CHOOSE
Strategic Network Planning
Optimize long-term infrastructure investments for energy transmission and distribution networks to support renewable integration, manage demand growth, and maintain reliability while minimizing capital expenditures.
Understanding the Problem
As global electricity demand is projected to double by 2050 with renewables providing over 90% of new generation capacity, utilities must make critical infrastructure decisions balancing massive capital expenditures (estimated at $2.4 trillion globally for 2024-2030) against uncertain demand growth, renewable integration requirements, and reliability constraints. Currently, nearly 3 terawatts of renewable energy projects are delayed due to grid constraints, highlighting the urgency of effective network planning that coordinates transmission and distribution with distributed energy resources creating bidirectional power flows.
THE CHALLENGE
What Makes it Hard
Strategic network planning involves optimizing where to build, upgrade, or reinforce grid assets over decades while balancing uncertain demand growth patterns, renewable generation profiles, technology evolution, and regulatory changes in massive capital investment decisions.
WHO FACES IT
Multi-period uncertainty: planning must account for decades of operational scenarios including demand growth, renewable profiles, technology evolution, and regulatory changes that traditional deterministic approaches fail to capture, leading to over- or under-investment
Transmission-distribution coordination: distributed energy resources creating bidirectional flows require coordinated optimization across multiple planning authorities while preserving proprietary information, posing computational and organizational challenges
Renewable integration complexity: intermittent solar and wind generation creates curtailment risk, stability concerns, and need for flexible resources requiring network expansion co-optimized with generation, storage placement, and flexibility procurement
BUSINESS IMPACT
Unlock 3,000 GW of queued renewables: grid-enhancing technologies deliver 20x savings versus traditional builds while avoiding $150B in annual outage costs.
Renewable Queue
3,000 GW
Waiting for Grid[1]
At least 3,000 GW of renewable projects are waiting in grid connection queues (5x solar/wind added in 2022) (IEA).
Optimization Savings
20x
vs Traditional Build[2]
PPL Electric solved congestion for <$1M with DLR vs $20M for reconductoring, saving $64M in year one.
Outage Cost
$150B
Annual US Business[3]
Power outages cost American businesses $150 billion annually, highlighting the value of improved grid reliability (DOE).
How We Solve It
Transmission expansion planning (TEP) formulated as large-scale Mixed-Integer Linear Programming (MILP) or Mixed-Integer Nonlinear Programming (MINLP). Binary variables for build/don't-build decisions on candidate lines, continuous variables for power flows and generation dispatch, constraints enforcing Kirchhoff's laws, line capacity limits, and N-1 security requirements. Multi-period models add temporal coupling through storage dynamics and investment timing, with stochastic formulations using scenario trees and robust optimization using min-max objectives.
Hybrid Compute
What We Bring
Large-scale MILP optimization using commercial solvers for networks up to 10,000 nodes and 50 time periods with parallel processing and aggressive cut generation
Decomposition methods enabling distributed solving across transmission and distribution planners for problems exceeding direct solution capabilities
Stochastic and robust optimization frameworks modeling multiple demand/generation scenarios to identify network expansion strategies performing well across uncertain futures
Integration with specialized power system modeling (MATPOWER, PowerModels.jl) providing domain-specific capabilities for power flow physics and security constraints
FUTURE POSSIBILITIES
The
Quantum Horizon
Network expansion planning can be formulated as QUBO problems suitable for variational quantum algorithms like QAOA, with early research showing favorable runtime scaling compared to classical methods for select instances, though current gate error rates limit problem sizes to under 100 variables.
Exploratory Work
While quantum computing has attracted significant research interest and industry pilot projects (NREL-Atom Computing testbed, German Q-GRID initiative), practical quantum advantage for strategic network planning remains years away. Current quantum hardware cannot handle the scale or precision requirements of real utility planning problems, with D-Wave applications limited to under 200-bus distribution networks and requiring classical refinement. Near-term value lies in quantum-inspired classical algorithms and preparing QUBO formulations for future hardware maturity.
Current Research Directions
QAOA approaches showing promise in small demonstration problems, though requiring 10+ years for practical advantage on realistic networks due to need for fault-tolerant quantum computers
Hybrid quantum-classical Benders decomposition separating discrete topology decisions (quantum) from continuous power flow (classical) for distribution network reconfiguration
Quantum-inspired classical algorithms (simulated bifurcation, coherent Ising machines) running on conventional hardware showing competitive performance on power system optimization benchmarks today
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 strategic network planning.
SOURCES