Post: Q&A | From design assumptions to market outcomes

Q&A | From design assumptions to market outcomes

Battery energy storage systems (BESS) are entering a new phase of maturity; defined less by deployment and more by performance. As markets become more volatile and revenue opportunities more fragmented, the challenge is no longer simply how to build and connect storage assets, but how to operate them intelligently. Capturing value now depends on the ability to interpret complex market signals, respond in real time, and balance short-term trading opportunities against long-term asset health.

Here we spoke to GridBeyond Director of Origination (Europe & APAC) Scott Berrie about the challenges of BESS optimisation, how value is created (and lost), and the characteristics that define high-performing platforms in today’s market.

Q: Why are battery energy storage systems (BESS) no longer just technical assets?

Battery storage has evolved into a market-facing asset class. Where systems were once designed primarily for grid support or renewable integration, they are now expected to generate revenue across multiple markets simultaneously. These include wholesale energy, balancing mechanisms, and ancillary services.

This shift means performance is no longer defined purely by uptime or response speed, but by how effectively an asset can interpret market signals, manage risk, and convert volatility into revenue, without compromising long-term health.

Q: What makes BESS optimisation inherently complex?

BESS optimisation is a multi-variable problem. Operators must continuously balance market prices that change every few minutes, physical constraints such as power limits and state of charge, degradation effects that accumulate over time and warranty conditions that restrict how the asset can be used. These variables are interdependent; maximising short-term revenue through aggressive cycling can accelerate degradation and reduce long-term returns.

This is why optimisation cannot be treated as a simple scheduling problem; it requires a system that understands both market dynamics and battery physics in real time. But many systems in the market still operate in silos.

Forecasting, trading, control systems, and asset analytics are often delivered by different providers, each optimising for their own objective. This creates gaps between financial models and real dispatch behaviour, market strategy and physical execution and expected and realised performance. This fragmentation can lead to inefficient cycling, missed trading opportunities, and increased wear on the asset.

A key lesson emerging across the industry is that optimisation quality is directly linked to how well these functions are integrated.

Q: Why is it important to link design assumptions with operational reality?

Every BESS project begins with a model that typically includes revenue forecasts, cycling strategies, and degradation assumptions, but these assumptions are often disconnected from how the asset is actually operated. When this happens, even well-structured projects can underperform.

A more robust approach is to ensure continuity between design and operation, so that the same logic used to evaluate the project also informs real-time decisions. Platforms that integrate modelling, forecasting, and control within a single framework are better positioned to maintain this alignment over time.

Q: How do forecasting and market access influence performance?

Forecasting is central to value creation. Optimisation platforms use probabilistic forecasting to anticipate price movements, volatility, and scarcity conditions across different markets. This allows assets to position ahead of events rather than simply reacting to them.

Equally important is market access. The ability to participate across day-ahead, intraday, balancing, and ancillary services, and to move between them dynamically, significantly expands the opportunities available. Solutions that combine forecasting with direct market integration and automated execution can respond faster and more consistently to changing conditions.

Q: What does “co-optimisation” really mean in practice?

Co-optimisation refers to evaluating all available revenue streams simultaneously, rather than in isolation. Instead of assigning fixed capacity to individual markets, an optimised system continuously reallocates energy and power based on relative value. For example, it may shift from intraday trading to balancing services within the same day if conditions change.

This requires high-frequency forecasting updates, continuous re-optimisation and tight integration between trading logic and asset control. Platforms that unify these capabilities can avoid the inefficiencies that arise when decisions are made sequentially or manually.

Q: How should trading strategies be integrated with physical assets?

Trading and physical operation should not be treated as separate functions. In many cases, trading decisions are made without full visibility of the asset’s physical state or long-term degradation cost. This can result in strategies that are theoretically profitable but operationally inefficient.

A more advanced approach embeds physical constraints directly into trading logic. This includes state of charge and power limits, degradation cost curves and warranty and cycling constraints. By aligning trading execution with real asset capability, operators can ensure that every market action is both feasible and economically justified.

Q: How is battery degradation incorporated into decision-making?

Degradation is one of the most important, and often underestimated, factors in BESS optimisation. Each charge and discharge cycle contributes to wear, but the impact varies depending on depth of discharge, temperature, and operating conditions. Assigning a cost to this degradation allows it to be treated as part of the optimisation problem.

More advanced systems go further by continuously monitoring state-of-health (SoH), updating degradation models based on real performance data and adjusting dispatch strategies dynamically. This enables a shift from static assumptions to adaptive lifecycle management, where short-term decisions are always evaluated against long-term impact.

Q: What role does system architecture play in performance?

System architecture determines how effectively data and decisions flow across the platform. Solutions that integrate EMS, SCADA, forecasting, optimisation, and trading within a single control stack can operate with greater speed, consistency, and transparency. They avoid the latency, data loss, and conflicting logic that often arise in multi-vendor setups. In addition, features such as local control resilience, vendor-agnostic integration, and secure communication with grid operators are critical for maintaining reliability while participating in fast-moving markets.

Q: How is risk managed in highly volatile markets?

Volatility creates opportunity, but also risk. Effective optimisation frameworks incorporate risk controls such as position limits and exposure thresholds, scenario-based stress testing, consideration of liquidity and execution risk. These mechanisms ensure that assets can capture upside during favourable conditions while avoiding overexposure during extreme events.

Balancing risk and reward is particularly important for assets operating across multiple markets with different time horizons.

Q: What defines a high-performing BESS platform today?

High-performing platforms share a common characteristic: integration. They bring together design and financial modelling, real-time forecasting, co-optimised dispatch, trading execution and asset and degradation management. By operating as a single, coherent system, they maintain alignment between assumptions and outcomes, reduce inefficiencies, and unlock more consistent value from volatility. This integrated approach is increasingly becoming the benchmark for how advanced optimisation providers differentiate in the market.

Q: What is the key takeaway for asset owners and investors?

The performance of a BESS asset is not determined solely by its hardware, but by the intelligence of the platform that operates it. Maximising value requires more than access to markets or basic control systems. It depends on how effectively forecasting, optimisation, trading, and battery intelligence are combined into a unified decision-making framework. As the market continues to evolve, solutions that can maintain continuity from design assumptions through to real-time execution will be best positioned to deliver consistent, long-term returns.