Smart Charging Plug-in Electric Vehicle Load Planning
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Solution Overview
Problem
The increased adoption of plug-in electric vehicles (PEVs) creates peak electric power loads that can strain utility grids, leading to potential distribution overloads and increased costs if not managed properly, necessitating a system for optimal planning of electric power demand.
Innovation Solution
A system and method for generating an optimized load and charging schedule for smart charging plug-in electric vehicles (SCPEVs) using operations research techniques, such as mathematical programming, to minimize costs and comply with constraints like transformer capacity and user preferences, thereby reducing distribution overloads and electric power generation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If PEVs charge simultaneously when arriving home in the evening, then all PEVs receive power, but peak electric power loads and transients increase significantly
Solution Approach 1:
The system performs preliminary scheduling of charging times before the actual charging occurs. The optimization algorithm calculates optimal charging schedules in advance, distributing charging loads across different time periods to avoid simultaneous charging peaks while ensuring all PEVs receive required power.
Solution Approach 2:
The charging schedule is dynamically optimized based on grid conditions, PEV arrival patterns, and power availability. The system adjusts charging rates and timing flexibly to balance individual PEV charging needs with overall grid load management, preventing excessive peak loads.
2Reliability
If utilities upgrade transformers and employ fast response power plants to meet peak demand, then power supply reliability improves, but infrastructure investment costs increase
Solution Approach 1:
The system implements feedback mechanisms where charging schedules are continuously optimized based on real-time grid conditions, historical data, and predicted PEV arrival patterns. This feedback loop enables utilities to manage peak demands through intelligent scheduling rather than physical infrastructure upgrades.
Solution Approach 2:
The optimization algorithm changes operational parameters such as charging rates, timing, and power distribution patterns to smooth peak loads. By adjusting these parameters dynamically, the system maintains power supply reliability without requiring additional transformer capacity or fast-response power plants.
3Device complexity
If charging schedules are optimized to reduce peak loads, then infrastructure investment is reduced, but charging time for individual PEVs may increase
Solution Approach 1:
The system applies partial charging during off-peak hours and completes remaining charge during moderate-load periods. Rather than charging at maximum rate continuously, PEVs receive distributed charging that spreads load over time, reducing peaks while still meeting individual charging requirements.
Solution Approach 2:
Charging is organized into periodic cycles with varying intensities. The system implements periodic charging schedules that alternate between higher and lower charging rates across different time periods, smoothing the overall load pattern while ensuring complete charging within acceptable timeframes.
Data Source
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AI summary
A system for optimal planning of electric power demand is presented. The system includes a node comprising one or more smart charging plug-in electric vehicles (SCPEVs), a processing subsystem, wherein the processing subsystem receives relevant data from one or more sources; and determines an optimized SCPEV load and optimal charging schedule for the node by applying an operations research technique on the relevant data.