AI Microgrid Planning Platform for Rapid DER Assessment
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Solution Overview
Problem
The high costs and inefficiencies associated with conventional methods for analyzing and modeling microgrid and Distributed Energy Resources (DER) projects, particularly due to the need for extensive data collection and computational power, hinder the ability to assess large geographic regions or multiple projects quickly, limiting scalability and practicality for investors, technology vendors, and regulatory authorities.
Innovation Solution
An AI-enabled microgrid and DER planning platform utilizing machine learning and neural networks to estimate planning parameters, trained on real-world project data, which simplifies data collection and processing, enabling fast and efficient analysis of multiple projects and regions without the need for extensive data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional modeling approaches are used to analyze microgrid and DER projects, then measurement precision and reliability are improved, but productivity and speed of analysis deteriorate
Solution Approach 1:
The system performs preliminary data collection and processing by gathering energy consumption data, weather data, tariff data, and building characteristics data in advance. This preliminary action enables the AI model to quickly generate accurate assessments without requiring extensive real-time data collection for each individual project analysis.
Solution Approach 2:
The system creates virtual copies of building models and energy systems that can be simulated and analyzed computationally. These digital twins or virtual models allow for rapid iteration and assessment of different microgrid configurations without physically implementing each scenario, thereby maintaining precision while improving productivity.
2Measurement precision
If conventional data collection methods are used for each building individually, then measurement precision is improved, but loss of time and productivity worsen
Solution Approach 1:
The system merges data collection and processing operations by integrating multiple data sources (energy consumption data, weather data, tariff data, building characteristics) into a unified platform. This consolidation allows simultaneous processing of multiple buildings and projects, reducing the time required from weeks to minutes while maintaining data accuracy through standardized collection protocols.
Solution Approach 2:
The system changes the scale and scope of analysis parameters by transitioning from individual building-level detailed modeling to region-wide aggregate analysis using AI algorithms. This parameter transformation enables the system to assess hundreds or thousands of buildings simultaneously by analyzing key parameters and patterns rather than every detail of each building.
3Reliability
If extensive data collection is performed for comprehensive analysis, then measurement precision and reliability are improved, but device complexity and computational requirements worsen
Solution Approach 1:
The system extracts and separates essential data elements from the complete dataset, focusing on the most critical parameters for microgrid feasibility assessment. By identifying and extracting only the necessary data (energy consumption patterns, weather conditions, tariff structures, building characteristics), the system reduces computational complexity while maintaining assessment reliability by concentrating on key decision-making factors.
Solution Approach 2:
The AI model acts as an intermediary between raw data and decision-making processes. It processes and transforms extensive collected data into meaningful insights and recommendations, mediating between the complexity of comprehensive data collection and the simplicity required for actionable business decisions. This intermediary layer maintains reliability by ensuring thorough analysis while simplifying the interface for users.
Data Source
AI summary
The embodiments disclosed in this document are directed to an AI-enabled microgrid and DER planning platform that uses AI methods and takes into account cost calculations, emission calculations, technology investments and operation. In an embodiment, the computing platform is deployed on a network (cloud computing platform) that can be accessed by a variety of stakeholders (e.g., investors, technology vendors, energy providers, regulatory authorities). In an embodiment, the planning platform implements machine learning (e.g., neural networks) to estimate various planning parameters, where the neural networks are trained on observed data from real-world microgrid/minigrid and DER projects.