4.5 Review

Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control Architectures

期刊

ENERGIES
卷 13, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/en13030547

关键词

microgrid; energy management system; centralized control architecture; review; research trends; clustering

资金

  1. Chilean Council of Scientific and Technological Research CONICYT-PFCHA/Doctorado Nacional [2017-21171695]
  2. CONICYT (FONDAP SERC Chile) [15110019]
  3. CONICYT (CONICYT/FONDECYT) [1181532]

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This paper presents both an extensive literature review and a qualitative and quantitative study conducted on nearly 200 publications from the last six years (based on international experience and a top-down analysis framework with five classification levels) to establish the main trends in the field of centralized energy management systems (EMS) for microgrids. No systematic trend analyses have been observed in this field in previous literature reviews. EMS attributes for several features such as objective functions, resolution techniques, operating models, integration of uncertainties, optimization horizons, and modeling detail levels are considered for main trend identification. The main contribution of this study is the identification of four specific existing research trends: (i) dealing with uncertainties (comprises 33% of the references), (ii) multi-objective strategy (29%), (iii) traditional paradigm (21%), and (iv) P-Q challenge (17%). Each trend is described and analyzed based on the main drive of these separate research fields. The key challenges and the way to cope with them are described based on the rationality of each trend, the results of previous reviews, and the previous experience of the authors. Overall, finding these main trends, together with a complete paper database and their features, serve as a useful outcome for a better understanding of the current research-specific challenges, opportunities, potential barriers, and open questions regarding the creation of future centralized EMS developments. The traditional numerical analysis is insufficient to identify research trends. Therefore, the need of further analyses based on the clustering approach is emphasized.

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