Demand-side management in Grid-Connected Energy Storage System using Deep Neural Network
The Grid and Energy Storage System
To set the stage, let's look at the basic setup of a grid-connected energy storage system:
Grid Specifications: The grid in this simulation has a capacity of 154 megawatts and operates at 34.5 kilovolts, which is then stepped down to 400 volts before being connected to the energy storage system.
Energy Storage System: This system includes a grid-connected battery, which manages energy storage and distribution.
Load Types: Various loads are connected to the system, including residential, commercial, and mixed loads. Each load type has a unique profile based on data collected over a 24-hour period.
Load Profiles and Data Management
Accurate load profiling is essential for effective energy management:
Residential and Commercial Load Data: Data for different types of loads (residential and commercial) are collected every 30 minutes over 24 hours. This data helps in creating accurate load profiles that can be used for demand-side management.
Load Profile Generation: Using data from local stations, load profiles are generated for both residential and commercial sectors. These profiles are essential for understanding energy requirements throughout the day.
Role of Neural Networks in Energy Management
Neural networks play a critical role in optimizing energy usage:
Inputs and Outputs: The DNN takes two primary inputs—time of day and the state of charge (SOC) of the battery. Based on these inputs, it provides commands to either supply power to the grid or charge the battery.
Power Management: During peak hours, the system uses the battery to supply power and reduce grid strain. Conversely, during off-peak hours, the system charges the battery, balancing energy storage and consumption.
Simulation and Implementation
The MATLAB simulation showcases the practical application of this system:
Without Neural Network: Initially, the simulation runs without the neural network, highlighting the power distribution solely managed by the grid.
With Neural Network: When the neural network is integrated, it dynamically adjusts power distribution and storage based on real-time data, optimizing both energy supply and battery charging.
Results and Comparisons
The simulation results highlight the effectiveness of the DNN approach:
Peak Hour Management: During peak hours, the energy storage system effectively clips excess power generation, reducing grid overload.
Off-Peak Efficiency: During off-peak hours, the system optimizes battery charging, ensuring energy is stored efficiently for future use.
Comparison: Comparing the results with and without the neural network shows significant improvements in power management and storage efficiency.
Conclusion
The integration of deep neural networks into grid-connected energy storage systems presents a significant advancement in demand-side management. By accurately predicting and managing energy needs, DNNs enhance the efficiency of energy storage systems, reduce peak load pressures, and ensure optimal energy usage.
Comments