BESS Controller Algorithms for Grid Stability and Peak Load
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Solution Overview
Problem
Current battery energy storage system (BESS) controllers lack the capability to effectively dispatch active and reactive power in a manner that benefits distribution systems and utilities, particularly in suppressing power swings, voltage regulation, and peak load management, while requiring human intervention and not optimizing renewables capacity firming and peak load shaving simultaneously.
Innovation Solution
A BESS control system that includes logic for photovoltaic station capacity firming, voltage support, and energy time shift algorithms, which derive optimal power output curves from historical data, monitor real-time voltage and phase values, and predict peak load times to manage power distribution autonomously, ensuring stable and efficient energy dispatch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a BESS controller is designed to handle multiple functions (PV capacity firming, voltage support, peak load shaving), then the system benefits distribution systems and utilities, but the device complexity increases
Solution Approach 1:
The BESS controller is designed to perform multiple functions including PV capacity firming, voltage support, and peak load shaving through a single integrated device. The controller executes different algorithms for each function based on real-time data from the distribution system, allowing one device to serve multiple purposes without requiring separate specialized controllers for each function.
Solution Approach 2:
The controller divides the multiple functions into separate executable algorithms that can be independently managed and optimized. Each algorithm (PV capacity firming algorithm, voltage support algorithm, peak load shaving algorithm) processes specific aspects of system control, allowing the complex control task to be segmented into manageable components that reduce overall system complexity.
2Productivity
If the controller optimizes both renewables capacity firming and peak load shaving simultaneously, then value maximization for grid operators is achieved, but the computational complexity and difficulty of control increases
Solution Approach 1:
The controller uses historical data to predict future PV output and feeder peak load times in advance. By performing preliminary analysis of data streams and pre-calculating optimal dispatch schedules, the controller prepares control actions beforehand, allowing simultaneous optimization of multiple functions without real-time computational overload.
Solution Approach 2:
The controller continuously monitors real-time data streams from the distribution system and adjusts its dispatch decisions based on feedback from actual system performance. This closed-loop control allows the system to optimize both PV capacity firming and peak load shaving dynamically while adapting to changing conditions, achieving value maximization through iterative refinement rather than complex open-loop calculations.
3Extent of automation
If the controller operates completely unsupervised without human intervention, then operational efficiency increases, but the measurement precision and detection accuracy requirements increase
Solution Approach 1:
The controller is designed to operate autonomously by processing its own input data streams and making independent dispatch decisions without requiring human supervision. The system self-adjusts its control parameters based on real-time measurements from the distribution system, performing self-diagnosis and self-optimization to maintain accurate operation throughout the day.
Solution Approach 2:
The controller replaces manual human analysis and decision-making with automated computational algorithms that process data streams and execute control decisions. This substitution of mechanical human operation with electronic computational systems enables unsupervised operation while maintaining or improving measurement precision through consistent algorithmic processing of sensor data.
4Reliability
If the controller uses real-time data streams for smart decisions, then the ability to suppress power swings and support voltage improves, but the loss of information and measurement requirements increase
Solution Approach 1:
The controller continuously processes real-time data streams from the distribution system without interruption, maintaining constant monitoring of PV output, voltage levels, and load conditions. This continuous data acquisition and processing ensures that the controller always has current information for making reliable dispatch decisions, suppressing power swings, and supporting voltage stability throughout operational periods.
Solution Approach 2:
The controller performs preliminary processing and filtering of incoming data streams to extract only the most relevant information for control decisions. By pre-processing data to identify key parameters and trends before full analysis, the controller reduces the volume of information that needs to be processed in real-time while maintaining the reliability needed for grid stability applications.
Data Source
AI summary
An energy storage system controller, including: an energy storage system coupled to a power distribution system; and a processor in communication with the energy storage system, wherein the processor executes: a renewables capacity firming algorithm operable for conditioning intermittent power of a renewable energy station using real time and historical input data such that it is made more stable and non-intermittent, optionally utilizing one or more parameter values associated with comparable time periods taking into account one or more factors comprising cloud state; and a peak load shaving algorithm operable for ensuring that the energy storage system is capable of transmitting full power capacity at a predicted feeder peak load time determined by the processor from real time and historical input data; wherein the performance of the renewables capacity firming algorithm and the performance of the peak load shaving algorithm are optimized in parallel.