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Special Issue "Machine Learning Algorithms for Operation and Control of Microgrids with Distributed Energy Sources"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 20 April 2023 | Viewed by 2584

Special Issue Editor

Dr. Wencong Su
E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA
Interests: power and energy systems; energy internet; electrified transportation systems; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to invite submissions to the Special Issue on “Machine Learning Algorithms for Operation and Control of Microgrids with Distributed Energy Sources”.

In recent years, microgrids have attracted attention of researchers due to their ability to sustain the penetration of renewables and supply power locally during emergencies. Various mathematical model-based optimization models have been developed and are widely applied in operation and control of microgrid systems. However, with the high uncertainty of distributed energy sources, traditional methods often face two major challenges, as follows.

(i) Mathematical-based optimization models usually require complex mathematical models and are often less flexible for dynamic environments with high uncertainties.

(ii) Re-optimization process is usually required to maintain the power balance in the system. This can take a long time and may not meet the requirements of a real-time operation.

Advanced machine learning algorithms have been rapidly growing to solve operational and control problems in microgrid systems with a fast response. In this Special Issue, we are looking for novel machine learning algorithms/methods, and technologies to enhance energy efficiency as well as to handle the aforementioned problems in the operation and/or control of microgrids. Review and survey articles on the following topics are also encouraging for submission.

Topics of interest for publication include, but are not limited to:

- Advanced machine learning algorithms for operation and control of microgrids

- Applications of Internet of Things (IoT) in power systems and microgrids operation and control

- Distribution system and smart grids optimization, planning and control

- Energy management systems for microgrids

- Machine learning-based predictive modelling in power systems

- Integration of renewables and EVs in microgrids

- Real-time operation and control of microgrids

- Resilience enhancement through/for microgrids

- Multiagent systems for microgrids

- Optimal economic dispatch of microgrids

- Self-healing strategies for islanded microgrids

Dr. Wencong Su
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligent
  • control system
  • deep learning
  • distributed energy sources
  • energy storage
  • machine learning
  • microgrids
  • multiagent system
  • operation
  • optimization
  • power system
  • reinforcement learning
  • renewable energy sources
  • smart grids

Published Papers (4 papers)

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Research

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Article
Load Forecast of Electric Vehicle Charging Station Considering Multi-Source Information and User Decision Modification
Energies 2022, 15(19), 7021; https://doi.org/10.3390/en15197021 - 24 Sep 2022
Viewed by 210
Abstract
In view of the current multi-source information scenario, this paper proposes a decision-making method for electric vehicle charging stations (EVCSs) based on prospect theory, which considers payment cost, time cost, and route factors, and is used for electric vehicle (EV) owners to make [...] Read more.
In view of the current multi-source information scenario, this paper proposes a decision-making method for electric vehicle charging stations (EVCSs) based on prospect theory, which considers payment cost, time cost, and route factors, and is used for electric vehicle (EV) owners to make decisions when the vehicle’s electricity is low. Combined with the multi-source information architecture composed of an information layer, algorithm layer, and model layer, the load of EVCSs in the region is forecast. In this paper, the Monte Carlo method is used to test the IEEE-30 model and the traffic network based on it, and the spatial and temporal distribution of charging load in the region is obtained, which verifies the effectiveness of the proposed method. The results show that EVCS load forecasting based on the prospect theory under the influence of multi-source information will have an impact on the space–time distribution of the EVCS load, which is more consistent with the decisions of EV owners in reality. Full article
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Article
A Novel Synchrophasor Estimation Based on Enhanced All-Phase DFT with Iterative Compensation and Its Implementation
Energies 2022, 15(19), 6964; https://doi.org/10.3390/en15196964 - 23 Sep 2022
Viewed by 276
Abstract
Synchrophasor estimation was mostly used in transmission systems in the past, and it is difficult to directly apply an existing synchrophasor algorithm to a distribution system with a more complex structure and environment. A synchrophasor estimation algorithm with a high accuracy and fast [...] Read more.
Synchrophasor estimation was mostly used in transmission systems in the past, and it is difficult to directly apply an existing synchrophasor algorithm to a distribution system with a more complex structure and environment. A synchrophasor estimation algorithm with a high accuracy and fast response speed is required to complete the calculation of the phasor in the face of the complex and changeable power signal of a distribution network. Therefore, an enhanced all-phase discrete Fourier transform (e-apDFT) algorithm is proposed for a distribution system in this paper, and the algorithm is deployed in a phasor measurement unit (PMU) prototype based on digital signal processing (DSP). Aiming to solve the problem of the accuracy of the traditional apDFT being reduced when the response speed is fast due to the influence of a dense spectrum, the existing algorithm is improved through iteratively compensating the spectral interferences to the main bin produced by adjacent bins. The experimental results show that the e-apDFT algorithm still has a fast response speed and that its estimation accuracy is much better than that of the traditional apDFTs in the presence of adjacent harmonic components. The proposed algorithm also complies with the IEEE standards for P-class PMUs. Full article
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Article
Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch
Energies 2022, 15(6), 2140; https://doi.org/10.3390/en15062140 - 15 Mar 2022
Cited by 1 | Viewed by 702
Abstract
Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency [...] Read more.
Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency variable instead of the local voltage for adjusting loads, which has led to obstacles in operating DC microgrids in the context of increasingly rising power electronic loads. Moreover, the consideration of voltage-dependent demand response and optimal battery dispatch has posed challenges for the traditional planning methods, such as stochastic programming, because of nonlinear constraints. Considering these facts, this paper proposes a model predictive control-based integrated voltage-based demand response and batteries’ optimal dispatch operation for minimizing the entire DC microgrid’s operating cost. In the proposed model predictive control approach, the binary decisions about voltage-dependent demand response and charging or discharging status of storage batteries are determined using a deep-Q network-based reinforcement learning method to handle uncertainties in various operating conditions (e.g., AC grid-connect faults and DC sources variations). It also helps to improve the DC microgrid operation efficiency in the two aspects: continuously avoiding load shedding or shifting and reducing the batteries’ charge and discharge cycles to prolong their service life. Finally, the proposed method is validated by comparing to the stochastic programming-based model predictive control method. Simulation results show that the proposed method obtains convergence with approximately 41.95% smaller operating cost than the stochastic optimization-based model predictive control method. Full article
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Review

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Review
Grid-Forming Converters for Stability Issues in Future Power Grids
Energies 2022, 15(14), 4937; https://doi.org/10.3390/en15144937 - 06 Jul 2022
Cited by 3 | Viewed by 1039
Abstract
Historically, the power system has relied on synchronous generators (SGs) to provide inertia and maintain grid stability. However, because of the increased integration of power-electronics-interfaced renewable energy sources, the grid’s stability has been challenged in the last decade due to a lack of [...] Read more.
Historically, the power system has relied on synchronous generators (SGs) to provide inertia and maintain grid stability. However, because of the increased integration of power-electronics-interfaced renewable energy sources, the grid’s stability has been challenged in the last decade due to a lack of inertia. Currently, the system predominantly uses grid-following (GFL) converters, built on the assumption that inertial sources regulate the system stability. Such an assumption does not hold for the low-inertia grids of the future. Grid-forming (GFM) converters, which mimic the traditional synchronous machinery’s functionalities, have been identified as a potential solution to support the low-inertia grids. The performance analysis of GFM converters for small-signal instability can be found in the literature, but large-signal instability is still an open research question. Moreover, various topologies and configurations of GFM converters have been proposed. Still, no comparative study combining all GFC configurations from the perspective of large-signal stability issues can be found. This paper combines and compares all the existing GFM control schemes from the perspective of large-signal stability issues to pave the way for future research and development of GFM converters for large-signal stability analysis and stabilization of the future low-inertia grids. Full article
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