Green Peak Tweak is designed to engage consumers in changing their energy usage patterns by showing when renewable energy is generated (1). Consumers learn what portion of their energy usage comes from renewable resources. For example, if a consumer used 10kWh from 10AM to 11AM and the system showed that 12.5% of all energy for that period came from renewable sources, then the consumer used 1.25kWh of renewable energy. Renewable energy production varies during by day, week, and season. (2) As data from CAISO shows, the ratio of renewable energy to load is much lower during peak hours and much higher off peak hours or on weekends. To use more renewable energy, consumers need to change their energy usage patterns.
GEU (Green Energy Usage) can be formulated as aggregation of renewable energy usage vs total energy usage.
GEU = 10000 * (accumulated green energy usage) / (total energy usage) (3)
GEU can be calculated for consumers for any period of time. Weekly intervals would be most effective. GEU represents a ratio of renewable energy used, but is not affected by the total amount of consumed energy. Thus, it can serve to compare renewable energy usage among consumers regardless of their total usage, house size, etc. Since green energy grows in popularity, GEU may help consumers use more renewable energy and avoid energy usage peaks with high non-renewable generation contribution.
Examples of how GPT can be used:
- For utilizing Green Button Data. Consumers can submit their usage data to service and have GEU calculated with suggestions on how to increase GEU by changing their energy usage patterns.
- For creating contests/competitions at the utility or ISO/RTO level and implement one of gamification platforms. GEU can be compared on a weekly or monthly basis for badges or rewards.
- For creating loyalty programs. Utilities can create loyalty programs and award consumers for maintaining appropriate levels of GEU for specific periods of time.
In many cases data needed to implement GPT already exists, such as hourly contribution of renewable generation, AMI systems with hourly data, and Green Button Data. The implementation of GPT involves predictable costs and low risk. Pilot programs can be developed relatively quickly.
(1) Data required for this idea is described as Green Energy Tracker submittal to “Wish List” Dataset.
(2) California ISO already presents renewable energy contribution on daily basis with hourly data intervals, but their data format is not convenient for automated data import.
(3) GEU can be also normalized to max of 100%.