Energy Usage Coordinator for Dynamic Building Load Management
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Solution Overview
Problem
The challenge lies in efficiently managing energy usage across multiple buildings to balance demand and supply, as high demand times lead to increased costs for electricity, and existing systems lack effective methods to predict and adjust energy needs dynamically, resulting in potential power shortages and costly purchases.
Innovation Solution
An energy usage coordinator system that measures and predicts energy needs, negotiates with energy companies for preferential rates, and adjusts energy usage by curtailment or generation based on marginal demand curves, enabling buildings to reduce consumption during peak times and sell excess energy, thereby optimizing energy distribution and cost management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If electricity is purchased in advance for future usage, then energy costs are reduced, but the system lacks flexibility to adapt to changing energy needs and demand fluctuations
Solution Approach 1:
The system dynamically adjusts energy purchasing strategies based on real-time demand predictions and actual energy needs. Instead of static advance purchasing, the system continuously updates energy acquisition plans by comparing predicted versus actual consumption, enabling flexible adaptation to changing conditions while optimizing cost through a mix of advance and spot market purchases
Solution Approach 2:
The system implements feedback loops where actual energy consumption data is continuously monitored and fed back into the prediction model. This feedback mechanism allows the system to learn from past performance, refine its demand forecasts, and adjust future energy purchasing decisions to balance cost efficiency with adaptability to changing needs
2Reliability
If additional electricity is purchased without advanced notice during high demand times, then energy availability is ensured, but energy costs increase significantly
Solution Approach 1:
The system performs preliminary actions by predicting future energy needs in advance and procuring the required electricity ahead of time through long-term contracts or advance purchasing. This proactive approach ensures energy availability is secured beforehand, avoiding the need for expensive last-minute spot market purchases while maintaining reliability
Solution Approach 2:
The system changes the timing parameter of energy purchasing from reactive (spot market) to proactive (advance purchasing). By shifting the purchase timing parameter to occur before peak demand periods, the system secures energy availability at lower off-peak prices, thereby reducing costs while ensuring power is available when needed
3Loss of energy
If energy usage is strictly controlled through curtailment, then energy costs are reduced, but operational flexibility and response to actual needs are diminished
Solution Approach 1:
The system dynamically adjusts energy curtailment levels based on real-time conditions, building operational requirements, and actual versus predicted consumption patterns. Rather than rigid curtailment schedules, the system flexibly modulates energy usage to match actual needs while maintaining cost efficiency, allowing operations to adapt to changing conditions without excessive restrictions
Data Source
AI summary
A controller and/or a gateway acts as a feedback-based energy estimator for controlling initial and refined estimates of energy usage of one or more buildings controlled by the controller or gateway. An initial estimate of a building's energy needs for a specified time in the future (e.g., a month or a week ahead) are calculated, and then over time, in conjunction with an energy company, the initial estimate (and subsequent estimates) is revised based on the costs of the energy predicted or quoted by the energy company. The controller may examine jobs to be performed at the time as well as predictive information that may affect the building's energy needs (e.g., the predicted temperature for the time of the predicted energy needs). As expectations change (or as predicted factors such as temperature change), the energy company may be informed of the additional energy that will be needed (e.g., if the predicted temperature is increasing and cooling will be needed) or the excess energy that is expected (e.g., if the predicted temperature is decreasing and less cooling will be needed).