Algorithms in the Field

One of the great new NSF programs in recent years is the introduction of the “Algorithms in the Field” program, which is a joint initiative from the CCF, CNS, and IIS divisions in CISE.  It’s goal is almost a direct match with what I try to do with my research:  it “encourages closer collaboration between (i) theoretical computer science researchers [..] and (ii) other computing and information researchers [..] very broadly construed”.  The projects it funds are meant to push the boundaries of theoretical tools and apply them in a application domain.

Of course this is perfectly suited to what we do in RSRG at Caltech!  We missed the first year of the call due to bad timing, but we submitted this year and I’m happy to say it was funded (over the summer when I wasn’t blogging)!

The project is joint with Steven Low, Venkat Chandrasekaran, and Yisong Yue and has the (somewhat generic) title “Algorithmic Challenges in Smart Grids: Control, Optimization, and Learning.

For those who are curious, here’s the quick and dirty summary of the goal…taken directly from the proposal.

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Videos on universal laws and architectures

I’ve posted the beginnings of what I hope will become an extensive library of videos, papers, notes, and slides exploring in more detail both illustrative case studies and theoretical foundations for the universal laws and architectures that I superficially referred to in my previous blog posts.  For the moment, these are simply posted on dropbox, so be sure to download them, since looking at them in a browser may only give a preview…

I’m eager to get feedback on any aspects of the material, and all the sources are available for reuse.

In addition to the introductory and overview material, of particular interest might be a recent paper on heart rate variability, one of the most persistent mysteries in all of medicine and biology, which we resolve in a new but accessible way.  There are tutorial videos in addition to the paper for download.

Business case for DER and utility

Climate and energy are critical, massive, and complex issues.  Whatever we talk about, it will be just a small piece of the overall puzzle and, by definition, unbalanced.  This post collects some tidbits that point to an underlying trend, focusing on the most commonly asked question “is there a business case for smart grid?” This trend suggests an indispensable role for distribution utility of the future.

Accelerating pace of DER (distributed energy resources)

I’m pleasantly surprised by the NYT report today (Dec 1, 2014) that one of the world’s largest investor-owned electric utilities, E.On of Germany, has decided to split itself into two, one focusing on the less (!) risky business of renewables and distribution, and the other on the more risky conventional generation business of coal, nuclear and natural gas.   “We are seeing the emergence of two distinct energy worlds,” E.On’s CEO said.  In case you think this is an irrational impulsive move, a financial analyst estimated that of E.On’s 9.3 billion euro in pretax profits in 2013, more than half came from the greener, more predictable businesses. The utility industry has entered a period of disequilibrium in recent years, contemplating how best to leverage emerging technologies and evolve their business models (we will return to this point below).  Initial response to E.On’s decision: its share price rose about 5% today.  E.On said it will present a plan in 2016 to spin off most of the unit that currently holds the conventional generation.

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A report from “The mathematics of planet earth”

The mathematics of planet earth is a joint initiative from a consortium of mathematical sciences organizations around the world (organized nominally by DIMACS) that has the goal of showcasing how mathematics can be useful in tackling our world’s problems.  It started last year as a year-long focus, but has now expanded and will continue for the coming years as well.   I’ve been to a few events organized under this program, but the reason for this post is to highlight the recent workshop on “Data-aware energy use” organized at UCSD a week or so ago.

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A Network of Intelligent DER

Energy and the environment are probably the most critical and massive problems of our time. The transformation of our energy system into a more sustainable form will take decades, determination, and sacrifices. In the case of power networks, several powerful trends are driving major changes. In this post, we will look at two of them.

The first trend is the accelerating penetration of distributed energy resources (DER) around the world. These DER include photovoltaic (PV) panels, wind turbines, electric vehicles, storage devices, smart appliances, smart buildings, smart inverters, and other power electronics. Their growth is driven by policies and incentive programs. California, for instance, has ambitious policy goals such as:

  • Renewable Portfolio Standard (2002): 33% of retail electricity will be procured from renewable sources by 2020.
  • Global Warming Solutions Act (2006): Reduce greenhouse gas emission to 1990 level by 2020.
  • California Solar Initiative (2007): Offers solar rebates for customers of three CA investor-owned utilities, from 2007 – 2016.
  • ZNE homes (2007): All new residential construction will be zero net energy by 2020.
  • Energy storage target (2010): The three investor-owned utilities will deploy 1.325 GW of non-hydro storage by 2020.

Leading the world, in terms of percentage share of non-hydro renewable generations (at approximately 20% now), is Germany.  Its relentless push for renewables, in the face of technical and financial challenges, will no doubt help find a way forward and benefit us all.  See a recent New York Times article, where a proud German reader commented, “And that’s what I love about my country, it is a pain, it causes frustration and malice, but nobody questions the vision.”   The question is not whether we should move to a sustainable future, but how we overcome the many challenges on the way (e.g., see Adam’s earlier post about Germany’s challenges), and the earlier we start, the less painful the process will be.

The second trend is the growth of sensors, computing devices, and actuators that are connected to the Internet. Cisco claims that the number of Internet-connected “things” exceeded the number of people on earth in 2008, and, by 2020, the planet will be enveloped in 50 billion such “Internet-of-things.”  Just as Internet has grown into a global platform for innovations for cyber systems in the last 20 years, Internet-of-things will become a global platform for innovations in cyber-physical systems.  Much data will be generated at network edges. An important implication on computing is that, instead of bringing data across the network to applications in the cloud, we will need to bring applications to data. Distributed analytics and control will be the dominant paradigm in such an environment. This is nicely explained by Michael Enescu (a Caltech alum!) in a recent keynote.

The confluence of these two trends points to a future where there are billions of DER, as well as sensing, computing, communication, and storage devices throughout our electricity infrastructure, from generation to transmission and distribution to end use. Unlike most endpoints today which are merely passive loads, these DER are active endpoints that not only consume, but can also generate, sense, compute, communicate, and actuate. They will create both a severe risk and a tremendous opportunity: a large network of DER introducing rapid, large, frequent, and random fluctuations in power supply and demand, voltage and frequency, and our increased capability to coordinate and optimize their operation in real time.

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Data centers & Energy: Did we get it backwards?

The typical story surrounding data centers and energy is an extremely negative one: Data centers are energy hogs.  This message is pervasive in the media, and it certainly rings true.  However, we have come a long way in the last decade, and though we certainly still need to “get our house in order” by improving things further, the most advanced data centers are quite energy-efficient at this point.  (Note that we’ve done a lot of work in this area at Caltech and, thanks to HP, we are certainly glad to see it moving into industry deployments.)

But, the view of data centers as energy hogs is too simplistic.  Yes, they use a lot of energy, but energy usage is not a bad thing in and of itself.  In the case of data centers, energy usage typically leads to energy savings.  In particular, moving things to the cloud is most often a big win in terms of energy usage…

More importantly, though, the goal of this post is to highlight that, in fact, data centers can be a huge benefit in terms of integrating renewable energy into the grid, and thus play a crucial role in improving the sustainability of our energy landscape.

In particular, in my mind, a powerful alternative view is that data centers are batteries.  That is, a key consequence of energy efficiency improvements in data centers is that their electricity demands are very flexible.  They can shed 10%, 20%, even 30% of their electricity usage in as little as 10 minutes by doing things such as precooling, adjusting the temperature, demand shifting, quality degradation, geographical load balancing, etc.  These techniques have all been tested at this point in industry data centers, and can be done with almost no performance impact for interactive workloads!

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Communication and Power Networks: Forward Engineering (Part II)

In Part I of this post, I have explained the idea of reverse and forward engineering, applied to TCP congestion control.   Here, I will describe how forward engineering can help the design of ubiquitous, continuously-acting, and distributed algorithms for load-side participation in frequency control in power networks. One of the key differences is that, whereas on the Internet, both the TCP dynamics and the router dynamics can be designed to obtain a feedback system that is stable and efficient, a power network has its own physical dynamics with which our active control must interact.

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