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

Abstract

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.

An Overview of Microgrids Challenges in the Mining Industry

“…To this end, the optimization problem requires inputs that are not only related to internal microgrid information (e.g. number of DGs, demanded power profiles, state of charge and health of ESS), but also external information such as the weather forecast and historical data from the energy market [25]. Because these tasks require considerable computational resources, the control period of tertiary controllers is between few minutes and one day.…”

“…Although the classical approach at this control level pursues an economical optimization, the multi-objective approach, which incorporates additional objectives such as carbon footprint mitigation or battery life optimization, is the currently considered cutting-edge. Because days-ahead models of generation and load profiles are required, the uncertainty of microgrid variables should be considered at this level to solve the dispatch problem [25]. In this way, information from DGs, controllable loads and smart meters in the microgrid, allows phenomenological and/or artificial intelligence-based structures to be taken into account in the construction of data-driven models.…”

“…Though it is possible to state power profiles, concerns such as cloudiness, changes in wind speed and direction, or rock hardness lead to power disturbances which should be managed properly. Although several EMS schemes have been reported for LV microgrids [25], to the best of our knowledge, reports on heavy industry applications are limited.…”

Reconfigurable solar photovoltaic systems: A review

“…Even though solar photovoltaic microgrids (PV MGs) can reduce the impact on the distribution network, SPVSs may suffer from issues related to power reliability, quality and conversion efficiency. Therefore, it is a necessity to modify existing solar PV MG architectures [10,11,12]. SPVSs and microgrids consist of power electronic interfaces (PEI) which make these systems highly controllable and flexible for modifications [13,14,15].…”

A Novel Cooperative Controller for Inverters of Smart Hybrid AC/DC Microgrids

“…Presently, with the progression of new digital technologies, like micro processed systems, and developments in power electronics, several applications have been deployed in the smart grid, particularly in the formulation of electronic energy converters and controllers. Of late, academics have made noteworthy contributions which have caused a significant effect on these domains, chiefly aimed at data acquirement, automation, and regulation of microgrids [9]. The microgrids do not merely assimilate the distributed generation with the utility grid in a dependable and tidy manner but also offer greater dependability in its capability to function despite natural phenomena and dynamic distribution grids, consequently causing reduced energy losses in transmission and dissemination and saving the time consumed in building and investment [10][11][12][13][14].…”