Distributed Generation Management System Forecasting Utility Requests
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Solution Overview
Problem
Distributed energy generation systems face challenges in optimizing power generation and consumption decisions due to uncertainty in future utility requests, generation characteristics, and load usage, leading to potential missed benefits if decisions are made based on immediate incentives rather than long-term forecasts.
Innovation Solution
The implementation of a method and system that analyzes real-time and future regulatory and economic incentives by predicting utility requests, energy generation, and load requirements, allowing energy resources sites to determine whether to comply with current or future utility requests based on estimated benefits, using mathematical models and energy storage systems to adjust power generation and consumption accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the ER site complies with the current utility request to increase or decrease net power generation, then the site receives immediate economic benefit or incentive, but the site may miss out on larger future benefits that would be available if it waited for more favorable future utility requests
Solution Approach 1:
The system performs preliminary forecasting of future utility requests and compares them with current requests before making compliance decisions. By predicting future conditions and evaluating potential benefits in advance, the system determines whether to comply now or wait for more favorable future conditions, thereby avoiding immediate compliance when it would be more beneficial to delay.
2Loss of energy
If the ER site uses forecasting models to predict future utility requests and optimize compliance timing, then the site can maximize long-term economic benefits, but the system complexity and computational requirements increase
Solution Approach 1:
The system introduces an intermediary forecasting module that acts as a mediator between the current utility request and the compliance decision. This module uses mathematical models to predict future utility requests and provides recommended compliance timing to the control system, thereby reducing the complexity of direct optimization while still achieving long-term benefit maximization.
3Loss of energy
If the ER site delays compliance with utility requests to capture future incentives, then the site can potentially earn larger economic benefits, but the site risks missing current revenue opportunities and facing penalty risks
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor current utility requests, forecast future requests, and evaluate the trade-offs between immediate compliance and delayed compliance. The feedback loop provides real-time recommendations on whether to comply with current requests or wait for future opportunities, balancing revenue maximization with reliable compliance to avoid penalties.
Data Source
AI summary
Embodiments may include a method of adjusting power in a distributed generation management system. The method may include receiving, by a processor, real-time load requirement data of an energy resources (ER) site. The method may also include receiving, by the processor, real-time energy generation (EG) data from the ER site. The method may further include determining, by the processor, a net power generation at a first level from the real-time load requirement data and the real-time energy generation data. The method may include receiving, by the processor, a first request from a utility to increase or decrease the net power generation from the first level. The method may also include determining a benefit complying with the first request. The method may further include estimating a benefit of not complying with the first request. The method may also include determining whether the determined benefit is greater than the estimated benefit.