Battery Energy Storage Charging Strategy for EV Load Variability
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Solution Overview
Problem
The increasing ratings of electric vehicle chargers pose challenges to the electrical grid, particularly in weak grids, where voltage drops can exceed permitted limits, and the unpredictability of electric vehicle charging loads complicates the optimization of battery energy storage systems, affecting their lifespan and operational efficiency.
Innovation Solution
The implementation of machine learning-based solutions for predicting site behaviors and optimizing battery energy storage system operations, using supervised and reinforcement learning algorithms, to determine optimal charging strategies that maximize battery lifespan and manage energy distribution effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a battery energy storage system is used to increase power output from the electrical grid, then the power output is improved, but the system complexity increases
Solution Approach 1:
The machine learning model autonomously predicts charging loads and determines optimal battery charging/discharging strategies without requiring complex manual configuration or intervention. The system self-optimizes by learning from historical data and adapting to unpredictable electric vehicle charging patterns, thereby managing complexity internally while delivering improved power output.
2Adaptability or versatility
If the battery energy storage system operates to support unpredictable electric vehicle charging loads, then the adaptability is improved, but the difficulty of setting optimal parameters increases
Solution Approach 1:
The machine learning model continuously learns from historical charging data and system performance feedback to automatically optimize battery operation parameters. By incorporating feedback loops that analyze actual charging patterns and system responses, the model adapts to unpredictable load profiles while eliminating the need for manual parameter tuning, thereby resolving the contradiction between adaptability and parameter optimization difficulty.
3Productivity
If the battery energy storage system provides loading cycles to support electric vehicle charging, then the productivity is improved, but the battery lifetime decreases
Solution Approach 1:
The machine learning model dynamically adjusts battery charging and discharging strategies based on real-time conditions and predictions. By flexibly modulating the rate and timing of battery cycles according to predicted electric vehicle charging patterns, the system maximizes productivity while minimizing stress on the battery, thereby extending its operational lifetime compared to fixed or aggressive cycling approaches.
Data Source
AI summary
An apparatus for performing the following: the apparatus maintains, in a database, one or more trained machine learning algorithms for predicting an optimal charging strategy for a time interval based on one or more values of a set of prediction parameters relating to a point of common coupling and one or more electrical load devices and on a state of charge level of the battery energy storage system. The apparatus obtains one or more recent values of the set of prediction parameters relating to one or more previous time intervals and predicts, using the one or more trained machine learning algorithm, an optimal charging strategy for a next time interval based on the one or more recent values and a current state of charge level of the battery energy storage system. Finally, the apparatus operates the battery energy storage system using the predicted optimal charging strategy.