Household Energy Scheduling for Grid Stability
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Solution Overview
Problem
The electric grid faces challenges in balancing electricity supply and demand, leading to shortages or excesses, which can be mitigated by optimizing the provision and consumption of renewable energy from households to reduce reliance on non-renewable sources.
Innovation Solution
A method and apparatus that optimize the allocation of renewable energy production devices and consumption plans across households to balance the electric grid's supply and demand, using quantum computing and classical techniques to determine when households should provide or consume electricity, minimizing non-renewable energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If households provide renewable electrical energy to the electric grid whenever produced, then the availability of renewable energy in the grid increases, but the grid may have excess energy at times when demand is low, leading to waste
Solution Approach 1:
The system performs preliminary actions by predicting future electricity demand and renewable energy production before the actual events occur. It uses machine learning models to forecast energy generation from household devices and grid demand, then proactively schedules energy provision and consumption tasks to optimize the use of renewable energy before excess or shortage situations arise.
Solution Approach 2:
The system implements continuous feedback loops where it monitors actual electricity production from household devices, actual grid demand, and compares these against predictions. This feedback is used to refine future predictions and adjust the scheduling of energy provision and consumption tasks, creating a closed-loop control system that adapts to changing conditions.
2Reliability
If the electric grid uses predetermined additional electrical energy reserves and generators to balance supply and demand, then consumers are always served, but the reliance on non-renewable energy sources increases
Solution Approach 1:
The system enables households to serve the grid directly by providing their self-generated renewable energy when demand exists. Through the scheduling system, households automatically provide energy to the grid during periods of high demand without requiring external intervention or non-renewable backup sources, making the system self-sufficient.
Solution Approach 2:
The system dynamically changes the operational parameters of household energy devices, switching them between providing energy to the grid, storing energy locally, or consuming energy from the grid based on real-time conditions. This flexible parameter adjustment allows the system to maximize renewable energy utilization while maintaining supply reliability.
3Reliability
If households store renewable electrical energy locally instead of providing to the grid, then self-sufficiency increases, but the overall renewable energy availability in the grid decreases
Solution Approach 1:
The system dynamically adjusts the operational mode of each household's energy devices based on real-time grid conditions and predictions. Households switch between storing energy locally, providing energy to the grid, or consuming from the grid as conditions change, optimizing the balance between self-sufficiency and grid contribution without fixed predetermined modes.
Solution Approach 2:
The scheduling system manages multiple functions across different household devices - some devices store energy, others provide to the grid, and others consume from the grid - all coordinated to achieve overall system optimization. This multi-functional approach allows flexible adaptation to different scenarios.
4Object-generated harmful factors
If the system optimizes energy provision and consumption scheduling, then non-renewable energy use decreases, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the energy management into independent controllable units at the household level, where each household's devices can be managed separately yet coordinated through a central scheduling system. This segmentation allows the complex optimization problem to be broken down into smaller, more manageable sub-problems that can be solved efficiently.
Data Source
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AI summary
A method comprising: receiving, by an apparatus or system, a first set of values for an electric grid, a second set of values for a first electricity supply, and a third set of values for a second electricity supply; receiving, by the apparatus or system, data from a plurality of electricity controllers, wherein all the received data form a fourth set of values, wherein each electricity controller of the plurality of controllers is associated with a household of a plurality of households and controls electricity supplied to the electric grid, stored and requested from the electric grid at the different time periods within the PT, wherein each household of the plurality of households comprises at least one renewable electrical energy production device and the fourth set of values is indicative of electrical energy producible by each device of the at least one renewable energy production device at the different time periods within the PT; solving, by the apparatus or system based on the first, second, third and fourth sets of values, an optimization problem.