- Optimisation Engine
Intelligent dispatch, every interval
AmpVerve’s optimisation engine computes safe, cost-optimal, and carbon-aware dispatch decisions across EVs, batteries, chargers, solar, and flexible loads continuously and autonomously.
Hybrid solvers combine mathematical precision with adaptive learning to operate reliably in real-world energy environments.
- Optimisation Engines
Built for Real-World Energy Control
Purpose-built models for each forecast type, continuously trained on your operational data.
Deterministic Optimisation (MILP)
Mixed-Integer Linear Programming is used where constraints are known and precision is critical such as transformer limits, charger ratings, contractual tariffs, and safety envelopes
- This enables predictable, explainable scheduling for enterprise and grid-facing use cases.
Adaptive Optimisation (Reinforcement Learning)
Reinforcement learning continuously adapts control policies based on observed outcomes, learning from operational feedback, behavioural patterns, and changing conditions.
- This allows the system to respond effectively when assumptions break, forecasts shift, or assets behave unpredictably
Hybrid Optimisation Mode
AmpVerve combines deterministic optimisation for baseline schedules with adaptive control for real-time adjustments
- This hybrid approach delivers both stability and flexibility ensuring safe operation while exploiting real-time opportunities for cost, carbon, and reliability gains.
- Impact
Measurable Outcomes at Scale
AmpVerve optimisation delivers consistent, measurable improvements across consumer, enterprise, and grid-connected deployment.
30%+
Carbon Reduction
By prioritising renewable generation, low-carbon grid periods, and carbon-aware dispatch strategies.
25-40%
Energy Cost Reduction
By prioritising renewable generation, low-carbon grid periods, and carbon-aware dispatch strategies.
95%+
High-Confidence Optimisation
Decisions are uncertainty-aware, balancing optimality with operational safety under volatile conditions.
- Impact
Autonomous Dispatch with Human Oversight
Optimisation runs continuously and autonomously, generating dispatch actions in real time.
Operators can monitor, approve, override, or constrain decisions as needed without disrupting automation.
- Example Optimised Schedule
00:00–06:00
Charge BESS from grid
Low-carbon, off-peak pricing window
09:00–16:00
Discharge BESS to site load
Peak demand mitigation
16:00–21:00
Solar to load + storage
Maximise on-site renewable utilisation
21:00–24:00
Grid to BESS
Pre-charge for next-day readiness
- Start Optimising Today
Ready to Optimise Your Energy Assets?
Deploy autonomous optimisation that adapts in real time, scales globally, and supports consumer, enterprise, and grid-connected use cases.
- Production deployments supported
- 14-day pilot program
- Enterprise support included