Peer-to-Peer Energy Trading With Scalable Blockchain Coordination
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Solution Overview
Problem
Existing smart grid technologies face challenges in managing decentralized energy systems, including scalability issues with blockchain architectures, inefficiencies in energy trading, and limited adoption of computed optimal power flow setpoints by prosumers.
Innovation Solution
The implementation of a blockchain-assisted crowdsourced energy system (CES) framework that enables peer-to-peer energy trading, a two-phase near real-time operation algorithm, and a scalable blockchain implementation using IBM Hyperledger Fabric to manage millions of energy-trading transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blockchain architecture is used to manage decentralized energy transactions, then trust and transparency are improved, but scalability and transaction efficiency deteriorate
Solution Approach 1:
The patent segments the blockchain system into multiple components: a permissioned blockchain for trust and transparency, a separate optimization layer for transaction processing, and a hybrid architecture that combines centralized coordination with decentralized verification. This segmentation allows each component to specialize in its strength without compromising the other.
Solution Approach 2:
The patent introduces an intermediary optimization layer that acts as a mediator between the blockchain and energy trading participants. This intermediary handles complex transaction processing, matching supply and demand, and coordinating settlements, thereby reducing the burden on the blockchain and improving overall transaction efficiency while maintaining trust through blockchain verification.
2Productivity
If computed optimal power flow setpoints are provided to prosumers, then grid efficiency is improved, but prosumer adoption and participation deteriorate
Solution Approach 1:
The patent implements self-service mechanisms where prosumers can autonomously participate in energy trading through automated smart contracts and algorithms. The system automatically matches prosumer supply with demand, executes transactions, and settles payments without requiring prosumers to manually adjust setpoints or understand complex grid operations, thereby maintaining high grid efficiency while simplifying prosumer participation.
Solution Approach 2:
The patent incorporates feedback loops that provide prosumers with real-time information about their energy production, consumption, and trading outcomes. This feedback mechanism helps prosumers understand the value of their participation, adjust their behavior to optimize benefits, and build confidence in the system, thereby improving adoption rates while maintaining grid efficiency through continuous optimization.
3Adaptability or versatility
If decentralized energy resources are increased, then energy distribution flexibility is improved, but system complexity and management difficulty worsen
Solution Approach 1:
The patent implements a universal platform that handles multiple functions: energy trading, optimization, settlement, and coordination. This multi-functional platform can accommodate various types of decentralized energy resources (solar panels, wind turbines, batteries, electric vehicles) and perform diverse operations through a single integrated system, thereby maintaining distribution flexibility while reducing management complexity through standardization.
Data Source
AI summary
A crowdsourced energy system includes a plurality of distributed energy resources managed by crowdsourcees of the system, a power network to which the distributed energy resources are connected, and a system operator that manages energy trading transactions and energy delivery within the system, the system operator operating at least one computing device configured to: obtain day-ahead peer-to-peer energy trading transaction requests from crowdsourcees for energy to be delivered from the distributed energy resources, estimate day-ahead energy load and solar forecasts, determine optimal power flow for the delivery of energy, and schedule delivery of energy from the distributed energy resources across the power network based upon the energy trading transaction requests, the estimated forecasts, and the determined optimal power flow.