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We discuss a framework for coordinating the response of distributed energy resources (DERs) connected to electric power distribution networks to provide frequency regulation services. These resources include plug-in electric vehicles, thermostatically controlled loads, and microturbines. In this framework, we consider an aggregator that participates in the real-time market by submitting an offer to provide frequency regulation services. If the offer is accepted, the aggregator needs to coordinate the response of a set of DERs. The DERs are compensated through bilateral contracts, the terms of which are negotiated in advance. The DER coordination problem the aggregator is faced with is cast as an optimal control problem, and we propose a bilayer framework to obtain a sub-optimal solution. In the first layer, we utilize model-predictive control techniques driven by regulation signal forecasts and parameter estimates to obtain a reference control signal for the DERs. A second control layer provides closed-loop regulation around the reference computed by the top layer, which minimizes the error that arises due to forecast error, plant-model mismatch, and the slower speed of the optimal control.

Oilfield power demand is extremely dynamic in both time and space, and a lack of accurate forecasting causes increased cost for electric utilities to extend their grids to the field. It also causes increased demand charges for producers, which increases their lifting costs. As well production declines with time, electric utilities may end up with stranded assets which represent a large investment that is costly to maintain but is not being fully utilized. Therefore, both producers and utilities have a common interest in improved load forecasting to cut down on cost of power, especially during times of low oil prices. In this research, we show how data analytics can be used to predict load growth evolution in time and space. We incorporate production operational data, well location and geometry, and historical power consumption of neighboring wells into a data analytics engine to develop a platform for improved oilfield load forecasting. This data-driven approach is shown to decrease a utility's kilowatt prediction error for new well pads by 48 to 78% for annual average power and by 10 to 26% for peak power.

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