Smart Appliance Power Management via Dynamic Pricing Signals
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Solution Overview
Problem
Current power management systems fail to efficiently match demand to supply in real-time, relying heavily on fossil fuels and neglecting the integration of renewable energy sources, leading to inefficiencies and environmental issues like global warming, while also disproportionately affecting users of modest means and not adequately addressing breaks in power infrastructure.
Innovation Solution
A system that utilizes a network, such as the Internet, to orchestrate supply and demand by sending real-time pricing signals to smart appliances, encouraging or discouraging usage based on available renewable energy sources, and rerouting demand around failed grids and fuel supply chain breaks, targeting demand at the appliance level to maximize efficiency and reduce emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fossil-fuelled electrical capacity is used to meet immediate large aggregate demand, then power supply reliability is improved, but environmental harm increases due to greenhouse gas emissions
Solution Approach 1:
The system implements real-time feedback loops where pricing signals are continuously adjusted based on supply-demand conditions, renewable energy availability, and grid status. This feedback mechanism enables dynamic coordination between fossil fuel and renewable sources, optimizing the mix to meet demand reliably while minimizing emissions by dispatching renewable energy when available and using fossil fuels as needed.
Solution Approach 2:
The invention changes the pricing parameter dynamically in real-time based on supply conditions, demand levels, and renewable energy availability. By varying prices continuously, the system incentivizes demand response that aligns consumption with renewable energy generation, reducing reliance on fossil fuels while maintaining supply reliability through market-based signals.
2Object-generated harmful factors
If renewable energy sources are used to reduce emissions, then environmental harm is reduced, but power supply reliability deteriorates due to intermittent availability
Solution Approach 1:
The system dynamically adjusts pricing signals in real-time to reflect the intermittent nature of renewable energy sources. When renewable generation is high, prices are lowered to encourage consumption; when renewable availability drops, prices increase to signal reduced supply. This dynamic pricing mechanism maintains reliability by aligning demand with available supply while maximizing renewable energy utilization.
Solution Approach 2:
The invention introduces a network-based pricing signal system as an intermediary between renewable energy sources and consumers. This intermediary translates complex supply conditions into simple price signals that guide consumer behavior, enabling seamless integration of intermittent renewable sources into the grid while maintaining reliability through market-based demand response.
3Loss of energy
If real-time pricing signals are implemented to match demand with supply, then energy efficiency is improved, but system complexity increases due to network infrastructure requirements
Solution Approach 1:
The system utilizes existing network infrastructure (such as utility meters, communication networks, and smart appliances) to deliver pricing signals and collect demand data. By making the network infrastructure multi-functional—serving both traditional billing and real-time demand response functions—the system achieves energy efficiency improvements without proportionally increasing complexity, as existing assets are leveraged for multiple purposes.
Solution Approach 2:
The invention enables self-service capabilities where smart appliances and systems automatically respond to pricing signals by adjusting their operation to optimize energy usage. This automation reduces the need for complex centralized control, as distributed devices make their own decisions based on received price signals, thereby improving energy efficiency while limiting the growth of system complexity.
4Loss of energy
If demand is targeted at the appliance level to maximize efficiency, then energy optimization is improved, but ease of operation deteriorates due to granular control requirements
Solution Approach 1:
The system implements self-service functionality where smart appliances autonomously respond to pricing signals by adjusting their own operation. Devices such as water heaters, thermostats, and washing machines automatically modulate their energy consumption based on real-time price information, eliminating the need for users to manually control each appliance while achieving granular energy optimization across the entire appliance fleet.
Solution Approach 2:
The invention segments the demand control function by appliance type and usage pattern, allowing different control strategies for different devices. This segmentation enables sophisticated energy optimization at the appliance level while presenting a unified, simple interface to users through automated systems that handle the complexity of granular control behind the scenes.
Data Source
AI summary
A system, method and apparatus provide management of power to meet demand of consumers that consume the power. An interface is configured to receive a pricing signal that indicates a price for the power. Wherein the interface is configured to control an appliance coupled to the interface to shift adjusting power consumption sooner in time than the appliance is predetermined to shift its power consumption.