Integration of intermittent resources with price-responsive loads

This paper concerns the near-real time supply and demand balancing problem in the changing electric energy industry. In particular, potential dispatch problems in the presence of hard-to-control wind and solar power have emerged as the key industry concern. Our work evolves around the idea that the more predictive information about wind power output is known and the more carefully this information is included dynamically, the more effective integration of intermittent power will be. In this paper we recognize that a combination of predicted wind power and ahead-of-time knowledge of customers' willingness to adjust their consumption at a price could result in even more effective system-wide supply and demand balancing. We generalize our recent work which is based on the look-ahead model-predictive control approach to the wind power integration. A Markov model for predicting wind power is used to determine the optimal amount of wind power to be sent to the grid in order to balance demand while accounting for the limiting ramping rates of other power plants. As expected, utilizing the full wind power output is sub-optimal and different technologies will be needed to utilize this clean energy resource fully as it is made available. Different storage technologies as well as adaptive demand-side response are such key candidates. In this paper we generalize our recently proposed look-ahead model predictive dispatch to integrate the price-responsive demand. We show using the IEEE 14 bus example potential benefits from simultaneous dispatch of both conventional power plants characterized by their inherently limiting ramping rates and the price-responsive demand characterized by its demand function. A comparison is made to the dispatch without active demand-side adaptation.