Edge Computing and Distributed Intelligence Enable the Neural Grid TransformationPosted to Guidehouse in the Digital Utility Group
- Apr 13, 2021 5:30 am GMTApr 12, 2021 11:51 pm GMT
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The distribution grid is quickly becoming the most dynamic section of the power delivery network. Yet, even in developed regions like North America and Europe, the distribution system lacks visibility, transparency, resiliency, and autonomy, especially compared to the transmission network. This comparative lack of distribution system instrumentation is largely due to the relatively low impacts of distribution system outages versus a transmission failure. Additionally, the size of the distribution network makes it difficult to monitor the entire system effectively, and the cost of distributing intelligence throughout the distribution grid is high. Guidehouse Insights estimates that less than 10% of the distribution network is automated and instrumented beyond basic SCADA systems.
The integration of distributed energy resources (DER), the increase in customer demand for grid reliability and stability, and recent advances in grid technology are driving utilities and grid operators to equip their distribution networks with monitoring, automation, and control equipment. Edge computing is a small part of the overall distribution automation and controls market, but it spans the entire distribution network—from the substations, to and through the meters, and onto DER networks, microgrids, and distributed generation. The following figure illustrates the functions and locations of various grid edge computing applications.
Grid Intelligence at the Edge
Source: Guidehouse Insights
Edge Computing Technologies
Across all grid segments, Guidehouse Insights expects edge computing platforms to be centered around four key technologies:
- Distribution automation (DA): Near-instantaneous fault detection, location, isolation, and service restoration (FLISR) uses the split-second action of DA assets around the grid for enhanced grid reliability and resiliency.
- Volt/VAR optimization (VVO): Utilities can control voltage and volt-ampere reactive (VAR) levels in real time to optimize power flows, reduce energy consumption, and manage challenges that arise with high penetrations of DER.
- Advanced metering infrastructure (AMI): Advanced meters can continuously calculate and analyze loads on individual distribution transformers, locate outages at the meter level, and integrate with VVO, FLISR, and active demand response (DR) programs.
- Smart inverters: Advanced smart inverters can integrate load flow data with VVO and conservation voltage reduction to provide reactive power compensation to the grid to offset fluctuations caused by DER and DR programs. Home energy management systems can leverage smart inverters to manage the interactions between DER and the grid.
Each of these applications enables intelligent grid edge assets to collect data, analyze data, and act based on the results of the analysis—all without involving any centralized IT system or requiring advanced communications networks. The benefits to the increased deployments of this technology are tremendous. While substantial barriers exist, Guidehouse Insights expects the market for grid edge computing to grow significantly over the next decade. A few of the most significant benefits of grid edge computing are outlined as follows:
DER Network Efficiency Gains
With the addition of renewable generation and energy storage on the customer side of the meter, the need for enhanced monitoring and control has become more important than ever. By integrating onboard processing power into sensing devices that measure power flow, power quality, frequency, and other vital grid characteristics, DER network utilization can be maximized, safety can be prioritized, and overall DER system efficiency can be increased. This increase in DER efficiency creates value across the network for both DER owners and network operators. Smart inverters are among the most important DER assets and are equipped with onboard computing capabilities. Additionally, voltage regulation has increased DER hosting capacity while maximizing energy efficiency benefits.
Demand Side Management and Energy Efficiency Program Benefits
The integration of distributed intelligence can help maximize the potential benefits of demand side management and energy efficiency programs. The ability of meters and other distributed control devices to communicate with household equipment and behind-the-meter generation to manage capacity and load vastly improves the effectiveness of these types of programs. Meters and other devices that can disaggregate residential, commercial, and industrial load profiles can also enable customer engagement in utility energy efficiency programs and support the reduction in demand, potentially deferring the need for new centralized generation and infrastructure.
Workforce Requirement Reductions
By increasing the distributed decision-making capabilities of grid assets throughout the distribution network, human intervention and control is minimized, providing financial savings for utilities and network operators while increasing grid stability. Applications such as FLISR and direct load control drastically reduce the manual labor required to maintain an operational grid. Utilities and grid operators will be able to transfer traditional operations and maintenance to CAPEX spending on distributed computing assets, allowing collection through rate cases.
Communications Congestion Alleviation
Historically, real-time or near-real-time data communications on the distribution grid have been minimal, if nonexistent. As sensing, measurement, and automation technologies have been added to the distribution network, the necessary communications infrastructure has been added as well. With the increase in data collection capabilities, the communications networks have witnessed growing congestion, which is slowing performance, adding stress on the networks, and reducing the effectiveness of the overall communications network. For example, Itron’s OpenWay Riva AMI solution can collect far more data than can be streamed back to a central IT platform and analyzed, highlighting the need for distributed intelligence. The ability of the AMI device to act on the collected data eliminates the need for communications network upgrades, deferring utility investment and improving communications and distribution network performance.
Asset Management Benefits
As utility asset management strategies shift from time-based to condition-based to reliability-based and begin to incorporate predictive capabilities, edge computing increases grid visibility with enhanced distributed data modeling, alleviates the stress on a centralized asset management platform, and defers capital investment in grid infrastructure upgrades. A centralized asset management platform captures data provided by edge computing devices and can integrate the device actions into its asset performance management and portfolio analysis.
Enabling the Transformation to a Digital and Decentralized Grid
As utilities and grid operators seek to transform the global electric grid into a digital, decentralized, and decarbonized intelligent network, they face mounting pressure from customers and regulators to make the most cost-effective and impactful grid modernization investments. It would be naïve to suggest the pervasive addition of grid edge computing technology is the optimal choice for every grid, but it would certainly be the right one for many. Yet, global penetration of edge computing technologies remains in the single digits.
Despite the low levels of deployment, the eventual integration of edge computing technologies on the grid is inevitable as the grid transforms into an intelligent Neural Grid. As data collection through sensing and measurement technologies continues to grow in both scale and complexity, data storage systems will be tested, communications networks will become congested, and centralized IT systems may become overloaded. By allowing intelligent devices on the grid edge to collect, analyze, and act on network data, all the aforementioned systems can achieve their own superior performance, leading to dramatic improvements in overall grid performance across utility networks.