Active Load Management for Dispatchable Operating Reserves
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Solution Overview
Problem
Current approaches for managing energy demand in electric power grids are inadequate, relying on statistical trends and sampling, which fail to accurately forecast demand and provide dispatchable operating reserves, especially for regulating and spinning reserves, leading to inefficiencies and frequency fluctuations.
Innovation Solution
An active load management system that determines power consumption patterns, generates expected behavior models, and calculates available operating reserves by projecting energy savings, allowing for precise control of power distribution and reserve generation through a network of connected devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If statistical trends and sampling methods are used to forecast demand, then the system complexity is reduced, but the measurement precision and reliability of demand forecasting deteriorates
Solution Approach 1:
The patent replaces statistical sampling methods with actual metered consumption data from smart meters and automated meter reading systems. This substitution of measurement methodology provides precise, real-time consumption information without requiring complex statistical modeling, thereby improving forecast accuracy while maintaining system simplicity.
Solution Approach 2:
The system implements feedback loops where actual consumption data is continuously measured, compared against forecasts, and used to refine future predictions. This feedback mechanism enables accurate demand forecasting through actual data rather than statistical assumptions, resolving the contradiction between simplicity and precision.
2Ease of operation
If traditional demand response methods are used, then the ease of operation is maintained, but the ability to provide dispatchable operating reserves deteriorates
Solution Approach 1:
The patent enables automated demand response where the system itself manages load control based on pre-established rules and real-time conditions. This self-service approach maintains ease of operation by removing manual intervention requirements while simultaneously improving reliability through automated, consistent execution of reserve generation strategies.
Solution Approach 2:
The system pre-configures demand response strategies and customer preferences before peak demand events occur. By establishing control rules, thresholds, and customer preferences in advance, the system ensures reliable dispatchable reserves are available when needed without requiring complex real-time decision-making, thus maintaining operational simplicity while ensuring reserve availability.
3Measurement precision
If actual consumption data and customer preferences are utilized, then the measurement precision and reliability improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent replaces complex statistical analysis systems with straightforward data collection from smart meters and automated meter reading infrastructure. By using actual metered data directly rather than processed statistical estimates, the system achieves high measurement precision while avoiding the complexity of statistical modeling and sampling methodologies.
4Productivity
If active load management is implemented to generate operating reserves, then the productivity and grid stability improve, but the ease of operation and customer impact increase
Solution Approach 1:
The system pre-establishes customer preferences, control rules, and acceptable load management parameters before implementing active load management. By obtaining customer input and configuring control strategies in advance, the system can efficiently generate operating reserves through automated load control while minimizing customer impact and maintaining ease of operation through pre-approved strategies.
Data Source
AI summary
A utility employs a method for generating 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, 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 generated 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, and associated with a power supply value (PSV) are determined based on the devices' power consumption behavior. An amount of available operating reserve is determined based on the projected energy savings.