Decentralized Energy Management Agents for Zone Adaptability
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Solution Overview
Problem
Conventional Building Energy Management Systems (BEMS), Home Energy Management Systems (HEMS), and Factory Energy Management Systems (FEMS) employ centralized integrated control schemes, which fail to account for environmental differences within zones, leading to suboptimal energy management, reduced user convenience, and increased maintenance costs due to inflexibility and the need for frequent system modifications.
Innovation Solution
An Energy Management System (EMS) utilizing a decentralized approach with machine learning algorithms, where community, building, and zone agents independently manage energy based on local data, allowing for plug-and-play functionality, optimal energy cost determination, and user convenience by reflecting current occupied status and energy use patterns, and enabling independent energy management policies for each zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized integrated control scheme is used to manage energy across the entire community, then system-wide coordination is achieved, but the system cannot reflect environmental differences between zones and user tendencies, leading to suboptimal energy management performance
Solution Approach 1:
The system divides the community into multiple zones, each with its own energy management agent that independently manages local energy resources and demands. This segmentation allows each zone to be optimized according to its specific environmental conditions and user behaviors, while the overall system maintains coordination through standardized communication protocols between agents.
Solution Approach 2:
Each zone is equipped with localized control capabilities that enable it to adapt to its specific environmental characteristics and user preferences. The local agents can make real-time decisions based on zone-specific data without requiring centralized control, thereby improving adaptability while reducing the complexity of centralized management.
2Productivity
If a single demand response incentive policy is applied to the entire community, then policy implementation is simplified, but the system cannot achieve high performance across diverse zones with different user tendencies
Solution Approach 1:
The system enables customization of demand response incentive policies at the zone level, allowing each zone to receive incentives tailored to its specific characteristics and user behaviors. This localized policy approach maximizes the effectiveness of demand response programs while maintaining overall system coordination through standardized agent communication.
3Productivity
If the system increases compulsion in demand response to achieve higher performance, then energy management efficiency improves, but user convenience is reduced
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor user responses to demand response incentives and adjust the level of compulsion accordingly. By providing real-time feedback on energy consumption patterns and incentive responses, the system can optimize demand response effectiveness while maintaining user convenience through adaptive, rather than purely compulsory, control strategies.
4Adaptability or versatility
If the centralized system is modified to add new configurations, then the system can handle new devices, but the entire EMS requires modification and recompilation, increasing maintenance costs
Solution Approach 1:
The system architecture segments the energy management functionality into independent, modular agents that can be deployed and configured separately. This modular structure allows new devices and configurations to be added at the zone level without requiring modification of the entire centralized system, thereby reducing maintenance costs while improving adaptability.
Solution Approach 2:
The agent-based architecture provides universal interfaces and standardized communication protocols that enable the system to handle diverse device types and configurations through a common framework. This universality allows the system to adapt to new configurations without requiring custom modifications, reducing maintenance complexity and costs.
5Productivity
If a single EMS algorithm manages the entire configuration, then centralized control is achieved, but energy supply for each power use pattern cannot be optimized and local load changes cannot be effectively managed
Solution Approach 1:
The control algorithm is segmented into multiple independent agents, each responsible for managing energy supply optimization for specific power use patterns and local load changes within their respective zones. This segmentation allows each agent to specialize in optimizing for its local conditions, improving overall energy supply efficiency while reducing the complexity of any single control algorithm.
Data Source
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AI summary
An embodiment provides an energy management system of a community in which a plurality of buildings, each divided into a plurality of zones, are located, the energy management system including: a community agent that is a device managing energy of the community and is configured to receive a first incentive about a Demand Response (DR) or a Frequency Regulation (FR) from a demand management device, calculate a first response capacity responding to the first incentive among a community management capacity, and transmit a second incentive to a building agent; a plurality of building agents, each of which is a device managing energy of a corresponding building and is configured to calculate a second response capacity responding to the second incentive among a building management capacity, and transmit a third incentive to a zone agent; and a plurality of zone agents, each of which is a device managing energy of a corresponding zone and is configured to calculate a third response capacity responding to the third incentive among a zone management capacity and transmit the third response capacity to a corresponding building agent, wherein each of the building agents is configured to receive third response capacities from the plurality of zone agents, and transmit a fourth response capacity obtained by adding the plurality of third response capacities and the second response capacity to the community agent, and the community agent is configured to receive fourth response capacities from the plurality of building agents, and transmit a fifth response capacity obtained by adding the plurality of fourth response capacities and the first response capacity to the demand management device.