  The next-generation energy network, the so-called smart grid (SG), promises a
tremendous increase in efficiency, safety and flexibility of managing the
electricity grid as compared to the legacy energy network. This is needed today
more than ever, as the global energy consumption is growing at an unprecedented
rate, and renewable energy sources have to be seamlessly integrated into the
grid to assure a sustainable human development. Smart meters (SMs) are among
the crucial enablers of the SG concept; they supply accurate high-frequency
information about users' household energy consumption to a utility provider,
which is essential for time of use pricing, rapid fault detection, energy theft
prevention, while also providing consumers with more flexibility and control
over their consumption. However, highly accurate and granular SM data also
poses a threat to consumer privacy as non-intrusive load monitoring techniques
enable a malicious attacker to infer many details of a user's private life.
This article focuses on privacy-enhancing energy management techniques that
provide accurate energy consumption information to the grid operator, without
sacrificing consumer privacy. In particular, we focus on techniques that shape
and modify the actual user energy consumption by means of physical resources,
such as rechargeable batteries, renewable energy sources or demand shaping. A
rigorous mathematical analysis of privacy is presented under various physical
constraints on the available physical resources. Finally, open questions and
challenges that need to be addressed to pave the way to the effective
protection of users' privacy in future SGs are presented.
