What is The Role of Data Analytics in a Smart Grid?

Numerous industries are currently being transformed by big data and analytics, and the utility industry is no exception. When faced with the numerous challenges of the energy transition – including multidirectional energy flows, intermittent renewable generation, increased disruptions from severe weather, and more – there is simply no way for utilities to successfully orchestrate a sustainable energy grid without harnessing the power of big data via smart grid analytics.

In this blog we will examine how data analytics is shaping the future of smart grids.

Smart grid analytics software is one of the most important investments the modern utility can make. Implementing such software enables data to be leveraged via a variety of advanced capabilities, many of which revolve around AI and machine learning (ML). With smart grid analytics, utilities can directly address some of their toughest challenges in the energy transition, including the following:

Grid reliability

Every utility strives to guarantee a consistent and reliable flow of power for its customers. That goal is vastly easier to achieve with the implementation of smart grid management analytics. The right analytics solution can unlock a variety of use cases that improve grid reliability. A perfect example is using sensor data to unlock insights about asset performance, enabling utilities to forecast potential equipment failures and intervene long before an outage results.

Energy efficiency

Using smart grid analytics, utilities can analyze consumption trends in minute detail, enabling them to better predict load requirements and identify times of peak demand. Such information can simplify energy efficiency initiatives like demand response programs, Volt/VAR optimization, and more.

Cost savings

Smart grid analytics lead to significant cost savings for utilities in many ways. For example, smart inertia management is a key analytics capability of the best advanced energy management systems (AEMS). This involves setting an analytics engine to monitor generators for signs of low inertia. Any red flags trigger the analytics engine to inform grid operators of the total amount of inertia needed to restore the grid to a safe point. This precise calculation helps users avoid the significant costs associated with excess inertia.

Asset management

Grids contain a staggering array of assets, from transformers to substations to sensors and beyond. They also sport many millions of sensors that generate masses of data recording specific grid behaviors. With the right smart grid analytics, utilities can translate that data into insights about their asset performance management. Those insights can unlock proactive planning and predictive maintenance that lead to reduced downtime and optimized asset lifecycles.

The implementation of smart grid analytics unlocks an array of benefits for utilities, including:

Longer asset lifecycles

Replacing a failed asset is incredibly costly and disruptive. By leveraging smart grid analytics to predict looming equipment failures and dictating proactive maintenance needs, utilities can minimize not just asset failures but downtime overall.

Optimized spend

As EVs become increasingly popular, many utilities are concerned about whether their grids can support the additional power needed for charging. Smart grid analytics software can process a variety of data on consumption patterns, peak loads, asset performance, and other information to create digital twins that utilities can use to run simulations. These simulations can help utilities determine if they do in fact need to upgrade their infrastructure to accommodate anticipated demand, or if there are more economical alternatives to

Increased reliability

Smart grid analytics software is a crucial tool for maximizing reliability. Increasing levels of renewable integration means that the power supply overall can be highly unpredictable, given the intermittent nature of renewable generation. With smart grid analytics, utilities can easily maintain the flow of power as needed, whether by automatically drawing on distributed energy resources (DERs) to close the gap, activating microgrids, or (typically as a last-ditch effort) tapping into fossil fuel-generated supply.

Clearly, smart grid analytics are a critical tool for modern utilities as they navigate the energy transition. However, utilities encounter a few challenges on the road to smart grid analytics.

Budgetary concerns

Utilities operate on rather tight budgets, and the prospect of acquiring an analytics solution can be deterring for Chief Financial Officers. However, the costs of investing in smart grid analytics are well worth it, especially when you compare them to the consequences of not making the investment. Curtailment of renewables, fines for failing to meet sustainability requirements, and more-frequent replacement of physical assets are just a few costs that grids will incur by not investing in smart grid analytics software.

Data access hurdles

It is notoriously difficult for utilities to access the big data that smart grid analytics software needs to work properly. Energy data is bigger, faster, and more scattered than ever before – and the fact that it often rests in silos only compounds the challenges. Luckily, all of the above difficulties can be resolved rather easily with a grid data fabric. The best smart grid analytics software solutions include access to a grid data fabric, which makes it much easier to discover, govern, and utilize the data needed by analytics engines.

Cybersecurity concerns

Smart grid analytics software needs open access to utilities’ most sensitive data and assets. This makes the risk of cyber-attacks a major concern. Luckily, grid cybersecurity has made enormous strides in its scope and magnitude of protections for data, assets, and systems. For exceptional protection, seek out smart grid analytics software with Zero Trust grid security principles built in.

Smart grid analytics capabilities are a hallmark of the best solutions for grid orchestration. Here are a few fundamental solutions with smart grid analytics baked in:

Advanced Energy Management System

A key tool for transmission operators, AEMS solutions use smart grid analytics for a variety of essential use cases. Key examples include intelligent inertia management, Wide Area Management and Control (WAMS & WAMC, and voltage management, among others.

Distributed Energy Resource Management System

DER integration is already increasing exponentially, and will only accelerate as the energy transition progresses. Thus, a distributed energy resource management system is crucial to integrating, visualizing, controlling and simulating the DERs connected to the grid. Smart grid analytics engines are a key feature of the best distributed energy resource management systems (DERMS), especially for powering use cases like flow stabilization, violation avoidance, intelligent dispatching, capacity simulations, and virtual power plant operations.

Advanced Distribution Management System

As the portion of the power network that directly supplies customers, there is no overstating the importance of an advanced distribution management system (ADMS) outfitted with smart grid analytics. An analytics-capable ADMS can unlock outage response and restoration, emergency response (e.g. load shed and distributed black start), coordinated Volt/VAR optimization, and integrated switching, among others.

Utility vegetation management

Analytics plays a crucial role in the best utility vegetation management (UVM) solutions. Built-in analytics engines overlay satellite, LiDAR, and photo imagery with network maps and identify the precise areas where vegetation must be trimmed to avoid damage to nearby assets. This leads to significant increases in efficiency and effectiveness for UVM efforts.

Asset inspections

With analytics, utilities can avoid imprecise, inefficient manual asset inspections. Some solutions have analytics engines capable of identifying incredibly minute details that indicate asset defects or damages, such as corrosion, structural weakening, loose wires, and more. This information is fed back to the operator, who can quickly log the finding and schedule corrective maintenance. The approach accelerates the process of detecting and correcting issues, in turn leading to increased safety and reduced downtime and costs.

Digital dynamic line ratings

As integration of renewables amps up, complex issues like congestion and curtailment become ever more common. A relatively new technology called digital dynamic line ratings (DDLR) can help. With DDLR, analytical algorithms process weather data to determine how certain conditions affect the grid, and can dynamically adjust a line’s rating based on actual conditions at any given time. Thanks to the analytics capabilities, utilities do not need to install a single hardware sensor to enable DDLR – the analytics engine handles all data capture and interpretation to determine to correct line rating.

The critical importance of data analytics for smart grids cannot be overstated. They play a crucial role in unlocking grid orchestration – an absolute must-have for accelerating the energy transition and achieving a reliable and resilient grid.

A grid data fabric makes it easy to access all the data you need to enable full analytical capabilities on your grid. For more information on GridOS(R) Data Fabric, the first grid-specific data fabric, check out our whitepaper on the topic.