Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

A Hybrid Agent-based Model Predictive Control Scheme for Smart Community Energy System with Uncertain DGs and Loads
Author:
Affiliation:

1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China;2.Department of Electrical and Computer Engineering, Mississippi State University, Starkville 39762, USA

Fund Project:

This work was supported by National Key R&D Program of China (No. 2017YFE0112600).

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    Abstract:

    A multi-agent consensus-based market scheme is proposed for the cooperation of community and multiple microgrids (MGs) in a distributed, economic and hierarchal manner. The proposed community-based market framework with frequency regulation (FR) market is formulated as a two-level scheduling problem: the global decision-making process of community agent (CA) to participate in the FR market and the interaction and control process of local MGs to achieve collaboration in response to the global target with efficient pricing rules. Specifically, the model predictive control (MPC) is integrated with the consensus-based theory to allow MG to obtain an economic and reliable dispatch in the presence of uncertainties of distributed generators and loads. Thanks to the distributed nature of the proposed scheme, its robustness to communication issues has been strengthened and a win-win situation for all energy stakeholders can be achieved. The robustness of the proposed scheme is investigated in various conditions, including different implementation strategies, communication topologies, and the level of uncertainties.

    表 1 Table 1
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    表 3 Table 3
    图1 DES在生物质预处理及其他领域的应用Fig.1 Application of DES in biomass pretreatment and other fields
    图2 DES的基本组成和结构Fig.2 The basic composition and structure of DES
    图3 DES制备纳米纤维素Fig.3 Preparation of nanocellulose by DES
    图4 DES与木质纤维素的作用机理[48]Fig.4 The reaction mechanism between DES and lignocellulose[48]
    图5 基于DES预处理的生物质精炼模式Fig.5 DES-based biomass refining model
    图1 Proposed community framework.Fig.1
    图2 Power transaction between CA and MGs. (a) MG1. (b) MG2. (c)MG3. (d) MG4. (e) MG5.Fig.2
    图3 Control sequences of MGs. (a) MG1. (b) MG2. (c) MG3. (d) MG4. (e) MG5.Fig.3
    图4 Communication topologies of investigated MG community. (a) Star connection. (b) Random connection.Fig.4
    图5 Evolution process of trading prices among MGs under different communication topologies. (a) Star connection. (b) Random connection.Fig.5
    图6 Evolution process of tie-line power and mismatched power under different communication topologies. (a) Star connection. (b) Random connection.Fig.6
    图7 Evolution process of the benefits of all agents under different communication topologies. (a) Star connection. (b) Random connection.Fig.7
    图8 Evolution process of trading prices among MGs with different adjustment factors. (a) Case A. (b) Case B. (c) Case C. (d) Case D.Fig.8
    图9 Evolution process of tie-line power and mismatched power with different adjustment factors. (a) Case A. (b) Case B. (c) Case C. (d) Case D.Fig.9
    图10 Evolution process of trading prices among MGs with communication failure. (a) Case E. (b) Case F.Fig.10
    图11 Evolution process of tie-line power and mismatched power with communication failure. (a) Case E. (b) Case F.Fig.11
    图12 Tie-line power curves between CA and main grid under different levels of uncertainties.Fig.12
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History
  • Received:October 10,2019
  • Online: May 19,2021