Distributed Energy Node Load Balancing via Centralized Optimization
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Solution Overview
Problem
The integration of renewable energy sources (RES) into energy grids faces challenges due to their intermittency and uncontrollability, leading to imbalances between energy supply and demand, which can result in increased costs and grid stability issues, as traditional storage solutions like batteries are insufficient to fully mitigate these issues.
Innovation Solution
A system comprising a central allocation server and local agent servers connected to energy nodes, which predict energy generation and consumption patterns over a planning horizon, solve optimization problems to allocate energy efficiently across the network, allowing for collaborative load balancing and sharing of resources among energy nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If batteries are used as electricity storage to store excess energy generation, then energy storage capacity is improved, but system cost and device complexity increase
Solution Approach 1:
The patent divides the energy storage function into multiple distributed energy nodes throughout the network, each with its own storage capacity. Instead of relying on a single large battery system, the solution segments storage across many smaller units at different locations, reducing the complexity burden on any single component while maintaining overall storage capacity.
Solution Approach 2:
The patent combines multiple distributed energy nodes with storage capabilities into a coordinated network system. By merging the functions of generation, storage, and consumption across multiple nodes and managing them through a centralized optimization system, the solution achieves enhanced overall storage capacity without proportionally increasing system complexity.
2Quantity of substance
If batteries are used as electricity storage to store excess energy generation, then energy storage capacity is improved, but cost increases
Solution Approach 1:
The patent segments the total energy storage requirement into smaller distributed units across multiple nodes. This allows for more economical small-scale storage solutions rather than requiring expensive large-scale battery systems, reducing the overall cost while maintaining adequate storage capacity.
Solution Approach 2:
Each energy node in the patent manages its own storage and generation resources autonomously to some extent, making self-service decisions about when to store or discharge energy. This distributed intelligence reduces the need for expensive centralized control infrastructure and optimizes resource utilization across the network.
3Adaptability or versatility
If distributed energy resources are widely used, then renewable energy penetration is improved, but load balancing difficulty increases
Solution Approach 1:
The patent implements a feedback mechanism where the centralized system continuously receives information about generation and consumption at each energy node, processes this data through optimization algorithms, and sends back control signals to adjust loading and storage. This closed-loop feedback system enables effective load balancing across the distributed network despite the variability of renewable energy sources.
Solution Approach 2:
The patent introduces a centralized optimization system as an intermediary between the distributed energy nodes. This intermediary coordinates the activities of multiple independent nodes, managing the complexity of load balancing by centralizing the decision-making process while allowing distributed execution, thus enabling high renewable energy penetration without proportionally increasing overall system complexity.
Data Source
AI summary
A system for collaborative load balancing within a community of a plurality of energy nodes includes a central allocation server and a plurality of local agent servers. Each of the local agent servers is connected to a respective one of the energy nodes and has a processor configured to: receive input variables or parameters; predict, using the received input variables or parameters, a non-zero energy generation amount that power generation equipment can generate over a planning horizon and an energy consumption amount that will be consumed over the planning horizon; solve, using the energy generation amount and the energy consumption amount, an optimization problem over the planning horizon; and communicate a solution to the optimization problem to the central allocation server. Each of the energy nodes includes power generation equipment, power transmission equipment, and power storage equipment.