Active Load Management System for Utility Energy Profiles
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Solution Overview
Problem
Power utilities lack the ability to collect detailed data on energy consumption patterns within customers' homes or businesses, limiting their capacity to create comprehensive customer profiles and effectively manage electrical load control events.
Innovation Solution
The Active Load Management System (ALMS) captures energy usage data at each service point, generates customer profiles including energy consumption patterns, and uses these profiles to manage load control events by selecting the most efficient service points for energy reduction, employing intelligent load rotation to distribute control events equitably.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If power utilities collect detailed data on energy consumption patterns within customers' homes or businesses, then the ability to create comprehensive customer profiles and manage load control events improves, but the complexity of the system increases
Solution Approach 1:
An energy management system with smart meters and communication networks is introduced as an intermediary between utility companies and customer premises equipment. This intermediary captures detailed energy consumption data at the service point level and transmits it to the utility company, enabling comprehensive customer profiling without requiring direct complex instrumentation throughout the customer's home or business facilities.
2Loss of energy
If load control events are applied to reduce energy consumption at service points, then energy efficiency improves, but customer comfort or service quality may deteriorate
Solution Approach 1:
The system applies different control strategies to different service points based on their specific energy consumption patterns, customer profiles, and load characteristics. Rather than uniform control across all customers, the system tailors load management approaches locally to each service point, optimizing energy reduction while maintaining service quality according to customer-specific requirements and preferences.
Solution Approach 2:
The system performs preliminary analysis of customer energy profiles and consumption patterns before implementing load control events. By pre-identifying optimal control opportunities and timing based on historical data and predicted demand, the system can apply control actions that achieve energy reduction while minimizing impact on customer comfort and service quality.
3Productivity
If the same service points are repeatedly selected for load control events, then energy savings are maximized, but equitable distribution of control events deteriorates
Solution Approach 1:
The system implements a rotational selection approach where service points are chosen for load control events in periodic cycles rather than continuously. This periodic action ensures that different service points receive control events over time, distributing the burden equitably across the customer base while still achieving cumulative energy savings through repeated optimization cycles.
Data Source
AI summary
A system and method for creating and making use of customer profiles, including energy consumption patterns. Devices within a service point, using the active load director, may be subject to control events, often based on customer preferences. These control events cause the service point to use less power. Data associated with these control events, as well as related environment data, are used to create an energy consumption profile for each service point. This can be used by the utility to determine which service points are the best targets for energy consumption. In addition, an intelligent load rotation algorithm determines how to prevent the same service points from being picked first each time the utility wants to conserve power.