The Grid with Intelligent Periphery

Abstract

Pressing environmental problems, energy supply security issues, and nuclear power safety concerns drive the worldwide interest in renewable energy. Renewable energy sources such as wind and solar exhibit variability: they are not dispatchable, exhibit large fluctuations, and are uncertain. Variability is the most important obstacle to deep integration of renewable generation. The current approach is to absorbthis variability in operating reserves. This works at today’s modest penetration levels. But it will not scale. At deep penetration levels (>30%) the levels of necessary reserves are economically untenable, and defeat the net carbon benefit. 

So how can we economically enable deep penetration of renewable generation? The emerging consensus is that much this new generation must be placed at hundreds of thousands of locations in the distribution system, and that the attendant variability can be absorbed by the coordinated aggregation and control of distributed resources such as storage, programmable loads, and smart appliances. Tomorrow’s grid will have an intelligent periphery. 

We will explore the architectural and algorithmic components for managing this intelligent periphery. Clusters of distributed energy resources are coordinated to efficiently and reliably offer services (ex: bulk power, regulation) in theface of uncertainty (ex: renewables, consumers). We begin by formulating a general class of stochastic dynamic programming problems that arise in the context of coordinated aggregation. We then consider specific real-time scheduling problems for allocating power to resources. We show that no causal optimal policy exists that respects rate constraints (ex: maximum EV charging rates). Next, we explore the benefits of coordinated aggregation in the metric of operating reserves savings. We close by suggesting several challenging problems in monetizing and incentivizing resource participation.