Power Dispatch Bounds for Adaptive Energy Storage Control
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Solution Overview
Problem
Conventional power dispatch methods employing fixed charging and discharging strategies for energy storage batteries fail to adapt dynamically to actual requirements, leading to suboptimal performance and inefficiencies in managing energy supply and demand.
Innovation Solution
A power dispatch system and method that dynamically adjusts power dispatch strategies by using a database to record historical electricity usage data, a prediction module to forecast demand, and a dispatch module to set upper and lower bounds for energy storage unit operation, ensuring optimal charging and discharging based on real-time conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed charging and discharging strategy is employed for energy storage batteries, then the system operation is simple, but the performance cannot be optimized for different scenarios and energy efficiency is reduced
Solution Approach 1:
The patent implements dynamic charging and discharging strategies by continuously adjusting the operating parameters of energy storage batteries based on real-time power demand predictions and historical data. The system transitions from fixed strategies to dynamic adaptation, allowing the energy storage system to optimize its performance across different scenarios while maintaining manageable complexity through automated control algorithms.
2Measurement precision
If historical electricity usage data with sufficient time span is used for prediction, then the prediction accuracy improves, but the data processing time and system response speed increase
Solution Approach 1:
The system performs preliminary processing of historical electricity usage data by pre-calculating statistical features and patterns during off-peak periods or when computational resources are abundant. This pre-processing creates condensed data representations that can be quickly analyzed during real-time operation, thereby improving prediction accuracy without proportionally increasing real-time processing time.
Solution Approach 2:
The patent segments the historical data processing into multiple stages: data collection and preliminary cleaning, feature extraction and pattern recognition, and real-time prediction. By dividing the processing workflow into discrete segments that can be executed in parallel or distributed across different computational units, the system reduces the overall processing time while maintaining comprehensive analysis of historical data.
Data Source
AI summary
A power dispatch system and a power dispatch method are provided. When a time span of the historical electricity usage data is greater than or equal to a preset value, an upper bound is determine according to the historical electricity usage data, the expected power demand and a dispatchable power. When the time span is less than the preset value, the upper and lower bounds are determined according to the historical electricity usage data. When an actual power demand is greater than the upper bound, the power system receives an amount of power equal to the upper bound, and the energy storage unit discharges to supplement the power required by the power system. When the actual power demand is less than the lower bound, the power system receives an amount of power equal to the lower bound, and the energy storage unit receives the surplus power for charging.