Adaptive Load Management Modules for Consumer Preference Integration
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Solution Overview
Problem
Current smart grid technologies lack effective methods to systematically integrate consumer preferences and voluntary behaviors into electricity management, leading to inefficiencies in energy usage and generation.
Innovation Solution
A system and method utilizing Adaptive Load Management (ALM) modules at both consumer and aggregator levels, which account for consumer values by optimizing electricity purchasing strategies and resource allocation through sensing, pricing information, and thermal modeling, enabling closed-loop control of distributed loads and generators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional electricity management systems are used, then system simplicity is maintained, but consumer preferences and voluntary behaviors cannot be systematically integrated into electricity management
Solution Approach 1:
The system segments electricity management into multiple hierarchical levels: consumer-level ALM modules, aggregator-level ALM modules, and utility-level systems. Each level handles specific functions independently, allowing consumer preferences to be integrated without overwhelming system complexity. The segmentation enables modular deployment where complexity is distributed and managed at appropriate levels.
Solution Approach 2:
Aggregators serve as intermediary entities between individual consumers and the utility system. The aggregator-level ALM module collects and processes preference data from multiple consumer-level modules, performing data aggregation and coordination functions. This intermediary layer shields the utility system from direct complexity of individual consumer preferences while enabling systematic integration of these preferences into grid management.
2Productivity
If real-time optimization of electricity purchasing strategies is implemented, then energy costs are reduced, but computational requirements and processing time increase
Solution Approach 1:
The ALM modules perform preliminary actions by continuously monitoring and analyzing electricity pricing signals, consumer preferences, and load characteristics in advance of peak demand periods. The system pre-computes optimization strategies and prepares purchasing decisions before critical time windows close. This allows real-time optimization without excessive processing delays during critical decision moments.
Solution Approach 2:
The system implements closed-loop feedback mechanisms where the ALM modules continuously receive feedback from smart meters about actual electricity consumption, pricing signals, and system conditions. This feedback is processed to dynamically adjust optimization strategies in near-real-time. The feedback loop enables adaptive optimization that responds to changing conditions without requiring exhaustive recomputation from scratch.
3Reliability
If distributed generation and renewable energy sources are integrated, then sustainability is improved, but system complexity and coordination requirements increase
Solution Approach 1:
The system merges distributed generation resources, energy storage systems, and conventional loads into unified ALM frameworks at both consumer and aggregator levels. By combining these diverse elements under common optimization objectives and control mechanisms, the system manages coordination complexity while maximizing the sustainability benefits of distributed renewables. The merging approach allows heterogeneous resources to be treated as integrated components of a coordinated energy system.
Solution Approach 2:
The ALM modules are designed as universal platforms that can manage multiple types of resources simultaneously - distributed generators, energy storage, flexible loads, and traditional electricity consumption. This multi-functionality reduces the need for separate specialized control systems for each resource type, thereby managing coordination complexity while enabling comprehensive integration of diverse renewable and distributed resources into the grid.
Data Source
AI summary
A method for determining an amount of electricity to purchase includes determining electrical power consumption characteristics of an electrical load at an end user of the electricity. A preference of the end user for an output of the electrical load is ascertained. The output varies with a rate of electrical power consumption by the load. A mathematical model is created of an amount of electrical power to be consumed by the load as a function of time and of monetary cost of the electricity. The model is dependent upon the electrical power consumption characteristics of the electrical load and the preference of the end user for an output of the electrical load. An amount of electricity is purchased based on the mathematical model of an amount of electrical power to be consumed by the load, and based on the monetary cost of the electricity.