Good afternoon, and thank you for the opportunity to speak today.I’m Neil Beckwith and I represent eMeter a business sector of Siemens.My topic today aims to share some of the ‘real world’ use cases eMeter has developed from experience gained with Smart Grid customers in North America.In particular I would like to make a case for some careful consideration around the potential role for MDM relative to any Smart Grid deployment.
To shine some light on the demand on this last leg of the gridSpecific algorithms to derive kva demand from kwh usage.Once we know the demand at each service point - massive-scale aggregation is possible.
Created hundreds of thousands of virtual meters at the transformer, circuit section, and substation level. Can we start to measure the likelihood of transformers failing?Virtual metering implemented in this way represents a multi million dollar avoided cost. Performance? It’s a lot of data: - One day’s 15 minute interval data aggregatedby distribution transformer for 1.6m meters.Result: 200million records processed = 20m output records in under 120 seconds.Running on IBM Netezza.
Load duration curve for a defined period provides engineers with useful metrics to predict the likelihood of transformer failure based on loading.
DR and TOU pricing are 2 solutions proposed solutions.Question is how do we best use these to drive consumer behavior?First need to understand the data – to identify the problem.
So we start by taking a look at overall peaks that are too high
Once we find those areas of the grid that are most overloaded, we can start to target the most fruitful customers as candidates for DR. ‘Bang for buck’.