Microgrids play a crucial role in modern energy systems by integrating diverse energy sources and enhancing grid resilience. This study addresses the optimization of microgrids through the deployment of high
This model effectively balances the economy and robustness of microgrid operation. The model uses robust equivalent to deal with the uncertain factors in the microgrid, adopts the form of a robust optimization model of min-max-min structure, and uses the Benders dual algorithm to solve and calculate the established optimization model.
Review of optimization techniques used in microgrid energy management systems. Mixed integer linear program is the most used optimization technique. Multi-agent systems are most ideal for solving unit commitment and demand management. State-of-the-art machine learning algorithms are used for forecasting applications.
When considering the optimized operation of multiple microgrids, reference uses the random optimization method of scene generation and scene reduction to deal with the uncertainty of renewable power.
A two-stage robust optimization model considering uncertainties is established. Uncertainty parameters are converted corresponding definite adjustable parameters. The Benders dual algorithm is used to solve the problem. The robust adjustment parameters of the microgrid can be obtained.
A microgrid energy management system based on robust convex optimization , which is used to provide a solution when the random load demand is large and the supply of renewable energy is insufficient. The demand response based on the time-of-use electricity price is considered in Ref. .
In this context, different researches have decided to reviewed optimization applied to microgrid-related technologies such as renewable power sources , , . Baños et al. review in optimization methods applied to wind power, solar energy, hydropower, bioenergy, geothermal energy and hybrid systems.
