DER Energy Allocation Using IoT Edge and Cloud Algorithms
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Solution Overview
Problem
Current methods for deploying distributed energy resources do not effectively consider local factors such as solar potential and grid limitations, leading to inefficient energy planning and accounting, with traditional methods being costly, time-consuming, and lacking consideration for lateral energy flows.
Innovation Solution
A system and method utilizing IoT edge devices connected to a cloud computing infrastructure for measuring and allocating exported energy, employing algorithms for optimal distribution based on demand, historic consumption, and energy production, with a simplified blockchain workflow that includes an embedded blockchain network for secure and efficient energy trading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrical consulting agencies perform detailed electrical simulation analysis, then measurement precision and reliability of energy allocation are improved, but device complexity and loss of time increase significantly
Solution Approach 1:
The patent creates a virtual copy of the electrical network as a digital twin that mirrors the physical grid's structure, components, and operational parameters. This digital replica enables rapid simulation and analysis without requiring physical measurements or lengthy field studies, thereby achieving high measurement precision while dramatically reducing analysis time from months to minutes.
Solution Approach 2:
The system performs preliminary actions by pre-building the digital twin model with all network characteristics, constraints, and operational parameters before actual energy allocation decisions are needed. This advance preparation allows the system to instantly simulate various scenarios and provide accurate energy allocation recommendations without requiring time-consuming analysis at the moment of decision-making.
2Productivity
If comprehensive electrical simulation analysis is conducted to optimize DER deployment, then productivity and energy value optimization are improved, but device complexity and cost increase
Solution Approach 1:
By creating a virtual digital twin of the electrical network, the system achieves comprehensive simulation capabilities without the complexity and cost of physical measurement devices and field studies. The digital model captures all necessary electrical characteristics, grid constraints, and operational parameters, enabling sophisticated energy optimization while keeping the physical infrastructure simple and cost-effective.
3Measurement precision
If lateral energy flows are tracked and accounted for in real-time, then measurement precision and energy allocation accuracy are improved, but device complexity and loss of time increase
Solution Approach 1:
The digital twin creates a virtual representation of lateral energy flows within the network, enabling precise tracking and accounting of power exchanges between distributed energy resources and consumers without requiring complex physical measurement infrastructure. The simulated model captures bidirectional flows, storage charging/discharging, and peer-to-peer transactions with high accuracy while maintaining system simplicity.
4Reliability
If embedded devices perform full blockchain functions locally, then reliability and security of energy trading are improved, but device complexity and use of energy by moving object increase
Solution Approach 1:
The blockchain system is segmented into distributed nodes across the network rather than requiring full blockchain functionality in each embedded device. Each device performs localized validation and transaction signing, while the broader network maintains the distributed ledger. This segmentation achieves high reliability and security through distributed consensus while keeping individual device energy consumption and complexity manageable.
Data Source
AI summary
A system and method for allocating exported energy in a utility network is provided. A system and method comprising measuring the exported energy and a consumed energy via one or more IoT edge devices connected to a cloud computing infrastructure and coupled to one or more distributed energy resources in a community of energy consumers via a telecommunication network. Storing the measured exported energy and consumed energy on a memory coupled to a processor in the cloud computing infrastructure. Selecting one or more allocation algorithms executed by the processor based on the measured exported energy. Distributing and assigning the exported energy according to the one or more allocation algorithms selected and differentiating the community of energy consumers based on the one or more allocation algorithms selected.