Grid-Forming Energy Storage Control for Real-Time Demand Response
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Solution Overview
Problem
Existing grid-forming energy storage systems face challenges in real-time data processing, event response, and resource allocation efficiency, leading to energy waste and instability in smart grids with high variability and complex energy inputs.
Innovation Solution
A grid-forming energy storage system utilizing a data interface and transfer module, state analysis and prediction module, event response and strategy adjustment module, and energy scheduling and management module, employing JSON formatting, TCP/IP protocol, Apache Kafka message queue, Spark Streaming, and Esper engine for real-time data processing and optimized energy distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional batch processing method is used for data processing, then system complexity is reduced, but data processing speed decreases and real-time energy management cannot be achieved
Solution Approach 1:
The patent replaces the conventional batch processing mechanical system with a stream processing system based on Spark Streaming and Apache Kafka. This substitution enables continuous real-time data processing of energy storage unit parameters (voltage, current, temperature, capacity) without the delays inherent in batch processing, while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent introduces Apache Kafka as an intermediary message queue between data collection and processing components. This mediator buffer decouples the data production and consumption rates, allowing high-speed data ingestion while enabling gradual processing through Spark Streaming, thus resolving the contradiction between speed and complexity
2Productivity
If real-time stream processing is implemented, then energy management efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the energy storage management system into distinct functional modules: data collection module, message queue module (Apache Kafka), stream processing module (Spark Streaming), and control module. This segmentation allows real-time processing capabilities to be implemented in a distributed, manageable manner, improving energy management efficiency while keeping individual component complexities low
Solution Approach 2:
The Spark Streaming engine serves multiple functions simultaneously: it consumes data from Apache Kafka, processes real-time analysis of energy storage parameters, generates control decisions, and outputs scheduling commands. This multi-functionality consolidates several operations into a single universal processing platform, enhancing productivity without proportionally increasing overall system complexity
3Speed
If conventional scheduling system is used, then system complexity is minimized, but response speed to grid faults and demand changes is insufficient
Solution Approach 1:
The patent implements a dynamic event response mechanism where the system continuously monitors energy storage unit states and automatically adjusts charging/discharging strategies in real-time based on detected events (grid faults, demand peaks). This dynamic adaptation enables rapid response to changing conditions, transforming the static conventional scheduling system into a responsive real-time system
Solution Approach 2:
The patent establishes a closed-loop feedback system where stream processing results feed back into control decisions, which are then applied to energy storage units, and the effects are continuously monitored. This feedback mechanism enables the system to learn from and respond to actual system behavior, achieving fast adaptive response to grid events while maintaining structured complexity through the feedback loop architecture
4Loss of energy
If real-time data processing is implemented, then energy waste is reduced, but processing delay in conventional systems persists
Solution Approach 1:
The patent implements continuous stream processing that operates without interruption, continuously analyzing energy storage data and generating control commands in real-time. This continuous action eliminates the idle periods inherent in batch processing, ensuring that energy management decisions are always based on current data, thereby minimizing both energy waste and processing delays
Solution Approach 2:
The system performs preliminary real-time analysis of energy storage unit states and predicts future conditions using stream processing, enabling proactive energy management decisions before critical situations arise. This preliminary action allows the system to prepare and execute optimal charging/discharging strategies in advance, reducing energy waste from reactive responses and eliminating processing delays associated with real-time crisis management
Data Source
AI summary
A grid-forming energy storage system includes a data interface and transfer module, a state analysis and prediction module, an event response and strategy adjustment module, and an energy scheduling and management module. In the present disclosure, the energy management efficiency in the smart grid is significantly improved through real-time data formatting and efficient information transfer, real-time transmission and accuracy of data are ensured by using a JSON formatted data interface and a TCP/IP protocol, the data processing flow is optimized in conjunction with Apache Kafka, and the throughput capacity is improved and the response time is shortened, such that the grid manages resources more efficiently. Through state analysis and demand prediction by Spark Streaming, the grid responds in real time and predicts energy demand changes to reduce the energy waste.