Abstract

In this paper, an improved power management strategy (PMS) for multi-agent system (MAS)-based distributed control of DC microgrid (DCMG) under communication network problems is presented in order to enhance the reliability of DCMG and to ensure the system power balance under various conditions. To implement MAS-based distributed control, a communication network is constructed to exchange information among agents. Based on the information obtained from communication and local measurements, the decision for the local controller and communication is optimally given to guarantee the system power balance under various conditions. The operating modes of the agents can be determined locally without introducing any central controller. Simultaneously, the agents can operate in a deliberative and cooperative manner to ensure global optimization by means of the communication network. Furthermore, to prevent the system power imbalance caused by the delay in grid fault detection and communication in case of the grid fault, a DC-link voltage (DCV) restoration algorithm is proposed in this study. In addition, to avoid the conflict in the DCV control among power agents in case of the grid recovery under communication failure, a grid recovery identification algorithm is also proposed to improve the reliability of DCMG operation. In this scheme, a special current pattern is generated on the DC-link at the instant of the grid recovery by the grid agent, and other power agents identify the grid recovery by detecting this current pattern. Comprehensive simulations and experiments based on DCMG testbed have been carried out to prove the effectiveness of the PMS and the proposed control schemes under various conditions.

Highlights

  • In recent years, the concept of microgrids (MGs) has been introduced as an effective and potential solution to integrate various renewable energy sources (RESs) such as wind and solar into the grid [1]

  • To deal with the aforementioned drawbacks caused by the communication network problems in both the grid fault and grid recovery cases, an improved power management strategy (PMS) using multi-agent system (MAS)-based distributed control of DC microgrid (DCMG) is presented in this paper

  • For the purpose of enhancing the reliability of DCMG and ensuring the system power balance under various conditions, this paper has presented an improved PMS for MAS-based distributed control of DCMG under communication network problems

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Summary

Introduction

The concept of microgrids (MGs) has been introduced as an effective and potential solution to integrate various renewable energy sources (RESs) such as wind and solar into the grid [1]. In [14], an intelligent control based on the MAS technique is presented to control the MG in both the grid-connected and islanded modes In this scheme, the optimization in real-time control of the MG is achieved by the negotiation among agents to share the available energy. In the MAS-based distributed control, the communication network problems such as delay or failure are ubiquitous during the process of information transmission among agents, which may cause the system malfunction, instability, or even collapse [19]. When DCMG operates in the grid-connected mode, the system power balance is normally achieved by implementing the DC-link voltage control mode (DCVM) by the grid agent. To deal with the aforementioned drawbacks caused by the communication network problems in both the grid fault and grid recovery cases, an improved power management strategy (PMS) using MAS-based distributed control of DCMG is presented in this paper. 10::LWoaPdGSpoagweenrt iss ngorteabteler ttohcaonnwtroinl DdCpVo.wer 1: Load power is greate(rPth≥anPwi)n.d power (PL ≥ PW). 0: Lo0a:dLpooawderpioswsmeralilse(Prsmth

Control Strategy of Grid Agent
Control Strategy of Battery Agent
Control Strategy of WPGS Agent
Proposed Control Strategies under Communication Network Problems
Control Strategy for Grid Fault Case
Control Strategy for Grid Recovery Case
Grid-connected Case
Islanded Case
Case of Communication Problems
Conclusions
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