Microgrid EMS Weighting for Predictive Multi-Objective Optimization
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Solution Overview
Problem
Current energy management systems for microgrids rely on local data and human expert input for optimization, which limits their ability to determine optimal operating points and is prone to errors, especially when dealing with complex scenarios involving renewable energy sources and energy storage systems.
Innovation Solution
An automated method and system that uses multi-objective optimization to determine asset operating points over a predictive time horizon, where weights for objective functions are automatically determined using utopia values and Pareto front analysis, allowing for objective and scenario-specific tuning of energy management systems without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human experts manually set weights for multi-objective optimization in the EMS, then the system can be tuned to specific scenarios, but human errors may occur and the process is time-consuming
Solution Approach 1:
The system automatically determines optimization weights through self-service mechanisms. The EMS performs autonomous multi-objective optimization by automatically selecting weights based on scenario characteristics without requiring manual human intervention. This self-service approach eliminates human errors in weight selection while maintaining adaptability to different microgrid scenarios through automated scenario recognition and parameter adjustment.
Solution Approach 2:
The system dynamically changes optimization parameters (weights) based on different operating scenarios. By automatically adjusting the weights of various objective functions according to the specific microgrid configuration, renewable energy availability, and operational conditions, the system adapts to different scenarios while removing the need for manual parameter tuning and associated human errors.
2Ease of operation
If the EMS uses only local data and past information for optimization, then the system is simpler to operate, but the optimization is limited and cannot account for future conditions
Solution Approach 1:
The system performs preliminary actions by incorporating forecasted future data into the optimization process. Before making real-time optimization decisions, the EMS uses predictive models to estimate future renewable energy generation, load demands, and market conditions. This preliminary analysis of future conditions enables more effective optimization that accounts for upcoming scenarios while maintaining operational simplicity through automated forecasting integration.
3Productivity
If the EMS performs multi-objective optimization with multiple weighted functions, then the optimization comprehensiveness is improved, but the system complexity increases
Solution Approach 1:
The system handles the complexity of multi-objective optimization through self-service automation. The EMS automatically determines appropriate weights for multiple objective functions based on scenario characteristics, eliminating the need for complex manual weight determination processes. This automated approach maintains comprehensive multi-objective optimization while reducing system complexity from the user perspective.
Data Source
AI summary
Methods and systems for operating an energy management system (EMS) for a microgrid are operative to automatically determine weights by which different objective functions are weighted in a multi-objective optimization performed by the EMS.