Transactive Energy Matching for Fair Distributed Load Allocation
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Solution Overview
Problem
Distributed energy systems face challenges in efficiently matching energy sources and loads due to varying capacities and requirements of producers and consumers, leading to inefficiencies in power distribution and management.
Innovation Solution
A method for determining selection options for loads, ordering them and energy sources based on specific parameters, and matching them to ensure fair and efficient energy distribution, considering factors like price, resilience, and energy source type, with a computing system to facilitate this process and manage energy transfer and settlement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed energy resources are used to support local environments and enable autonomous operation, then grid resilience and carbon footprint are improved, but matching producers and consumers becomes challenging due to variety in power characteristics and consumer requirements
Solution Approach 1:
The patent segments the energy distribution problem into discrete selection options for each load (e.g., priority sources, acceptable sources, prohibited sources). This segmentation allows complex consumer requirements to be broken down into manageable categories that can be systematically matched with energy sources, resolving the contradiction between grid resilience and matching complexity.
Solution Approach 2:
The patent introduces an intermediary matching system that acts as a mediator between distributed energy producers and consumers. This intermediary establishes selection options and performs the matching process, simplifying the complexity of directly matching diverse producers with diverse consumers while maintaining grid resilience through proper source-load pairing.
2Productivity
If strong control over the grid is used to manage the balance between producers and consumers, then matching efficiency is improved, but the system loses flexibility and efficiency in reflecting party interests
Solution Approach 1:
The patent implements dynamic selection options that can be adjusted based on system conditions and party interests. The matching system adapts to changing requirements by allowing selection options to be modified, maintaining both matching efficiency and system flexibility through dynamic rather than static control mechanisms.
Solution Approach 2:
The patent changes key parameters of the matching process by introducing selectable options with different priorities and constraints. This allows the system to adjust matching criteria based on varying conditions, achieving both efficiency through structured matching and flexibility through parameter adjustability.
3Reliability
If multiple selection criteria are used to match energy sources and loads (price, resilience, source type), then fairness and efficiency are improved, but computation and matching complexity increases
Solution Approach 1:
The patent segments multiple selection criteria into distinct selection options for each load, organizing complex matching criteria into manageable categories. This segmentation reduces computation complexity by structuring the matching process around discrete options rather than continuous multi-criteria optimization.
Solution Approach 2:
The patent applies local quality by allowing different selection options and criteria to be applied to different loads based on their specific requirements. This localized approach to matching criteria ensures fairness for each load while avoiding the need for complex global optimization across all loads simultaneously.
Data Source
AI summary
A method of energy distribution from a plurality of energy sources to a plurality of loads wherein a set of selection options for a load are determined. Values are established for the selection options for each loads. The loads are then ordered into a load order according to a load ordering parameter. The energy sources are ordered in a plurality of sequences, where each sequence corresponds to a possible set of values for the selection options and wherein at least one energy source appears in more than one of the plurality of sequences. The loads are then matched with the energy sources according to the load order, with the sources in the sequence corresponding to the set of values for the selection options established for that load. A computing system designed to perform this method, and an electrical grid incorporating such a computing system are also described.