Multi-Agent BESS Control for Degradation-Aware Energy Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery energy storage systems (BESSs) face challenges such as battery capacity and power degradation over time, especially under non-ideal charging and discharging conditions, and lack effective management systems that can optimize multiple factors simultaneously in complex and dynamic environments.
Innovation Solution
A degradation-aware multi-agent machine learning framework is introduced to manage and control BESSs, coordinating multiple software agents to optimize energy arbitrage, battery health, charging schedules, and backup power simultaneously, while considering battery degradation and dynamic market conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If single-agent machine learning methods are used to optimize energy management in BESSs, then a single objective (e.g., energy arbitrage or EV scheduling) can be optimized, but conflicting and changing objectives and interdependencies among complex multi-factor systems cannot be addressed
Solution Approach 1:
The system divides the complex energy management problem into multiple independent agent modules, each responsible for a specific function (energy arbitrage agent, EV scheduling agent, battery management agent, backup power agent). This segmentation allows each agent to specialize in optimizing its specific objective while the coordinator integrates them, resolving the contradiction between optimization effectiveness and ability to handle multiple objectives.
Solution Approach 2:
The system merges multiple single-agent machine learning models into a unified multi-agent framework coordinated by a central coordinator. This combining approach enables the system to simultaneously address multiple conflicting objectives (energy arbitrage, EV scheduling, battery health, backup power) that a single agent could not handle alone, while maintaining the optimization capabilities of individual agents.
2Device complexity
If rule-based/heuristic controls are used for energy management in BESSs, then system complexity is simplified, but the system becomes inadequate for adapting to complex dynamic changes and uncertain events
Solution Approach 1:
The system replaces traditional rule-based/heuristic mechanical control systems with machine learning-based intelligent agents. These agents use learned patterns and predictions to adapt to dynamic changes in energy markets, battery conditions, and EV charging demands, providing the necessary adaptability while maintaining manageable system complexity through modular agent architecture.
Solution Approach 2:
The control system transitions from static rule-based controls to dynamic machine learning agents that continuously learn and adapt to changing conditions. The agents update their strategies based on real-time data from the environment, enabling the system to respond flexibly to dynamic changes in energy prices, battery degradation patterns, and charging schedules without requiring complex manual reconfiguration.
3Power
If battery energy storage systems operate under non-ideal charging and discharging conditions to meet high-power requirements, then grid capacity problems are addressed, but battery capacity and power degradation worsen
Solution Approach 1:
The battery management agent uses machine learning to predict future battery degradation and charging/discharging conditions. Based on these predictions, the system proactively adjusts operating parameters (charge rates, discharge power levels, temperature management) to prevent excessive degradation before it occurs, allowing the system to maintain high power output while preserving battery reliability through advance planning and constraint enforcement.
Solution Approach 2:
The system implements continuous feedback loops where the battery management agent monitors real-time battery conditions (state of charge, temperature, degradation metrics) and adjusts charging/discharging operations accordingly. This feedback mechanism enables the system to dynamically balance power output requirements against battery health preservation, reducing degradation by modifying operations based on observed battery responses to previous charging/discharging cycles.
Data Source
AI summary
A system and method for management and control of battery energy storage systems in complex and dynamic multi-factor environments using a degradation-aware multi-agent machine learning framework. The management and control methodology involves coordination of a plurality of software agents to simultaneously optimize multiple factors in battery energy storage system environments such as energy arbitrage, battery health, charging schedules, and backup power.