ADAPT
Dynamic Pricing and Tariff Optimization
Design time-varying electricity prices that balance revenue maximization, demand shaping, renewable integration, and grid stability while encouraging efficient customer consumption patterns.
Understanding the Problem
Energy utilities must set dynamic prices that reflect real-time supply conditions, demand patterns, and system constraints while aligning customer behavior with grid needs. California now requires utilities to offer hourly dynamic rates to all customers, while EU regulations mandate dynamic pricing availability since 2025. Studies estimate nationwide deployment could yield $33-50 billion in annual system savings through improved demand flexibility and renewable integration.
THE CHALLENGE
What Makes it Hard
Setting time-varying electricity prices in a leader-follower structure where utilities set prices (leader level) and customers respond with consumption decisions (follower level), creating bilevel optimization problems.
WHO FACES IT
Bilevel decision hierarchy is strongly NP-hard even with linear objectives, requiring coordination between utility pricing and customer response
Unknown and varying demand response functions across customer segments, time periods, and external factors requiring online learning
Peak synchronization risk where automated customer systems responding to identical price signals create new unexpected demand peaks
BUSINESS IMPACT
Dynamic pricing delivers 10-20% efficiency gains and up to 42% consumer savings, with $100-200B in potential U.S. power system benefits.
Efficiency Gains
10-20%
From Time-Varying Rates[1]
Time-of-use and critical-peak pricing deliver 17-20% of the efficiency gain possible with real-time pricing.
Consumer Savings
42%
Annual Cost Reduction[2]
Swedish households with dynamic hourly contracts that adapted consumption saved 42% on annual electricity costs (2021-2023).
System Savings
$100-200B
Over 20 Years[3]
National deployment of grid-interactive buildings with dynamic pricing could provide $100-200 billion in U.S. power system savings.
How We Solve It
Bilevel mixed-integer optimization models the retailer-consumer interaction with the utility maximizing profit at the upper level subject to customer response optimization at the lower level. We tackle bilevel problems by advanced reformulation techniques, branch-and-cut methods or decomposition-based heuristics. Stochastic programming or robust optimization handles uncertainty in demand response, renewable generation, and wholesale prices.
Hybrid Compute
What We Bring
Bilevel MILP formulation balancing cost minimization and comfort preferences at customer level
Online convex optimization for sequential pricing under uncertain consumer elasticity
Two-stage stochastic models with scenario trees for forecast uncertainty
Decomposition methods (Benders, column generation) for large-scale instances
FUTURE POSSIBILITIES
The
Quantum Horizon
Dynamic pricing problems are fundamentally continuous, parametric, and multi-period with bilevel hierarchical structure poorly aligned with current quantum hardware designed for binary combinatorial optimization.
Exploratory Work
Quantum approaches remain impractical for core pricing optimization due to continuous nature, hierarchical structure, and real-time responsiveness requirements favoring classical convex optimization and MILP solvers. Quantum-inspired algorithms may provide incremental improvements in preprocessing or decomposition (2/5 realism), while quantum machine learning shows promise for long-term forecasting components but not yet practical (2/5 realism). Classical methods remain superior for 5+ years minimum.
Current Research Directions
Quantum-inspired optimization techniques for customer clustering and scenario reduction preprocessing
Quantum neural networks (QNNs) showing similar predictive capacity to classical models with fewer parameters for demand forecasting
Theoretical QAOA formulations requiring significant problem reformulation away from natural continuous structure
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 dynamic pricing and tariff optimization.