Microgrids and More: Decentralized Generation Relies on Distributed Intelligence

Aug. 8, 2019
Microgrids have garnered interest beyond the role of supporting critical infrastructure

When Superstorm Sandy blacked out a large swath of the Eastern seaboard for days in 2012, a few islands of light stood out. Co-generation facilities at New York University, Princeton University and the U.S. Food and Drug Administration’s White Oak Research facility in Maryland powered microgrids to meet energy needs through the extended grid outage.

In the years since this incident, microgrids have garnered interest beyond the role of supporting critical infrastructure. In fact, they’re now seen as a potential distributed energy resource tool to both bolster resiliency and integrate energy from clean sources. 

Because the current distribution system was not designed to support distributed generation, adoption of these resources is often hindered by power quality concerns, voltage and frequency management challenges and inability to manage two-way power flows.

But the ongoing digitalization of the grid is providing the distributed computing and communications capabilities necessary for microgrids to more seamlessly transition from an islanded to interconnected mode. With the right smart grid technology, microgrids can be leveraged to shed measurable load at strategic points on the circuit to help balance resources – increasing reliability and efficiency while reducing emissions. In other cases, they’ve been proposed as a possible solution for towns in remote fire-prone regions of California, where communities face the possibility of days-long blackouts when utilities shut-off transmission lines to reduce fire risks.

Navigant Research forecasts global microgrid capacity to grow from 1.4 GW in 2015 to 7.6 GW by 2024. It also anticipates a big jump in distributed energy resource (DER) capacity over the next five years with as many as 528 GW of distributed generation, microgrids, energy storage, EVs and demand response installed by 2026. That includes decentralized energy sources like rooftop solar, small scale wind, energy storage and other technologies.

This prediction follows two trends in the industry. First, the mission of utilities is changing in ways that puts less emphasis on ownership of grid assets and more on optimizing their value regardless of ownership. Secondly, addressing climate change objectives will require both an increased use of electricity and simultaneous decarbonization of generation.

Transforming microgrids into integrated nodes on the grid depends on real-time supply and demand data, along with the ability to monitor power quality and automate voltage adjustments to efficiently blend traditional grid-scale operations with microgrids.

Much of the enabling technology, including grid-edge sensing provided by advanced meters, is in the field or being deployed already. The next-generation smart meter not only can monitor load and voltage levels at each premise but will be able to analyze waveform data similar to grid meter used at traditional interconnection sites. This capability opens a window into accurately predicting the grid’s response to secondary power flows at any given moment.

Data from meters and grid devices can be used to model system behavior over months and years, as well as give a near real-time model of the microgrid itself as it interacts with the larger electric grid.

Installing this kind of smart infrastructure throughout the grid transfers the heavy lifting of data collection and response across a larger network, akin to how blockchain leverages the decentralized computing power of many different end users to collectively power its system.

This makes smart meters and other advanced metering infrastructure essential operational components and data sources for the analytics that facilitate modern power grid operation.

The planning process for designing, siting and operating a grid-connected microgrid involves many considerations, such as size, generation resources, load forecasting and configuration. Using intelligent grid devices like smart meters and their data analytics capabilities can help utilities make more realistic forecasts for standard and peak load requirements throughout the year.

Leveraging this technology, utilities can also know the performance of existing infrastructure under various microgrid scenarios before a project begins. The surge of data allows simulations to consider real-time operational data from meters, sensors and control systems.

Not all utilities have taken the path of including advanced load management technologies in smart metering projects, but this option is increasingly drawing interest from companies and municipalities across the globe. 

As experiments with integrating distributed renewables and microgrids continue around the globe, the advances being made in distributed intelligence will continue to play a vital role.

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