Utility Portal Enrollment for Smart-Home Demand Response
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Solution Overview
Problem
Utility companies face challenges in managing peak electricity demand due to limited tools for intuitive and flexible management of demand response events, leading to consumer discomfort and potential undermining of demand response programs.
Innovation Solution
The development of utility portals that enable communication between utility companies and energy management systems, allowing for intelligent management of demand response events through network-connected devices, including enrollment processes, energy consumption prediction, and participant selection based on residence characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If utility companies build additional power plants to satisfy peak demand, then the ability to meet peak demand is improved, but the cost and efficiency worsen due to prohibitive construction costs and underutilization of capacity
Solution Approach 1:
The system performs pre-cooling of residences during off-peak hours before the anticipated peak demand period. By lowering the temperature setpoint in advance (e.g., from 72°F to 68°F), the thermal mass of the building stores cooling energy, allowing the cooling system to be reduced or shut off during peak demand without compromising comfort. This preliminary action shifts energy consumption away from peak periods, eliminating the need for additional power plant capacity.
2Productivity
If utility companies implement traditional load shedding by directly controlling cooling systems, then peak demand is reduced, but consumer comfort and program effectiveness worsen due to inadequate cooling during hot periods
Solution Approach 1:
The system pre-cools residences before the load shedding interval by lowering the temperature setpoint during off-peak hours. This creates a thermal buffer that maintains comfortable temperatures during the peak demand period when cooling is reduced or stopped, thereby preserving consumer comfort while achieving peak demand reduction.
Solution Approach 2:
The system dynamically changes the temperature setpoint parameter based on the timing relative to peak demand events. During pre-cooling, the setpoint is lowered below the normal comfort level; during the load shedding interval, the setpoint is raised or cooling is curtailed; after the event, the setpoint returns to normal. These parameter changes are optimized based on residence characteristics such as thermal mass, insulation, and occupancy patterns.
3Productivity
If utility companies use direct load control with periodic on-and-off cycling, then peak demand is reduced, but consumer control and cooling system reliability worsen due to loss of consumer authority over their cooling system
Solution Approach 1:
Instead of cycling the cooling system on and off during peak demand, the system performs the necessary cooling action in advance during off-peak hours. This preliminary cooling establishes a thermal buffer that passively maintains comfort during the peak period, eliminating the need for active control intervention and preserving consumer autonomy over their cooling system operation.
Solution Approach 2:
The pre-cooled thermal mass of the residence serves itself during the peak demand period by releasing stored cooling energy, maintaining comfortable temperatures without requiring active cooling system operation or utility control intervention. This self-service mechanism preserves consumer control while achieving demand reduction.
Data Source
AI summary
A method for authorizing a smart-home device for enrollment with a demand-response program may include receiving, at a control server of an energy management system and for the smart-home device, identifying information for a user account. The method may also include sending the identifying information from the control server to an Application Program Interface (API) with an enrollment request. The method may additionally include receiving, at the control server, a determination from the API as to whether the identifying information for the user account was matched to an existing utility account. The method may further include based on the determination from the API, determining whether the smart-home device can be enrolled with the demand-response program.