Thermal networks are a new asset class for utilities and energy providers, and there are many opportunities for different rate structures with these systems. Simulation tools and data science can help optimize these cost structures for minimal impact on the ratepayer

Thermal networks are a large upfront investment, and require creative cost-recovery mechanisms to finance their construction. Scenario-based prediction of loads and usage can be used to define how a utility can recoup that investment.

Thermal networks need to run at peak performance to maximize the economic and environmental benefits of the system. Optimization algorithms and load case studies can showcase opportunities for improvement, and help develop rate structures and programs that guide ratepayers towards efficient usage.