical studies, we investigate the impacts of the privacy loss parameter on the output distribution of the market-clearing quantities and payoffs of the market participants. In addition, we demonstrate the inherent trade-off between privacy protection and social welfare in the market under different privacy regimes.