Solar Microgrid Digital Twin for Predictive Energy Yield Control
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Solution Overview
Problem
Existing Distributed Energy Resource Management Systems (DERMS) are not designed for end users like Commercial and Industrial customers who wish to be independent from the grid and lack real-time optimization and predictive capabilities for renewable energy systems.
Innovation Solution
An Energy Yield Management Software (EYMS) framework that includes a control unit, digital twin, and predictive models for optimizing energy production, storage, and consumption in Industrial Grade Solar Microgrids (IGSM), using real-time and historical data to manage energy yield and financial performance, with features like prescriptive maintenance and dynamic resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If DERMS control systems are used for utility grid management, then grid stability and efficiency are improved, but the system is not suitable for end users who wish to be independent from the grid
Solution Approach 1:
The system dynamically adapts its operational mode based on user needs and grid conditions. The control system can switch between grid-tied operation, islanded microgrid operation, and hybrid modes, allowing the same hardware infrastructure to serve both utility-scale grid management and end-user energy independence requirements. This dynamic reconfigurability resolves the contradiction by making the system versatile enough for end users while maintaining grid independence capability when needed.
Solution Approach 2:
The control system is designed with multi-functionality to serve dual purposes: it can operate as a traditional utility-scale DERMS for grid stability management, and simultaneously function as an energy independence platform for end users. The system incorporates universal communication protocols, flexible control algorithms, and adaptable user interfaces that accommodate both utility operators and end users, thereby resolving the contradiction between grid management suitability and end-user independence capability.
2Productivity
If traditional DERMS focus on operational control and monitoring, then real-time control is achieved, but predictive analytics and optimization capabilities are lacking
Solution Approach 1:
The system incorporates predictive analytics that perform preliminary analysis of energy production, consumption patterns, and equipment performance trends. By using machine learning models and historical data analysis, the system forecasts future energy needs and identifies optimization opportunities before they occur, enabling proactive rather than reactive control. This preliminary action enhances energy optimization efficiency while the modular architecture manages complexity by separating predictive analytics modules from core control functions.
Solution Approach 2:
The system introduces an intermediary layer of intelligence between the basic monitoring hardware and the control execution layer. This intermediary comprises predictive analytics engines, optimization algorithms, and decision-support software that process raw operational data and translate it into optimized control commands. This intermediary layer enhances productivity by adding predictive and optimization capabilities while managing device complexity through a clear architectural separation of concerns, where each layer has defined responsibilities.
3Reliability
If solar energy systems are deployed for renewable energy generation, then sustainability is improved, but real-time optimization and predictive capabilities are insufficient
Solution Approach 1:
The system implements comprehensive feedback loops that continuously monitor solar panel performance, weather conditions, energy storage state, and consumption patterns. Real-time sensor data feeds into the control system, which immediately adjusts operational parameters such as inverter settings, battery charge/discharge rates, and load management strategies. This closed-loop feedback mechanism ensures that real-time data is fully utilized to optimize energy management, resolving the contradiction between improving reliability through efficient management and preventing loss of real-time information by demonstrating its active utilization.
Data Source
AI summary
In one aspect, a computerized system of an Energy Yield Management Software (EYMS) framework includes an Industrial Grade Solar Microgrids (IGSM) deployment comprising a control unit configured to communicate with a power generating source, collect data from the power generating source, and issue instructions to the power generating resource. The control unit is further configured to communicate with a sensor, an automation module, a local load, an energy storage systems, or a generation resource within a customer IGSM deployment. The EYMS is configured to communicate through a communication network to the IGSM deployment. A Digital Twin configured to actively use real time and historical data to learn how each component in the IGSM performs under a plurality of operational conditions and characteristics, wherein a predictive model is employed by the Digital Twin for a prediction operation, an optimization operation and a prescriptive maintenance operation of the IGSM.