Microgrid internal dispatch

Can deep reinforcement learning solve the optimal dispatch of microgrids under uncertaintes?

This paper presents an improved deep reinforcement learning (DRL) algorithm for solving the optimal dispatch of microgrids under uncertaintes. First, a multi-objective interval optimization dispatch (MIOD) model for microgrids is constructed, in which the uncertain power output of wind and photovoltaic (PV) is represented by interval variables.

What is a multi-objective interval optimization dispatch model for microgrids?

First, a multi-objective interval optimization dispatch (MIOD) model for microgrids is constructed, in which the uncertain power output of wind and photovoltaic (PV) is represented by interval variables. The economic cost, network loss, and branch stability index for microgrids are also optimized.

What are the limitations of a generalized microgrid dispatch model?

In view of these limitations, in this work, a generalized microgrid dispatch model that incorporates multiple uncertainties introduced by EVs, including arrival and departure times, initial and final SOC, and charging types, has been proposed.

How does a microgrid work?

Typically modern microgrid systems can either be operated in the grid connected mode or in the islanded mode. In the grid connected mode, the microgrid is connected with the main grid, whilst in the islanded mode, the microgrid can be disconnected from the main grid in the event of a system emergency and still supply local load.

What is the optimal control strategy for a microgrid operating in islanded mode?

An optimal control strategy for a microgrid operating in the islanded mode and containing RES is investigated . The objective is to minimize the electricity generation cost and determine the optimal operational schedule of the microgrid considering the stochastic nature of RES.

What is the economic dispatch problem of multi-microgrids?

This paper investigates the economic dispatch (ED) problem of multi-microgrids considering the flexible loads based on distributed consensus algorithm.