Generator Power Dispatch Control for Time-of-Use Cost Switching
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Solution Overview
Problem
Consumers face challenges in accurately determining when and how to manage the delivery of electric power from multiple sources, such as utility power and alternative sources like solar panels, due to varying costs based on time, day, and season, making it difficult to predict cost-effectiveness.
Innovation Solution
A computer-implemented method and system that uses historical energy usage data and performance characteristics of an electric generator to determine a set point for dispatching power, accessing utility metered load data, and controlling the dispatch of power from the generator, while collecting and transmitting performance and usage data for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consumers manually manage power from multiple sources, then they can control power delivery, but it becomes difficult to accurately determine when and how to dispatch power cost-effectively
Solution Approach 1:
The system enables self-service by allowing the generator and controller to automatically determine and execute cost-effective power dispatch decisions without requiring manual consumer intervention. The controller monitors utility rates, generator performance, and load conditions to autonomously make dispatch decisions, thereby improving measurement precision while reducing the perceived complexity for consumers.
Solution Approach 2:
The controller acts as an intermediary between the generator, utility power source, and consumer load. It processes information about utility rates, generator status, and power demands, then coordinates dispatch decisions to optimize cost-effectiveness. This intermediary function resolves the contradiction by managing the complexity internally while providing accurate dispatch timing guidance to consumers.
2Loss of energy
If utility power rates vary by time, day, and season, then consumers can potentially save costs by timing power usage, but it becomes impossible to accurately predict which power source will be most cost-effective
Solution Approach 1:
The system implements feedback by continuously monitoring utility power rates, generator performance characteristics, and actual power dispatch outcomes. This feedback loop enables the controller to learn from past dispatch decisions and adjust future timing to maximize cost savings. The accumulated data about rate variations and generator performance eliminates the unpredictability of selecting the most cost-effective power source at any given time.
Solution Approach 2:
The system performs preliminary action by pre-determining optimal dispatch set points based on anticipated utility rate structures and generator performance. By analyzing historical data and predicting future rate variations, the controller can proactively schedule power dispatch to coincide with the most cost-effective timing, thereby securing cost savings before the actual dispatch occurs and eliminating prediction uncertainty.
3Reliability
If consumers rely on the utility grid, then they have access to reliable power, but they incur higher costs and greater reliance on external infrastructure
Solution Approach 1:
The system applies dynamics by enabling flexible switching between utility power and generator power based on real-time conditions. Rather than statically relying on the utility grid, the controller dynamically adjusts the power mix to optimize both reliability and cost-effectiveness. The generator provides backup capability and cost savings during periods when utility rates are high, while maintaining the option to use utility power when rates are low, thereby resolving the contradiction between reliability and cost-effectiveness.
Data Source
AI summary
Systems and methods for determining how to dispatch power to a property from a generator are provided. According to certain aspects, a controller associated with the generator may retrieve or access a set of data indicating time of use rates associated with utility power, performance characteristics of the generator, and/or energy usage data. Based on the data, the controller may determine a set point corresponding to when it may be beneficial to dispatch generator power to the property instead of utility power. At the set point, the controller may facilitate supplementing power from utility power with power from the generator. Additionally, the controller may collect usage and performance data associated with dispatch of the generator power.