ADAPT
Energy Storage Management and Real-Time Optimization
Optimize battery charging and discharging in real-time to maximize revenue while preserving battery lifespan.
Understanding the Problem
Battery Energy Storage Systems (BESS) are critical infrastructure for grid stabilization, renewable integration, and energy arbitrage. The U.S. added 10 GW of utility-scale batteries in 2024 alone, with costs falling to $165 per kWh. The optimization challenge is balancing immediate revenue opportunities against long-term degradation costs, as batteries endure only 6,000+ charge-discharge cycles before end-of-life. Effective scheduling must respond to minute-by-minute price signals while considering location-specific grid conditions and multi-market participation opportunities.
THE CHALLENGE
What Makes it Hard
Energy storage operators must decide when to charge and discharge batteries in response to volatile electricity prices, grid conditions, and multiple revenue streams. Each cycle degrades the battery, creating a fundamental tension between capturing today's arbitrage profits and preserving tomorrow's asset value.
WHO FACES IT
Deep discharge increases revenue but accelerates battery aging, requiring constant trade-off calculations between short-term profit and long-term asset value
Prices change minute-to-minute, demanding sub-second optimization that forecasts demand, renewables, and market signals simultaneously
Batteries can participate in arbitrage, frequency regulation, capacity markets, and ancillary services with conflicting operational requirements
BUSINESS IMPACT
Advanced BESS scheduling delivers 33-95% operational cost savings while hardware costs fell 40% in 2024 and U.S. capacity grew 66%.
Every charge and discharge cycle is an optimization opportunity.
BESS Cost Reduction
40%
Year Over Year[1]
Global average turnkey energy storage system prices fell 40% from 2023 to $165/kWh in 2024, the largest annual reduction since 2017.
Operational Savings
33-95%
Cost Reduction[2]
Advanced BESS scheduling algorithms deliver microgrid operational cost savings ranging from 33.6% to 94.8% across various scenarios.
U.S. Battery Growth
66%
Capacity Increase[3]
U.S. utility-scale battery storage capacity increased 66% in 2024, with 10.4 GW added bringing cumulative capacity to 26+ GW.
How We Solve It
ML-fed (stochastic) mixed-integer linear programming (MILP) formulations optimize charge/discharge decisions across multiple time periods with fine-grained intervals (5-15 minutes). Models include binary variables for operating mode selection and continuous variables for power flows and state-of-charge, with objectives balancing revenue maximization against degradation costs.
Hybrid Compute
What We Bring
Real-time optimization with sub-minute execution for dynamic market response
Degradation-aware scheduling that maximizes total lifetime value
Multi-market coordination across arbitrage, regulation, and capacity services
Location-specific optimization accounting for LMP and grid topology
FUTURE POSSIBILITIES
The
Quantum Horizon
BESS scheduling can be reformulated as QUBO for quantum optimization research. The Q-GRID project and similar initiatives are exploring quantum utility for grid applications, making this an active research area despite current limitations.
Exploratory Work
Proof-of-concept implementations on simplified grid models with fewer than 100 decision variables, preparing QUBO formulations for future hardware generations while classical methods handle production workloads.
Current Research Directions
QAOA-based frameworks for microgrid scheduling with renewable variability
D-Wave annealing for EV charging optimization on small test systems
Quantum-inspired classical algorithms showing competitive performance
Interested in quantum research?
Explore proof-of-concept implementations with our team.
Ready to solve this problem?
Talk to our experts about how Strangeworks can help with energy storage management and real-time optimization.