What Is a Smart Grid?

Many factors are driving the need for a modernized power grid that can dynamically and efficiently manage how we generate, distribute, and consume energy. Some factors include:

• Global electricity demand is projected to increase 2.5x by 2050.¹
• Electricity generation from renewable sources, like solar, wind, water, and distributed energy resources (DERs), including solar panels and wind turbines, is rapidly expanding.
• Traditional, centralized power plants with one-direction transmission cannot adequately support growing demand, and new bidirectional power transmission needs to and from distributed sites.
• Existing infrastructure—a patchwork of siloed, aging systems with limited manageability—severely limits visibility and control across systems, the potential of automation to dynamically balance supply and demand, and efficiency improvement efforts to meet decarbonization goals and manage costs.
• Aging infrastructure also increases the cost of monitoring, maintenance, and upgrades, opening up potential vulnerabilities in the security perimeter.

The smart grid—an interconnected network of technologies with digital control, analysis, monitoring, and telecommunications capabilities—offers a path to a more intelligent and responsive energy system to address these and other challenges and unlock new capabilities.

The introduction of virtualization and software-defined networking, intelligence brought to IoT devices, and advanced connectivity enables two-way communication with shared data access among disparate systems across the energy supply chain. Artificial intelligence (AI) and edge computing unlock near-real-time data collection and analysis. This enables automated decision-making and near-real-time optimizations, providing increased visibility to inform generation planning, energy demand predictions, and operational and maintenance planning.

Benefits of Smart Grids

Through predictive analytics, proactive operations, and other AI and IoT-enabled capabilities, the smart grid enables electricity companies, grid system operators, and engineers to:

• Monitor, predict, and optimize demand and distribution: With granular insight into energy consumption patterns and AI-enabled predictive analytics, utilities can balance demand with supply and minimize waste caused by overprovisioning.
• Reduce capital expenditures: Utilities can meet peak demand without waste and distribute electricity efficiently, minimizing transmission costs and optimizing every component.
• Increase renewable capacity: Using powerful machine learning (ML) capabilities to analyze massive datasets, utilities can incorporate more onshore and offshore renewables into the energy mix and take advantage of recent advances in emerging energy storage technologies. Issues such as intermittent supply and smaller, distributed energy generation can be overcome while still ensuring a steady electricity supply.
• Reduce maintenance costs: Insight into every generation, transmission, and distribution asset enables remote fault diagnosis, minimizes site visits, and supports predictive maintenance that focuses engineering teams on areas of greatest need.
• Improve regulatory compliance: Smart grid technology helps electricity and utility companies meet regulatory obligations to reduce carbon emissions by enabling greater use of renewables; supporting more efficient generation, transmission, and distribution; and facilitating more efficient consumption. AI-based analysis of operational and system data can be used to measure, model, forecast, and report on emissions and identify suggestions for improvement.
• Enhance customer engagement: Electricity companies can use data-driven insights to accurately predict customers’ usage patterns, on which to base more tailored and competitive product and service offerings. This is especially critical to compete within deregulated markets and meet energy-savings targets in regulated markets.

Types of Smart Grid Technology

Ultimately, future energy demands and increased reliance on renewable energy sources require a data-driven smart grid that is flexible and configurable and can maximize energy resources at the transmission level.

From advanced metering infrastructure (AMI) to analytics, smart grid technologies are crucial in transforming how energy is generated, distributed, and consumed.

Advanced Metering Infrastructure (AMI)

AMI is a core smart grid technology that has the potential to modernize utility customer interactions—and the benefits may appeal to consumers who might otherwise be reluctant to consider new programs and offerings.

The system combines intelligent metering devices, robust communication channels, and advanced analytics platforms to facilitate two-way information exchange between energy providers and end users.

In its ideal state, AMI can provide consumers with tools such as in-home displays and programmable thermostats, empowering them to manage their consumption. Positive impacts for energy providers may include better grid reliability, renewable energy integration, and data-driven decision-making.

Networking and Data Transmission

Network and data transmission technologies, enhanced by AI, form the backbone of smart grid systems, enabling real-time communication between the complex array of grid components. This infrastructure combines wide area networks (WANs), neighborhood area networks (NANs), and home area networks (HANs) to create a comprehensive communication system.

AI algorithms can be integrated to optimize network routing, predict and prevent potential failures, and enhance cybersecurity through intelligent threat detection. Machine learning models also assist in data compression and prioritization, improving overall network efficiency and responsiveness in the smart grid ecosystem.

Security

Electricity grids are large, valuable, and critical targets requiring a multilayered approach to security:

• Virtualization and consolidation of smart grid monitoring and control systems on industrial computers allow for centralized threat management, enhancing cybersecurity efficacy. These systems can leverage AI-assisted threat detection to predict potential vulnerabilities and orchestrate rapid, coordinated responses to detected threats. This significantly reduces response times and minimizes potential damage from cyberattacks.
• AMI must include security measures to protect consumer data and prevent tampering.
• Network and data transmission technologies can use encryption and secure protocols to safeguard communication.
• Grid management systems can employ access controls and monitoring to prevent unauthorized operations.

Analytics

The smart grid offers millions of data points that present opportunities for insights and improvements. Energy stakeholders across the industry can process data from smart grid IoT sensors, smart meters, and other grid devices and use AI to expedite powerful analysis and decision-making. Analytics provides a range of positive benefits:

• Real-time visibility into operations supports more efficient maintenance planning.
• Predictive analytics enables more precise generation planning and load balancing.
• Simulation models help renewable energy plants better predict energy demand, factor in weather conditions, and optimize capacity.
• Real-time optimizations by distribution service operators, transmission service operators, and vertically integrated utilities help address issues such as service voltage and phase of substations, thereby improving energy efficiency.

Smart Grid Technology Use Cases

Progress toward smart grid innovation is driven by smart technologies that help enhance efficiency, reliability, security, and sustainability.

Demand Management

Energy generators and sellers need real-time visibility into market demand, production output, and efficiency to make accurate predictions and be profitable.

By leveraging smart grid advanced metering and two-way communication, utilities can use real-time insights for a range of cost savings and efficiency use cases. This includes forecasting consumption, integrating DERs and balancing intermittent renewable generation with other sources, enabling automated switching to prevent overloads, and supporting dynamic pricing and incentive programs that encourage off-peak usage and reduce grid strain.

Remote Manageability

Remote system and device management becomes possible because of the virtualized nature of smart grid infrastructure and the two-way communication between devices across the energy supply chain. Remote diagnostic, control, and repair capabilities can dramatically increase equipment availability and reduce costs, particularly when assets are geographically dispersed or otherwise hard to access.

Remote management also provides impactful value in crises. For example, during natural disasters or cyberattacks, system operators can enable rapid isolation of affected areas and reroute power, significantly enhancing grid resilience.

Energy Theft

Utilities face substantial economic and efficiency losses due to energy theft; however, adopting a smart grid technology approach offers potential solutions. For example, the implementation of AI-enabled metering and analytics can help facilitate the quick identification of unusual patterns or tampering attempts. AI-enabled cybersecurity solutions can detect, alert, and act on potential threats. Remote manageability can restrict access or cut off supply to thwart efforts and reduce revenue losses.

Predictive Maintenance

The interconnected smart grid provides near-real-time access to massive datasets that can be rapidly analyzed using machine learning capabilities to identify patterns and anomalies. When applied to power grid physical infrastructure, this analysis can help proactively identify potential issues requiring maintenance. This helps to reduce costs and downtime, extend asset lifespan, and improve efficiency, performance, safety, and customer satisfaction.

Microgrids

A microgrid is a small-scale, local energy system that can operate independently from the traditional utility grid or connect back into a smart grid when more resources are needed. Microgrids incorporate many smart grid technologies, including virtualization and software-defined infrastructure, intelligent IoT devices like smart meters and sensors, and edge and AI capabilities for near-real-time data collection, analysis, and response.

Challenges of Smart Grids

Like other new and evolving technologies, transitioning to a smart grid presents some challenges, including:

• High CapEx investment: For many projects, especially at generation plants or alternative energy installations, the funds required for extensive, complex projects are very high. ROI from large systematic projects can also take a long time to recoup. Fortunately, in some instances, costs can be offset by government grants, rebates, and credits. Open source architectures can also help reduce costs through standardization and accelerated development, which shortens project timelines.
• Legacy equipment: Replacing or even retrofitting legacy equipment is not simple. Original builds were intended to last decades, and vendor lock-in is a major factor. Moreover, in the case of utilities, end customers often must be persuaded or incentivized to opt into new meters and programs.
• Complexity: Smart grids rely on a vast system of components and technologies, making them immensely complex. There is also little standardization or interoperability in the edge and IoT solutions space. Working closely with technology partners to map, plan, and phase grid modernization efforts is critical to ensuring connectivity and successful operation.
• Remote environments: Many organizations operate at remote sites or in emergency conditions, where there is little or no surrounding infrastructure, and it takes a considerable effort to send personnel. Ruggedized equipment, light infrastructure, creative connectivity solutions, and remote manageability capabilities are necessities to integrate these locations into the smart grid.
• Security and data privacy: Due to their increased reliance on digital technologies, energy grids must be protected from physical attacks and cyber threats. Any solution used in the smart grid should provide robust, multilayered protection; be rigorously vetted to meet stringent security standards; and be actively managed to protect against the theft of personal data.

Transformation of the energy system is a global, complex, and lengthy challenge. Collaboration across industry, public, and private sectors is critical to individual and collective success.

The Future of Smart Grids

The potential of smart grid technology is vast, complex, and exciting. As utilities advance smart grid implementations, it is crucial that they maintain flexibility and compatibility across new and legacy infrastructure.

Fortunately, standardized, open source architectures support a wide range of software applications, preventing vendor lock-in and enabling seamless application migration between hardware solutions. This approach ensures long-term adaptability and cost-effectiveness in smart grid deployments, facilitating the integration of emerging technologies like blockchain for energy trading and advanced demand response systems. Indeed, blockchain could streamline microgrid operations, automate processes through smart contracts, and enable real-time grid management.

From demand response management to integrating renewable energy sources, smart grid technology continues to revolutionize how we generate, distribute, and consume electricity.