Federated Reinforcement Learning for Privacy-Aware Microgrid Energy Trading
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Solution Overview
Problem
Existing microgrid systems face challenges in efficiently managing renewable energy sources, balancing energy demand and supply, and minimizing carbon emissions while addressing privacy concerns in data collection and usage.
Innovation Solution
A federated learning framework for hierarchical multi-agent reinforcement learning systems that includes household, microgrid, and distributor agents, trained to manage energy transactions and minimize carbon emissions, with a federated learning engine combining model parameters to update global agents without sharing raw data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional centralized training methods are used to train multi-agent reinforcement learning systems for microgrid management, then the system can achieve optimal energy management performance, but the privacy of participant data is compromised due to centralized data collection
Solution Approach 1:
The training process is segmented into local training at individual microgrids and centralized aggregation at the server. Each microgrid trains its own policy gradient using local data, and only model parameters (not raw data) are transmitted to the central server for aggregation. This segmentation maintains privacy while achieving collaborative optimization across the microgrid network.
Solution Approach 2:
The patent introduces a federated learning server as an intermediary that coordinates the training process. The server aggregates model parameters from multiple microgrids using federated averaging, enabling collaborative learning without directly accessing or storing sensitive local data. This intermediary facilitates optimal energy management while preserving data privacy through parameter-only exchange.
2Loss of information
If federated learning is used to train multi-agent reinforcement learning systems, then data privacy is maintained, but the complexity of the training system increases due to distributed architecture
Solution Approach 1:
The system segments training responsibilities between local microgrids and the central server. Each microgrid independently performs local training iterations, and the server performs centralized parameter aggregation. This segmentation distributes computational complexity while maintaining a clear hierarchical structure that simplifies implementation compared to fully distributed approaches.
Solution Approach 2:
The federated learning process implements feedback loops where model parameters are exchanged between the server and microgrids in iterative rounds. The server provides aggregated model updates to microgrids, which then use these updates to improve their local policies. This feedback mechanism enables coordinated optimization while maintaining privacy, with the iterative process converging to optimal solutions despite the distributed architecture.
3Object-generated harmful factors
If renewable energy sources are used to reduce carbon emissions, then environmental impact is minimized, but energy supply becomes stochastic and difficult to balance with demand
Solution Approach 1:
The patent implements dynamic pricing policies that adjust energy prices in real-time based on supply-demand conditions and renewable generation levels. When renewable energy is abundant, prices are reduced to encourage consumption; when supply is low, prices increase to balance demand. This dynamic adjustment enables the system to adapt to the stochastic nature of renewable sources while maintaining grid stability and reducing carbon emissions.
Solution Approach 2:
The multi-agent reinforcement learning system continuously monitors renewable generation, energy demand, and grid conditions, using this feedback to optimize trading decisions and pricing policies. The system learns from historical data and real-time observations to predict renewable availability and adjust energy transactions accordingly, balancing the stochastic supply with demand while minimizing carbon emissions through increased renewable utilization.
Data Source
AI summary
A federated learning framework including household agents configured to continuously learn model parameters for managing charging periods and discharging periods of household batteries, and microgrid agents to maximize use of local energy based on a pricing policy, including accessing power from other microgrids when there is insufficient local energy to cover local demand. and selling surplus energy to the other microgrids when power generation by the microgrid surpasses the local demand. Each household machine learning agent is configured to control household energy demand from and supply to a microgrid which they are connected in order to minimize household energy cost while adapting to changes in the energy price that is determined based on the pricing policy of the microgrid agent that encourages reduction of carbon emission. A federated learning engine combines the model parameters from the household machine learning agents to update a global household machine learning agent.