Battery Asset Management Agenda

Merging the SOC accuracy and stranded capacity conversation: how inaccurate battery data flows from monitoring through to trading performance.

• SOC can be off by up to 15%, where does that inaccuracy originate, and who is responsible for fixing it?
• What does 'useful' imbalance data look like from an asset manager's perspective versus an OEM's?
• How much capacity is routinely left stranded by OEM-mandated buffers, and how is that number changing as analytics improve?
• What level of SOC precision and failure prediction would meaningfully change how trading desks size their positions?
• How do degradation and cell imbalance interact over the asset lifecycle and when does a technical monitoring problem become a revenue problem?
• Can third-party analytics close the gap, or does unlocking stranded capacity ultimately require working directly with the OEM?
• Should SOC accuracy be a contractual obligation, and if so, who holds the risk?

Getting an asset to COD is one challenge. Making sure the team running it has everything they need from day one is another.

• Which assets perform best in year one, and what did their handover look like?
• How do you keep an accurate record of what was designed, what was built, and what's actually running?
• What does a good OEM relationship look like at the point of handover, and what should operations teams make sure they have before the construction team leaves site?
• How do you structure accountability across developer, operator, and OEM when things aren't tracking?

Warranty structures, LTSA obligations, and what asset owners are failing to negotiate before it is too late.

• What is the practical difference between a warranty claim and a plant underperformance claim, and why does that distinction cost asset owners millions?
• How should asset owners structure back-to-back warranties between offtakers, themselves, and OEMs- and what happens when a counterparty fails?
• Round-trip efficiency has no agreed definition, so what should the contractual standard be, and who should bear the measurement risk?
• How big is the gap between lab-tested and field-tested performance, who absorbs the cost when it shows up?
• Regarding data access rights in LTSAs, what clauses are asset owners routinely failing to negotiate?
• When a vendor goes bankrupt mid-contract, what options does an asset owner have, and what should they have secured at procurement?

Inaccurate State of Charge (SOC) data, as communicated by the OEM-provided BMS, is a revenue leak worth over $1M per GWh annually.  Drawing on real customer cases from live BESS projects, this session unpacks how inaccurate SOC and SOC imbalances erode state-of-energy accuracy, what that costs traders, and how you mitigate it.

How operators are structuring revenue across market types, and where the stack is under pressure.

• How do tolling and capacity agreements with utilities differ from private market arrangements, and what are the trade-offs?
• At what point does merchant exposure become unacceptable, and how do operators hedge without giving up the upside?
• How is the revenue picture changing as more BESS capacity comes online and margins compress?
• What role can ancillary services play as a stable floor, and which markets make this easiest to access?

How interconnection timelines, costs, and reform efforts are reshaping BESS economics

• Interconnection queues now stretch to five years in some markets, how are developers and operators adapting their project pipelines?
• How does rising load growth driven by data centres and electrification affect the economics and queue position of new BESS projects?
• How should the cost of connecting new large loads be allocated and is the current framework fair to existing asset owners?
• What would meaningful interconnection reform look like, and is the political will there to deliver it before the queue problem becomes a crisis?

Participate in intimate, small-group discussions focused on topics selected by you and your fellow delegates. With only four roundtable sessions available and a total of 40 places, these highly interactive discussions will be allocated on a first-come, first-served basis to ensure every participant secures their preferred topic.

How purpose-built operators balance battery health against trading revenue, and where the model breaks down.

• How do you decide when to prioritize battery health versus maximizing short-term trading revenue, and who in the organization makes that call?
• What does it mean to absorb merchant risk as an operator, and how is that risk managed on a day-to-day basis?
• What do operators rely on to catch performance degradation before it becomes a revenue event, and what do they routinely miss?
• How do you communicate real-time asset health constraints to trading desks without killing their upside or destroying the commercial relationship?
• Forward curves, analyst consistency, and financing assumptions versus operational reality.
• Why are some revenue forecasts wrong, and what that costs the industry.

CAISO is the largest battery storage market in the U.S., yet one of the hardest to evaluate as resources and market outcomes are anonymized. This session presents findings from Gridmatic's CAISO Storage Report, an industry-first methodology that unmasks anonymized data to reveal how 30 real battery assets actually performed.

• Gridmatic unmasked CAISO batteries and built an industry-first proprietary shadow-clearing engine to reconstruct actual asset-level revenue
• Performance varied enormously: revenue ranged from under $1 to over $6, with the top asset earning 2.45x the median
• Location and availability explain some of the gap but not all
• Adaptive, forecast-driven bidding is what separates top performers yet many systems deploy static bidding strategies
• The fleet left an estimated $98 million in unearned potential on the table, raising the question: are storage assets being fully utilized?

As BESS fleets grow, asset teams are being asked to manage far more complexity without adding people at the same rate. Shyam Srinivasan, EVP, BESS Software at Elysia Battery Intelligence (A Fortescue Zero Company), will share field examples of the issues that make battery performance so difficult to manage, including hidden availability losses, sudden power setpoint deviations, inaccurate SoC and power signals, and block imbalance that quietly strands usable capacity.

The session will show how Elysia BESS Copilot combines cloud diagnostics with real-time on-premise intelligence to identify root causes faster, improve the signals operators rely on, and guide corrective action, helping asset managers recover performance and manage more GWh with the teams they already have.