The AI Revolution Is Unlocking Value in Battery Energy Storage
How AI Solutions Are Increasing Battery ROI, Creating New Opportunties and Extending Asset Life
Renewable energy continues to be the fastest growing source of new energy generation, and in areas where there already is a lot of renewable adoption, battery energy storage systems (BESS) have emerged as a critical component for grid stability and energy transition. Stationary energy storage deployments surged in 2024, with installed capacity doubling in just twelve months! As margins compress in maturing markets, AI provides the competitive edge needed to identify and capture remaining high-value opportunities. This is the landscape that makes it a meaningful time in the industry for artificial intelligence—a game-changing technology that's revolutionizing how we plan, operate, and maintain battery storage systems. Companies that fail to integrate AI strategies across their battery operations risk being left behind as the industry rapidly evolves
After reviewing available literature and resources, I found the industry lacking in any definitive guide to what AI could mean for energy storage. This gap is particularly problematic as AI adoption becomes a critical competitive differentiator in an increasingly crowded market. Today, I'll explore how AI is–or could be–transforming every stage of the battery energy storage value chain and highlight some of the innovative companies leading this technological revolution.
Understanding AI for Modern Energy Systems
While AI is receiving unprecedented attention across industries, before diving into the energy storage applications, it's worth taking a moment to understand what we mean by "artificial intelligence." AI refers to the simulation of human intelligence in machines designed to think and act like humans. While the concept may seem futuristic, AI technologies are already deeply embedded in our daily lives and across industries, building on decades of research and development.
AI encompasses several key components, including:
Machine Learning (ML): The ability of systems to learn from data and improve performance without explicit programming
Natural Language Processing (NLP): Technologies that allow machines to understand and generate human language
Computer Vision: Systems that can interpret and understand visual information
Robotics: The integration of AI with physical systems for real-world tasks
Today, we primarily use what experts call "Narrow AI" (ANI) - systems designed to perform specific focused tasks without the ability to self-expand functionality. These systems excel at specialized functions but operate within defined boundaries. The energy storage applications we'll discuss utilize this type of AI, employing machine learning algorithms and data analysis to optimize specific aspects of battery systems.
As energy markets grow increasingly volatile and complex, software tools are becoming essential for maximizing the value of battery investments while ensuring their safety, longevity, and optimal performance. AI-powered solutions excel at pattern recognition and making sense of large amounts of data, which the energy industry now has. In terms of application to the energy storage industry, I’ll focus on the development and deployment aspects of the industry. In particular, how AI is beginning to (or could) impact:
Project planning and grid integration
Operations & maintenance
Financial performance
Safety and battery health
Value chain efficiencies
Project Planning and Grid Integration with AI-Driven Decision Making
The journey of a battery storage project begins long before installation. In this critical planning phase, AI is already proving its worth through sophisticated modeling and forecasting capabilities.
AI-powered platforms now provide essential building blocks for creating revenue optimization models that inform crucial early decisions around siting, design, and commercial agreements. These tools deliver market analysis and performance forecasting that gives developers, investors, and grid operators unprecedented insight into potential revenue streams and expected performance—critical information for development and financing decisions.
The predictive capabilities of AI extend to optimizing system sizing as well. Rather than oversizing battery systems at the outset (a common but costly approach), AI can inform more precise capital decision-making through accurate forecasting and analysis of future needs.
Grid operators can use AI to rapidly run through power flow calculations on a variety of scenarios that simulate possible grid conditions and the interaction of distributed resources, enabling faster processing of interconnection queues.
Examples of startups in the space:
Tyba has created a battery optimization platform that provides building blocks for revenue optimization models, supporting everything from initial siting decisions to in-life optimization.
Modo Energy focuses on performance benchmarking, allowing operators and analysts to track battery asset revenue performance against market indices and develop strategies using sophisticated market forecasts.
Fly Power leverages AI to process vast amounts of public sentiment and historical permitting information to help developers of utility-scale projects assess risk at a county-level and design effective community engagement strategies.
Piq Energy automates grid interconnection study and project sizing through use of AI for developers.
Battery System Operational Excellence with AI
While planning sets the foundation, operational excellence determines day-to-day performance. Optimal operation of batteries involves complex balancing of numerous technical parameters. A mismanaged battery can have a shorter-than planned lifetime and increased safety risks. AI systems excel at monitoring and adjusting these internal variables – from cell-level performance to thermal management – ensuring batteries deliver maximum power when needed while minimizing degradation. Unlike market-focused applications that optimize for financial returns, these performance management systems focus on the technical foundation that makes market participation possible in the first place.
AI solutions empower facility managers and system owners by processing and interpreting the enormous quantities of real-time data required for effective energy storage operation. This enables more efficient energy management and system optimization without requiring operators to become technical experts in battery chemistry or electrical engineering.
Example startups in this space:
Power Factors offers an integrated suite of open and smart apps, which are designed for asset management, field service optimization, and performance optimization. These apps leverage machine learning-based advanced analytics to help customers maximize the value of their renewable assets.
Avathon Inc. provides an Industrial AI platform that enhances operational efficiency for battery storage investments, as evidenced by their partnership with UBS Asset Management for BESS projects in Texas.
Bidgely helps utilities leverage data to improve customer engagement, grid management, and energy efficiency. Their UtilityAI platform uses AI to analyze real-time smart meter data, providing insights into customer behavior and energy consumption, allowing utilities to personalize customer interactions and optimize grid operations.
Improved Financial Returns with Real-time Optimization
With technically optimized batteries ready to perform, the next challenge becomes determining when and how to deploy that capacity for maximum financial return. Finance-focused AI platforms transform batteries from passive assets into active market participants by continuously analyzing price signals, forecasting opportunities, and executing sophisticated trading strategies across multiple value streams – capabilities that go far beyond basic operational management.
This includes handling:
Energy Arbitrage: AI systems can manage energy purchase, storage, and sale based on fluctuating electricity prices, effectively time-shifting energy to maximize value.
Revenue Optimization: By continuously analyzing market conditions and making decisions about when to charge and discharge, AI helps operators extract maximum value from their assets.
Intelligent Forecasting: Combining weather data, market intelligence, and historical patterns, AI creates sophisticated models that predict energy production and consumption patterns.
Multi-Stream Value Capture: Rather than focusing on a single revenue source, AI can optimize for multiple value streams simultaneously, balancing wholesale market participation with demand charge management, solar charging, and grid services.
Responsive Adaptation: As grid conditions and market signals change, AI enables real-time response and adjustment of operational strategies.
Examples of startups in the space:
Evergen offers an AI-powered platform for optimal management of solar and battery resources through power trading, peak demand management, demand response, and grid stabilization.
Stem's Athena platform processes massive datasets, forecasts demand and market conditions, predicts system responses, and optimizes flexibility across multiple value streams including utility bill management, solar+storage integration, and market participation.
Enhancing Safety and Extending Battery Life Through Predictive Asset Management
One of the most promising applications of AI in battery storage is in the domain of battery health and safety. By combining machine learning with physics-based modeling, AI platforms can:
Predict Failures Before They Occur: Automated warnings can be generated weeks before problems become critical, allowing for preventative intervention.
Detect Anomalies: AI systems learn to recognize patterns across electrical, chemical, and thermodynamic subsystems, identifying potential issues that might otherwise go unnoticed.
Assess Fire Risk: Real-time monitoring and predictive analytics can identify potential fire risks long before an event occurs—a crucial safety feature for large-scale deployments.
Support Maintenance Planning: Predictive maintenance enabled by AI reduces downtime, improves battery lifespan, and increases profitability through better scheduling of service interventions.
Monitor State of Health: Tracking historical degradation patterns and predicting future performance allows for better lifecycle management and more accurate valuation of assets, including for resale and second-life applications.
Enhance Battery Management Systems: AI augments traditional BMS capabilities with more sophisticated monitoring and protection mechanisms.
Examples of startups in the space:
ACCURE Battery Intelligence has developed a predictive analytics platform that combines AI, machine learning, and physics-based modeling to improve battery safety, performance, and useful life. Their system issues automated warnings, enables condition-based maintenance, tracks performance metrics, and provides a Battery Safety Score recognized by insurers for risk reduction.
TWAICE offers comprehensive battery intelligence software for monitoring health, safety, and performance. Their platform tracks performance relative to warranty terms and includes a Battery Quick Check that provides State of Health assessments during transactions.
Brill Power is tackling sub-optimal system operation through AI solutions that increase operational revenues while reducing lifetime costs.
Value Chain Efficiencies That Reduce Costs Across the Lifecycle
Beyond direct operational benefits, AI could be instrumental in addressing the "soft costs" associated with battery storage systems. Soft costs are a significant portion of the overall project costs for Battery Energy Storage Systems (BESS) (~40% for smaller systems), particularly in the installation phase. These non-hardware expenses constitute a significant portion of installation costs and are expected to grow as deployments scale.
AI-powered software solutions streamline deployment, control, and management processes, creating efficiencies throughout the value chain. From supply chain optimization and manufacturing improvements to sales process automation and service delivery, AI can enhance every facet of a company's operations.
The resulting cost savings can be passed on to customers, accelerating adoption and deployment of energy storage technologies. Improved data transparency, process efficiency, and workflow enhancements in areas like cell selection, site development, and decision-making further contribute to overall value creation.
Examples of startups in the space:
Symbium is using AI to enable instant permits for solar and battery projects, streamlining the work of building departments across a number of cities in California.
Greenlite streamlines construction permitting and reviews, greatly accelerating permit approvals and improving the likelihood of approval.
The Future of AI in Energy Storage
As battery deployments accelerate globally, the role of AI in maximizing their value will only grow in importance. The integration of AI across the battery lifecycle—from planning and deployment through operation and end-of-life management—represents a fundamental shift in how we approach energy storage.
For grid operators, developers, and investors, embracing these AI-powered solutions is becoming not just advantageous but essential for remaining competitive in a rapidly evolving market. The companies that successfully leverage artificial intelligence to optimize their battery assets will be well-positioned to lead the energy transition and capture the enormous value creation opportunities that lie ahead.
Looking ahead, as AI technology continues its rapid evolution and adoption, we can expect even more profound impacts on energy storage. While today's AI applications focus on optimizing specific functions, future systems may provide more holistic management of entire energy ecosystems, dynamically balancing multiple objectives simultaneously and adapting to changing conditions with minimal human intervention.
We're still in the early stages of the AI revolution, but the pace of innovation is accelerating rapidly. The applications we're seeing today—impressive as they are—represent just the beginning of what's possible when we combine advanced data analytics with energy storage technologies. Energy storage stakeholders must develop comprehensive software and AI strategies now to maintain competitive advantage in this rapidly evolving landscape.
The question for energy storage stakeholders is no longer if they should implement AI strategies, but how quickly they can deploy these solutions to maintain competitive advantage. As you evaluate your own organization's AI readiness, begin by identifying which aspects of your battery operations would benefit most from increased intelligence, then explore the rapidly expanding ecosystem of solutions designed to address these specific challenges.
Curious how to make best use of AI in your organization or where to get support? Book a call with our team today.
This article explores the transformative impact of artificial intelligence across the battery energy storage value chain, from project planning through operation and maintenance. As the energy transition accelerates, these AI-powered solutions will become increasingly critical for maximizing the value of storage assets while ensuring their safety, longevity, and optimal performance.