There is a considerable amount of buzz in the industry about the promise of a distributed energy resource management system (DERMS). It has been referred to by some as “the most far-reaching transformation in terms of design and implementation.” DERMS are offered as a solution to the current and future challenges posed by distributed energy resources (DERs) but is yet to be a proven solution capable of managing the growing number of heterogeneous metered and behind-the-meter DERs that will grow into the hundreds of thousands.

DERMS have functional benefits, but they are just a component of the interim and long-term solution. The broader “far-reaching transformation” has to be a system-of-systems solution that is capable of modeling every service point and every asset on the distribution system. This includes aggregate DER asset models from multiple DERMS platforms to create localized load and generation curves that feed the advanced distribution management system (ADMS) power flow analysis. The modeling of DERs by a DERMS is only one component of the local distribution system model. As more and more behind-the-meter DERs, such as renewable generation, electric vehicles, energy storage and load control devices, are deployed, the more critical it will be to model every asset on the system.

The Train Has Left the Station

A few years ago, I did not foresee a future in which there would be a solar panel on every house and an electric vehicle in every driveway. And I still don’t. Yet the recent regulations, building codes, manufacturing production trends and the rapid adoption of these heterogeneous, distributed and sometimes mobile DERs are having an indisputable impact on the current electric distribution system.

Building codes are starting to incorporate more than just energy efficiency standards to include smart thermostatic communications protocols and even DERs consideration for new construction. Beginning in 2019, all thermostats installed during new construction starts in California will have to be able to receive an advanced demand response through the open automated demand response (OpenADR) protocol. These smart thermostats, like Nest, Ecobee, Honeywell and others, will enable the control of behind-the-meter smart devices. There are no mandates that currently require demand response participation, but enabling the OpenADR protocol behind-the-meter opens the gateway to utility or third-party control of smart appliances and other DERs. In 2020, all new homes three stories or higher in California, will have to incorporate DERs into their design. This new building code requirement is the first of its kind, and if both of these California building codes are adopted across the country, then we will see an increased adoption, or in the case of building codes, requirements to include DERs into new construction and renovations.

And as manufacturing costs and production trends continue to drive down the price of DERs and increase the availability and options to consumers, the deployment of DERs will move from the early adopter phase into mass-market participation. Consider the recent news about Tesla meeting its production target of 5000 electric vehicles per week, along with announcements from most of the global auto manufacturers indicating their commitment to EV production, and one can see the sea change ahead. Many believe that this is the beginning of a broader transition across the entire transportation industry. The International Energy Agency forecasts 125 million electric vehicles on the road worldwide by 2030.

Continued gains in battery technology are the key driver to this transition. A byproduct of this research – continued cost decreases in batteries – makes the economics of on-site and even residential behind-the-meter energy storage more likely in the near future. How long before we see a 5kW battery at Home Depot in place of the pad mount generator set?

The adoption of these technologies is occurring during a period where household spending on electric utility service as a percentage of monthly household expenditures is the lowest it has been since 1959. As Steve Mitnick, editor-in-chief of Public Utilities Fortnightly, outlines in a recent Today’s Public Utility Fortnightly digital column, “Electric bills were 1.36 percent of consumption expenditures in the second quarter this year.” Clearly, the consumer adoption of these expensive new energy technologies is not driven solely by economics.

This groundswell change in consumer habits is influenced by broader social and environmental factors that speak to a growing segment of the population. More importantly, as a consumer-driven transformation, the train has left the station. As outlined above, utilities have little influence over how and when consumers use their product and what new technologies they may adopt.

Changing consumer habits, growth in and lower costs of DERs, declining costs of energy storage and more state-wide renewable energy policies all lead to the inevitable reshaping of the power industry. As the grid transforms into a bi-direction transactive platform, utilities need to develop the systems capable of managing distribution system power flow to maintain the fidelity of the grid.

Death by a Thousand … NO, a Million Cuts

While some utilities have deployed DERMS in pilots to help manage the growth of DERs, in reality, a DERMS might be a stopgap measure that provides only some visibility and control over larger DERs. Its limitations are considerable and analogous to only controlling the schedule of express trains that share the same tracks as local service trains. Without full visibility of how all assets are performing, you can’t maintain a safe and reliable system. With reliability remaining job number one for utilities, less than full visibility could lead to “blue sky” outages due to uncoordinated DER performance… and that won’t fly with customers or regulators. No one wants to see a train wreck! Modeling and scheduling larger DER assets only – as DERMS do – is an incomplete solution. To gain full visibility across the system, every asset, down to the service point and including behind-the-meter assets, has to be modeled.

As more behind-the-meter DERs are installed on the system and more homes open their home area networks to utility or third-party control of thermostats, water heaters and other smart appliances, the cumulative and localized impact of these micro assets have to feed into the hour-to-hour distribution power flow analysis. Current DERMS manage hundreds or maybe a few thousand DER assets disbursed across the system. As consumers continue to adopt behind-the-meter appliances and DERs, modeling and managing millions of assets will be essential. The DERMS will continue to be an input to the power flow analysis, but only if it can provide next-hour localized asset models and schedules. Localized distribution power flow has to be capable of modeling millions of metered assets, behind-the-meter assets and the aggregate grouping of assets by phase, circuit, transformer, etc., to ensure the safe and reliable operation of the system.

It is not enough to model hundreds or thousands of DERs across the system. Modeling every asset, including millions of service points, provides system operators with the granular visibility needed to ensure the fidelity of the grid.

Operating Closer to the Rails

The need for more localized modeling is driven by hour-to-hour locational energy supply requirements and capacity constraints. As the electric grid begins to shift from a primarily centralized generation platform to a more decentralized distributed generation platform, scheduling energy and managing capacity on local circuits will become more critical.

As mentioned previously, in order to reliably integrate the deployment of potentially millions of micro assets into the distribution grid, utilities have to develop modeling capabilities that forecast near-real-time system requirements on every circuit to ensure there is sufficient electric energy and capacity to serve all customers. These local circuit requirements – energy, the net volumetric consumption in kWh and capacity and the magnitude or cumulative demand in kW at any moment in time – are crucial determinants to ensuring grid fidelity.

Scheduling energy on an hourly basis with wholesale markets will have to include modeling the performance of all assets, with or without DERs, on the distribution system. This bottom-up approach to modeling provides utilities with the capability of determining the hourly net load requirements for every circuit by simply aggregating the hourly forecasts for each asset on the circuit.

Unlike energy, capacity is a physical constraint, a demand limit determined by manufacturer nameplate equipment ratings on higher level system assets, such as transformers.

This physical limitation is a critical operational threshold that, if exceeded, could affect all downstream assets (customers) and, potentially, customers on adjacent circuits. System planners continually review the loading on these critical assets to ensure sufficient capacity to manage peak demands.

But across the country regulators and utilities have been adopting a new approach to system planning: locational resource planning or non-wire alternatives (NWA). As an alternative to costly capital investments to increase physical system capacity, system planners are modeling the effect that downstream energy efficiency and load shifting programs, along with DERs incentives and time-of-use rates, can have on reducing hourly peak demand on upstream assets.

Locational resource planning alternatives can defer capital investments in system assets and help to increase capacity utilization, but also present a looming risk. With less headroom in capacity, we are asking system operators to run “closer to the rails.” The capacity limits on critical system equipment become more important on NWA program circuits. The performance of all downstream assets, especially those variable assets that are incentivized to reduce demand during peak capacity periods, has to be modeled and forecasted in next-hour frequency to provide grid operators with the actionable information needed to avoid overloading on critical system assets.

The Confluence of Technologies – Enabling Platforms

The wave of innovation hitting the electric utility industry is a disruptive force that is changing a century-old industry that was, for most of that century a vertically integrated, uni-directional system. The new and emerging technologies available to consumers are drastically changing the operational and business models in ways that were not possible to conceive at the turn-of-the century. At the same time, utilities are faced with an onslaught of disparate data growing minute-by-minute. Utilities need to harness this big data to manage the proliferation of DERs and the evolving energy IoT platform.

Complementing advances in digital communications and controls in information technology are helping utilities manage these disruptive end-use technologies. An energy IoT platform is evolving, and as utilities gather data from behind-the-meter devices, advanced meter infrastructure (AMI), advanced distribution management systems (ADMS) and other OT & IT data sources, enterprise solutions are needed to unlock operational and commercial value from this big data.

Measurement data from energy IoT smart devices, including meters, inverters, appliances and thermostats provide utilities with information that can be used to assess the deployment and value of DERs on each circuit and to optimize the physical and economic value of these assets. However, the data can only be made actionable and intelligent for utility operations if it can be processed and presented in near-real time. Some utilities have adopted costly colocation strategies using Hadoop distributed file system architecture, centrally located data warehouses or data lakes to manage the growing big data. These solutions require the replication of data and increased data latency, thereby reducing the source data’s value to the enterprise, particularly operations. An optimal solution is to minimize the replication of source data and leverage a data virtualization layer to pull disparate data into a real-time operational platform.

Data virtualization allows utilities to integrate data from disparate sources, such as AMI, DERMS, EV chargers, ADMS, etc., without replicating the data, to create an enterprise virtual data layer that supports grid operations, system planning and commercial evaluation of all DER assets across the system.

As noted earlier, the need for near-real-time data is essential to providing system operators with the visibility needed to ensure the fidelity of the grid. Therefore, data latency has to be minimized when processing data. Data virtualization does not wholesale replicate downstream source data, thereby minimizing data latency and providing actionable information to grid operators. Data virtualization also provides strong data management features which ensure data integrity, security and auditable. This functionality is not only critical to the operational integration of DERs, but also in the validation of DER performance and transactional settlement. This business platform is a keystone to integrate and extract the full value potential of the modern asset distribution grid.

The Emerging Transactive Energy Platform

The GRIDWISE Architecture Council (GWAC) defines transactive energy as, a system of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter. A system is defined as a set of connected things or parts forming a complex whole. In the case of the electric distribution system, the set of connected things must include all micro assets that form the complex whole.

This is the shortcoming of DERMS. It is a disaggregated top-down modeling platform that only considers the impact that large DER assets have on the distribution system and does not incorporate how the performance of DERMS controlled DER assets and localized micro assets will affect individual circuit performance. An aggregate bottom-up approach that models every asset on a circuit is the best method.

System of Systems with Data Virtualization

Utilities need a transactive energy platform capable of integrating data and models from other systems to provide load and generation forecast data for every asset on the distribution grid, with the spatial and temporal granularity required to support operational decision management. This bottom-up approach provides utilities with aggregate asset forecasts by circuit, substation or other spatial attributes to analyze the impacts of DER assets on other grid assets. The platform must be able to dynamically model local resource requirements based upon actual historical and forecasted load, as well as any DER performance data for each local circuit. This system of systems platform will pull data from DERMS, EV charging stations, volt-VAR optimization (VVO) platforms and near-real time AMI data to model hour-to-hour, circuit-by-circuit system requirements.

While DERMS are part of the solution to managing DERs on the grid, a much broader system of systems approach with a configurable data virtualization layer is the optimum solution for modeling a transactive energy platform. Utilities need a holistic view of the distribution grid in which every asset is modeled for near-real time performance.

Consider the Following Scenario

There will come a time when solar panels or other self-generating and energy storage units are required as part of every new home or more likely, offered by home builders as additional features that get rolled into a first mortgage. That same home may also be fitted with fast=charging electric vehicle outlets in the garage. More than likely the home will have a home area network with smart thermostats and security system controlled by a mobile device. Is this scenario five years out, ten years out? How long can you wait?