AI Solar-Plus-Storage Dispatch

Optimal dispatch strategies for combined solar and battery systems

The Problem

“Optimize Solar-Plus-Storage Dispatch Amid Volatility”

Organizations face these key challenges:

High forecast error and intraday volatility cause missed price spikes, inefficient charging, and increased imbalance exposure

Manual or rule-based dispatch fails to co-optimize energy, ancillary services, and curtailment management under tight interconnection/SoC constraints

Battery degradation and warranty constraints are difficult to model, leading to over-cycling, reduced capacity, and unexpected augmentation costs

Impact When Solved

Increase market revenues 3–10% by capturing volatility and optimizing multi-market participationCut imbalance penalties 10–30% with probabilistic forecasting and continuous re-dispatchReduce degradation-driven costs 5–15% and extend battery life by ~0.5–2 years through cycle-aware optimization

The Shift

Real-World Use Cases

Data-driven optimal configuration of hybrid energy storage in park micro-energy grids

This is like designing the right mix and size of batteries for an industrial or campus-sized “mini power grid” so it can quickly ramp power up and down when needed, without overpaying for equipment or risking reliability.

End-to-End NNEmerging Standard

8.5

Energy Storage Optimization using AI

AI helps batteries work better by deciding when to store or release energy.

optimizationgrowing

7.0