BESS Optimization

Congestion arrangements are intended to allow more assets to connect to a congested grid while giving grid operators controls to keep flows within grid limits. These arrangements come in different forms and names: flexible connection agreements (FCAs), capacity-limiting contracts, time of use etc. KYOS simulated a 1 MW / 4 MWh battery trading Dutch intraday (2027–28). Fixed congestion windows (no discharge 12–14h, no charge 18–20h) cut revenue by ~4%. Blocking the two cheapest charge and two priciest discharge hours each day cut it by ~13%, more than three times as much. ...

Cannibalized solar revenues in Europe can be partly recovered through co-location with batteries and algorithmic market channel optimization. That’s how I read Gerard Reid’s post in which he highlights the renewables buildout in Europe over the past twenty years, where the goal was increasing installed capacity and support schemes often reduced asset owners’ exposure to capture prices. As we have now reached an impressive buildout capacity, the effects of solar cannibalization are visible: Many hours of negative wholesale prices around midday in several European markets and compression of revenues for solar asset owners exposed to those prices. Low demand and inflexible generation also contribute. ...

In their new white paper, Volue argue that competitive advantage in BESS increasingly depends on algorithmic optimization and trading. Building optimizers myself, I am probably biased. But in my opinion both this white paper and the market trends below paint this as an exciting field to work in. Volue argue that: BESS hardware has become increasingly commoditized: turnkey storage equipment prices fell approximately 81% between 2017 and 2025, excluding EPC and grid connection costs. Alongside grid and market access, trading skill is an increasingly important source of repeatable advantage. The hard part is earning a profit repeatedly while adapting to changes in market rules, revenue streams, and asset makeup. Trading decisions are interconnected. A commitment now changes the opportunities available later. Degradation also means higher revenue today can reduce lifetime value. Volue identify three reasons behind this shift in importance from hardware to algorithm: ...

With robust optimization we look for the highest-profit asset dispatch under the worst price realization allowed by our uncertainty set: max_{sell volume, buy volume} min_{prices ∈ uncertainty set} profit(dispatch, prices) Vidan et al. look into robust optimization both for a thermal generator and for a battery. Their assets are always price takers. This does not mean that prices are fixed before the auction. It means that prices are external: The assets assume that their own orders do not affect the clearing price. ...

Noticed this Modo Energy index-linked swap agreement between Statkraft and SSE. From what I understand, there doesn’t have to be a physical battery involved in the swap itself. It’s two parties taking opposite positions on an index of how much batteries earn. SSE already has battery exposure, through its own batteries or other contracts. When batteries earn less, they earn less. So they add a position that pays when the index settles below an agreed strike. If that tracks what their existing exposure suffers, the payment offsets some of the loss. In return, they pay when the index settles above the strike. ...

BESS long-term state of health (SoH) and ageing is something I often ignore but likely has a material impact over battery lifetime. A preprint that popped up on my feed combines a mixed-integer linear model for market channel optimization with a separate battery model to account for degradation. In this UK-based case study assuming perfect foresight, the authors co-optimize intraday energy trading with all six Dynamic Containment, Moderation and Regulation products. ...

A three-stage stochastic battery optimization model solved with SDDP.jl, comparing the learned policy to the hand-derived continuation value logic.

A three-stage stochastic battery optimisation problem solved by hand, showing how market price and marginal continuation value determine charging and discharging decisions.

Saw the Capalo AI / European Energy solar + BESS announcement and got curious what kind of optimizer uplift is actually realistic for co-location. A UK study gets an interesting result: Compared with a similarly sized standalone BESS, co-located solar + BESS has higher NPV but lower IRR. So the hybrid creates more absolute project value, but a lower return on invested capital. At the same time, co-location increases the solar capture rate and grid connection utilisation relative to solar-only. ...

An interesting Modo Energy podcast episode with Centrica Energy on the secret sauce of BESS optimization and intraday as 96-dimensional chess: My main takeaways: Operational excellence matters at least as much as optimizer sophistication. Detecting asset faults, finding the root cause and fixing them quickly can easily overshadow incremental algo improvements if you get this wrong. BESS optimizer performance seems to be converging across Europe. Good forecasts, correct dispatching, etc. increasingly become the baseline. The edge is temporary and keeps moving. ...