Market Data Unit for Adaptive Energy Service Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current energy service control models are inflexible and unable to adapt dynamically to the changing nature of power supply from traditional generators to renewable sources and demand side load control, necessitating more adaptive and dynamic control systems.
Innovation Solution
The implementation of a Market Data Unit (MDU) that employs a control model to manage energy service units, including batteries for energy storage, which can adjust its operational mode between a default standalone mode and a mode controlled by the MDU, using market data to select and execute appropriate control algorithms for frequency regulation and other ancillary services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional control models are used for energy service units, then the system structure is simple and easy to operate, but the system cannot adapt dynamically to changing power supply conditions and market data
Solution Approach 1:
The control system dynamically selects between multiple control models (economic dispatch model, unit commitment model, etc.) based on real-time market conditions and power supply demands. This dynamic model selection capability allows the system to adapt to changing conditions without requiring a completely complex reconfiguration of the control architecture.
Solution Approach 2:
The system changes operational parameters by switching between different control models with varying degrees of complexity. The economic dispatch model is used for real-time adjustments, while the unit commitment model is used for longer-term planning, allowing the system to adapt parameter sets based on time scales and market conditions.
2Adaptability or versatility
If multiple control models are simulated and selected based on market data, then the adaptability and flexibility of energy service management is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system pre-simulates and evaluates multiple control models offline to determine their performance characteristics under different market conditions. This preliminary analysis allows the real-time system to quickly select the most appropriate model without performing full simulations at runtime, significantly reducing processing time while maintaining flexibility.
Solution Approach 2:
The system continuously monitors market data and power supply conditions, using feedback mechanisms to select and switch between control models based on current operational context. This feedback-driven model selection optimizes the balance between adaptability and computational efficiency by choosing models appropriate for current conditions.
3Reliability
If the battery state of charge is maintained within optimal ranges through dynamic control, then the reliability and stability of the energy grid is improved, but the control algorithm complexity increases
Solution Approach 1:
The control algorithm dynamically adjusts battery charge/discharge operations based on real-time market signals and grid conditions, maintaining state of charge within optimal ranges. This dynamic control ensures grid stability by preventing battery depletion or overcharging while adapting to changing economic and operational conditions.
Solution Approach 2:
The control system serves multiple functions simultaneously: it manages battery state of charge, responds to market signals, maintains grid stability, and optimizes economic performance. By integrating these functions into a unified control framework, the system achieves grid reliability without proportionally increasing algorithmic complexity.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
ABSTRACT OF THE DISCLOSURE Apparatus, systems, computer readable media and methods for controlling power realized by energy service equipment based on market data are disclosed. A method simulates execution of control models, as simulation results; selects one of the control models during a first period; and controls operation of the energy services equipment in a second period, using the selected control model. According to an exemplary apparatus, each control model is based on a set of changing market data and controls operation of energy service equipment. A system controls power realized from different energy service equipment using market data. The system includes a model processor that simulates execution of model sets using market data to generate a simulation result; a model selector that selects a model of each set based on simulation results; and a processor that controls operation of the different energy service equipment using the selected model of each respective model set.