Cost/Privacy Co-optimization in Smart Energy Grids
Alma Pröbstla, Sangyoung Parkb, Sebastian Steinhorstc and Samarjit Chakrabortyd
Technische Universität München, Germany
aalma.proebstl@tum.de
bsangyoung.park@tum.de
csebastian.steinhorst@tum.de
dsamarjit.chakraborty@tum.de
ABSTRACT
The smart energy grid features real-time monitoring of electricity usage such that it can control the generation and distribution of electricity as well as utilize dynamic pricing in response to the demands. For this purpose, smart metering systems continuously monitor the electricity usage of customers, and report it back to the Utility Provider (UP). This raises privacy concerns regarding the undesired exposure of human activity and time-of-use of home appliances. Photovoltaics (PV) and a residential Electrical Energy Storage (EES) have proven to be effective in mitigating the privacy concerns. However, this comes at several costs: Installation of PV and EES, their subsequent aging and the possibly increased electricity cost. We quantify the trade-off between privacy exposure and financial costs by formulating a stochastic dynamic programming problem. Our analysis shows that i) there is a quantifiable trade-off between the financial cost and privacy leakage, ii) proper control of the system is crucial for both metrics, iii) a strategy solely focusing on privacy results in high financial costs, and iv) that for a typical residential setting, the costs for a trade-off solution lie in the range of 600 US$-1700 US$. As the load flattening has a peak shaving effect desirable for UPs, increasing privacy is mutually beneficial for both, customers and UPs.
