Decentralized PEV Charge Management via Power Packets
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Solution Overview
Problem
The increasing adoption of plug-in electric vehicles (PEVs) strains the existing power distribution infrastructure, particularly transformers and underground cables, due to the additional electricity demand, which can lead to rapid aging and overloading, and existing charge management schemes face challenges in adapting to the stochastic nature of charging behavior and customer privacy concerns.
Innovation Solution
A decentralized charge management approach that treats PEV charging as a random access problem, using 'charge-packets' to distribute electricity, allowing nodes to request power packets based on probabilistic automaton states, reducing the need for centralized coordination and maintaining customer privacy, and adapting to varying system capacity and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized optimization control methods are used to manage PEV charging, then the power distribution system can achieve economically optimal outcomes and avoid constraint violations, but vehicle owners must provide information to a central authority and give up autonomy over charging
Solution Approach 1:
The charging control is segmented into decentralized autonomous vehicle-side control and centralized coordination. Each PEV independently manages its own charging based on local conditions (battery state, arrival/departure times) while the centralized system provides coordination signals. This segmentation allows vehicle owners to maintain autonomy over their charging decisions without requiring full centralized control, thus preserving ease of operation while still achieving system-wide reliability through coordinated management.
Solution Approach 2:
The invention introduces an intermediary coordination mechanism that mediates between centralized optimization goals and decentralized vehicle autonomy. The centralized system acts as an intermediary that receives anonymized aggregate data and provides coordination signals back to vehicles, enabling constraint violation avoidance without requiring direct control over individual vehicle charging decisions. This intermediary role resolves the contradiction by facilitating information exchange and coordination while preserving vehicle owner autonomy.
2Productivity
If PEV charging demand increases to support more vehicles, then transportation electrification goals are achieved, but the existing power distribution infrastructure experiences strain and rapid aging
Solution Approach 1:
The charging system implements dynamic load management that adapts to real-time infrastructure conditions. The centralized coordination mechanism monitors power distribution capacity and dynamically adjusts charging schedules to prevent overloading transformers and cables. This dynamic approach allows the system to support increased PEV charging demand while protecting infrastructure durability by avoiding operation beyond thermal limits, thus resolving the contradiction between productivity and reliability.
Solution Approach 2:
The system employs periodic charging schedules that distribute charging loads across different time periods rather than concentrating them simultaneously. By coordinating charging sessions to occur during off-peak periods or staggered time windows, the system increases overall PEV charging capacity while preventing infrastructure overload. This periodic action pattern allows more vehicles to be served without straining the power distribution infrastructure, thus maintaining both productivity and reliability.
3Ease of operation
If decentralized charge management methods are used to preserve customer privacy and reduce communication bandwidth, then autonomy and privacy are maintained, but coordination efficiency may be reduced compared to centralized optimization
Solution Approach 1:
The system uses anonymized aggregate copies of charging data rather than individual vehicle information. The centralized coordination mechanism receives and processes anonymized aggregate data about charging patterns and infrastructure conditions, enabling efficient coordination decisions without accessing or transmitting sensitive customer privacy information. This copying approach maintains customer privacy while preserving coordination efficiency, as the centralized system can optimize charging schedules based on aggregate patterns without needing detailed individual data.
4Measurement precision
If charge management schemes require detailed information exchange between vehicles and central authority, then optimization accuracy is improved, but communication bandwidth requirements increase
Solution Approach 1:
The invention extracts only the essential anonymized aggregate information needed for coordination while leaving detailed vehicle-specific data at the local level. The centralized system extracts and processes only the minimum necessary information (aggregate charging patterns, infrastructure status) to provide coordination signals, eliminating the need for continuous exchange of detailed vehicle data. This extraction approach maintains optimization accuracy by focusing on critical coordination parameters while dramatically reducing communication bandwidth requirements.
Data Source
AI summary
Systems and methods for distributing electric energy in discrete power packets of finite duration are presented. Systems may include an aggregator for providing power packets to one or more nodes. An aggregator may receive requests for power packets from nodes. In other embodiments, an aggregator may transmit status broadcasts and nodes may receive power packets based on the status broadcasts.