Unified Incentive Signal for Distributed Energy Resource Scheduling
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Solution Overview
Problem
Current systems for distributed energy resource optimization lack a unified approach to incentivize peak-to-off-peak energy shifting, leading to inefficiencies in reducing peak load and carbon generation.
Innovation Solution
A network-based system that combines multiple incentives into a single price signal, using an optimization server to generate schedules for energy consumption based on customer preferences and real-time pricing data, controlling distributed energy resources like thermostats and electric vehicles to optimize energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate incentive programs are used to encourage peak-to-off-peak energy shifting, then customer participation in individual programs may increase, but system complexity and inefficiency increase, reducing overall productivity in reducing peak load and carbon generation
Solution Approach 1:
The patent combines multiple separate incentive programs (demand response, carbon pricing, renewable energy incentives) into a single unified incentive signal that is communicated to distributed energy resources. This consolidation eliminates the inefficiencies of multiple separate programs while maintaining comprehensive coverage, directly resolving the contradiction between program versatility and productivity.
Solution Approach 2:
The unified incentive signal serves multiple functions simultaneously: it provides demand response coordination, carbon emission pricing, and renewable energy integration guidance. This multi-functional approach allows a single signal to replace multiple separate incentive programs, improving overall system productivity while maintaining adaptability across different energy management objectives.
2Object-generated harmful factors
If distributed energy resources are controlled to reduce peak consumption, then carbon emissions and costs are reduced, but customer comfort may be compromised without proper optimization
Solution Approach 1:
The optimization server generates schedules that pre-cool or pre-heat buildings before peak demand periods, storing thermal energy in advance. This preliminary action allows the system to reduce peak consumption and carbon emissions while maintaining customer comfort during high-price periods, as the thermal mass already contains the necessary energy to sustain comfortable temperatures.
Solution Approach 2:
The system dynamically adjusts energy consumption schedules based on real-time pricing signals, weather conditions, and customer comfort preferences. This dynamic optimization ensures that comfort constraints are continuously respected while maximizing peak load reduction and carbon emission savings, resolving the contradiction between environmental benefits and operational ease.
Data Source
AI summary
Distributed energy resources (DERs) are remotely controlled devices that are connected to the electric grid and can affect the grid. The effect could be the result of consuming electricity, generating electricity, or storing electricity. Common examples of DERs include thermostats, electric vehicles (EVs), home batteries, and electric water heaters. DERs may be controlled to take advantage of multiple incentive programs offered by one or more incentive providers. The DER optimization may involve interacting with a carbon marketplace, a renewable energy credit marketplace, or both. A customer may provide preference data that indicates preferred and acceptable values for conditions of one or more DERs. Multiple incentives are combined into a single price signal. Based on the price signal and customer preference data, a schedule for energy consumption is generated. One or more DERs are controlled according to the generated schedule.