• The Grid of Things

Across all industries, a growing buzz has surfaced around the term “Internet of Things” (IoT).

This interest has been fostered by applications that leverage greater connectivity and the widespread use of smart devices. Given this new paradigm of information access and actionable results, it is no wonder that the power grid is at the epicenter for what some are calling the Grid of Things (GoT).

Within the electric distribution segment, utilities have progressively moved toward greater situational awareness and control by using intelligent end devices (IEDs), which are connected over varying communications networks. However, the potential of a new era of automation and autonomy is what could create an advent of new models of operation, which, consequently, could transform the grid modernization efforts of numerous utilities.


At the Internet of Things World Forum (IoTWF) held last year, the 1,500-plus cross-industry attendees took a straw poll. The question posed to the audience was “Which of the following industries will adopt the Internet of Things the fastest: manufacturing, retail, medical, retail, utilities or transportation?”  A large display streamed the live results as participants entered their answer on their smart phones and devices. The manufacturing industry emerged as a marginal leader over the utility industry.

The results did not reveal whether or not people viewed the proliferation of smart meters as the foundation for this viewpoint. However, the subsequent industry-specific breakout sessions revealed that forum attendees expect that utilities will use an increasing number of smart devices within the network in the future. Additionally, utilities will use these devices to improve quality of infrastructure, provide greater transparency about service restoration estimates, and to become part of the greater ecosystem of connected devices.

While it is very likely attendees at this conference may not  have a realization of how much automation already exists in the utility environment,  it is clear that the audience’s expectation is that there are opportunities to increase the use of sensors and control points to improve performance, reliability and to further increase grid intelligence.


Unsurprisingly, radical changes are driving the power industry in new directions, which include customers, regulators, and infrastructure.

The growth of distributed energy resources (DERs), driven by State Renewable Portfolio Standards (RPS), is one factor (refer to Figure 2), which is setting supply goals in many states, some of which have set aggressive targets in the range of 20 to 30 percent by 2020.

While costs of natural gas continue to drop, the interest to reduce carbon footprints and greenhouse gases are dominating the push for solar and wind resources. However, these resources have an inherent intermittency of supply, which without some energy storage to mitigate these fluctuations, present a factor that both utilities and customers must manage.

The cost of solar resources continues to drop, thus making solar photovoltaic panels and systems more affordable for the masses. While Solar Renewable Credits (SRECS) may be on the decline, an increasing number of consumers are looking at rooftop solar as a means to become “green” and to undertake levels of self-sufficiency. Producer/consumers (prosumers) are on the rise, and firms who provide solar installation, financing, and support are becoming significant participants in this new energy market.

Microgrids are growing in popularity. Many mid-sized and large consumers are both exploring and undertaking efforts to be energy self-reliant. The U.S. Department of Defense (DoD) has had the vision and directive to become “grid independent”, driven by a need for more resilience as well as an offset to potential threats.

Many states such as Connecticut and New York are offering incentives to deploy community-based microgrids. Often, these incentives are, as reflected in the New York State Energy Research and Development Authority (NYSERDA) NY Prize mission statement, to “reduce costs, promote clean energy, and build reliability and resiliency into the grid”. According to NYSERDA’s website, the NY Prize is “a first-in-the nation $40 million competition designed to help communities create microgrids: standalone energy systems that can operate independently in the event of a power outage”.

The challenges for utilities that have customers with microgrids are complex (refer to the “Microgrid Challenges” sidebar).  Full microgrid implementation will require collaboration and coordination among utilities, stakeholders, regulators, and customers, and most likely be where the Grid of Things will play an important role.



•  Microgrids are located close to the customer, not necessarily the utility. This could pose supply or reliability challenges such as power quality and/or capacity issues.

•  The composition of the local supply will most likely be composed of various combinations of supply, which could pose such as solar, combined heat and power, and in some cases, battery energy storage, all of which have varying performance characteristics.

•  Despite the potential loss of load, utilities must know what reserves are necessary to fulfill client needs should a local power source fail •  Utilities must know what impacts intermittency of load and supply will have on the distribution network.


Electric utilities have used connected end devices in the past. Throughout the grid, utilities have used many connected assets including line fault sensors, power quality monitoring points, automatic transfer switches, reclosers, sectionalizers, capacitor banks, voltage regulators, line tap changers, and protective relays. However, utilities have traditionally connected these devices in a hierarchical system of command and control (refer to Figure 2 on page 42).

In this top down, bottom up network, a high dependence exists on communications connectivity and throughput, so that status, events, and actions are processed in sufficient time to ensure the efficacy of the application. An example of this interdependence of throughput and efficacy can be found with smart meters in an AMI network. The primarily function of these devices is to collect consumption data for billing purposes and to capture interval data for analysis. The difference between a monthly consumption read and 15 minute interval data can be a factor of

2600 to 1. Often any limitations in available bandwidth can be mitigated by time stamping the interval data and storing this within the meter. This information can be compressed and transferred on a batch (non-real-time) basis. However, exceptions such as alarms and events, like tamper indications and outage, is information that has value as happen, and therefore, these messages are sent in real-time and often are treated by network as prioritized packets.

In the distribution automation arena, status, events, and exceptions are more the norm, rather than the exception. Therefore, network and master station (advanced distribution management system) requirements are similar to SCADA (supervisory control and data acquisition) systems- and operate in real—or near-real—time.

A new trend is emerging – that is, what interaction must a utility have with a microgrid. Eeven though many of these are customer sided (behind the meter) systems, they have the potential of being grid-shared assets. As such, these elements will likely play an active role in grid stability and reliability.


Under the current specifications of IEEE Standards Association’s specification 1547: “National Standard for Interconnecting Distributed Generation”, any grid-connected generation asset must automatically disconnect from the grid if any grid-side situations, such as under voltage or frequency deviations occur.

However, California and other states are driving the new IEEE specification to consider ride-through requirements that would set different boundaries for operation. As shown in Figure 4, the California Smart Inverter Working Group (SIWG) has defined voltage and frequency ride-through and power factor correction curves for intelligent distributed energy resource inverters.

With these ride-through requirements under consideration, the traditional utility centralized command and control environment may not be adequate. Take as an example the mapping of control and information that would be required to integrate a microgrid into the utility distribution environment (refer to Figure 3 on page 44).

The integrated solution packages include systems that orchestrate the devices and tools, some of which would be highly dependent on robust communications networks to support the flow of information and actuation of control.


In the machine-to-machine (M2M) arena, devices that are communicating directly to other devices in an autonomous manner have been more common than in utility systems. For instance, consider wearable health monitors such as the Fitbit. This device telemeters personal biometric data information directly to the wearer’s smartphone, monitoring activities and medical information such as heart rate.

Hospital technology is also rapidly adopting the Internet of Things concept. Medical professionals are now using smart medical patient bracelets to verify the identity of the individual before dispensing medication. Remote monitors and sensor networks provide patient telemetry to hospital staff on their intelligent tablets.

Popular smart phone devices now include near field communication (NFC) capability, so that paying for items at a merchant eliminates the inconvenience of physically removing the credit or debit card from the wallet and swiping it in a reader. Instead, the user’s fingerprint acts as authorization, effectively providing a digital signature. In a smartphone, the credit card information is stored as a unique, encrypted token, and stored in a dedicated memory chip. When a customer pays the merchant, this token passes to the merchant.

Utility applications such as automatic reclosers use local operation; however, some utilities resist the idea of devices acting directly on information received from other devices. Largely, the safety of line crews who are dependent on certainty of conditions is the motive behind the resistance. These are indeed very valid and significant concerns.

However, in the case of the operation of a microgrid in response to stimuli that happen in seconds or, in some cases, sub-seconds, the latency of a command/control scheme may not be adequate to meet the ride-through requirements.


Recently, Duke Energy announced its Coalition of the Willing Phase II (COW II). In this pilot, Duke Energy is setting up a live microgrid in one of their facilities. While many utilities have established test beds for emerging technologies, what makes this situation unique is that the participants in this endeavor will be demonstrating how devices such as inverters, battery storage, and power quality meters interoperate in a common peer-to-peer environment. The selected methodology demonstrated is based on the data distribution system (DDS) specification.

In this environment, communication among devices will operate in a common field message bus. Elements can both publish and subscribe to information.


As the power grid becomes more complex, and the operational requirements of the distribution network become more intertwined with devices possibly owned by other parties, the necessity for connectedness among elements will become critical for safe and reliable operations.

Key factors such as cybersecurity and control vulnerability are essential, which means that utilities must support and embrace timely local operational modes. While full-scale operation in an IoT environment may be too challenging a step to drive immediate changes in the industry, it is important to recognize and accept the value of the potential of greater intelligent and autonomy. Centralized functions will always be necessary; the challenge of scripting and anticipating all possible scenarios is nearly impossible, so utilities will require greater situational awareness at a common point.

Likewise, even if devices operate locally, information that is both historical and operational in nature must be pushed up the pyramid of control. The archiving and use of this data will be used for future modeling, and for developing analytic tools that will help define the future control algorithms that would be pushed down to these devices.

Ron Chebra is a Principal in the Utility Consulting organization within Schneider Electric Services. He and the team of seasoned professionals are helping utilities with their strategies, often developing technology and process roadmaps, to deal with the emerging trends of renewable integration and microgrid management.