Microgrid EV Charging Strategy for Power Balance Optimization
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Solution Overview
Problem
Current simulation tools for decentralized energy systems do not adequately consider electric vehicle (EV) charging stations, failing to optimize energy infrastructure and charging strategies effectively, which affects the overall energy balance and efficiency of the system.
Innovation Solution
A computer-implemented method that simulates power generation by various electric sources in a decentralized energy system, including EV charging stations, and optimizes the charging strategy based on EV schedules and power generation patterns, allowing for the integration of EVs as both consumers and potential energy storage units, thereby tailoring the charging process to match power availability and reduce reliance on external grids.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If EV charging stations are integrated into decentralized energy system simulations, then the energy management optimization is improved, but the simulation complexity increases
Solution Approach 1:
The simulation system is divided into separate functional modules: power generation simulation, EV charging station simulation, and optimization algorithm execution. Each module handles specific aspects independently, allowing the complex overall system to be managed through modular components that can be developed, tested, and maintained separately.
Solution Approach 2:
The simulation platform is designed to handle multiple functions within a unified framework: simulating various power sources (solar, wind, conventional), modeling different EV charging strategies, and performing optimization calculations. This multi-functional approach avoids the need for separate specialized tools for each aspect.
2Loss of energy
If charging strategy optimization is performed considering power generation patterns, then energy balance efficiency is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary simulation of power generation patterns and EV charging demands over the simulation period before executing the optimization algorithm. By pre-calculating and storing power generation data, vehicle schedules, and charging requirements, the optimization phase can focus solely on finding optimal charging strategies without re-simulating physical processes, significantly reducing computational requirements.
3Adaptability or versatility
If EVs are treated as energy storage units in the decentralized system, then system adaptability is improved, but control complexity increases
Solution Approach 1:
Instead of treating EVs purely as energy consumers, the system inverts the conventional approach by allowing EVs to function as distributed energy storage units that can discharge power back to the grid or local loads. This inversion enables EVs to actively participate in balancing supply and demand, providing flexibility and adaptability to the decentralized energy system while using standardized charging infrastructure for bidirectional energy flow.
Data Source
AI summary
The disclosure pertains to optimizing a charging strategy for one or more charging stations to which one or more electric vehicles are connected. The charging strategy is optimized taking into account power generation within a decentral energy system, also referred to as microgrid.