How Are Leading Energy Market Players Using AI to Optimise Trading and Monetise Flexibility?

In today's power markets, characterised by increasing renewable penetration, volatile supply and demand dynamics, and growing regulatory complexity, conventional trading approaches are struggling to keep pace. The convergence of intermittent renewable energy output, distributed energy resources (DERs), and real-time market fluctuations requires more than static forecasting and manual decision-making. Against this backdrop, AI-driven energy trading optimisation has emerged as a robust and proven solution that is already delivering measurable value for asset owners, utilities, and market participants. 

The Growing Role of AI in Energy Markets 

Over the past decade, digitalisation and the rapid growth of operational and market data in the power sector have fundamentally accelerated the adoption of intelligent systems. Digital solutions now play a central role across advanced forecasting, system optimisation, asset management, and energy trading operations, enabling organisations to make faster and more informed decisions in increasingly complex and data-rich environments. 

As electricity generation becomes more distributed and variable, supported heavily by solar, wind, and battery storage assets, there is a clear need for tools that can dynamically adjust to changing conditions. AI's ability to ingest large and diverse datasets, such as weather inputs, generation patterns, price signals, and grid constraints, while learning from them, makes it a natural fit for trading optimisation. Industry analysis suggests that artificial intelligence is rapidly becoming the backbone of modern, decentralised, and digital energy markets. 

What AI-Driven Energy Trading Optimisation Actually Does 

Real-world AI-driven energy trading platforms deliver operational capabilities that traditional trading methods struggle to achieve at scale. 

• Advanced Forecasting for Generation, Demand, and Price: AI and machine learning models combine historical data with external inputs such as weather and real-time signals to more accurately forecast renewable generation, market demand, and pricing. Improved forecast precision reduces error margins and hedging risk, creating more efficient participation in wholesale markets. 

• Automated Short-Term Trade Optimisation: Platforms automate decision-making for short-term electricity markets such as day-ahead and intraday. By integrating generation forecasts, price forecasts, and risk parameters, AI helps traders respond quickly to market volatility and maximise value creation. 

• Flexible Asset Monetisation for Storage and DERs: For battery energy storage systems or other flexible assets, AI determines optimal charging and discharging cycles to capture revenues through arbitrage and balancing services. This unlocks monetisable value streams for owners of flexible capacity. 

• Risk Management and Operational Efficiency: Algorithmic trading reduces manual workload, lowers operational risk, and improves response times to price movements or grid events. This helps organisations streamline trading processes and strengthen resilience. 

As a result, AI-driven energy trading is not an experimental concept but an operational reality across multiple energy markets. 

Selected Real World Players and Their Contributions 

To demonstrate practical adoption, the following companies are notable providers of AI-enabled trading optimisation solutions: 

• Dexter Energy, based in the Netherlands, offers AI-based forecasting and short-term trade optimisation for renewable power portfolios and battery-enabled assets. The platform integrates price forecasting, generation forecasting, and automated trading to manage balancing exposure and maximise revenue opportunities. 

• Enspired GmbH, headquartered in Vienna, Austria, provides AI-driven trading as a service for battery storage systems and flexible energy assets. The company recently incorporated battery health diagnostics into its optimisation platform, enhancing long-term asset performance evaluation. 

• GridBeyond, operating internationally with headquarters in Ireland, delivers intelligent energy management and trading capabilities for commercial and industrial energy users. Its AI platform enables DER participation in day-ahead and intraday markets and supports demand response and flexibility monetisation. 

• Volue ASA, based in Oslo, Norway, develops software solutions for energy trading, power grid operations, and infrastructure management. The company supports energy traders and utilities by enabling advanced market simulation, forecasting, and algorithmic execution. 

These examples demonstrate that AI-enabled trading has already become mainstream across Europe and continues to gain momentum globally. 

Strategic Benefits Realised by Adopters 

 

For power producers, utilities, storage operators, and DER aggregators that deploy AI trading optimisation platforms, the strategic benefits are substantial: 

• Enhanced Profitability and Asset Monetisation through more accurate forecasting and improved trade timing. 

• Lower Operational Overheads by reducing manual work and allowing trading teams to scale without proportional resource increases. 

• Improved Risk Management enabled by real-time updating of forecasts, market signals, and operational constraints. 

• Renewable and Storage Integration Support by coordinating decentralised and intermittent assets more intelligently. 

• Acceleration of the Energy Transition by enabling flexible resources to participate in markets, helping balance variability and improve grid stability. 

Implementation Realities and Key Considerations 

Despite these advantages, successful deployment requires a strong technical and market foundation. Key considerations include: 

• Data Infrastructure and Quality: Reliable and high-frequency data streams are essential for accurate modelling and automated decision support. Research shows that digital infrastructure is a critical dependency for scaling AI capabilities in power systems. 

• Integration with Physical Assets and Grid Systems: Storage, DERs, and flexible loads must be controllable and digitally accessible to execute trading strategies effectively. 

• Regulatory and Market Design Factors: Market structure and participation rules determine the extent to which flexible assets can access value streams. 

• Governance and Transparency: Algorithmic controls must include risk limits, audit logs, and human oversight to ensure compliance and accountability. 

• Scalability and Adaptability: Successful platforms need to evolve continuously with new asset classes, geographies, and market rules. 

What the Latest Research and Industry Trends Indicate 

Recent academic and industry publications confirm the mainstream transition of AI in the power sector. A 2023 global study cataloguing AI-focused companies across the energy value chain highlighted substantial activity in forecasting, DER management, trading, asset optimisation, and predictive maintenance, indicating that AI is now a critical enabler of the energy transition. 

Industry reporting also notes increased integration of advanced analytics, particularly in trading for renewable assets and storage. For example, battery health analytics are now being used to complement trading optimisation strategies, supporting long-term asset value planning in addition to short-term revenue capture.

Collectively, these trends signal that AI-driven optimisation will become foundational to modern power markets, particularly as renewable generation and flexibility requirements expand. 

Conclusion 

AI-driven energy trading optimisation is already transforming trading operations in multiple markets, delivering tangible commercial and system-level benefits. Companies such as Dexter Energy, Enspired, GridBeyond, and Volue ASA demonstrate that advanced data-driven trading capabilities are not futuristic but operational and impactful today. 

For organisations operating in energy markets, particularly those holding renewable, storage, or distributed energy assets, the adoption of AI-based trading optimisation offers a material strategic advantage. These technologies improve monetisation, enhance risk management, and support more dynamic and flexible engagement with evolving electricity markets. 

As power systems become increasingly distributed, digital, and decarbonised, AI-driven optimisation is likely to shift from competitive edge to operational necessity. Firms that combine analytics capability, disciplined execution, and forward-looking strategy will be best positioned to lead in the next era of energy markets.