Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side

TL;DR

This paper proposes a deep reinforcement learning strategy for microgrid optimization that considers priority flexible demand side, aiming to improve energy dispatch, cost savings, and supply reliability amid renewable energy variability.

Contribution

It introduces a novel reinforcement learning approach incorporating priority response of flexible loads and energy storage, enhancing microgrid control and efficiency.

Findings

Effective management of flexible loads and energy storage improves microgrid stability.

The proposed method reduces power input costs and ensures reliable supply.

Simulation results demonstrate improved learning efficiency and control performance.

Abstract

As an efficient way to integrate multiple distributed energy resources and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads, energy storage systems, price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor-critic with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training.