Home Energy Management System for Peak Demand Reduction
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Solution Overview
Problem
The existing power management systems face inefficiencies due to peak demand sizing, leading to low asset utilization and high costs in electricity generation, transmission, and distribution, necessitating improved home energy management systems that enable end-use devices to actively participate in grid control.
Innovation Solution
A method and system for managing electrical power usage by determining estimated market prices and operating times for controllable devices, generating schedules based on these factors, and transmitting control signals to optimize power consumption while maintaining user comfort and performance constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If system components are sized to meet peak demand, then power supply adequacy is ensured, but asset utilization becomes low
Solution Approach 1:
The system performs preliminary scheduling of controllable loads during off-peak hours before peak demand occurs. By pre-cooling spaces, pre-heating water, or completing laundry cycles during low-price periods, the system reduces peak demand without compromising service adequacy, thereby improving asset utilization while maintaining reliability
Solution Approach 2:
The system dynamically adjusts load operation schedules based on real-time or forecasted electricity pricing signals and grid conditions. Controllable loads are flexibly shifted between off-peak and peak periods according to price variations, enabling adaptive optimization of asset utilization while ensuring power supply adequacy is maintained through automated scheduling
2Reliability
If traditional power generation and transmission capacity is increased, then peak demand is met, but costs increase significantly
Solution Approach 1:
The system extracts and schedules controllable loads from the overall demand profile, separating them from non-controllable essential loads. By identifying and scheduling specific controllable appliances (laundry, dishwashers, water heating, HVAC) during off-peak periods, the system reduces peak demand without requiring additional generation or transmission capacity, thereby avoiding the $2,000/kW infrastructure cost
Solution Approach 2:
The system changes the operational timing parameter of controllable loads from fixed schedules to flexible, price-responsive schedules. By modifying when these loads operate based on electricity pricing signals, the system shifts demand away from peak periods, reducing the need for expensive peak-capacity infrastructure while maintaining service reliability
3Loss of energy
If controllable loads are shifted to off-peak periods, then cost is reduced, but user convenience may be affected
Solution Approach 1:
The system provides automated scheduling of controllable loads based on user-defined preferences and electricity pricing signals, eliminating the need for users to manually monitor prices or adjust schedules. The automated system handles the complexity of optimization while users simply define their constraints and preferences once, maintaining convenience while achieving cost reduction
Solution Approach 2:
The system incorporates user feedback mechanisms where users can review generated schedules, adjust preferences, and override decisions. This feedback loop ensures that scheduling optimizations align with user convenience requirements while still achieving cost reduction goals through iterative refinement of the scheduling algorithm
4Productivity
If automated scheduling systems are implemented, then energy optimization is improved, but system complexity increases
Solution Approach 1:
The system employs a universal scheduling framework that can handle multiple types of controllable loads (laundry, dishwashers, water heating, HVAC) through a single integrated optimization engine. This multi-functional approach consolidates what would otherwise require separate control systems for each appliance type, managing complexity while achieving comprehensive energy optimization across diverse load categories
Data Source
AI summary
Disclosed herein are representative embodiments of methods, apparatus, and systems for controlling and scheduling power distribution in a power network, such as household power network. One disclosed embodiment is a system comprising a central controller and a control device coupled to a household appliance. The central controller can comprise computing hardware coupled to a wireless transceiver. The central controller can be configured to generate and transmit control signals for controlling the operational state of the household appliance according to the schedule. Furthermore, the control device can be configured to receive the transmitted control signals from the central controller and to control the operational state of the household appliance in response to the control signals.