Not every customer will drive the same valueLet's say you install projects with identical measures at two buildings across the street from one another. Even though your inputs are the same, each incentive payment will be determined by the actual energy consumption of each building over the next year.
If one of these buildings produces consistent savings during the evening 4pm-9pm peak window and the other saves energy primarily between 10am-2pm, the building that drives peak period savings will generate much more value to the grid and therefore will generate much higher incentive payments. Optimize offerings to customersWith perfomance-based incentives, implementers are rewarded for both increasing the volume of total savings and maximizing the value of the resource curve (savings load shape) to maximize project value and subsequent incentive payment.
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Targeting
Learn how to work with Recurve to get a list of prioritized addresses with the potential to drive the most value and higher incentives Avoided Cost Calculator
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Higher Peak Savings Means Higher Incentive Payments
Maximizing Your Portfolio's Value and Make More Money!
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Implementers have an incentive to both increase the volume of total savings and maximize the value of the resource curve (savings load shape) to maximize project value. Depending on a FLEXmarket's cost-effectiveness rules, implementers may also reduce customer cost in order to increase the available cost-effective incentive (per the Total Resource Cost test). We encourage all aggregators to take time using the Project Value Estimator to understand these interactive effects to develop a model that maximizes grid impacts and customer offerings.
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We can also work with you to identify customers with the highest potential for savings. Our targeting tools can also be used to identify the buildings that offer flexibility opportunities during summer evening ramp, leading to much more valuable avoided cost savings. Recurve will work with aggregators to identify the attributes of these high-potential customers and identify targeted groups that are the most likely to have cost-effective outcomes for all parties involved.
For example, an aggregator targeting small business air conditioner savings would have the greatest potential impact by focusing on the customers with the greatest AC usage during the grid peak periods when savings are worth the most. These customers are most likely in need of help, and will have dramatically higher bill savings and grid benefits.
Aggregators also have the freedom to optimize their offerings toward the most beneficial mixes of technologies, develop innovative customer engagement strategies, or explore automation. Aggregators will be given maximum flexibility to determine their own business models, and customer value propositions.
For example, an aggregator targeting small business air conditioner savings would have the greatest potential impact by focusing on the customers with the greatest AC usage during the grid peak periods when savings are worth the most. These customers are most likely in need of help, and will have dramatically higher bill savings and grid benefits.
Aggregators also have the freedom to optimize their offerings toward the most beneficial mixes of technologies, develop innovative customer engagement strategies, or explore automation. Aggregators will be given maximum flexibility to determine their own business models, and customer value propositions.
Targeting Case Studies
In this example based on the results of a California based commercial lighting and BMS program, by targeting the 20% of commercial customers that use significant energy during the peak period with a high evening ramp, it is possible to increase customer savings by 40% (resulting in higher levels of adoption and satisfaction) while doubling the grid value that comes from driving peak period savings.
These parameters can then be used in the population to identify a cohort of customers that have much higher than average potential to deliver valuable outcomes, with dramatic increases in both kWh and hourly avoided cost.
These parameters can then be used in the population to identify a cohort of customers that have much higher than average potential to deliver valuable outcomes, with dramatic increases in both kWh and hourly avoided cost.
Using targeting to Identifying commercial buildings with a high potential to save energy during system peak periods.
