Optimization Control Method for Low-Voltage DC Microgrid with Low Carbon, Economy, and Reliability.

Area of Science:

• Electrical Engineering
• Energy Systems
• Optimization Algorithms

Background:

• DC microgrids offer economic, low-carbon, and safety advantages.
• Managing uncertainty in renewable energy sources (like photovoltaics) and loads is crucial for efficient operation.
• Existing methods may not fully address the multi-objective optimization needs of DC microgrids.

Purpose of the Study:

• To propose a multiscenario optimization control method for low-voltage DC microgrids.
• To enhance economic benefits, reduce carbon footprint, and ensure system safety.
• To optimize the operation throughout the entire lifecycle of energy storage systems.

Main Methods:

• Utilized Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and K-means clustering for scenario generation.
• Developed energy exchange strategies based on time-of-use electricity prices and operating modes.
• Established a multiobjective optimization model with objectives for net income, energy storage utilization, and CO2 emissions.
• Employed nondominant sorting arctic puffin optimization algorithm (NSAPOA) and multi-attributive border approximation area comparison (MABAC) for optimization.

Main Results:

• The proposed NSAPOA effectively generates Pareto optimal solutions for the multiobjective problem.
• The WGAN-GP and K-means approach successfully handles uncertainties in photovoltaic output and load.
• The integrated method achieves economic and low-carbon operation, considering the service life of energy storage.

Conclusions:

• The developed optimization control method provides a robust solution for economic and low-carbon operation of DC microgrids.
• The approach effectively balances competing objectives, including profitability, energy efficiency, and environmental impact.
• This method contributes to the sustainable and efficient integration of renewable energy in DC microgrid systems.