Microgrid Optimization Dispatch Graduation Thesis Defense PPT

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.

How to optimize a microgrid?

The economic cost, network loss, and branch stability index for microgrids are also optimized. The interval optimization is modeled as a Markov decision process (MDP). Then, an improved DRL algorithm called triplet-critics comprehensive experience replay soft actor-critic (TCSAC) is proposed to solve it.

What is a microgrid?

loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid. A microgrid can connect and disconnect from the grid to enable it to operate in both grid-connected or island mode.”

Why are microgrids important?

Nowadays, a growing proportion of the generated renewable energy, such as wind and solar power, is employed to power loads via microgrids (MGs) rather than the traditional distribution grids [2-4]. Therefore, optimal dispatch of the MGs is critical for promoting the application and accommodation of renewable energies .

What is the main task in microgrid operation phase?

Abstract: Dispatching the output of distributed power sources is the main task in the microgrid operation phase.