Category Archives: Smart Grid 2.0

Smart Grid 2.0 consists of value-added products and services overlaid on the infrastructure of Smart Grid 1.0

The Smart Grid: 100% Offset of Grid Modernization Costs (Part 5)

SG IIoT Logo Powerpoint 10.12.15Part 5: We Can Replace Aging Infrastructure “for Free” with a Least Cost Deployment of the SG

Dom Geraghty

Excerpts from Part 5 of “The SG National Necessity Series

  • The transition to the SG IIoT and grid modernization will cost about $2.1 trillion over the next three decades , of which $1.7 trillion will be spent on the replacement of aging infrastructure and $400 billion on SG IIoT applications
  • There is a dearth of quantitative information on the benefits of SG IIoT applications -- the availability of benefit to cost ratio (B/C) studies is meager (particularly on the benefits side), thus hampering prioritization of SG IIoT applications
  • Nevertheless, there are a number of  SG IIoT applications that are commonly viewed as offering high B/C ratios, e.g., conservation voltage management, power factor correction, intelligent faulted circuit indicators, and combined volt/VAR management
  • Some important benefits in the benefits “stack” of a SG IIoT project may be “soft” or qualitative -- they are usually societal benefits, e.g., environmental improvements, increases in safety, increasing the end-use customer’s choice
  • The best-available B/C analyses of SG investments are the analyses of Automated Metering Infrastructure (AMI) projects published in conjunction with regulatory rate hearings -- in two cases in CA, the AMI investment was justified by “stacking” a demand response (DR) benefit on top of the AMI benefits; in CT, a significant amount of “soft” benefits was included in the benefits “stack” to justify the investment
  • The cost of the transition to the SG IIoT can be minimized by using situational awareness data to locate SG IIoT applications selectively within the grid in an 80% value/20% cost approach – a least cost deployment (LCD) strategy
  • Our economic analyses of a LCD strategy suggests that we can reduce the total cost of the transition to the SG IIoT  from the $2.1 trillion above to $450 billion, a reduction of about $1.65 trillion
  • Bottom line: The savings from the LCD strategy can thus offset the majority of the total infrastructure investment of $1.7 trillion needed over the next 30 years, i.e., it results in a “Net Zero Capacity Cost” for replacing aging infrastructure
  • Prerequisites for the “Net Zero Capacity Cost” scenario: first and foremost, investing in situational awareness applications; second, using this data to implement a least cost deployment (LCD) strategy for the transition to the SG IIoT; third, accelerating the introduction of regulatory incentives and the elimination of existing regulatory disincentives

Money, money, money
Must be funny
In the rich man's world
Money, money, money
Always sunny
In the rich man's world
Aha-ahaaa
All the things I could do
If I had a little money
It's a rich man's world

-ABBA, 1970

As always, comments welcome and appreciated.

The Smart Grid: Stacking the Benefits (Part 4)

SG IIoT Logo Powerpoint 10.12.15Part 4: Starting with Situational Awareness, Each SG IIoT Application Generates a “Stack of Benefits”

Dom Geraghty

Excerpts from Part 4 of “The SG National Necessity Series

  • Primary benefits of the SG IIoT are increased operating efficiency, reduction in outages (as measured by SAIDI), increased asset utilization factors, and optimized replacement of aging infrastructure
    • Efficiency: A 1% reduction in transmission and distribution losses would reduce U.S. customers’ annual bills by about $4.3 billion
    • Outages: There is a wide variation world-wide in SAIDI performance, e.g., SAIDIs for Germany’s power system at 24 minutes and South Korea’s distribution system at 3.2 minutes (!) are far less than those in the U.S. -- even allowing for situational differences, there appears to be significant potential for decreasing the duration of outages
    • Asset utilization factor: Power sector asset utilization factors are surprisingly low in the  U.S., estimated as follows: generation: 47%, transmission: 43%, and distribution: 34% -- other capital intensive industries that experience high peak to average demand ratios have used very sophisticated  variable pricing techniques to create substantial improvements in utilization factors – the value of improving U.S. power production utilization factor from 47% to 48% is between $10 and $40 billion
    • Aging infrastructure: SG IIoT applications allow us to move from empirical asset replacement to intelligent replacement of aging assets -- intelligent replacement offers economic optimization – the total amount of savings is yet to be determined
  • Many SG IIoT applications generate more than one of the above benefits, i.e., a “stack” of near-term and long-term benefits – the most appropriate benefit to cost ( B/C) ratio is based on the full “stack” of benefits
  • Some significant benefits of SG IIoT applications are qualitative/societal requiring human value judgements
  • The fundamental SG IIoT application is situational awareness -- all SG IIoT applications are based on it -- situation awareness data enable the development of a “least cost” deployment of the SG IIoT, leveraging the 80% value/20% cost rule
  • Yield management is a potentially powerful tool for increasing power system asset utilization, which is the largest economic benefit created by deploying the SG IIoT - examples are provided of SG IIoT applications that can “create” incremental capacity by increasing the utilization factors of existing power sector assets

“The way to solve the conflict between human values and technological needs is not to run away from technology. That’s impossible.

The way to resolve the conflict is to break down the barriers that prevent a real understanding of what technology is ... not an exploitation of nature, but a fusion of nature and the human spirit into a new kind of creation that transcends both.

When this transcendence occurs in such events as the first airplane flight across the ocean or the first footstep on the moon, a kind of public recognition of the transcendent nature of technology occurs.”

-Robert Pirsig, “Zen and the Art of Motorbike Maintenance”, 1974

As always, comments welcome and appreciated.

The Smart Grid: It’s a “Crazy Quilt” (Part 3)

SG IIoT Logo Powerpoint 10.12.15

Part 3: Plan for a Slow and Untidy Transition to the SG IIoT

Dom Geraghty

Excerpts from Part 3 of “The SG National Necessity Series

  • The U.S. power sector is heterogeneous and its governance and coordination is balkanized: over 3,200 utilities implementing a broad range of different business strategies; 50 state regulatory commissions that more often than not implement different regulatory policies, e.g., on mandated RPS targets; independent siting boards; 100 balancing authorities; 6 ISOs with very different market protocols
  • The transition to the SG IIoT is like the construction of a "crazy quilt", project by project, utility by utility, one disparate patch (project) at a time with many zigs and zags along the way – a “random walk down grid street”
  • The pathway to the SG IIoT is indeterminate because it is governed by thousands of independent decisions and uncoordinated projects that are randomly implemented
  • Today, the SG is a hodge-podge of opportunistic situation-specific application projects (“patches” in the crazy quilt) with different functionalities which don’t communicate across applications and which operate in different time domains
  • There is no universal architecture driving the SG transition, although a number have been proposed – in the U.S., we are undergoing a laissez-faire “transition” to the SG in contrast to, for example, Germany’s top down “transformation” of its power sector
  • New SG applications have to interface with a range of different product and technology vintages because power system assets have a 40-year lifetime and often undergo multiple upgrades during their lifetime
  • Interoperability between SG IIoT applications and the existing power system is being achieved mostly by customized Application Programming Interfaces (APIs)
  • The “real-world” transition to the SG IIoT is protracted because of:
    • The requirements to “do no harm”
    • The need to provide due process
    • The modest level of technology readiness
    • The reluctance of vendors to cannibalize existing revenue-producing products
    • Uncertainties in prospective investment returns, and
    • The lack of broad-based interoperability standards
  • The slowness of the SG IIoT transition raises its cost and duration
  • Part 5 of this series suggests ways to accelerate the transition to the SG IIoT while meeting regulatory mandates, achieving a least cost national deployment, and maintaining service reliability

“You have your way. I have my way. As for the right way, the correct way, and the only way, it does not exist.” 

          -Friedrich Nietzsche, “Thus Spake Zarathustra”, 1885

“His way had therefore come full circle, or rather had taken the form of an ellipse or a spiral, following as ever no straight unbroken line, for the rectilinear belongs only to Geometry and not to Nature and Life.” 

          -“The Glass Bead Game”, Hermann Hesse, 1946

“What need is there of suspicious fear? And if you see clearly, go by this way content, without turning back: but if you do not see clearly, stop and take the best advisors.”

          -Emperor Marcus Aurelius, “Meditations”, 174 A.D.

As always, comments welcome and appreciated.

The Smart Grid: It’s the IIoT of the Power Sector (Part 2)

SG IIoT Logo Powerpoint 10.12.15Part 2: The SG Is the First and the Largest Implementation of the Industrial Internet of Things (IIoT)

Dom Geraghty

Excerpts from Part 2 of “The SG National Necessity Series

  • The IIoT refers to the integration of complex physical machinery with networked intelligent sensors and software -- it draws together fields such as machine learning, big data, machine-to-machine communication and cyber-physical systems to ingest data from machines, analyze it (often in real-time), and use it to adjust operations
  • The goals of the SG and the IIoT are the same -- to create automated, interconnected, intelligent networks
  • The SG is the first IIoT and unique within the IIoT because it spans an entire industrial sector – electric power generation, delivery, and end-use
  • We are in the midst of a transition from traditional Operating Technology (OT) to the Industrial Internet of Things (IIoT) technology manifested by intelligent automation and control systems with real-time capabilities -- for several decades, industrial control and automation have increasingly consisted of project-specific systems that used such technology as wireless asset identification, machine-to-machine (M2M) communications, intelligent sub-systems, advanced sensors, wireless communications and/or other IIoT elements -- examples of these systems in the context of SG applications are discussed
  • As we transition from an OT perspective to the IIoT, we need to think differently about how we manage the electric grid. Examples of new applications which the IIoT can support to address the emerging needs of the power sector include:
    • Delivery of highly granular situational awareness data simultaneously to multiple SG applications
    • Conversion from point-to-point, client/server, publish/subscribe, queuing architectures to data-centric architectures, e.g., DDS
    • Substitution of closed solution approaches by iteratively convergent methodologies leveraging high-performance computing
    • Development of advanced control algorithms with co-optimization functionality
    • Delivery of system-wide, nodal  synchronized measurements across all voltage levels
  • The SG is also the largest IIoT implementation, both physically (nation-wide) and financially (a $2.1 trillion investment) - this represents an immense business opportunity – the power sector is one of the most capital-intensive, highest operating leverage sectors of the economy
  • Creating the SG IIoT is an enormous technical challenge – it involves transitioning a huge, interconnected, pulsing, sentient synchronous network that must continuously remain in dynamic equilibrium – its reactions to stimuli obey the implacable laws of physics
  • Examples of this interconnectedness in action in the electric power grid include:
    • In November 2006, the European grid collapsed into three separate domains that were thousands of kilometers apart as phase angles sharply separated between the north, south and east due to insufficient inter-transmission service operator coordination and non-fulfilment of an N-1 contingency criterion
    • The proven ability of a simple 120-volt wall socket in a University of Austin building to sense disturbances in the ERCOT grid caused by the outage of a electricity generator over 350 miles away
  • It will take 25 - 30 years to achieve something close to the SG IIoT

Machines take me by surprise with great frequency.

        -Alan Turing, 1912 - 1954

There is geometry in the humming of the strings; there is music in the spacing of the spheres.”

        -Pythagoras, 569 B.C. – 500 B.C.

As always, comments are welcome and appreciated.

The Smart Grid: A National Necessity (Part 1)

 

SG IIoT Logo Powerpoint 10.12.15Part 1: “Real-World” Situational Analysis for a Smart Grid (SG) Transition

Dom Geraghty

Excerpts from Part 1 of "The SG National Necessity Series"

  • New energy and regulatory policy initiatives, especially the mandate to increase the percentage of renewable generation, are creating unintended reliability and cost-of-service consequences for the power grid that must be addressed – a list of the most important initiatives is presented and their impacts discussed
  • Aging electricity infrastructure threatens service reliability – replacement cost estimated to be in the region of $1.7 trillion over the next two decades
  • Implementation of the SG is necessary to address these two challenges, at an estimated cost of $400 billion over the same period
  • SG infrastructure can substantially offset the total $2.1 trillion cost with operating and capital cost savings -- a broad-based regulatory incentive framework, coupled with the elimination of current regulatory disincentives, would be an important catalyst in achieving these savings -- it  is even conceivable that the operating and capital cost savings that the SG generates could pay for the replacement of aging infrastructure (see Part 5)
  • To address the unintended undesirable outcomes resulting from the new policy initiatives, we need to move from the current “nice to have” improvements created by selective use of SG applications to a “need to have” broadly-based, cyber-secure SG
  • More granular grid situational awareness is the first, prerequisite step in the transition to the SG -- especially awareness of the traditionally sparsely-monitored distribution system and behind-the-meter operations - we must move beyond static optimal power flow models
  • Today, SG applications are delivering sensing, monitoring, and diagnosis – collectively, situational awareness, and also some control functionality -- as we increase our understanding of operations of the newly-configured grid, and develop more sophisticated algorithms, we will progress to automation, and ultimately to optimization of the grid’s operations
  • Some of the required SG technology is already commercial, e.g., high accuracy sensing, wide-spectrum capture, very low noise floors, miniaturized high-performance processing, low-latency communications links; some technology needs to be developed and/or demonstrated in the field, e.g., advanced control and optimization algorithms
  • Still, lags in enabling regulatory incentive policies are inhibiting the transition to the SG
  • National power sector goals are proposed that provide meaningful metrics and motivation for the transition to the SG, including, for example, targeting SAIDI at 60 – 70 minutes (4 x 9s is equivalent to 53 minutes), and improving asset utilization percentage by five percentage points

"Human, All Too Human"

           -Friedrich Nietzsche, 1878

“It is time for man to fix his goal. It is time for man to plant the seed of his highest hope…What is great in man is that he is a bridge, and not an end; what can be loved in man is that he is an overture…

Look here my brothers! Do you not see it, the rainbow and the bridges of the Übermensch?”

            -“Thus Spake Zarathustra”, Friedrich Nietzsche, 1885

As always, comments are welcome and appreciated.

In the Smart Grid, Über-Sensors Declare “When = Where”

Final Avatar 80x80-Logo-SG-1-and-2-and-IX-LOGO-e1363114874895-150x150

 

Dom Geraghty

 

Transitioning to the power sector’s Smart Grid (SG) involves delivering the full continuum of functionality for SG applications, as follows:

  1. Sensing
  2. Monitoring
  3. Diagnosis
  4. Control
  5. Automation, and
  6. Optimization.

Of these functionalities, most of the SG applications today deliver sensing, monitoring (i.e., sensing plus communication), and some level of diagnosis which may require additional post-processing. The remaining three functionalities -- control, automation, and optimization -- are much more difficult to implement for a variety of reasons, not the least of which is the dearth of algorithms available to support these applications in a grid that is often operating in ways for which it was not originally designed and in electricity markets that are not fully integrated.

The Importance of High-Performance Sensors

DSC_1310-150x150All SG applications require some form of sensing – it is a prerequisite functionality upon which all of the other functionalities in the chain of applications depend.

But sensing, while basic on the surface, is not “a walk in the park” for a grid that is undergoing major physical and market structural changes even as it moves towards increasing automation.

Traditionally, we have used static optimal power flow (OPF) models for state estimation of the nodes in the distribution system and for designing power security protection schemes. With the computer processing capability available today, these models have become quite fast, with state recalculations for the entire distribution system now possible in seconds using blade servers. Today a full simulation can be completed well within a typical SCADA cycle. But the results are still “estimates” of the state of the distribution system.

While better OPF models are necessary, they are not sufficient to address the upcoming challenges of the SG because the distribution system is being operated in ways never contemplated when it was designed, due to the introduction of:

  1. Two-way power flows associated with distributed resources, micro-grids, and virtual power plants
  2. Intermittency introduced by PV installations in distribution systems
  3. Electric vehicle charging
  4. Automated demand response, and
  5. M2M appliances in end-user facilities

The most challenging impacts of these changes are:

  1. Volatility of distribution power flows is increasing significantly
  2. The rate of change of power flow metrics is accelerating, and
  3. Short-term load forecasts of “net demand” are exhibiting a broader range of uncertainty than before, creating challenges for operators to schedule production that meets minute-to-minute demand

As a result, even the fastest OPF models need to be supplemented (as well as re-calibrated) with real-world data that reflect these new operating regimes and uncertainties.

Bottom line: to maintain acceptable reliability, security, and stability levels as the physical grid and its associated markets are re-structured, we need to have a much more granular, real-time situational awareness of the distribution system.

How do we address this need for heightened situation awareness as we continue to implement interoperable SG applications?

Introducing the Über-Sensor Platform

Let’s assume that we have installed a set of ultra-accurate, ultra-fast, sensor platforms in our distribution system. The sensors are synchronized to the same “very exact” nano-second accuracy clock. All sensor measurements are time-stamped and concurrent.

What do the sensor platforms consist of? Each sensor consists of a high accuracy analog front-end (AFE) and a flexible, high-performance field programmable gate array (FPGA) integrated as a system-on-module (SOM), connected to, or embedded in, power measurement devices or intelligent electronic devices (IEDs).

DSC_1334 150x150Using technology available today, combined with some proprietary technology configured in a novel manner, the AFE can be highly accurate – providing, for example, a steady noise floor of ~-150 dB all the way out to 105 Hertz and perhaps ~-120 dB at 106 Hertz. The AFE can be supplemented by fast digital signal processors (DSPs) and hardware accelerators. Each sensor could then provide wide spectrum capture, power system hardware DSP, programmable DSP cores, programmable response, a customizable IED firmware stack, and the ability to communicate securely in milliseconds. It is, in effect, a platform that can support all six of the SG functionalities discussed in the opening paragraph of this dialog.

A sensor platform with these performance characteristics could be used in point-on-wave, phasor, power/energy, and power quality measurement applications. Electricity waveforms could be sampled with high accuracy at high frequency. For example, a 2 µ-second transient could be captured and displayed with high fidelity. For distribution system voltage levels, total vector error would need to be in the range of ~<0.2%, which is attainable today, but not without some difficulty.

With an Über-Sensor, We Can Equate “When” with “Where” in the Distribution System

Imagine what we could do with this Über-sensor platform/network in the electric distribution system.

Here’s an example: an incipient fault, or a 2 micro-second transient created by a lightning strike, has created an aberration in the wave form emanating from a particular location in the distribution system.

It spreads like the ripples from a stone cast into a pool. “I felt a great disturbance in the Force”, says Obi-Wan Kenobi. In Gridiopolis, the Engineer Ultra (υπερ-και για το μηχανικό) feels the hair stand out on his Greek neck and he reaches quickly for his krater. But seriously……

In the power system, just like in “The Force”, we know that everything is connected to everything in dynamic meta-stable equilibrium – it is a network of inter-dependent nodes in constant motion, maintained in equilibrium by grid operators.

Emanating outwards from the location of the above transient event, associated disturbances in the wave-form propagate across the distribution system and are sensed by the distributed über-sensor platforms. Because all of the sensor measurements are synchronized and concurrent, the time taken for the propagation to reach an individual sensor is known, and thus the disturbance can be traced back to a particular location by a form of triangulation between the sensors. (Of course, such a locational algorithm is yet to be developed, and we would also need pattern recognition and out-filter algorithms to eliminate the modulations of the disturbance introduced by intermediate devices.)

Yes, “When” Can Equal “Where” in the Distribution System

In effect, if we know very precisely the “when” of a disturbance, then, with a high-performance set of sensor platforms, we should be able to determine the “where” (the origin of the disturbance). In addition, with pattern recognition, the type and likely cause of the disturbance could also be determined.

Pink-middle-of-tower-42-New-Image-150x150And so, we wouldn’t merely detect a highly transient fault or an incipient fault, we would also locate its origin with the same sensors, and very quickly too.

With a network of über-sensor platforms, we would not need an OPF model to estimate states of the distribution grid, because we would “know” the states in real-time. We could archive and use this operations knowledge to re-calibrate the OPF model for off-line planning applications and post-mortems of disturbance events and remediation schemes.

The above application would be useful in terms of two high-value SG applications:

  1. High-speed fault detection, location, and causality
  2. Intelligent replacement of aging power system infrastructure through the identification of incipient failures

Availability of Über-Sensors Today

Über-sensor platforms like this are available today as “evaluation boards”. They go beyond ARM capability to add all of the additional functionality above, with a much-reduced chip-count per module. After they’ve being thoroughly tested and refined in upcoming field applications, they will ultimately be turned into high volume, low-cost ASICs, embeddable in IEDs commonly used in distribution systems.

Why Do We Need Über-Sensors?

Policy-driven structural changes are occurring, and are expected to occur, in the power grid and its associated markets. These changes will have profound operational implications for the grid. The transition to “Smart Grid” applications is being driven by the need to cope with these operational challenges.

In this context, if we are to deliver the same or better reliability of service at an affordable cost to electricity customers, we will need the above high-performance/low-cost sensors as fundamental building blocks to support our ultimate goal -- an interoperable, automated, optimized SG capable of maintaining resilient dynamic equilibrium while under constant fire from millions of continuously-occurring potentially destabilizing events.

 As always, comments are welcome and appreciated.

Implementation of Interoperability in the “Real World” of the Smart Grid (SG)

Final Avatar 80x80-Logo-SG-1-and-2-and-IX-LOGO-e1363114874895-150x150

Dom Geraghty

Abstract

U.S. energy policy initiatives are changing the structure of the physical power system and power system markets. While achieving policy goals, they also create undesirable side-effects for service reliability and power costs. Smart Grid (SG) applications can mitigate these side effects. However, the SG can only work if its applications are interoperable because objects in the power grid network are inter-dependent.  In the “real world”, interoperability comes in many forms. Whatever form it takes, it is a prerequisite for the implementation of SG applications, which in turn are required to ameliorate the undesirable and/or unintended side-effects of salutary and broadly-supported energy policy initiatives.

Situation – Structural Changes in the Power Sector

Today, there are two primary structural changes occurring in the U.S. power grid: (1) physical (ongoing) and (2) power markets (early phases). These changes are being driven by the following energy policy initiatives:

  • Renewable Portfolio Standards (RPS) mandate which introduces increasing amounts of intermittent/variable power production
  • Promotion of, and subsidies for, distributed energy resources (DERs), micro-grids, virtual power plants (VPPs)
  • Promotion of electric vehicles (EVs)
  • Availability of demand response (DR)/load dispatch programs
  • Availability of increased end-use customer choice/energy management options such as smart thermostats, “Green Button”, home automation systems, building automation systems, dynamic rates
  • Integration of wholesale and retail markets, and integration of physical and power market operations

Side-Effects of Energy Policies – There Is a Disturbance in the Force

The above structural changes resulting from energy policy changes have some undesirable side-effects that impact service reliability and create challenges for grid operators.

The operations-related side effects of policy-related structural changes in the grid include:

  • More volatile operations as a result of intermittent resources
  • Events/actuations happen faster – machine-to-machine (M2M), some automation – create a need to manage system security/protection and system stability more tightly
  • Un-designed-for operation of traditional distribution systems, e.g., two-way flows in distribution systems, high-gain feedback loops due to price-responsive demand management programs
  • Visualization of the instantaneous “state of the grid” becomes more challenging
  • The power dispatcher’s job becomes more complex in terms of matching supply and demand on a quasi-real-time basis, e.g., load-following is more demanding
  • Forecasting the “net” load curve is more uncertain
  • More reserves are required to maintain service reliability targets

Two-t-lines-56-mauve-New-Image1-e1355772451904-150x150The side-effects occur because the electricity grid is an interconnected network. Energy policies can affect service reliability negatively because an undesigned-for change in one part of the grid’s operation affects other parts of the grid to an unknown extent, e.g., Lorenz’s “butterfly effect” -- the sensitive dependency on initial conditions in which a small change at one place in a deterministic nonlinear system can result in large differences in a later state. Everything in the electricity grid is interdependent – everything is connected to everything else.

Examples of this interconnectedness in action in the electric power grid include:

  • The November 2006 European grid collapse into three separate domains as phase angles sharply separated between the north, south and east due to insufficient inter-transmission service operator coordination and non-fulfilment of an N-1 criterion
  • The proven ability of a 120V wall socket in a University of Austin building to sense disturbances in the ERCOT grid over 350 miles away

Other, More Generic, Undesirable/Unintended Side-Effects of New Energy Policies

The policy-created structural changes in the power sector can also create other undesirable side-effects:

  • Increased costs because the “first cost” of SG-related equipment is almost always higher than existing (less-smart) equipment
  • Reductions  in system load factor

Bottom Line – Undesirable Side-Effects Need to Be Addressed

If unmitigated, the implementation of the above broadly-supported policy initiatives creates undesirable reliability, cost and asset utilization side-effects under business-as-usual power grid operations -- enter the SG with solutions to mitigate or even eliminate some of these side-effects.

A Brief Digression - Definition of the SG as an Intelligent Network

Before discussing how SG applications can mitigate or eliminate undesirable side-effects of new energy policies, it is important to define the SG.

The ultimate SG is a network of physical objects related to the generation, delivery, and utilization of electricity -- the objects are provided with unique identifiers and the ability to transfer data over Continue reading

Physical and Market Drivers in the Power Sector

Final Avatar 80x80-Logo-SG-1-and-2-and-IX-LOGO-e1363114874895-150x150

 

Dominic Geraghty

 

The Power Sector's Transition to the Smart Grid

The power industry has developed a vision of the ultimate “plug-and-play” smart grid (SG). Many detailed and thoughtful architectures for this ultimate fully automated and optimized SG have been proposed.

There is a wide variety of opinions as to how we’ll get there from here, but there is consensus that it will take decades for a robustly implemented SG to come to fruition. There is also consensus that the total investment requirements will be enormous. Lastly, it is understood the journey will not be without obstacles because field implementation is always full of surprises.

DSC_0190-150x150A credible implementation scenario will not be just about technology; it will consist of business cases that take strategic drivers of the power sector into account. To implement the SG, we need a thorough understanding of the strategic drivers of its evolution – as a basis for planning, evaluating, and investing risk capital in R&D&D, new products and the infrastructure of this future grid.

Various players in the industry have developed a profusion of listings of the strategic drivers of the evolving power sector in the past few years. We have consensus, more or less, on the common elements of a “master list”, but not necessarily on their relative importance, given the differing agendas of impacted industry stakeholders.

This brief paper summarizes and clarifies the strategic drivers of the SG evolution, using a unique new and simplified categorization. The paper then presents the potential impacts of these drivers on service reliability and the cost of service, impacts that logically lead to the need for embedded intelligence in the power grid, i.e., the ultimate SG.

Strategic Drivers of the Power Sector

1. Structural Changes

The power industry is responding to the very real structural changes occurring in the electric sector, changes occurring in both its (1) physical and (2) market configurations.

Physical structural changes are occurring as a result of a broad set of energy policy mandates promoting:

  • Renewable energy production, distributed energy generation and storage, micro-grids, electric vehicles, energy efficiency, increased end-use customer choices

Market structural changes are occurring as a result of efforts to increase the efficiency of electricity markets:

  • New products such as demand response, frequency regulation
  • Promotion of peak-shifting wholesale generation and transmission rates and dynamic pricing  for end-use customers
  • Competition from non-utility providers and end-use customers
  • Broader participation in, and pending integration of, wholesale and retail markets
  • Expansion of “incentive regulation” program

2. Aging Infrastructure

Pole w/Wires 150x150The infrastructure of the power sector is not just aging – it is aged, with much of it now well past its original design life. Legacy control systems are the norm, providing far less functionality than that available from automated intelligent digital devices available based on today’s technology. There is a strong concern within the power sector that this aging infrastructure, unmitigated, will inevitably lead to lower levels of service reliability.

3. Cybersecurity

Lastly, there is continuing verification of increased levels of cyber-based intrusions within the power sector. This has raised concerns in particular about the vulnerability of unprotected legacy operations technology (OT) -- the devices and software that control the grid. Legacy monitoring and control systems are widespread in our aging power system, designed and installed in an era when cybersecurity was not an issue.

So that’s it – our definitive list of strategic drivers of the SG – condensed into three categories.

How Do These Strategic Drivers Affect the Power Sector’s Performance?

The power sector’s performance is measured primarily by service reliability and the cost of service.

The physical structural drivers listed above, if unmitigated, will:

  • Decrease service reliability due to the intermittency of renewable power production, the increased uncertainty of “net” load (demand) resulting from unpredictable end-use customer use of on-premises energy production and management equipment, and the operation the distribution system in ways for which it was not designed, i.e., two-way power flows
  • Increase the cost of service because:
    • The capital costs of renewable energy, energy storage, and some customer-owned production and energy management devices are currently much higher than traditional grid technologies (1, 2)
    • Increased spinning and regulation reserves are needed to maintain existing levels of reliability as the proportion of renewable energy increases in the production mix (3, 4)
    • The load duration curve’s shape is shifting unfavorably towards a lower asset utilization rate across the grid as the ratio of peak load to average load increases (5)

Fortunately, we have shown elsewhere that SG applications can fully mitigate the above negative outcomes. (6, 7)

In contrast, the market structural changes, when implemented, will increase service reliability and decrease the cost of service. However, this implementation is subject to a lengthy political and analytical process involving regulators, various stakeholders, and likely the legal system as well.

Most of the structural market changes presented here have been under discussion for decades with very little progress being made in terms of implementation. Structural market changes represent a major opportunity to lower the deployment cost of the smart grid, while maintaining acceptable reliability levels.

We Are Driving Embedded Intelligence into the Power Grid

To mitigate the negative and support the positive impacts of the above strategic drivers, we will be embedding intelligence across the power grid. This intelligence will be supported by today’s and tomorrow’s advanced information, operational, and communications technology.

DSC_0316_2-150x150The ultimate SG will be an optimized, automated system meeting a prescribed level of service reliability and security, delivering commodity-priced electricity. To achieve this, SG applications will introduce, on a project by project basis, automated intelligent digital devices distributed across the grid. The transition will take multiple decades.

Initially, the SG will deliver sensing, monitoring, diagnosis, and control functionality. As we progress in our understanding of the grid and develop more sophisticated algorithms, we will progress to automation, and ultimately to optimized operations.

The SG applications will have shorter working lives than the long-lived assets of today’s grid, but they will be substitutable, because interoperability will be the norm for all produced SG devices and applications (8).

The good news: planned properly, the net cost of the transition over its multi-decade duration should be zero, relative to continuing on a business-as-usual basis (5).

As always, comments are welcome and appreciated.

 References

  1. Chris, Namovicz, “Assessing the Economic Value of New Utility-Scale Renewable Generation Projects”, US-EOIA, EIA Energy Conference, June 17, 2013
  2. “Distributed Generation Renewable Energy Estimate of Costs”,  NREL, August 2013
  3. CPUC, “33% Renewable Portfolio Standard: Implementation Analysis – Preliminary Results”, June 2009
  4. Robert Gross, et al., “The Costs and Impacts of Intermittency“, U.K. Energy Research Centre, Imperial College, London, March 2006
  5. Geraghty, Dominic, “Shape-Shifting Load Curves”, smartgridix.com, January 25, 2014
  6. Geraghty, Dominic, “The Elephant in the Room: Addressing the Affordability of a Rejuvenated, Smarter Grid”, smartgridix.com, November 21, 2013
  7. “Estimating the Costs and Benefits of the Smart Grid”, EPRI Technical Report 1022519, March 2011
  8. Geraghty, Dominic, “ Implementation of the Interoperability in the Real Smart Grid”, October 2014, smartgridix.com (to be published, draft under review, available from the author)

 

Internet of (Smart Grid) Things – Achieving Interoperability

Final Avatar 80x80-Logo-SG-1-and-2-and-IX-LOGO-e1363114874895-150x150

 

Dom Geraghty

In the previous dialog, we introduced the “Internet of (Smart Grid) Things”, or “Io(SG)T”, a real-world microcosm of Cisco’s IoT or IoE.  We make no excuses for accepting the admitted Cisco “spin” -- we have already been living this “spin” since the advent of the SG.

The SG is a microcosm of the IoT because we have defined the ultimate SG as an automated plug and play system, just like we increasingly plug and play today on the Internet, moving inexorably towards the universal plug and play IoT or IoE in the future. The concept is similar to “The Feed” in Neal Stephenson’s book, “The Diamond Age” – an ultra-reliable commodity mix priced at marginal costs.

Our ultimate SG replicates the IoT concept as a power sector subset or “vertical” – it is a textbook crucible because we can draw a clear unbroken boundary around the sector. Within this bounded system are the inter-operations of power production, transmission, distribution, and end-use of electricity, representing as a whole the networked infrastructure of the SG.

Pink-71-two-t-lines-150x150Here, we define this automated, interactive power sector vertical as the Io(SG)T. The power sector is the first of many networked industry sub-sets of the IoT likely to emerge over time. While we have not been explicitly calling it the Io(SG)T until now, as professionals within this sector we’ve been working on it for more than couple of decades.

However, there is still a long way to go to automate this very complex machine -- our electricity grid. We posit a tentative time-frame of at least 30 years for the ultimate SG, the Io(SG)T, and, as we’ve said before, there will be many zigs and zags along the way.

Let’s Back Up a Little – Why Is the Io(SG)T Needed?

Energy and environmental policies, regulations, and mandates are creating operational challenges for the electricity grid – they call for the grid to be operated in ways for which it was not designed, e.g., accommodating intermittent renewable generation, two-way power flows in distribution systems, evolving integration of wholesale and retail power markets, autonomously operated distributed generation and storage, M2M-enabled smart appliances and buildings, and microgrids.

These new developments make it a challenge to maintain traditional 4 x 9s service reliability levels, increase the stresses on the aging infrastructure of the grid, increase electricity costs, decrease asset utilization factors, and add substantial uncertainty to the net load/demand curve against which grid operators dispatch generation and delivery assets.

DSC_0026-150x150Fortunately, today’s new SG technologies and applications can provide the processing power, reaction speed, bandwidth, accuracy, interoperability (sometimes), and instant situational awareness across the grid necessary to accommodate the new operational challenges above while operating the grid reliably and safely.

Actually, we don’t really have any option – intelligent automated functionality of the SG has become a prerequisite for economical, reliable, and safe operation of the power grid in the future.

The Costs of the Io(SG)T Can Be Managed

Turning to the costs, we’ve recently presented a comprehensive analysis of the elements of a least cost Io(SG)T deployment strategy that includes (1) locational selectivity (using the 80%/20% rule) in our deliberate shift from a “blanketing” approach for SG applications to a prioritized deployment approach, and (2) equipment rejuvenation -- retrofitting rather than “rip and replace” approaches.

We estimate that this strategy has the potential to decrease initially estimated Io(SG)T costs by half an order of magnitude – that is, that the deployment of the Io(SG)T will cost about $400 billion through 2030.

The approach recognizes that SG applications will offset capital requirements over time and reduce operating costs going forward. For example, SG applications can increase asset utilization (freeing up “latent” capacity), reduce power system losses, and increase the economic efficiency of power markets.

We Are Beginning to Stack Up the Benefits of the Io(SG)T

11. DSC_0118-150x150-150x150For many individual SG applications, we are still in early days in terms of calculating and crediting all of the benefits in the “stack” of benefits, but it is happening. For example, we are beginning to see numerous utilities leverage the very first SG applications, i.e., AMI systems, to improve outage management, reduce truck rolls, improve billing, identify and eliminate electricity theft, manage voltage, and monitor transformers. All of these applications add incrementally to the AMI benefits stack.

Other real-world examples of accruing multiple benefits from an SG application:

  • Some grid operators are using SG applications with energy storage to “smooth” intermittency, shift and shave peak, and create arbitrage opportunities in wholesale power markets, presenting a “stacked” benefits analysis of energy storage systems
  • The power industry is in the middle of deploying phasor measurement units (PMUs) across regional transmission grids to provide very sophisticated situational awareness and safely operate our systems much closer to their capacity limits. The benefits stack includes freeing up latent capacity in the power infrastructure, reducing reserve requirements for the increasing proportion of intermittent generation, and relieving congestion on transmission lines

Bottom line: the Io(SG)T is a prerequisite for accommodating energy and environmental policy goals and we continue to quantify in the field additional benefits of Io(SG)T applications that improve benefit-to-cost ratios.

But how do we connect all of these dispersed and opportunistic SG applications so that they work together seamlessly in an Io(SG)T?

APIs – Learn to Love Them in the Io(SG)T

Sub-Title: The Interoperability Continuum

The SG automation roadmap involves measurement (advanced sensors -  we need these first to enable the rest of this roadmap), monitoring (communications), diagnosis (analytics and visualization), and control (algorithms) all operating simultaneously across the physical and market nodes of the SG, and across the multiple operating and back-office systems of grid operators.

For the automation roadmap to be realized, the SG applications and utility systems upon which they operate must be interoperable.

To date, interoperability between applications or systems has been achieved by using standards developed by Standards Development Organizations (SDOs) or by purpose-built Application Programming Interfaces (APIs). It is actually a little more complicated than that, however. The graphic below presents the “Interoperability Continuum”. The left-hand side represents a situation where no standard exists for a proposed interface/integration. On the right-hand side, a mature standard exists which can be used for the interface. Moving from left to right denotes increased general interoperability.

Proprietary APIs

We define a proprietary API as a customized interface that is developed by a vendor, system integrator, or grid operator for a special-purpose application that is usually one-off. It usually connects proprietary systems to other proprietary or home-grown systems. For example, an AMI vendor may develop a proprietary API to connect its system to the utility’s home-grown OMS or billing system.  Vendors do not share proprietary protocols because they believe that they enhance their competitiveness, capture the customer for “up-sells”, and increase the value of their ongoing Service Level Agreements (SLAs).

The next step in the continuum of increasing interoperability is a hybrid interface consisting of the combination of a proprietary API and an existing standard that allows vendors, system integrators and grid operators to connect proprietary systems to systems with standardized interfaces. While a standard is utilized in the hybrid, the interface is still controlled by the developer of the API. In this case, an example would be the interface between a proprietary AMI system and the CIM standard.

Open APIs

By providing an Open API (i.e., an open-source API available to third-party developers), the vendor relinquishes control of its interface. In return, the vendor with a superior product will attract independent third-party developers who create interfaces for value-adding SG applications, thus increasing the demand “pull” for the vendor’s equipment or systems.

This is the business model used today by Facebook, Apple, and others to create customer “stickiness” through an interoperable applications portfolio that they do not have to develop themselves. It’s a strategy for increasing market share. As part of the Web 2.0 business model, it was initially conceived to (1) allow web-sites to inter-operate, (2) create virtual collaborative services environment to support, for example, the professional interface between a designer and an architect, and (3) expand social media platforms.

7. DSC_0150-150x150The Open API is the future of SG applications if it logically follows the IoT (r)evolution. As part of the interoperability continuum, it can inter-operate with a widely deployed standard, or it can be offered as a stand-alone independent API. While this goes against traditional business protection instincts of vendors, it in effect “outsources” applications development relevant to its offerings – development resources that it gets for free-- in return for the internal cost of developing and offering the Open API itself.

Furthermore, the Open API, while provided free to developers, can have an associated “revenue license” where the vendor receives some portion of any revenue earned by a third-party developer commercializing a SG application on top of the Open API. Again, the Web 2.0 business model can apply. In the long run, the vendor may even decide to acquire some of the third-party developers of interfaces based on its Open API. The advantage to the vendor, along with the potential for increased product demand, is that the initial business/development risk and investment are undertaken by a third-party.

Mature Standards

Finally, at the right-hand end of the continuum above, there are scores of mature standards that already allow plug and play to happen between SG systems.

But……, at least at present, it is common to find that a mature standard that facilitates some interfaces may be “partial” in the sense that it does not cover (1) other (perhaps newer) interfaces with grid systems that the application also impacts, or (2) different layers within SG application interfaces which may not be compatible with layers in the systems to which they interface – thus requiring the combination of an API and a mature standard to accomplish the integration/interoperability for an advanced SG application.

So, a mature standard will remain a moving target as the Io(SG)T continues to evolve, and APIs will continue to be needed (and create value) as the automation of the SG progresses.

For a more detailed discussion of this last point, see a very interesting position paper by Scott Neumann that he wrote for GWAC.

A Glimpse into the Future: Could We Leapfrog the Development of SG Control Algorithms/Standards Altogether?

DSC_1253-150x150Compared to generation and transmission systems, the distribution system in the SG has less-well developed grid control algorithms and virtually no associated standards. These algorithms will sit within distributed intelligent processors embedded in IEDs dispersed throughout the distribution system. Using state estimation tools, dynamic load flow modeling, and real-time state information from high-speed ultra-accurate PMU chipsets, we are just beginning to develop control algorithms  for actuators in the distribution system and for remedial action scheme firmware aimed at protecting the system during contingencies.

Could we instead leap-frog today’s relatively mature “system on chips” (SOCs) technology by substituting memristor chipsets embedded in IEDs? That is, adaptive learning chips based on how the brain’s synapses operate by conducting processing and memory activities simultaneously -- using this approach, couldn’t we derive control algorithms empirically? OK, assuming that we also had ultra-fast sensors.

But memristors and ultra-fast sensors already exist today………

So maybe we don’t need to worry too much about the last (most difficult) step in the Io(SG)T roadmap, i.e., the development of SG control algorithms, which would take place in earnest perhaps a decade from now. Why? Because we won’t need standard grid control algorithms per se -- memristors, with processing speeds six orders of magnitude faster than today’s best technology, will sort it all out for us empirically - we just need to interface them with those ultra-fast sensors that measure and communicate the requisite data to the memristors over interoperable SG networks.

More on these cutting edge technologies in a future dialog………………

As always, comments welcome and appreciated.

Smart Sensors for the SG: You Can’t Manage What You Don’t Measure

Final Avatar 80x80-Logo-SG-1-and-2-and-IX-LOGO-e1363114874895-150x150

 

Dom Geraghty

 

An Integrated Measurement Strategy for the SG

Obviously, since the SG is inanimate, we don’t expect it to intuit how to do “smart” things by itself (!). We have to provide it with data, and with analytical rules. Even the recent introductions of AI-based algorithms in some SG applications must still be derived from “learnings” from empirical data.

At present, the deployment of SG applications can be characterized as “tactical”-- uncoordinated with other activities in the SG, and special-purpose in nature – certainly not following the holistic, long-term visions of SG architectures and power markets developed by such entities as GWAC, NIST, EPRI, IEC, IEEE, SGIP, etc.  The result is a hodge-podge of application-specific sensors with different capabilities which don’t communicate across applications and which operate in different time domains. But it does not need to be like that, as we shall outline below.

Let’s Define Measurement, Sensors, and Smart Sensors

10. DSC_1334-150x150Smart sensors are the fundamental building blocks for the implementation of a truly “smart” grid. They are an essential part of every SG solution. Regular analog sensors become the “smart sensors” of the SG when they add intelligence to the measurement function, i.e., analog to digital conversion, processing power, firmware, communications, and even actuation capability.

We can think of smart sensors as the first link in a four-link SG decision-making chain that consists of:

(1) Location-specific measurement -- sensor function only

(2) Monitoring -- a sensor with one-way communications functionality

(3) Diagnosis at the “edge” -- a sensor with localized diagnostic intelligence based on data analytics and/or centralized diagnosis based on communicated sensor data

(4) Edge-embedded control actions (based on embedded algorithms, including Remedial Action Schemes (RAS)) -- a sensor with intelligence and control/actuator capability. The algorithms for this functionality could also be centralized and use two-way communications with an “edge” sensor/actuator, and/or they could drive peer-to-peer coordination of control actions at the “edge”; however, a substantial amount of R&D still needs to be done to develop autonomous real-time or quasi-real-time control algorithms for power distribution systems

To Date, Smart Sensor-Based Measurement in the SG Has Been “Tactical”

Granted, as we’ve said before, there is a reason for this tactical approach to sensor deployment – up to now the choices of SG projects are driven by energy and regulatory policies and rules that target a limited set of SG applications. Fair enough -- none of us expect that the evolution of the SG will follow a “grand deployment plan” – it will be imperfect, following the zigs and zags of these real-world drivers.

Continue reading