Improve BESS Performance Through Enhanced State-of-Charge Estimation: The Case for Probabilistic Cloud-Based Battery Analytics

Key Takeaways:

• Unlocking confidence in usable capacity can increase returns by up to 10%.
• Costly penalties from System Operators can be avoided.
• The volume of tradeable energy can be significantly increased by tracking dynamic uncertainty in State-of-Charge (SOC).

The Evolving Role of BESS in Energy Markets

Rapid advancements in LFP battery manufacturing have significantly lowered costs, making utility-scale Battery Energy Storage Systems (BESS) an increasingly critical component of modern electricity networks. BESS not only provide essential grid stability services but also enable a higher share of renewable generation in the energy mix.

The primary revenue streams for BESS in most liberalised energy markets are built on a combination of energy arbitrage, grid stability services (such as frequency response and reserve services), and capacity market services. Effective delivery of any of these services requires an accurate determination of the present and forecasted State-of-Charge (SOC). However, the widespread use of SOC estimates that are solely derived from the Battery Management System (BMS) can introduce significant errors due to the inherent uncertainty in the techniques used. When trading energy, this can mean that at the point of delivery there is insufficient energy remaining in the BESS to deliver on its obligations. If SOC estimations fail in grid stability services, assets may be unable to provide the necessary energy, resulting in potential blackouts or system disruptions.

To mitigate these risks, System Operators worldwide have implemented stringent SOC monitoring and reporting requirements. Over-reporting SOC and under-delivering energy or power can lead to substantial financial penalties. In our experience, operators’ confidence in the accuracy of SOC tends to fluctuate; sometimes it will be accurate, while other times it may have significant errors, with no obvious cause. This perceived volatility can drive inefficient mitigations – for example, many BESS operators attempt to safeguard against this by setting (somewhat arbitrary) restrictive operational buffers at the top and bottom of the useable energy range. While this does reduce the risk of penalties, it also reduces tradable energy and financial performance in the context of an increasingly competitive market. Others may over-use disruptive and lengthy calibration cycles, which may not be necessary at the time they are performed and has the opportunity cost of lost revenue.