Mini-Grid Energy Allocation Using Battery Charge Prediction
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Solution Overview
Problem
Existing solutions for load shedding and energy optimization in mini grids powered by solar energy do not effectively account for subscriber consumption and battery properties, leading to inefficiencies and potential blackouts.
Innovation Solution
A method for optimizing energy distribution in mini grids that involves predicting the final state of charge and fictitious extra-energy, using a set of prediction algorithms that consider weather forecasts, battery capacities, and consumption habits, to adapt energy quantities and prevent blackouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional load shedding solutions are applied based on voltage measurement or decision trees, then network integrity is protected, but energy availability for subscribers is not optimized and battery properties are not considered
Solution Approach 1:
The system performs preliminary prediction of the battery's final state of charge and fictitious extra-energy before the given day begins. This allows the energy distribution plan to be optimized in advance, ensuring both network integrity and maximum energy availability by pre-calculating the optimal energy allocation based on predicted battery performance and subscriber consumption patterns.
Solution Approach 2:
The system uses actual consumption data from subscribers and real-time battery state of charge measurements as feedback to continuously refine energy distribution decisions. By monitoring the difference between predicted and actual consumption, the system adapts its predictions and optimization strategies to improve both reliability and energy availability over time.
2Productivity
If energy distribution is optimized to maximize subscriber consumption, then energy availability increases, but battery state of charge may drop below safe levels causing blackouts
Solution Approach 1:
The system introduces the concept of 'fictitious extra-energy' as an intermediary parameter that represents the difference between actual solar production and what would be needed to meet all subscriber demands. This intermediary allows the optimization algorithm to balance energy distribution to subscribers against battery charging needs, ensuring continuous supply while maximizing productivity by identifying the optimal energy allocation point.
Solution Approach 2:
The system dynamically changes the energy distribution parameters based on the predicted final state of charge and fictitious extra-energy. By adjusting the authorized energy quantity for subscribers as a variable parameter rather than a fixed value, the system can optimize energy availability while maintaining battery state of charge within safe operational limits, preventing blackouts.
3Reliability
If battery capacity is increased to ensure continuous supply during night, then energy availability improves, but system cost and complexity increase
Solution Approach 1:
Instead of increasing battery capacity, the system performs preliminary prediction of the battery's final state of charge before each given day. This allows the optimization to determine the maximum energy that can be authorized to subscribers while ensuring the battery maintains sufficient charge for continuous supply, avoiding the need for larger batteries and reducing system complexity.
Solution Approach 2:
The system changes the operational parameters of the existing battery by dynamically adjusting the authorized energy quantity for subscribers based on predicted battery performance. This parameter optimization allows the existing battery capacity to be used more efficiently, ensuring continuous supply without requiring additional battery capacity or increasing system complexity.
4Productivity
If solar panel capacity is increased to meet peak demand, then energy production increases, but system cost and land requirements increase
Solution Approach 1:
The system uses feedback from actual solar production data and subscriber consumption patterns to optimize energy distribution. By analyzing the difference between solar production and consumption (fictitious extra-energy), the system can maximize the utilization of existing solar panel capacity, reducing the need for additional panels while maintaining high energy production efficiency.
Solution Approach 2:
The system optimizes the operational parameters of the existing solar panel array by dynamically adjusting the authorized energy quantity based on predicted solar production and battery state. This allows the existing solar capacity to be used more effectively, achieving high productivity without increasing the physical solar panel installation or system complexity.
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
This approach optimizes energy distribution by ensuring that energy quantities meet subscriber demands and battery charging needs, reducing the likelihood of blackouts and improving energy availability while utilizing fictitious extra-energy effectively.
Implementation Method 1
production station comprising at least one solar panel and at least one battery
Implementation Method 2
batteries for storing the electrical energy produced from solar energy
Data Source
Figure 1~2
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Figure 5~7
AI summary
A method for distributing electrical energy produced by a production station (11) comprising at least one solar panel (12) and at least one battery (14), comprising the steps of: - defining a quantity of energy for the given day; - predicting a final state of charge equal to a state of charge of the battery at the end of the night of the given day, as well as a fictitious extra energy, the fictitious extra energy being energy not produced by the solar panel during the day of the given day but which could be produced if a consumption of the subscribers and/or a demand for energy from the battery increases; - adapting the quantity of energy according to the final state of charge and the fictitious extra energy; - distributing the quantity of energy to the subscribers of the network during the given day.