Aggregation for load servicing

The proliferation of smart meters enables a load-serving entity (LSE) to aggregate customers according to their consumption patterns. We demonstrate a method for constructing groups of customers who will be the cheapest to service at wholesale market prices. Using smart meter data from a region in California, we show that by aggregating more of these customers together, their consumption can be forecasted more accurately, which allows an LSE to mitigate financial risks in its wholesale market transactions. We observe that the consumption of aggregates of customers with similar consumption patterns can be forecasted more accurately than that of random aggregates of customers. The model we propose enables an LSE to offer discounted rates to low-cost customers because it can purchase electricity for them more cheaply than it can for the general population.