Distributed Agent Energy Distribution for Dynamic Demand Management
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Solution Overview
Problem
Existing energy distribution systems in local networks face challenges in achieving dynamic demand-side management and continuous adaptability due to imperfect forecasting, changing resource availability, and the need for recalculating energy distribution plans in response to fluctuations in energy demands and resource changes.
Innovation Solution
A distributed agent-based approach for energy distribution decision-making, where each system in the network has an agent with a data processor and transmitter to continuously assess and reassess energy delivery plans, allowing for dynamic adjustments and communication sequences to manage energy distribution efficiently and adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized calculation approach is used to determine optimal energy distribution, then an initial energy distribution plan can be established, but the system lacks continuous adaptability when real demands differ from forecasts or when resources change
Solution Approach 1:
The centralized energy distribution system is segmented into multiple autonomous agents, each responsible for a specific component (generator, boiler, or demand point). These agents independently make decisions based on local information and communicate through standardized messages, eliminating the need for continuous centralized recalculation while maintaining adaptability to changing conditions.
Solution Approach 2:
The system transitions from a static centralized plan to a dynamic distributed architecture where agents continuously adjust their behavior based on real-time conditions. The asynchronous message-passing mechanism enables the system to dynamically respond to changes in demand forecasts, resource availability, and operational constraints without requiring complete recalculation.
2Reliability
If energy distribution plans are recalculated in entirety when circumstances change, then optimal distribution can be maintained, but significant time and computational resources are consumed
Solution Approach 1:
Agents pre-negotiate and commit to energy profiles for future time periods in advance. These preliminary agreements establish binding energy distribution plans that are executed without real-time negotiation, reducing the need for frequent recalculation while maintaining optimality through advance planning.
Solution Approach 2:
The system implements feedback mechanisms where agents monitor actual energy consumption and generation against planned profiles. When deviations occur or conditions change, agents receive feedback messages and adjust their behavior accordingly, maintaining optimal distribution through continuous adaptation rather than complete recalculation.
3Adaptability or versatility
If a distributed agent-based approach is used for energy distribution, then continuous adaptability and dynamic demand-side management are achieved, but communication protocols and coordination mechanisms become more complex
Solution Approach 1:
A universal message-passing protocol is implemented that enables agents to communicate various types of information (energy profiles, constraints, commitments, deviations) through a standardized interface. This universal communication mechanism handles multiple functions (negotiation, monitoring, adjustment, coordination) without requiring separate protocols for each interaction type.
Solution Approach 2:
The communication protocol uses parameter-based messaging where agents exchange structured data containing key parameters (energy quantities, time periods, cost signals, constraint values). By changing and adjusting these parameters dynamically, agents can adapt to different situations and conditions without modifying the underlying communication structure.
4Productivity
If advance calculation of energy distribution is performed, then a planned energy profile can be established, but the system cannot respond dynamically to changing resource availability or price signals
Solution Approach 1:
The system maintains continuous energy distribution operations through pre-negotiated profiles that are executed without interruption. Agents continuously monitor and adjust their behavior based on real-time conditions, ensuring uninterrupted energy supply while adapting to changing demands, resource availability, and price signals throughout the operational period.
Data Source
AI summary
This invention concerns a method and apparatus for coordinating energy distribution over a local energy network having at least one generator and a plurality of systems requiring an energy supply for operation, each system in the local network being connected to the at least one generator and/or another system in the network for distribution of energy there-between. An agent for each respective one of the generator and systems has a data store, a data processor and a data transmitter and receiver for transmission and receipt of data communication with one or more of the other agents. The data processor of each agent is programmed to recognize a predetermined communication sequence having a plurality of ordered data communication steps required to initiate supply of energy between the generator or system of said agent and the generator or system associated with another agent. Each communication sequence is assigned a future time period for which the energy supply is to be enacted.