Microgrid Virtual Model for Real-Time Market Simulation
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Solution Overview
Problem
Conventional systems for managing distributed energy generation, such as microgrids, lack real-time monitoring and predictive capabilities, leading to inaccurate market forecasts and operational inefficiencies due to the absence of real-time data integration and virtual modeling.
Innovation Solution
A system that utilizes real-time data from sensors to create and synchronize a virtual model of a microgrid's electrical system, allowing for real-time performance monitoring, prediction of energy costs and reliability, and simulation of scenarios like maintenance and unplanned events, thereby optimizing energy consumption and market-based pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional off-line market-based pricing systems are used for distributed energy generation, then system complexity is reduced and ease of operation is improved, but measurement precision of real-time power network conditions deteriorates and reliability of market forecasts worsens
Solution Approach 1:
The patent creates a virtual model that copies the physical power network's structure, components, and operational characteristics. This virtual replica enables real-time simulation and analysis of market conditions without requiring complex physical modifications to the actual grid infrastructure. The virtual model captures essential dynamics while simplifying the computational burden compared to full-physics real-time simulations.
Solution Approach 2:
The system performs preliminary calibration of the virtual model using historical and real-time data before conducting market forecasts. By pre-synchronizing the virtual model with actual grid conditions and pre-computing baseline scenarios, the system reduces real-time computational complexity while maintaining measurement precision for market-based pricing decisions.
2Reliability
If real-time virtual modeling and simulation systems are implemented for microgrid optimization, then reliability of market forecasts and operational efficiency are improved, but device complexity and data processing requirements worsen
Solution Approach 1:
The virtual model dynamically adapts its complexity based on operational needs, adjusting the level of detail and computational resources allocated to different simulation scenarios. The system transitions between simplified and detailed modeling approaches depending on whether real-time monitoring or comprehensive market analysis is required, optimizing the balance between reliability and complexity.
Solution Approach 2:
The system segments the power network into functional zones and models each with appropriate detail levels. Critical components requiring high measurement precision are modeled in greater detail, while less critical elements use simplified representations. This segmentation reduces overall system complexity while maintaining reliability for decision-critical parameters.
3Productivity
If real-time data acquisition and model synchronization are performed continuously, then productivity and operational efficiency are improved, but use of energy and computational resources worsen
Solution Approach 1:
The system implements periodic model synchronization at strategically determined intervals rather than continuous updates. The synchronization frequency adapts based on the rate of change in grid conditions and the criticality of upcoming market decisions. This periodic approach maintains productivity by providing timely updates while reducing computational energy consumption by eliminating redundant continuous synchronization operations.
Solution Approach 2:
The system dynamically changes the sampling rate and synchronization frequency based on operational parameters such as load variability, generation uncertainty, and market volatility. During stable conditions, parameter updates are reduced to minimize energy use, while during transitional or critical periods, the system increases update frequency to maintain productivity and forecast accuracy.
Data Source
AI summary
Systems and methods for optimizing energy consumption in multi-energy sources sites are provided. These techniques include developing a real-time model and a virtual model of the electrical system of a multi-energy source site, such as a microgrid. The real-time model represents a current state of the electrical system can be developed by collecting data from sensors interfaced with the various components of the electrical system. The virtual model of the electrical system mirrors the real-time model of the electrical system and can be used to generate predictions regarding the performance, availability, and reliability of cost and reliability of various distributed energy sources and to predict the price of acquiring energy from these sources. The virtual model can be used to test “what if” scenarios, such as routine maintenance, system changes, and unplanned events that impact the utilization and capacity of the microgrid.