Active Load Management System for Dispatchable Operating Reserve
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Solution Overview
Problem
Current approaches for managing energy demand in electric utility systems are inadequate, relying on statistical trends and sampling, which fail to accurately forecast and provide dispatchable operating reserve, especially for regulating and spinning reserve, leading to fluctuations in line frequency and inefficiencies in power distribution.
Innovation Solution
An active load management system (ALMS) that remotely monitors and manages power consumption at individual service points, using empirical data and customer preferences to estimate and provide dispatchable operating reserve by interrupting or reducing power to devices, thereby creating additional regulating, spinning, and non-spinning reserve capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If statistical trends and sampling methods are used to manage energy demand, then the system complexity is reduced, but the measurement precision and reliability of operating reserve forecasting deteriorate
Solution Approach 1:
The patent replaces statistical sampling methods with an active load management system that uses empirical data from remote monitoring of individual service points. The ALMS substitutes traditional statistical approaches with direct measurement and control mechanisms, enabling precise forecasting of operating reserve needs through real-time load management data.
2Reliability
If active load management is implemented to provide dispatchable operating reserve, then the reliability of power supply is improved, but the device complexity increases
Solution Approach 1:
The ALMS enables self-service load management at individual service points, where the system automatically monitors and adjusts power consumption based on predetermined criteria. This self-service approach improves reliability by enabling rapid response to operating reserve needs without requiring complex centralized control for each adjustment decision.
Solution Approach 2:
The patent segments the power distribution system into individually monitorable service points, allowing granular control and measurement of load management actions. This segmentation enables the system to provide dispatchable operating reserve by aggregating small adjustments across multiple service points, improving reliability while managing complexity through modular architecture.
3Productivity
If remote monitoring and control of power consumption is implemented, then the productivity of load management is improved, but the ease of operation deteriorates
Solution Approach 1:
The ALMS introduces an intermediary control layer between the utility and end-use devices, automating the complex tasks of remote monitoring and control. This intermediary system handles the operational complexity of coordinating load management across multiple service points, improving productivity while shielding operators from direct interaction with complex control mechanisms.
4Measurement precision
If empirical data from individual service points is collected and analyzed, then the measurement precision of power consumption patterns is improved, but the loss of information increases due to data management requirements
Solution Approach 1:
The ALMS extracts only the essential empirical data needed for operating reserve forecasting from individual service points, rather than collecting and managing all possible operational data. This selective extraction approach improves measurement precision for load management purposes while minimizing data management overhead by focusing on critical power consumption patterns.
Data Source
AI summary
A utility employs a method for estimating available operating reserve. Electric power consumption by at least one device serviced by the utility is determined during at least one period of time to produce power consumption data. The power consumption data is stored in a repository. A determination is made that a control event is to occur during which power is to be reduced to one or more devices. Prior to the control event and under an assumption that it is not to occur, power consumption behavior expected of the device(s) is estimated for a time period during which the control event is expected to occur based on the stored power consumption data. Additionally, prior to the control event, projected energy savings resulting from the control event are determined based on the devices' estimated power consumption behavior. An amount of available operating reserve is determined based on the projected energy savings.