EV Charging Decision Support via Dynamic Pricing
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Solution Overview
Problem
The uncontrolled connection of a large fleet of electric vehicles to the power grid causes stress on the power system infrastructure, leading to increased losses for utilities and energy service providers.
Innovation Solution
A hierarchical multi-agent system that includes a higher-level agent, virtual block agents, charging station agents, and electric vehicle agents, which work together to manage electric vehicle charging requests by manipulating charging prices and redistributing vehicles across charging stations, thereby balancing load and minimizing losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If electric vehicles are allowed to connect freely to charging stations, then user convenience and charging accessibility are improved, but power system infrastructure stress and energy losses increase
Solution Approach 1:
The system dynamically adjusts charging prices in real-time based on grid conditions, vehicle demand, and time of day. The virtual block agents continuously optimize price signals to balance load distribution, preventing infrastructure stress during peak periods while maintaining accessibility during off-peak times. This dynamic pricing mechanism resolves the contradiction by making the system adaptive rather than static.
Solution Approach 2:
The hierarchical multi-agent system implements continuous feedback loops where virtual block agents monitor grid conditions, charging station status, and vehicle requests, then adjust pricing strategies accordingly. This feedback mechanism enables the system to respond to changing conditions and optimize both accessibility and energy efficiency simultaneously.
2Ease of operation
If more charging stations are deployed to improve coverage, then charging accessibility is improved, but infrastructure complexity and investment costs increase
Solution Approach 1:
The system segments the charging infrastructure management into hierarchical layers: higher-level agents for strategic decision-making, virtual block agents for regional optimization, and local charging station agents for operational control. This segmentation allows complex infrastructure to be managed through decentralized, modular agents, reducing overall system complexity while improving coverage.
Solution Approach 2:
Virtual block agents serve as intermediaries between the central higher-level agents and individual charging station agents. These intermediary agents aggregate information from multiple charging stations, simplify decision-making processes, and coordinate load distribution, thereby managing infrastructure complexity while enabling extensive charging coverage.
3Productivity
If charging prices are lowered to attract more electric vehicles, then market adoption is improved, but energy losses and grid stress increase
Solution Approach 1:
The system changes the pricing parameter dynamically based on grid conditions, time of day, and demand levels. Instead of fixed low prices that would always cause grid stress, the virtual block agents adjust prices in real-time, offering lower prices during off-peak hours when grid stress is low and higher prices during peak periods. This parameter change strategy promotes market adoption while minimizing energy losses.
4Ease of operation
If load is concentrated at popular charging stations, then user convenience is improved, but power system stress and losses at specific locations increase
Solution Approach 1:
The system applies local quality by allowing different pricing strategies and load management approaches at different geographic locations and time periods. Virtual block agents tailor pricing signals to local conditions, encouraging users to visit less-popular stations in certain areas or times, thereby distributing load more evenly across the network while maintaining overall user convenience.
Data Source
AI summary
Methods and systems for an electric vehicle charging decision support system are provided. A system can include: a higher level agent configured to be connected to an energy grid and to receive a charging request from an electric vehicle and transmit the charging request to a virtual block agent; and a plurality of charging station agents connected to an energy service provider, the energy grid, and the virtual block agent. The virtual block agent can be configured to receive a respective power set-point and availability from the plurality of charging station agents and transmit a recommended energy price charged at a respective charging station to the energy service provider. The recommended price can maximize a probability of an electric vehicle agent choosing a particular charging station. The system facilitates a win-win situation for the mutual and simultaneous benefit of electric vehicles and the power grid.