Power Grid Real-Time Dispatch Using Reinforcement Learning
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Solution Overview
Problem
Existing power grid real-time dispatch optimization algorithms face challenges in modeling multiple uncertain factors and suffer from slow computation due to the rapidly growing control scale and complexity of new power systems, making it difficult to achieve efficient and accurate dispatch adjustments.
Innovation Solution
A method and system utilizing reinforcement learning and training models to optimize power grid dispatch, incorporating power grid model parameters and operation data to dynamically adjust strategies, including an agent and reinforcement learning environment for interactive decision-making, with reward feedback mechanisms to enhance efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If model-driven optimization algorithms (genetic algorithm, particle swarm optimization) are used for power grid real-time dispatch, then optimization can be performed, but computation speed is slow and difficulty in modeling multiple uncertain factors increases
Solution Approach 1:
The patent replaces traditional model-driven optimization algorithms (genetic algorithm, particle swarm optimization) with a data-driven deep reinforcement learning approach. The DQN agent learns optimal dispatch strategies through interaction with the power grid environment, substituting complex mathematical modeling and iterative optimization computations with a trained neural network that directly outputs dispatch decisions based on input state features, dramatically improving computation speed while handling uncertainty through learned patterns from training data
Solution Approach 2:
The patent transforms the optimization problem from solving complex objective functions with multiple constraints into a state-action mapping problem where the DQN agent learns to map grid state parameters (power outputs, voltages, frequencies) directly to dispatch actions. This parameter transformation eliminates the need for explicit modeling of uncertain factors and constraint satisfaction, as the agent learns optimal mappings during training that inherently satisfy system constraints
2Adaptability or versatility
If control scale of power grid grows exponentially with transformation to new power systems, then system capability increases, but real-time dispatch optimization becomes more difficult due to high dimension, nonlinearity and non-convexity
Solution Approach 1:
The patent segments the complex power grid control problem into manageable state features and actions that the DQN agent processes independently. The state space is divided into key observable parameters (power outputs of different units, voltage levels, frequency deviations) and the action space is segmented into discrete control decisions for each controllable unit. This segmentation allows the agent to learn optimal policies for each component while coordinating overall system optimization, making the high-dimensional non-linear problem tractable
Solution Approach 2:
The patent transitions from traditional optimization in the parameter space (power outputs, voltages, frequencies) to learning in the state-action space using deep neural networks. The DQN agent adds a learning dimension by mapping observed grid states through a neural network to determine optimal actions, effectively transforming the high-dimensional non-linear optimization problem into a pattern recognition task that can be solved efficiently through experience-based learning rather than exhaustive search
Data Source
AI summary
Disclosed in the present application are a power grid real-time scheduling optimization method and system, a computer device and a storage medium. The method comprises: acquiring power grid model parameters and power grid operation data; and according to the power grid model parameters and the power grid operation data, obtaining a power grid real-time scheduling adjustment strategy by means of a preset power grid real-time scheduling reinforcement learning training model. Massive operation data of a power grid and load flow calculation simulation technologies can be fused by means of reinforcement learning, and unlike a conventional algorithm, a complex and difficult-to-solve calculation model does not need to be established, so that rapid optimization adjustment of power grid real-time scheduling is achieved, the optimization adjustment cost is reduced, and the matching degree of power grid real-time scheduling and actual operation is improved. The problem of real-time scheduling optimization of a power grid is solved, and the defects of difficulty in modeling in consideration of uncertain factors and slow calculation for solving large-scale optimization in existing algorithms due to the characteristics of strong uncertainty, rapidly increasing control scale and the like of novel power systems are overcome.