Residential Microgrid Load Dispatch Optimization Using GRU Forecasting
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Solution Overview
Problem
Current short-term load forecasting and photovoltaic output power forecasting methods in residential microgrids have low accuracy, leading to unsuitable load dispatch schemes and high operating costs due to the volatility and randomness of residential electricity consumption.
Innovation Solution
A load dispatch optimization method using a computer-based system that collects environmental and time data to input into pre-trained GRU-based recurrent neural networks for load and photovoltaic output power forecasting, followed by a particle swarm algorithm to determine an objective function that minimizes the total cost of the microgrid, resulting in a more suitable dispatch scheme.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional load forecasting methods are used, then the system is simple to implement, but the forecasting accuracy is low
Solution Approach 1:
The patent replaces traditional mechanical/statistical forecasting methods with a GRU-based recurrent neural network model. This substitution enables the system to capture temporal dependencies and non-linear patterns in load and photovoltaic data, significantly improving forecasting accuracy while accepting increased computational complexity.
Solution Approach 2:
The patent changes the parameters and structure of the forecasting model by introducing GRU units with gating mechanisms (update gate, reset gate) that dynamically adjust information flow. This allows the model to adapt to varying patterns in residential electricity consumption and photovoltaic output, resolving the contradiction between accuracy and complexity.
2Measurement precision
If GRU-based forecasting models are used, then the forecasting accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-training the GRU model on historical data and storing the trained model parameters. This allows the forecasting system to make accurate predictions with reduced computational overhead during operation, as the complex learning process has already been completed in advance.
Solution Approach 2:
The patent applies partial action by using a moderate-sized GRU architecture that provides sufficient forecasting accuracy without excessive computational resources. The model processes only the necessary features (load data, photovoltaic output, environmental factors) rather than all possible inputs, balancing accuracy and resource consumption.
3Loss of energy
If load dispatch optimization is implemented, then the operating cost is reduced, but the system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the GRU forecasting model continuously provides predicted load and photovoltaic output data to the load dispatch optimization system. This feedback loop enables dynamic adjustment of dispatch strategies based on accurate predictions, reducing operating costs while managing system complexity through automated control.
Solution Approach 2:
The load dispatch optimization system performs self-service by automatically generating optimal dispatch schemes based on forecasting results and operational constraints. This reduces the need for manual intervention and complex external control systems, achieving cost reduction with manageable system complexity.
Data Source
AI summary
The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm. The invention can provide load dispatch scheme suitable for current microgrid and reduce operating cost of residential microgrid.