Modular Energy Management Architecture for Dynamic Model Updates
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Solution Overview
Problem
Modern power systems with renewable energy sources and storage face complexity due to variability in equipment, energy sources, load characteristics, and environmental conditions, requiring a versatile and dynamically adjustable energy management system for effective setup and operation.
Innovation Solution
A computer-implemented energy management system (EMS) with a forecasting model for predicting power consumption and generation, and an optimization model for generating control parameter values, integrated with a communication module for equipment control, allowing for dynamic updates and extensibility to adapt to changing conditions and new protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a versatile energy management system is designed to handle various power system configurations and changing conditions, then the system's adaptability and functionality are improved, but the system's complexity and difficulty of setup increase
Solution Approach 1:
The EMS is divided into distinct functional modules including a forecasting module with multiple forecasting models, an optimization module with multiple optimization models, a communication module, and a user interface module. Each module can be independently configured and updated, reducing overall system complexity while maintaining versatility.
Solution Approach 2:
The system employs universal interfaces and standardized communication protocols that allow the same EMS architecture to manage diverse power system configurations including renewable energy sources, energy storage systems, and various load types. The modular design enables a single system to perform multiple functions across different应用场景.
2Measurement precision
If multiple forecasting models and optimization models are integrated into the EMS to improve prediction accuracy and optimization performance, then the system's functionality is enhanced, but the computational resources and processing time required increase
Solution Approach 1:
The system dynamically selects and switches between different forecasting models and optimization models based on current system conditions, data availability, and performance metrics. This allows the EMS to use more computationally intensive models only when necessary, while relying on lighter models for routine operations, thus balancing accuracy with resource consumption.
Solution Approach 2:
The system adjusts model parameters and complexity levels based on operational context. For example, during stable operating conditions, simpler models with fewer parameters are used, while during transitional or critical periods, more complex models are activated to improve prediction accuracy without consistently consuming high computational resources.
3Adaptability or versatility
If the EMS is designed to be extensible and allow dynamic updates of models and protocols, then the system's longevity and adaptability are improved, but the system's reliability and stability may be compromised due to frequent changes
Solution Approach 1:
The system includes pre-configured model libraries, standardized communication protocol templates, and validated optimization algorithms that are prepared in advance. These pre-established components reduce the risks associated with dynamic updates, as new models can be integrated using proven frameworks rather than ad-hoc implementations.
Solution Approach 2:
The system incorporates monitoring and validation mechanisms that continuously assess the performance and stability of integrated models. When updates are deployed, the system monitors for anomalies and can automatically rollback to previous stable configurations if performance degradation is detected, thus maintaining reliability while enabling extensibility.
Data Source
AI summary
An energy management system (EMS) and corresponding EMS manager are provided that provide improved extensibility and dynamic updating of models for predicting and optimizing power system management. In one aspect, an EMS predicts generation and consumption of a power system and optimizes operation of the power system using various forecasting and optimization models. The models may be managed and updated by the EMS manager based on data received from the EMS and other EMSs in communication with the EMS manager. The EMS manager may be configured to dynamically update and promulgate updates to models used by the EMS and other aspects of the EMS. The EMS may have an architecture including an application layer configured for the specific management system and a collection of updatable and expandable modules to facilitate forecasting, optimization, communication, and data management for the managed system.