Microgrid Power Management with Predictive Market Modeling
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Solution Overview
Problem
Existing microgrid power generation and management systems lack efficient optimization methods for selectively engaging emergency power generation equipment based on real-time energy pricing and demand fluctuations, leading to suboptimal power distribution and storage.
Innovation Solution
Implementing a predictive modeling-based system that includes a Price Resource Management System (PRMS) to monitor and analyze wholesale energy market prices, enabling selective activation and coordination of emergency power generation equipment for distribution and storage within a microgrid, using devices such as generators, solar arrays, and energy storage mechanisms, to optimize power supply based on market rates and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If emergency power generation equipment is continuously operated, then reliable backup power is ensured, but energy waste increases and operational costs rise
Solution Approach 1:
The system performs preliminary actions by continuously monitoring wholesale energy market prices and predicting future price trends before making activation decisions. The PRMS analyzes market conditions in advance and pre-determines optimal activation times, allowing emergency power equipment to be activated only when market prices indicate favorable conditions, thus avoiding unnecessary operation and energy waste while maintaining reliability.
Solution Approach 2:
The system implements feedback mechanisms where the PRMS continuously monitors both market price signals and actual power generation outcomes. This feedback loop allows the system to learn from past decisions, adjust activation strategies based on observed market patterns, and optimize the balance between maintaining backup power reliability and minimizing energy waste over time.
2Productivity
If emergency power generation equipment is activated based on real-time market prices, then energy revenue is maximized, but system complexity increases
Solution Approach 1:
The PRMS serves multiple functions within a single integrated system: it monitors wholesale energy market prices, predicts price trends, determines optimal activation times, coordinates equipment operation, and tracks revenue outcomes. By consolidating these diverse functions into one multi-functional platform, the system maximizes energy revenue through intelligent market-based decisions while avoiding the complexity of multiple separate systems working in isolation.
Solution Approach 2:
The PRMS acts as an intermediary layer between the emergency power generation equipment and the complex wholesale energy market. It translates market price signals into actionable activation decisions and coordinates equipment operation accordingly. This intermediary function simplifies the interface between the power equipment and market dynamics, maximizing revenue potential while containing system complexity within the PRMS itself.
3Productivity
If predictive modeling is implemented for power generation optimization, then operational efficiency improves, but computational requirements and system complexity increase
Solution Approach 1:
The predictive modeling implementation focuses on the most critical factors influencing power generation decisions, such as wholesale energy market price trends and key load demand patterns. Rather than attempting to model every possible variable, the system applies partial action by concentrating computational resources on the most impactful predictors, thereby improving operational efficiency without requiring excessive computational complexity.
4Loss of energy
If selective engagement of emergency power equipment is optimized, then energy utilization increases, but control system complexity increases
Solution Approach 1:
The control system merges multiple decision-making functions into a unified optimization process. The PRMS combines market price analysis, load demand forecasting, and equipment activation decisions into a single coordinated control mechanism. This merging approach improves energy utilization efficiency by ensuring equipment is activated only when both market conditions and local demand align, while containing control complexity through integration rather than multiplication of separate control systems.
Data Source
AI summary
Systems and methods for coordinating selective activation of at least one power generation equipment component and/or at least one power storage device over a predetermined geographic area for distribution and/or storage, and/or for sale and distribution in a utility grid.