Dynamic Stochastic Optimal Power Flow Control for Grid Stability
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Solution Overview
Problem
Current electrical power systems face challenges in reliably and efficiently operating with high penetration of intermittent renewable energy due to uncertainty and variability, leading to issues like wind curtailment and inadequate real-time system security, as traditional control methods are based on deterministic and linear controllers that fail to account for dynamic stochastic disturbances.
Innovation Solution
A dynamic stochastic optimal power flow (DSOPF) control system using nonlinear optimal control techniques, such as adaptive critic designs and model predictive control, is implemented to provide multi-objective optimal control capability, coordinating secondary AC power flow control and integrating intermittent renewable energy sources by monitoring conditions with sensors and outputting control data to active and reactive power generation controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional deterministic control methods are used, then system operation is simplified, but system reliability deteriorates under intermittent renewable energy penetration
Solution Approach 1:
The patent transitions from static deterministic control to dynamic stochastic control that adapts to changing system conditions. The control method incorporates real-time state estimation and dynamic optimization to handle the variability of renewable energy sources, making the control system responsive to stochastic disturbances while maintaining reliability.
Solution Approach 2:
The patent changes the fundamental parameters of the control approach by incorporating stochastic models and probability distributions to represent renewable energy uncertainty. This allows the control system to account for variability in wind and solar generation, improving reliability without requiring excessive complexity through optimized parameter selection.
2Reliability
If conservative operation with high probability of exceedance forecasts is used, then system security is maintained, but productivity decreases due to wind curtailment
Solution Approach 1:
The patent implements a feedback-based stochastic control system that continuously monitors system state and adjusts control actions accordingly. By using real-time state estimation and feedback from renewable energy generation and system loads, the controller can dynamically balance security requirements with maximizing renewable energy utilization, reducing unnecessary curtailment while maintaining system security.
Solution Approach 2:
The patent performs preliminary stochastic optimization to determine optimal control strategies before real-time operation. By pre-calculating control actions based on forecasted renewable energy scenarios and system conditions, the system can prepare appropriate responses that maximize renewable energy utilization while ensuring security constraints are met, reducing reactive curtailment.
3Device complexity
If linear controllers are used, then device complexity is reduced, but adaptability to stochastic disturbances deteriorates
Solution Approach 1:
The patent replaces static linear controllers with dynamic nonlinear controllers that adapt to stochastic disturbances. The control method uses dynamic optimization techniques that adjust control parameters in real-time based on system state and renewable energy variability, providing the necessary adaptability without excessive complexity through efficient computational approaches.
Solution Approach 2:
The patent enables the control system to self-adjust to stochastic disturbances through autonomous stochastic optimization. The system automatically adapts its control strategy based on real-time observations of renewable energy generation and system conditions, eliminating the need for complex manual tuning while maintaining high adaptability to varying disturbance patterns.
4Device complexity
If steady-state optimization is used, then computational simplicity is improved, but measurement precision of dynamic behavior deteriorates
Solution Approach 1:
The patent transitions from steady-state optimization to dynamic optimization that captures transient behavior. The method incorporates time-varying system models and real-time state estimation to accurately represent dynamic behavior, improving measurement precision while managing computational complexity through efficient dynamic optimization algorithms and selective state monitoring.
Data Source
AI summary
A dynamic stochastic optimal power flow (DSOPF) control system is described for performing multi-objective optimal control capability in complex electrical power systems. The DSOPF system and method replaces the traditional adaptive critic designs (ACDs) and secondary voltage control, and provides a coordinated AC power flow control solution to the smart grid operation in an environment with high short-term uncertainty and variability. The DSOPF system and method is used to provide nonlinear optimal control, where the control objective is explicitly formulated to incorporate power system economy, stability and security considerations. The system and method dynamically drives a power system to its optimal operating point by continuously adjusting the steady-state set points sent by a traditional optimal power flow algorithm.