Thermal Energy Scheduling Using Grid Supply-Demand Forecasts
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Solution Overview
Problem
Existing systems for scheduling the operation of electrical apparatuses that supply thermal energy lack efficiency in managing supply-demand ratios, leading to variability in electrical grid loads and inefficiencies in thermal energy delivery.
Innovation Solution
A system comprising a server that generates a control instruction schedule based on predictive information about supply-demand ratios and thermal energy requirements, optimizing thermal energy supply to minimize output during low supply-demand periods and maximize output during high supply-demand periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thermal energy is supplied continuously to meet setpoint temperature requirements, then temperature control reliability is improved, but electrical grid load variability increases
Solution Approach 1:
The system performs preliminary action by pre-heating or pre-cooling the medium during time slots with high supply-demand ratios (excess energy availability) before the peak demand period. This allows the electrical apparatus to meet future temperature requirements by storing thermal energy in advance, thereby reducing the need for continuous operation and decreasing electrical grid load variability during peak periods.
Solution Approach 2:
The system maintains continuity of useful action by ensuring that thermal energy supply is optimized across different time slots while meeting the overall temperature requirements. By strategically scheduling energy supply during periods of high supply-demand ratio, the system maintains effective thermal management without continuous operation, thus reducing grid load variability while preserving temperature control reliability.
2Productivity
If thermal energy supply is optimized based on supply-demand ratio classification, then energy delivery efficiency is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing time slots into different categories based on supply-demand ratio classifications (e.g., high availability, low availability). This segmentation allows the control system to apply different strategies for each category, optimizing energy delivery efficiency by targeting specific time periods with appropriate thermal energy supply levels while managing system complexity through structured classification.
Solution Approach 2:
The system utilizes parameter changes by adjusting the thermal energy supply level based on the classified supply-demand ratio parameters of different time slots. By changing the output parameter of the electrical apparatus according to the classified time slot characteristics, the system optimizes energy delivery efficiency while maintaining manageable complexity through parameter-based control strategies.
Data Source
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AI summary
A system (100) comprising an electrical apparatus (102) operable to supply thermal energy to a medium in a space to attain a setpoint temperature of the medium; and a server (104) is disclosed. The server (104) is configured to generate a control instruction schedule comprising a required output from the electrical apparatus (102) in each of the plurality of time slots. The control instruction is generated based on a determined required thermal energy to be supplied and on the classification of each of the plurality of time slots based on first predictive information indicative of a supply-demand ratio of power supplied. The control instruction schedule is generated to minimise a proportion of output required from the electrical apparatus (102) in a time slot where the first predictive information indicates a low supply-demand ratio. Also provided are a method and a computer program.