Fleet Depot Charging Profiles for Peak Power Shaping
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Solution Overview
Problem
The high peak power demand from the grid for charging electric vehicle fleets results in increased operational costs, particularly during peak demand hours when electricity prices are highest.
Innovation Solution
A system comprising a plurality of vehicle chargers connected to an electric power grid and a controller that determines vehicle and charger characteristics to optimize charging opportunities, perform peak power optimization, and generate a vehicle charging profile to minimize peak power demand and downtimes of chargers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple vehicles are charged simultaneously at the depot, then the charging efficiency and vehicle availability are improved, but the peak power demand from the grid increases resulting in higher operational costs
Solution Approach 1:
The system performs preliminary analysis of vehicle missions, charger availability, and electricity pricing to create optimized charging schedules in advance. The controller determines charging profiles before peak demand periods, scheduling charges to occur during off-peak hours when electricity rates are lower, thus avoiding high peak power demand while ensuring vehicles are charged and available when needed
Solution Approach 2:
The charging system dynamically adjusts charging schedules based on real-time conditions including vehicle return times, mission requirements, charger status, and fluctuating electricity prices. The controller continuously monitors and reoptimizes charging profiles to balance the need for vehicle availability with the goal of minimizing peak power demand and operational costs
2Reliability
If charging is performed during peak demand hours to ensure vehicle availability, then the vehicle readiness is improved, but the operational cost increases due to higher electricity prices
Solution Approach 1:
The system calculates and stores preliminary charging profiles that identify optimal charging windows based on historical and predicted vehicle usage patterns, mission schedules, and electricity pricing structures. These pre-computed profiles schedule charging to complete before vehicles are needed, avoiding peak pricing periods while ensuring vehicle readiness
Solution Approach 2:
The controller continuously monitors actual vehicle availability, charging progress, and electricity prices, comparing real-time conditions against the optimized charging schedules. When deviations occur or pricing changes, the system provides feedback and reoptimizes charging profiles to maintain vehicle availability while minimizing operational costs
Data Source
AI summary
A system for charging electric vehicles is disclosed, comprising: vehicle chargers coupled to an electric power grid; and a controller in communication with the chargers and vehicles and configured to execute software to cause the controller to: determine characteristics of each vehicle, the characteristics including a charge capacity of a battery system of each vehicle and a mission schedule; determine characteristics of each charger, the characteristics including a type of each charger and a charging capacity; process the characteristics of each vehicle and each charger to identify charging opportunities for each vehicle over the course of a time period; and perform a peak power optimization analysis to generate a vehicle charging profile configured to activate a minimum number of chargers simultaneously and to minimize downtimes of the plurality of chargers to thereby distribute the power demand from the electric power grid and result in an initial peak power demand.