New Insights on Data-Driven Control for Active Power Distribution Networks

Historically, power distribution networks were simple, one-way systems designed to deliver electricity from large, centralized generators to customers. Today, however, the rise of distributed energy resources (DERs) is turning these networks into dynamic ecosystems where power flows bidirectionally, and loads are more variable than ever. These modern grids are dynamic, complex, and require new approaches to monitoring, control, optimization, and decision-making.

A recently published paper titled “Data-driven control, optimization, and decision-making in active power distribution networks” — co-authored by John Dirkman, P.E. — explores how cutting-edge, data-driven techniques are revolutionizing grid operations. This blog highlights key findings from the paper, the implications for utilities and grid operators, and how these insights can guide the next generation of power systems.

Challenges in Managing Modern Distribution Networks

Active distribution networks present a host of challenges that strain traditional grid management tools:

• Complexity and scale: The growing number and diversity of DERs make it difficult to model and control networks using physics-based methods alone.
• Uncertainty and variability: Solar generation and customer demand can change rapidly due to weather or behavior, complicating forecasting and real-time management.
• Limited observability: Many distribution systems have sparse sensor coverage, leading to incomplete data on network conditions.
• Computational constraints: Real-time decision-making requires fast algorithms that can handle large datasets and produce actionable control signals.
• Integration of multiple objectives: Grid operators must balance competing goals such as minimizing energy losses, maintaining voltage stability, reducing costs, and maximizing DER utilization.

These challenges underscore the need for data-driven approaches that can extract insights from vast amounts of historical and real-time data, adapt to changing conditions, and automate decision-making.

What Is Data-Driven Control and Optimization?

At its core, data-driven control and optimization uses techniques from machine learning, statistics, and operations research to manage and enhance grid performance, based not on fixed models, but on patterns observed in real-world data.

This approach typically involves:

• Data acquisition: Collecting data from smart meters, sensors, DERs, and other grid components.
• Data processing: Cleaning, validating, and organizing data to handle noise, missing information, or inconsistencies.
• Model learning: Using historical data to train models that capture network behavior and forecast future states.
• Optimization: Identifying the most effective control actions to achieve specific operational goals.
• Decision support: Providing operators with actionable insights or automating control decisions through software systems.

The power of data-driven methods lies in their ability to handle nonlinearities, uncertainties, and changing network configurations that are difficult to model explicitly.

Implications for Utilities and Grid Operators

The advances detailed in this paper have direct implications for utilities and grid operators navigating the energy transition:

• Improved grid reliability: Real-time, data-informed control helps maintain stable voltage and frequency, minimizing equipment stress and reducing outages.
• Enhanced DER integration: Utilities can better leverage rooftop solar, batteries, and EVs, avoiding costly upgrades by optimizing existing infrastructure.
• Operational cost savings: Automated decision-making reduces the need for manual interventions and allows for optimized asset utilization.
• Flexibility and adaptability: Data-driven methods can scale as DER penetration grows and adapt to shifts in customer usage patterns.
• Environmental benefits: By enabling higher renewable penetration and reducing reliance on fossil fuels, these techniques support decarbonization goals.

For utilities looking to modernize their distribution systems, adopting data-driven frameworks is a critical step toward building the grids of the future.

Evolving with the Grid

The power system will continue to evolve—driven by electrification, decentralization, and decarbonization. Data-driven control and optimization will be at the heart of this transformation, enabling utilities to make smarter decisions faster, even as system complexity grows.

At Resource Innovations, we’re proud to contribute to thought leadership that bridges academic research and practical utility solutions. Our team members bring decades of deep technical expertise to help utilities harness emerging technologies and unlock the full potential of smart, distributed energy systems. If you have any questions or want to explore how data-driven solutions can help your utility or organization, feel free to reach out.