Allocative and strategic effects of privacy enhancement in smart grids

Local energy markets are a promising approach for automatic and efficient matching of renewable energy with household demand in smart grids. Therefore, such markets can help to improve power system reliability and at the same reduce emissions. However, to participate in such markets, customers need to disclose private consumption data. A number of studies show that such data records may reveal a broad range of personal, sensitive information on the inhabitants. Privacy-enhancement mechanisms can be applied to preserve the privacy of individuals to modify the data reported to the market. Yet, these mechanisms can lower allocative efficiency and alter theoretical properties of the market mechanism.In this paper, we characterize both theoretically and numerically the effect of privacy mechanisms applied in a local energy market scenario. Our model considers demand side flexibility as well as energy storage systems. Furthermore, we allow for a free specification of the desired privacy enhancement level. We show that under certain natural assumptions market mechanisms retain in-expectation incentive compatibility despite the presence of privacy enhancement. Our numerical analysis based on real-world data shows that the welfare impact of privacy enhancement mechanisms is limited. Furthermore, energy storage can mitigate this efficiency loss to a large extent.