Hierarchical Energy Resource Control for Volatile Load Fluctuations
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Solution Overview
Problem
Higher-level energy management systems struggle to react to highly volatile generation and load fluctuations due to long computing cycles and inaccurate model predictions, leading to suboptimal energy system operation.
Innovation Solution
A control unit with a shorter cycle time than the energy management system is used to regulate energy system resources, allowing for quicker reaction to fluctuations and indirect control of components like the grid connection point, enhancing operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a higher-level energy management system with model-based optimization is used to regulate operating resources, then the operational management of the energy system is improved, but the system cannot respond quickly enough to highly volatile generation and load fluctuations due to long computational cycle times
Solution Approach 1:
The control system is segmented into two hierarchical levels: a higher-level energy management system that performs model-based optimization at extended time scales, and a lower-level control unit that executes fast responses to volatile fluctuations. This segmentation allows each level to operate at its optimal time scale, resolving the contradiction between comprehensive operational management and rapid response capability.
Solution Approach 2:
The higher-level energy management system performs preliminary optimization calculations and determines setpoint trajectories in advance for less volatile operating resources. These pre-calculated setpoints serve as reference trajectories for the lower-level control unit, which then handles real-time adjustments. This preliminary action allows the slow computational system to prepare optimal solutions while the fast system handles immediate responses.
2Device complexity
If the energy management system uses a longer cycle time for optimization calculations, then computational complexity is reduced and operational management is improved, but the system cannot adequately account for unpredictable fluctuations in volatile generators and loads
Solution Approach 1:
The control architecture segments responsibilities between two levels: the higher-level system handles strategic optimization with reduced computational complexity at extended time scales, while the lower-level control unit manages tactical adjustments for volatile fluctuations. This segmentation allows the complex optimization problem to be solved at a manageable complexity level while maintaining reliability through the fast response capability of the lower-level system.
Solution Approach 2:
The lower-level control unit acts as an intermediary between the higher-level energy management system and the volatile operating resources. It receives setpoint trajectories from the higher-level system and continuously adjusts operating points to track these trajectories while compensating for unpredictable fluctuations. This intermediary layer bridges the gap between slow optimization and fast response requirements.
3Loss of time
If model simplifications are used in the energy management system, then computational time is reduced, but model inaccuracies lead to unforeseen deviations in system behavior
Solution Approach 1:
The higher-level energy management system performs preliminary optimization using simplified models to generate reference setpoint trajectories within acceptable computational time. These trajectories serve as guidance for the lower-level control unit, which then performs real-time adjustments based on actual system behavior. This preliminary action with simplified models reduces computational time while the fast control layer compensates for model inaccuracies.
Solution Approach 2:
The lower-level control unit continuously monitors actual system behavior against the reference trajectories generated by the higher-level system and applies corrective adjustments in real-time. This feedback mechanism compensates for model inaccuracies and unforeseen deviations, allowing the use of simplified models in the higher-level optimization without sacrificing overall system precision.
Data Source
AI summary
A method for controlling an operating resource (2) of an energy system (3) is proposed, characterized in that the energy system (3) comprises an energy management system for the higher-level control of the energy system (3) and a control unit (1) for the lower-level control of the operating resource (2), wherein the energy management system (3) determines setpoints for the operating resource (2) and/or other components (2) of the energy system (3) in the respective time ranges according to a first cycle time, and the control unit (1) uses the respective determined setpoint as the setpoint for its control in the respective time range, wherein the control of the operating resource (2) by the control unit (1) takes place within the time ranges according to a second cycle time that is shorter than the first cycle time. Furthermore, the invention relates to a control unit (1) for controlling an operating resource (2) of an energy system (3).