Dynamic Energy Management Models for Variable Power Systems
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Solution Overview
Problem
Modern power systems with renewable energy sources and storage devices are highly variable due to equipment, environmental, and communication factors, making their management complex and resource-intensive.
Innovation Solution
A computer-implemented energy management system (EMS) that generates power forecast data using a forecasting model and control parameter values using an optimization model, dynamically updating these models based on operational data to optimize power system operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If renewable energy sources and storage devices are added to power systems, then energy generation and storage capability is improved, but system variability and management complexity increase
Solution Approach 1:
The energy management system is divided into multiple independent modules including forecasting module, optimization module, communication module, and database module. Each module handles specific functions independently, making the complex system manageable and maintainable while supporting renewable energy integration.
Solution Approach 2:
The EMS is designed with universal interfaces and protocols that can accommodate various renewable energy sources (solar, wind), storage devices (batteries), and communication protocols. This multi-functionality allows the system to manage diverse equipment without increasing operational complexity.
2Productivity
If forecasting and optimization models are used to manage power systems, then operational efficiency is improved, but computational resources and system complexity increase
Solution Approach 1:
The forecasting model predicts future power consumption and generation before operational decisions are made. By performing preliminary forecasting, the optimization model can prepare optimal control strategies in advance, improving operational efficiency while distributing computational load over time.
Solution Approach 2:
The system dynamically adjusts forecasting and optimization parameters based on real-time operational data and changing conditions. This dynamic approach allows the models to adapt to varying renewable energy availability and load patterns without requiring excessive computational resources for static analysis.
3Adaptability or versatility
If the energy management system is made extensible and dynamically updated, then adaptability to changing conditions is improved, but system complexity and update management difficulty increase
Solution Approach 1:
The system continuously receives operational data from power system equipment and uses this feedback to dynamically update forecasting and optimization models. This closed-loop feedback mechanism enables the EMS to adapt to changing conditions automatically without manual intervention, improving adaptability while simplifying update management.
Solution Approach 2:
The EMS performs self-updating of its models and parameters based on accumulated operational data. The system automatically learns from past performance and adjusts its forecasting and optimization strategies without requiring external reconfiguration, enabling adaptability while reducing update management complexity.
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.