Energy Operation Apparatus for Stable Supply and Demand Control
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Solution Overview
Problem
Existing energy operation systems face challenges in accurately predicting and controlling energy supply and demand due to variations in weather conditions and the influence of natural phenomena, leading to difficulties in balancing energy distribution across management areas in distributed energy systems.
Innovation Solution
An energy operation apparatus comprising a demand predictor, planner, evaluator, and solution quality controller, which predicts energy demand and power generation, evaluates conditions such as weather and energy density, and adjusts the quality of prediction solutions to ensure accurate energy supply planning and control based on specific accuracy targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If average weather prediction data is used for energy demand prediction, then prediction robustness against positional errors is improved, but prediction accuracy for rare weather conditions deteriorates
Solution Approach 1:
The patent segments the prediction process into multiple independent prediction units, each handling specific weather conditions or time periods. By dividing the prediction task into segments, the system can apply different prediction strategies for different conditions, maintaining robustness while improving accuracy for rare events.
Solution Approach 2:
The patent implements dynamic adjustment of prediction parameters based on actual weather conditions. The prediction system dynamically selects or weights different prediction models according to the current weather state, allowing it to adapt to rare weather conditions while maintaining overall reliability.
2Ease of operation
If relaxation in exact solution is increased for scheduling flexibility, then ease of operation is improved, but manufacturing precision of energy supply plan deteriorates
Solution Approach 1:
The patent applies dynamic adjustment to the relaxation parameters in the scheduling process. Rather than using fixed relaxation levels, the system dynamically adjusts the degree of relaxation based on current system state, demand patterns, and available resources, thereby maintaining flexibility while preserving solution accuracy.
Solution Approach 2:
The patent changes key parameters of the scheduling solution based on evaluation results. By adjusting parameters such as relaxation factors, time windows, and constraint weights according to actual system performance and prediction accuracy, the system achieves both operational flexibility and planning precision.
3Productivity
If distributed energy operation apparatuses cooperate with each other, then productivity of energy supply system is improved, but device complexity increases
Solution Approach 1:
The patent segments the distributed energy operation system into independent but coordinated units, each with its own prediction and planning capabilities. This segmentation allows each apparatus to operate autonomously while contributing to the overall system productivity, reducing the coordination complexity compared to a highly centralized approach.
Solution Approach 2:
The patent designs the energy operation apparatus with universal functionalities that can be applied across different management areas and weather conditions. This multi-functionality allows distributed apparatuses to cooperate effectively using standardized interfaces and protocols, improving productivity while managing complexity through reuse of proven components.
4Measurement precision
If prediction accuracy target is increased for rare weather conditions, then measurement precision is improved, but loss of time for computation increases
Solution Approach 1:
The patent performs preliminary preparation for rare weather conditions by pre-computing prediction models, parameters, and response strategies during periods when such conditions are not occurring. This preliminary action allows the system to quickly respond to rare events without requiring extensive real-time computation, thereby improving prediction accuracy while minimizing computation time loss.
Solution Approach 2:
The patent applies higher prediction accuracy requirements and computational resources locally only when and where rare weather conditions are detected or predicted. Rather than uniformly increasing computation across all conditions, the system concentrates computational effort on specific local situations, improving accuracy for rare events while avoiding unnecessary computation during normal conditions.
Data Source
AI summary
An energy operation system, apparatus, and method capable of stably supplying energy and providing adjustment control based on prediction precision and planning precision for energy supply and demand. An energy operation apparatus includes a demand predictor, planner, evaluator, and solution quality controller. The demand predictor predicts demand and/or a power generation amount of future energy in a management area. The planner prepares a future energy supply plan in the management area based on a prediction. The evaluator evaluates supply and/or demand conditions including at least one of future weather in the management area, energy demand in the management area, a demand density and/or a generation density of future energy in the management area. The solution quality controller controls the quality of at least one of a prediction solution of the demand predictor and the energy supply plan of the planner based on an evaluation result from the evaluator.