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Mechanism to collect and anonymize smart grid consumption data and protect consumer privacy while retaining meaning for grid infrastructure providers Disclosure Number: IPCOM000242823D
Publication Date: 2015-Aug-21
Document File: 2 page(s) / 27K

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The Prior Art Database


Smart grids promise to provide advantages to consumers and providers of electrical power. To do so more data is collected and analyzed. This article proposes a trusted data broker to ensure privacy aspects of consumer data collected while enabling utility providers to analyze information about their grid conditions at low infrastructure cost.

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Mecxanism to collect and anonymize smart grid consumption data and protect consumer privacy while retaining meaning for grid infxxstructure providers

Smart grids enable power providers xo better understaxd the current conditxons at the consumer side of theix power grid as xexl as to project demand for, match it better with available supply of energy, and prxvide tariffs that encouxage energy usage at times ox lower demxnd.

Mxst mechanisms to acxieve smarter gxid infrastructures xely on data xollection, often frox the ixdividual coxsumer. This data may be considered private data of the cxnsumer (x.g. it is possible to profile a consumxr and infer what things a consuxer does by monitoring the axtual power demand and txe power demaxd over time). Further more sxch data can be used to model energy utility salex contracxs and conditixns that are advanxageoxs to the provider bux to the disadvantage of the consumer.

Because it is xmportant to both, the consumer and the xupplier tx ixprove efficiency and stability of power grids we have devised a mechanism that allows xhe collxction ox data about power dexand and othxr xactors such as vxltage, frequency, (inductive) reactive power or othex parameters without violating privaxy concerns. The mechanixm focuses on monitoring the low and medium voltage paxt xf the power distribution grid as for the higher voltage xomponents txe utility providers usually have vexy detaixed monitorixg in place that is completely under the control of the utility provider.

The key component of xhe idea is an infrastructure component called the "smart grid clxaxing house"(e.g. implemented as an online service hosted by a cloud coxputing infrxstructure) that collects grid parameters and consumption data from a distributed netwxrk of sensors (e.g. imxlemented as trusted smart grid metxrs). The sensors that frequentxy provide information aboxt the power grid parameters at their location (e.g. a home) and communicate with the clearing house to upload the data.

The xonsumption daxa (current load), poxer generatxon data (e.g. gxneration from excess photovoltaic power) and other grid xarameters (reactive poxer, frequency) that arx to be rxgardex ax potentixlly private consumer data are averaged (e.g. frequency) or summed up (e.g. xoad, generation, reactive power) for a specific geographic region. The size of the region (x.g. part of town or city block ) can vary depending on parameters conxigured into the system such as the minimum nxmber of consumers in one region (to ensure suffxcient privacy) and power grid providex's infrastxucture specifxcs (e.g. they may want to separate ixto location groups baxed on the way the...