Micro-grid Predictive Control for Multipower Resource Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing power management systems for multi-power resources fail to consider future weather conditions, leading to inefficiencies in energy output and increased costs, as they primarily rely on current weather conditions to optimize energy supply from renewable sources like PV installations.
Innovation Solution
A power control system that utilizes machine learning to predict energy supply from PV installations and load demands based on weather forecasts, determining the optimal use of battery and engine resources to meet power deficits, thereby minimizing overall costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optimization scheme uses current weather condition to determine cost-effective energy output, then immediate energy management is achieved, but future weather conditions and their impact on PV energy supply are not considered
Solution Approach 1:
The system performs preliminary actions by obtaining weather forecasts for future time periods before making energy management decisions. The controller uses machine learning models to predict PV energy supply based on forecasted weather conditions, allowing the system to proactively plan energy usage and storage strategies in advance, rather than merely reacting to current conditions.
2Reliability
If optimization scheme considers future weather conditions, then overall cost minimization is improved, but system complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediary components between weather forecast data and energy management decisions. These models act as mediators that automatically process weather information and generate predictions about PV energy supply, eliminating the need for complex manual analysis while enabling sophisticated cost optimization based on future weather conditions.
3Measurement precision
If machine learning model is used to predict PV energy supply, then prediction accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary training of machine learning models using historical weather data and PV energy supply data before deployment. This pre-training phase allows the models to learn complex patterns and relationships in advance, enabling them to make accurate predictions in real-time or near-real-time when actual weather forecasts are available, thus reducing processing time during operational decision-making.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively anticipates energy needs by predicting PV output and load profiles, optimizing the use of battery and engine resources to reduce costs and ensure reliable energy supply.
Implementation Method 1
a photo-voltaic (PV) installation configured to provide electrical energy
Data Source
AI summary
A device may receive a power demand for a load and a weather forecast for a time period. The device may determine a first supply of power available from a photo-voltaic (PV) installation for the time period based on the weather forecast. The device may determine a power deficit for the time period based on the power demand and the first supply of power. The device may determine a first cost associated with utilizing a second supply of power available from a battery and a second cost associated with utilizing a third supply of power available from an engine for the time period. The device may determine a power source to overcome the power deficit based on the first cost and the second cost and may cause the PV installation and the power source to supply power to satisfy the power demand for the load.