Building EV Battery Charging Control for Energy Cost and Grid Balance
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Solution Overview
Problem
Existing building energy systems lack efficient optimization for charging and discharging battery-powered devices, particularly in managing energy consumption and generation to balance demand and supply, optimize costs, and participate in frequency regulation and ramp rate control.
Innovation Solution
A building optimization system that includes a charging system and an optimization controller to manage battery charging and discharging based on constraints, predict departure times, and optimize energy transactions to balance energy use and revenue generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the battery is charged at high rates to meet departure time constraints, then the charging speed increases, but energy costs increase and grid stability deteriorates
Solution Approach 1:
The system performs advance charging during off-peak hours when energy costs are lower, rather than charging at high rates immediately before departure. The optimization controller predicts departure times and schedules charging operations beforehand to avoid peak pricing periods and reduce overall energy costs.
Solution Approach 2:
The charging rate is dynamically adjusted based on real-time energy prices, grid conditions, and predicted departure times. The system transitions from static high-rate charging to adaptive charging that modulates power intake according to varying economic and operational conditions.
2Loss of energy
If the battery is discharged to power building equipment or sell to grid, then revenue generation increases, but the battery charge level decreases
Solution Approach 1:
The system maintains a minimum charge level buffer in advance of predicted departure times to ensure the vehicle is ready when needed. Discharge operations are scheduled to occur during periods when the battery can be recharged, preventing situations where the vehicle would be unavailable due to depleted charge.
Solution Approach 2:
The system dynamically adjusts the discharge rate and timing based on energy price signals, building load requirements, and predicted vehicle usage patterns. By changing operational parameters adaptively, the system maximizes revenue while maintaining sufficient charge for required vehicle operations.
3Loss of energy
If the optimization system manages multiple charging constraints and predictions, then energy cost optimization improves, but system complexity increases
Solution Approach 1:
The optimization controller integrates multiple functions including departure time prediction, energy price analysis, charging rate optimization, and discharge scheduling into a single unified system. This multi-functional approach consolidates what would otherwise require separate systems, managing complexity through integration rather than proliferation of components.
Data Source
AI summary
A building optimization system includes charging and discharging a battery of a battery power vehicle. The building optimization system includes a charging system configured to cause the battery of the battery powered vehicle to charge or discharge. The building optimization system also includes an optimization controller including a processing circuit. The processing circuit is configured to receive charging constraints for the battery powered vehicle, determine whether to charge discharge the battery of the battery powered vehicle based on the charging constraints, and cause the charging system to charge or discharge the battery of the battery powered vehicle based on the optimization.