Vehicle-Specific Incentive Pricing for Demand Response
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Solution Overview
Problem
Demand response programs face challenges in effectively incentivizing electric vehicles to participate in demand response events, as existing methods do not maximize participation and profit for vehicle manufacturers and users, leading to suboptimal energy consumption reduction and revenue distribution.
Innovation Solution
A computer-implemented method and system that determine vehicle-specific incentive price ranges for electric vehicles, allowing for the selection of vehicles to form a subgroup that maximizes participation and profit in demand response events by offering varying incentives based on individual vehicle profiles, ensuring alignment with utility company incentives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a uniform incentive amount is provided to all vehicles, then the implementation is simple, but participation is not maximized and profit distribution is suboptimal
Solution Approach 1:
The patent implements vehicle-specific incentive pricing by determining individual incentive price ranges for each vehicle based on their unique DR profiles, battery capacity, and historical behavior. This local differentiation maximizes participation and profit for each vehicle rather than applying a uniform incentive structure to all vehicles.
Solution Approach 2:
The system dynamically adjusts incentive amounts based on real-time factors including current battery state of charge, projected state of charge at DR event completion, vehicle usage patterns, and market conditions. This dynamic pricing allows the system to adapt to changing conditions and optimize participation rates.
2Productivity
If incentive amounts are optimized for each vehicle, then profit and participation are maximized, but the system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-determining incentive price ranges for each vehicle before DR events occur. Historical data is analyzed and stored in advance to establish baseline incentive structures, which are then adjusted in real-time based on current conditions. This reduces the computational complexity during actual DR events.
Solution Approach 2:
The patent introduces an intermediary selection algorithm that acts as a mediator between the utility company's DR requirements and individual vehicle characteristics. This algorithm processes multiple vehicle profiles and incentive offers, selecting optimal participants while simplifying the overall system coordination and maximizing revenue distribution.
3Loss of energy
If more vehicles are selected for DR events, then energy consumption reduction is maximized, but the incentive cost increases
Solution Approach 1:
The system changes key parameters including incentive price ranges, battery state of charge thresholds, and vehicle selection criteria to optimize the balance between energy reduction and incentive costs. By adjusting these parameters based on market conditions and DR event requirements, the system maximizes energy consumption reduction while controlling incentive expenditures.
Data Source
AI summary
A computer-implemented method for implementing a demand response (DR) event includes receiving a demand request (DR) signal for the DR event, determining a vehicle specific incentive price range for each vehicle of a group of vehicles, and selecting vehicles from the group of vehicles to form a subgroup of vehicles. The subgroup of vehicles maximize a number of vehicles participating in the DR event and maximize a profit of each vehicle in the subgroup of vehicles based on the vehicle specific incentive price range for each vehicle in the group of vehicles, subject to the DR incentive amount. The DR signal is transmitted to each vehicle in the subgroup of vehicles.