The Internet of Things, or IoT, is vast, consisting of nearly 50 billion “things” by 2020 according to Philip Howard. The IoT is also nebulous. Defined as a network of physical objects or “things” embedded with electronics, software, sensors and connectivity, this IoT as we know it today includes devices as diverse as heart monitoring implants, biochip transponders on farm animals, automobiles with built-in sensors, refrigerators providing online status, bio-hazardous particulate sensors, centrally scheduled and monitored outdoor lighting, distributed net-energy meters, and many more. Plus who knows what kinds of things are on the drawing board.

Connecting all these things to the Internet is a certainty. Economics insure it. Publicly traded companies making data center hardware and software, delivering connectivity plumbing like fiber, providing cloud services, offering mobile services like smart phone connectivity, are all looking for that next hundred million in revenue offered by an emerging market of 50 billion things. Plus like those bulls in Pamplona, startups are running toward this bullring of opportunity too, hoping to create the killer app or uncover the dominant business model for all these things. How, though, will all these things get connected? Why wirelessly of course.


The edge of the Internet is hard to get to, because it is either remote or always moving or both. If it were easy, it would already be part of the Internet! Imagine an IoT application that utilizes RFID tags to track tools and equipment assigned to a truck and used by a field service worker. Depending upon the job and the day, this field service truck and worker may be in town where connectivity is easy, or way out of town at a remote, high-value asset like a pipeline pump station. Only a wireless connection works in both situations. One glimpse of a typical cellular provider’s mobile data coverage map and it’s easy to see that cellular coverage is prodigious. Ever increasing ARPU powering a never ending rollout of data connection speeds (… 2G, 3G, 4G/LTE …) has insured that cellular is very nearly everywhere. This fact lies in stark contrast to the many failures of Municipal Wi-Fi, doomed by technical, economic and business model shortcomings, foreshadowing a future bathed in cellular.


Cellular, however, is not the Internet per se because it does not operate using the Internet Protocol known as TCP/IP. Yet cellular routers and mobile applications with their cloud services have become quite adept at marshaling TCP/IP payloads across cellular networks, so cellular is perfectly capable of being used to extend the edge of the Internet as far and wide as cellular networks reach today, and tomorrow.

Spanning Networks

At the Internet’s edge, IoT application developers are hard at work building businesses on devices connected to the Internet via a spanning network. First, a spanning network extends the reach of the Internet using cellular, from a cell tower outward, as far as a cell tower can reach – the so-called last mile of connectivity. Then from the edge of a cell tower’s reach, a spanning network extends the Internet even further. Wired protocols like PLC for gas, water and power meters or RS-485 for fieldbus devices have been used in the past, but the ease and economics of wireless mesh networks like Zigbee and its many variants are rendering wired protocols obsolete. So more commonly, the last foot of connectivity is provided by wireless mesh protocols.

Each IoT application needs a spanning network. The nuts and bolts of this network are built from standard, off-the-shelf components like cellular routers and RF radios, but the performance characteristics of a spanning network are very application specific. How big are the payloads? How far and wide must payloads be distributed and through what routes? With what frequency must payloads be uploaded and downloaded? These application details along with the economics involved in operating a spanning network at scale drive the success of an IoT application, perhaps even more than its functionality.


Designing, testing and then operationalizing a spanning network at scale is nontrivial. Delicately balancing throughput requirements against cellular data plan requirements and costs is just one of the key drivers, but one that has an oversized effect on operational costs. How many wireless mesh nodes worth of payloads can a single cellular edge router manage? How many cellular edge routers are required to cover an IoT application’s service area? How big a machine-to-machine data plan does each cellular edge router need and what will the costs be in full operation?

Early investigations on these topics during the IoT application design phase can be dramatically simplified by a cellular edge router with the right functionality. The same is true during testing, operational scaling and even maintenance over a service agreement’s term. A cellular edge router designed for IoT application developers would support these features and functions:

  • Application Agnostic
  • Cloud Connected
  • Virtualization
  • Wireless Mesh NAT and DHCP
  • Bandwidth Modeling
  • Energy Management
  • Differential Monitoring

Application Agnostic

Attempting to be the Ginsu Knife of cellular edge routers for IoT developers is folly. There is no way to predict how a customer in a particular vertical market segment will wish to integrate IoT devices with a spanning network, and even if you could, satisfying such a disparate set would bloat a cellular edge router and render performance for any specific customer lame. Companies have struggled for years trying to be just such a solution.

Instead, the paradigm needs to change. The interface between a specific IoT application and the cellular edge router becomes Ethernet only, with the app developer encapsulating their app logic into an Ethernet push device like Synapse Wireless’ SNAP Connect E10 and E20 or EKM Metering’s EKM Push. This push device has intimate knowledge of the payloads being exchanged between the wireless mesh and the cloud as well as message semantics, recovery strategies and the like, which frees the cellular edge router to focus solely on optimized TCP/IP payload exchange.

Cloud Connected

Public IP addresses are too valuable a resource to dole out willy-nilly so instead, carriers dole out private IP addresses to cellular edge routers, which require a VPN connection into the carrier’s network for direct access to a cellular edge router’s configuration. These private IP addresses are used to configure, troubleshoot and optimize the performance of a cellular edge router. For initial configuration a VPN client and configuration may not be necessary because the router can be connected locally to a computer via an Ethernet cable, but once the router heads into the R&D lab or out into the wild it’s a different story. VPN client licensing and configuration across multiple roles within an organization is an unwieldy proposition at best, and one that gets disproportionately worse as the number of routers grows. Edge router support, troubleshooting and optimizations over time to maintain an IoT application’s spanning network must be simple and low-cost.


The solution is for a cellular edge router targeting IoT application developers to be “cloud connected”. Instead of connecting directly to an edge router’s configuration webpage through a VPN pipe, the IoT app developer securely logs into a cloud service provided by the edge router manufacturer in order to manage the edge router’s configuration initially as well as over time. Once provisioned into the carrier’s network, the edge router receives all of its configuration parameters from this manufacturer cloud service while pushing router monitoring and status information to the cloud service as well. No VPN client is required. No direct connection to the edge router’s webpage is needed either. The IoT app developer can then manage roles, authentication and authorization to specific edge routers in a way that is consistent with other managed devices. What’s more, a cloud service enables a stickier, longer-lasting relationship between the edge router manufacturer and the IoT app developer that improves monetization over time and can help fund cloud service development for the cellular edge router manufacturer.


Virtualization has been an economic boon for corporate IT and datacenters because of its dramatic improvement in utilization. Shared connectivity was the enabling technology, lower operating costs the benefit.

A similar benefit occurs when all of the edge routers in a spanning network share connectivity as they do when cloud connected. Cellular data plan utilization improves. In fact, the IoT application developer can optimize this part of their business, which can have a huge impact on the bottom line when considered across many customer installations.


Additionally, spanning network reliability improves. Each cellular edge router’s configuration for a particular IoT application resides in the cloud, simplifying failover and reducing downtime. A cellular edge router can be re-flashed in minutes. Or the router can be swapped in the field by lower cost field resources that know nothing about the IoT application, and then flashed and spun up remotely by the application developer. A spanning network can even be designed with overlapping meshes and overhead bandwidth so that a single cellular edge router can temporarily backhaul multiple meshes should an edge router fail in the field. This temporary releveling can be done remotely to preserve uptime at the expense of throughput, but then returned once the edge router gets replaced, all without rolling a truck.

Wireless Mesh NAT and DHCP

Low power, low throughput wireless meshes are the norm for IoT applications because the operating expenses are more favorable. Unfortunately, these meshes do not use the Internet Protocol. Neither IP addresses nor protocols like DHCP are supported, but a cellular edge router designed for IoT application developers could deliver these capabilities. Using IPv6, a cellular edge router could individually address each node in an 802.15.4 mesh, and provide NAT as well as DHCP to simplify management from the cloud. These edge router features would further simplify the application design process for IoT developers, adding even more value.

Bandwidth Modeling

Many, possibly even most, IoT applications will be delivered as financed services so that the “customer” pays over time, often in the context of a performance contract. Financing adds a time dimension to the economics of a solution, and heightens the importance of operational expenses like cellular backhaul data plans to the overall value proposition of an IoT application. Initially identifying the optimal data plans becomes crucial. Maintaining the optimal data plans over time as carriers change plans also becomes essential.

A cellular edge router will not know the dollars per megabyte for its backhaul pipe, but it will know the megabytes moved through the gateway per month, which obviously drives data plan economics. Early on in the design of an IoT application, the megabytes per month for each class of IoT device must be determined and then used to model the throughput of each router in the IoT application’s spanning network. Making this analysis and optimization easy and then simplifying verification in the lab as well as out in the wild during a performance contract has huge value to the IoT application developer.

A set of bandwidth modeling steps might unfold like this:

  1. Connect an IoT device to the router using a wireless mesh and enable real time throughput monitoring, then run the device through its usage scenarios, keeping track of payload size and frequency (i.e., data usage) per scenario.
  2. Assemble a collection of IoT devices sharing a single wireless mesh along with a single router into a scaled down spanning network, then put the IoT devices through their combined usage scenarios to determine the maximum number of IoT devices a single router can effectively backhaul.
  3. Operate the scaled down spanning network beyond the router’s capacity to understand throughput failure modes and how to set alert thresholds for managing to a performance contract.
  4. Expand to a multi-router, multi-wireless mesh spanning network to fine tune the IoT application’s operational parameters and alerts, all the while using the router’s bandwidth modeling tools as the design feedback loop.

Throughout, easily accessible bandwidth information provided by the cellular edge router enables the IoT developer to economically deliver optimized spanning networks for the IoT application.

Energy Management

Remote IoT applications may be battery powered, so understanding the energy characteristics of a cellular edge router across a broad set of scenarios, as well as being able to affect the router’s energy profile programmatically and in real time, is crucial. However, even when the IoT application is not remote, energy matters. The cost of energy comes out of the IoT application developer’s top line revenue when providing a service. Nowhere is this truer than in IoT applications targeting the energy sector, where every kilowatt generated or saved gets monetized.

The solution involves insuring the IoT application developer has access to rich energy usage data for the cellular edge router as well as a programmatic way to affect the router’s energy profile over time. Like bandwidth data, energy usage data provided by the router helps during the design and test phase and then rolls into differential monitoring to help the IoT application developer craft, then meet, a customer performance contract.

Differential Monitoring

A typical wide area IoT application requires a spanning network with numerous cellular edge routers for backhaul. A successful IoT application developer will have many customers, each with an instance of a wide area IoT application spanning network. Therefore, a successful IoT application developer must manage a large number of routers. Proactively assessing each router’s ongoing performance is untenable at scale, even if doing so can be accomplished using a cloud service. Instead, differential monitoring hosted at the cellular edge router’s cloud service is the key. Granular, side-by-side, near real time graphs of many routers performing similar functions is the simplest way to identify anomalies at scale. Once identified, troubleshooting can begin.

Exceptions to normal behavior, surfaced as alerts, are another form of differential monitoring. Performance thresholds that trigger alerts can be configured to proactively manage a spanning network’s performance to a service level agreement, a competitive advantage in the IoT space. Facilitating the performance analysis of an IoT application’s spanning network in order to craft the terms and conditions of a service level agreement, and then creating the associated alerts, enables the IoT developer to over deliver in the eyes of the customer.

Three Examples

A cellular edge router with these features could be used to design IoT applications where the spanning network provides stateless connectivity only as well as where the spanning network is state-full and provides unique capabilities to the application. A few examples should help illustrate.

Remember the tool tracking and utilization service for truck fleets from above? This application is primarily a monitoring application, so no algorithms need to reside and run at the edge and the spanning network simply passes data from RFID tags on tools through the truck’s cellular edge router and up to the cloud. No app aggregator would be needed in the wireless mesh because no state or semantics reside there. However, IPv6 addressing and DHCP from the wireless mesh all the way through to the cloud would be very beneficial and easily delivered by this cellular edge router.

Similar to the tool tracking example, imagine a service for tracking a department’s fleet of police cars. This application is also primarily a monitoring application without algorithms at the edge, but there are additional store and forward semantics that would improve data collection and bandwidth utilization. Desired data might include location of the vehicle, of course, but also identifiers for the officers in or near the vehicle as well as gun access and stow events. An application aggregator, in this case, would be needed in the wireless mesh and would include a GPS chip for location, an RFID receiver for officer and gun identification plus a holster detector. These monitoring payloads would be packaged up by the aggregator and then passed through the Ethernet interface of the cellular edge router to the cloud. The cellular edge router would know nothing of the application beyond throughput requirements.

At the other end of the spectrum, imagine a mesh of off-grid solar lights that coordinate a shared dusk and dawn demarcation for a cloud-based lighting schedule. Each solar light has a solar collector and can detect dawn and dusk, but these detections will vary from light to light causing a rolling turn on across the entire mesh. For a single on and off across all lights, a coordinated on/off can be determined by majority. Once a majority of the lights within the mesh detect dusk, for example, all lights turn on simultaneously. Ditto for dawn, except that the lights turn off. These semantics are handled within the mesh rather than in the cloud to eliminate the data throughput requirements and connectivity latency. Here an application aggregator would be required, with the algorithm to determine “majority” and then broadcast the turn on or turn off message to the mesh. This aggregator would include a wireless mesh radio so that it can communicate with all the solar light nodes in the mesh, as well as a processor to execute the coordinated on/off algorithm and an Ethernet chip for communications with the cellular edge router. Though more functionality resides at the edge in this example, the cellular edge router still knows nothing about these semantics because they are encapsulated in the application aggregator.


A Missed Opportunity

Nobody seems to be targeting IoT application developers and building this cellular edge router, which seems like a missed opportunity. Fifty billion reasons seems like plenty of motivation, I wonder where the takers are.

A Solar Tale

Remember “The Long Tail”? Maybe not. Unless you were up to your eyeballs in the nuances of search engines and niche marketing around the turn of the century, you wouldn’t. The phrase originated with a Wired article by Chris Anderson, but more generally Marziah Karch describes it like this; traditionally records, books, movies, and other items were geared towards creating “hits.” Stores could only afford to carry the most popular items because they needed enough people in an area to buy their goods in order to recoup their overhead expenses. The Internet changes that. It allows people to find less popular items and subjects. It turns out that there’s profit in those “misses,” too. Amazon can sell obscure books, Netflix can rent obscure movies, and iTunes can sell obscure songs. That’s all possible because the Internet, search engines and search advertising provide easy access to these niches out on the long tail of the demand curve, allowing them to compete with the head of the curve where the big hits and brick and mortar stores reside.

What does this have to do with solar energy? Plenty as it turns out. Demand for solar has traditionally been met by large, centralized solar farms that generate many megawatts of energy per system, per day, like the big-box retail stores of yore selling blockbuster records, books and movies, the hits at the head of the solar demand curve.

These centralized solar farms are comprised of rows and rows of identically mounted flat crystalline solar modules tilted at the ideal angle for the latitude. With their economies of scale they deliver the lowest installed system costs, in the $2 per watt range according to Greentechsolar, if you ignore the typical transmission infrastructure additions and upgrades required to deliver this energy to market. String inverters are a key ingredient in delivering such favorable economics. Large strings of solar modules, devoid of shading and other sources of performance differences between modules, can be connected to a single, rather expensive string inverter. The number of solar modules per string inverter, and therefore the number of watts by which the cost of the string inverter gets divided, is large, rendering favorable dollars per watt.

Centralized solar farms also fit neatly into the existing utility-driven paradigm and business model. Energy is generated centrally, delivered over wide area networks of transmission and distribution lines to paying customer loads and then paid for and recouped by regulated returns over long time horizons. These are the big hits.

Like the big box retail stores with search advertising, though, this centralized utility-scale model is being disrupted. Land acquisition and permitting for new solar farms combined with the challenges of adding net new or even upgrading existing transmission and distribution lines is constraining big solar. At the same time the cost of crystalline solar modules and supporting electronics has plummeted, opening up the first wave of distributed solar, known more commonly as rooftop solar. Rooftops are smaller than the acres devoted to centralized solar farms, by a lot, so the fixed costs of a rooftop solar generating system – e.g., solar modules, inverters, mounting infrastructure – are divided by fewer watts. As a result, the dollars per watt for rooftop solar initially suffered by comparison, but continues to get rosier and rosier as these costs continue their precipitous decline, sitting just under $4 per watt according to the same Greentechmedia study.

Rooftop solar is more distributed than a centralized solar farm, and more varied. A single rooftop may have several different pitches and possibly even directions these pitches face. Since economics will always drive towards maximizing the number of watts installed per rooftop, these variations become more and more common. Plus, shading plays a role. Rooftops are not pristine like single-purpose solar sites. Trees, neighbor houses, nearby foothills and the like can cause seasonal shading during times of the day, emphasizing the point that a rooftop is first and foremost, a rooftop. Fortunately for the rise of distributed solar, a Module-Level Power Electronics (MLPE) market has emerged to assuage the technical ramifications. Microinverters and power optimizers are examples of MLPEs. Each optimizes a single solar module’s output, an important innovation when adjacent solar modules may perform very differently due to shading or even their orientation relative to the sun. Mating a microinverter or power optimizer with every solar module costs more in dollars per watt, but as the distributed solar market grows and gains economies of scale for MLPE manufacturers, costs are coming down rapidly as they have with solar modules, while overall system generation across varied solar modules increases.

 Many Facet Rooftop Solar

Rooftop solar is filling out the inflection point between the head of the solar demand curve and the tail, but it cannot fuel the long tail all on its own. As of the third quarter of 2014, nearly 600,000 home and business owners already generate their own solar electricity from rooftop systems. Unfortunately, only as many as 20% of rooftops are suitable to host solar generation. Plus socially, rooftop solar contributes to the electrical divide, the increasing cost of energy low-income families will face as part of the utility death spiral – i.e., the concept where falling barriers to distributed generation coupled with rising electric bills will cause consumers to defect from the grid, leaving a smaller population to pay for the costs of maintaining the electrical infrastructure. This smaller population is filled with low-income families, families without the means or often even the rooftops to participate in the benefits of rooftop solar.

What will fuel the long tail? What is at least as distributed and local as rooftop solar, more egalitarian and offers unlimited surface area to cover and generate solar energy? Infrastructure Solar! Imagine the ability to economically cover all shapes and sizes of existing infrastructure out in the wild with solar generation, like light and utility poles of all heights and diameters, traffic intersection poles and arms and supports, bus and rail stops, wind turbine towers, water towers, floating bridge barricades, the list goes on and on. Each system is small in terms of nameplate generation – a 75 kilowatt lighting system, a 4.5 kilowatt traffic intersection – but like the Long Tail of the Internet, the sum of all installed Infrastructure Solar kilowatts will eventually dwarf the centralized and rooftop kilowatts being installed today because, well, the tail is really, really long.

Solar Cells and Modules

Standing between today and the explosion of Infrastructure Solar are a few innovations. Traditional flat crystalline solar modules can be added to existing infrastructure such as rooftops using mounting rails and attach points that depend on the type of roof material and structure. These flat solar modules work well on rooftops with large, flat, generally south-facing surfaces. When mated with MLPEs like a microinverter, each flat solar module’s generation is optimized. Localized shading only affects the generation of the shaded module, unlike string inverters where the performance of shaded solar modules can affect the performance of other solar modules sharing the same string inverter. Or when rooftops have multiple flat surfaces with different slopes and orientations, flat crystalline solar modules with microinverters per module perform optimally as well. However, these flat crystalline solar modules are big. A typical 60-cell solar module is in the 65 by 40 inch range, and getting bigger. Sunpower is now producing a 128-cell, 435 watt solar module that is a whopping 82 by 41 inches and over 20 percent efficient!

While bigger and more efficient is better for solar farms and most rooftops because the dollars per watt decrease, bigger is worse when the goal is to cover existing infrastructure. Curvature is the problem. Flat crystalline solar modules are, well, flat and rigid. They do not bend, so the bigger the flat crystalline solar module the less curvature it can effectively cover. Much less existing infrastructure can be transformed into solar energy generating devices with big, efficient crystalline solar modules.

Flexible Solar Modules

Flexible amorphous-silicon and CIGS solar modules can more easily attach to and cover existing curved infrastructures like poles and arms, but the cell efficiencies are less than crystalline cells and the orientation of bypass diodes between cells may or may not align optimally for the infrastructure being wrapped or the position of the sun throughout the day. When not ideally oriented, module generation performance suffers. For example, wrapping an amorphous silicon solar module designed to lay flat between spars on a metal roof, around a vertically oriented cylinder like an aluminum light pole, yields less than optimized generation because the cells were not wired with this geometry in mind.

The first innovation needed to unlock Infrastructure Solar combines the best of both crystalline and flexible solar cells into an articulating solar module; a solar module designed to transform existing infrastructure into optimized solar energy generating devices by attaching to and covering with articulating facets comprised of crystalline solar cells. This new class of solar module is comprised of two or more facets that articulate relative to one another, while each facet is comprised of one or more solar cells whose size and shape is determined by the geometry of the existing infrastructure being transformed and whose orientation relative to the sun is the same.  The size and shape of a facet’s crystalline solar cells need not be square or rectangular, but instead should be determined by the infrastructure being transformed and its curvature. These cells may take on the shape of all kinds of polygons such as triangles, pentagons, hexagons, octagons and the like, all to facilitate covering arbitrarily curved, already standing infrastructures.


Second, like the optimization benefits gained from mating microinverters with today’s solar modules, MLPEs must be applied more granularly than a single 60 or 70 or 128-cell solar module. Each articulating crystalline cell, or each group of crystalline cells that articulate together (i.e., facet), must be mated with an MLPE to optimize its performance regardless of orientation relative to the sun. Generalizing this notion and extending it across years of technological advancements, the logical result is the incorporation of a direct-current, solid state, Maximum Power Point Tracking (MPPT) power optimizer directly into each facet, and then sharing a single, separate, grid-tied inverter across numerous so-equipped facets to create an articulating solar module. An Infrastructure Solar system is then constructed from as many articulating solar modules as are necessary to cover the existing infrastructure being transformed.

Power Optimization

Obviously economics plays a big part in Infrastructure Solar too. The previous two technical innovations open up the market, but the dollars per watt must also be compelling. Balance of system costs should be less for most types of Infrastructure Solar because the infrastructure already exists and the cost is already sunk. However, a new type of articulating solar module employing more granular MLPEs will drive up system cost, initially.  Fortunately, if we have learned anything from the solar boom these past several years it’s the fact that solid state technologies and manufacturing processes consistently outperform predictions about economies of scale, solar modules and MLPEs included.

Data Center

The final innovation that will unlock the potential of Infrastructure Solar involves big data. Microsoft and Google both have truly massive geocoded data sets along with ecosystems seeded with platform development tools and services to extend these data sets. Think about the mapping app on your mobile device and all the supporting data overlays you see when following directions, like restaurants with their menus and star ratings, gas stations with their gas prices, etc. Now what if this same machinery were used to geocode existing infrastructure like street lights, traffic signals, water towers, and so on, and then overlay these locational data with ever more detail like height and diameter of street and traffic poles, easement ownership information for the land on which these poles reside, specifics about the below-ground power available to the poles like voltage and the nearest circuit panel, and so on? This level of detail would dramatically reduce the cost of standing up the first wave of Infrastructure Solar. Infrastructure will need to be cherry picked initially because economies of scale will not have kicked in, so easily and cost effectively identifying these cherries will be crucial initially. Yet even after this first wave helps to drive down system costs, the data will remain invaluable as a tool to reduce balance of system costs, perpetuating the economies of scale cycle.

Eleven years ago it was The Long Tail of the Internet. Eleven years from now it may very well be The Long Tail of Solar, with every size and shape of existing infrastructure transformed into solar energy generating devices. When summed, all these small, niche, solar generating systems will dwarf the kilowatt-hour capacity of the big solar farms just like Internet search advertising did for niche products relative to big product hits. Maybe then we will finally be able to put the 1 kilowatt of direct sunlight that hits every square meter of the Earth’s surface to good use.

Grid, Grid Everywhere, But Not An Erg To Drink

Everywhere you read it’s energy grid this, electrical grid that. The grid is getting smart, and clean. The grid can store energy. Two-way communications throughout the grid is commonplace, as are renewable energy sources. Microgrids are improving grid reliability and the overall grid architecture is becoming more and more distributed.

Yet looking out my window, nothing appears any different. Is my grid smarter or communicating or more distributed? Are today’s ergs somehow richer or more glamorous?

Distributed Is the New Black

No. Well, not yet anyway. But change is afoot! In the beginning there was the electrical grid, a truly dizzying feat of real-time engineering. Hit brew on your coffee machine and viola, it turns on. 120 volts at 60 hertz (in the U.S.) delivered to your doorstep from a dam or coal-fired power plant or nuclear power plant hundreds or even thousands of miles away. It’s magic!

Then there was the microgrid. A microgrid is a localized grouping of electricity sources and loads that normally operate connected to and synchronous with the traditional centralized grid, but can disconnect and function autonomously as physical and/or economic conditions dictate.[1] The scale of a microgrid is smaller than a traditional, centralized energy source like a coal-fired power plant. A microgrid might generate 1 to 10 megawatts into a 20 kilovolt distribution system, as opposed to 667 megawatts for an average coal-fired power plant into a 110 kilovolt transmission system on the centralized grid. Of course the service territory for a microgrid is smaller too, so its capacity can be smaller, matching a smaller demand.

Nanogrid Figure 2

And now we have the nanogrid. A nanogrid is an autonomous, self-contained grouping of electricity sources and loads that need not be connected to the traditional centralized grid, or microgrid, yet can be aggregated together and connected to the grid when the economic value of its available energy overcomes the infrastructure cost of the connection. As its name implies, we have moved the decimal on scale to the left a few more places. Nanogrid electrical sources are smaller still, from a few watts up to many hundreds of watts. More fundamentally, nanogrids are autonomous. They need no grid in the traditional sense, nor any of the costly infrastructures required to be grid-connected. They are able to generate their own energy, enough energy to perform their function, plus more in many cases. Examples include personal kinetic chargers like the nPower PEG product that can charge mobile devices, street side trash compactors like the BigBelly that use solar energy to compact garbage, solar-powered transportation fleets like eNOW Energy Solutions that generate and store enough energy to control the environment inside a trailer during transport, and outdoor lighting like Inovus Solar-Enhanced Lighting that captures solar energy during the day and uses it to provide lighting at night. Electric and hybrid vehicles may also qualify as nanogrids to the extent their batteries have available charge.

Connections Are the Key

Energy is becoming more and more distributed, even mobile in some cases, and autonomous. Yet being connected remains fundamental, whether grid-connected, control-connected or both. Being grid-connected allows a nanogrid to operate as just another electricity source on the grid, or redundantly alongside the grid, but the connection requires costly infrastructure. This type of connection is electrical. Control-connected is different. This type of connection involves two-way communications. Electricity sources and loads in a nanogrid communicate, coordinating behavior to insure autonomy. For example, a solar light directs its solar energy to one or more loads while the sun is shining, and may also top off the batteries when low. Then when the moon is shining, its behavior changes to run the loads off the batteries. So a nanogrid doesn’t need to be grid-connect, but it may be, and it is always control-connected.

Nanogrid Figure 3

Together We Stand

A single, autonomous nanogrid has at most a relatively small amount of energy at the ready. However, nanogrids can be aggregated. When the economics are favorable, large numbers of grid-connected and control-connected nanogrids can be summoned to wield large and meaningful amounts of energy. For example, all the electric vehicles within a county that are plugged in and have more than 10 extra kilowatt-hours of stored energy onboard can be selected and then targeted to deliver their energy to the grid during a 15 minute window in the afternoon while the natural gas peaker plant spins up.

Nanogrid Figure 4

A centralized aggregation point with detailed, near real-time information about these nanogrids is required for this example to be realized, but these can be high value uses for the highly distributed energy in nanogrids that warrant the economics of being connected.

Distributed Ergs

Outside your window things may not look any different today, but they will. It’s inevitable. The electrical grid must evolve. Energy generation is moving to the edge – to the county and city and neighborhood and commercial building and residential house – closer to the consumer. Your ergs may not be richer or more glamorous, but they will be less haggard from their travels.