EV Demand Response Incentives for Driver and Passenger Participation
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Solution Overview
Problem
Existing methods for managing electric power supply and demand in power grids, such as those involving electrified vehicles, often fail to effectively engage occupants other than the driver, limiting participation in demand response programs.
Innovation Solution
A method that involves sending messages to both the driver and passenger of an electrified vehicle, acquiring their attributes and preferences, and setting personalized incentives based on this information to encourage participation in demand response programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only the driver is given incentive to participate in demand response, then the incentive system is simple, but passenger participation is not facilitated
Solution Approach 1:
The incentive system is segmented into driver-specific incentives and passenger-specific incentives. The server identifies passengers in the vehicle and provides tailored incentives to each passenger based on their preferences, while separately managing driver incentives. This segmentation enables comprehensive participation facilitation without overwhelming system complexity.
Solution Approach 2:
The system transitions from a single-dimension incentive approach (driver only) to a multi-dimensional approach by adding passenger incentives as a new dimension. The server manages multiple incentive dimensions simultaneously, targeting different occupants based on their roles and preferences, thereby enhancing overall participation reliability.
2Reliability
If personalized incentives are set for passengers based on their preferences, then passenger participation is strongly prompted, but information processing complexity increases
Solution Approach 1:
The server performs preliminary actions by identifying passengers, acquiring their preference information, and pre-setting personalized incentives before demand response events occur. This advance preparation reduces real-time processing complexity and ensures incentives are ready when needed, enhancing participation reliability without overwhelming processing demands during critical moments.
Solution Approach 2:
The system enables self-service by allowing passengers to input their own preference information directly to the server. This self-provided data reduces the burden on the system to actively gather and process extensive preference information, thereby lowering information processing complexity while maintaining personalized incentive effectiveness.
3Measurement precision
If passenger information is acquired through multiple methods (activity learning, questions, image data), then preference estimation accuracy is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system dynamically selects and combines multiple data acquisition methods based on available resources, context, and passenger cooperation. The server can adaptively switch between activity-based learning, question-based gathering, and image/voice data analysis, optimizing preference estimation accuracy while managing system complexity and resource consumption flexibly.
Data Source
AI summary
A method of managing electric power supply and demand with a computer includes sending, from the computer, a message for making a request of an electrified vehicle with a driver and a passenger to participate in a demand response for adjusting supply or demand of electric power in a power grid, acquiring, by the computer, passenger information on at least one of an attribute and a preference of the passenger, and setting, by the computer, a first incentive for prompting the passenger to participate in the demand response based on the passenger information.