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Energies
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Modeling and Intelligent Control for Microgrids and Smart Grids
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Modeling and Intelligent Control for Microgrids and Smart Grids

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1555

Editors

Dr. Alessandro Pilloni

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, 09123 Cagliari, Italy
Interests: sliding mode control; smart grids; renewable generation; demand-side management; multi-agent systems; optimization; distributed parameter systems
Dr. Stevan U. Grabić

E-Mail Website
Guest Editor
Department of Power, Electronic and Telecommunication Engineering, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Interests: microgrids; smart grids; renewable energy sources; power electronic converters
Dr. Marko Vekić

E-Mail Website
Guest Editor
Department of Power, Electronic and Telecommunication Engineering, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia
Interests: microgrids; smart grids; power quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue of Energies (MDPI), “Modeling and Intelligent Control for Microgrids and Smart Grids.”

The ongoing evolution of microgrids (MGs) and smart grids (SGs) aims to transform the traditional paradigm of energy management into a sustainable, flexible, and resilient power system. Future power networks are expected to effectively integrate increasing energy demand, fully exploit renewable sources, promote distributed generation near the point of use, enable efficient energy dispatching, and leverage energy storage systems and advanced smart energy services. In such scenarios, all network nodes progressively become active agents interacting through heterogeneous communication channels. Designing appropriate multi-layer control architectures requires a deep understanding of both power system dynamics and communication infrastructure characteristics. The increasing availability of data and computational resources fosters the integration of data-driven methods, neural models, and learning-based control techniques into modern MG and SG architectures. From the perspective of the current development and application level, it is clear that there are many opportunities to explore and challenges to resolve.

This Special Issue aims to gather and disseminate the latest achievements in the theory, modelling, and applications of MGs and SGs. Topics of interest include, but are not limited to, the following:

  • Novel topologies of power electronic converters for DC and AC microgrid applications.
  • Innovative protection and circuit breaker schemes for MGs and SGs.
  • Advanced methods for MG and SG parameter identification and system modelling.
  • Stability analysis methods for MG and SG dynamics.
  • Decentralized and distributed control algorithms for primary, secondary, and tertiary control layers.
  • Decentralized observers and estimation schemes for state reconstruction in microgrids and smart grids.
  • Neural-based models for the identification, prediction, and emulation of microgrid behaviour.
  • Learning-based methods (e.g., reinforcement learning, deep learning) for system estimation and control in MG and SG energy management systems and demand-side management strategies with optimization for efficient microgrid and smart grid operation.
  • Advanced energy services and their impact on system behaviour.
  • Devices and control methods for power quality enhancement in MGs and SGs.
  • Advanced protection and control strategies for specific operating conditions (e.g., black start, anti-islanding, fault scenarios).
  • Plug-in services (e.g., vehicle-to-grid), their stability implications, and related control strategies.
  • The characterization and modelling of communication channels and protocols for MGs and SGs (PLC, 5G, 6G, Ethernet). 

Dr. Alessandro Pilloni
Dr. Stevan U. Grabić
Dr. Marko Vekić
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 2600 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

  • microgrids
  • smart grids
  • modeling and simulation
  • stability analysis
  • advanced control algorithms
  • distributed control
  • decentralized control
  • plug-in services
  • vehicle-to-grid (V2G)
  • power quality, identification, and optimization
  • energy management
  • demand-side management
  • communication protocol characterization
  • communication channels modeling
  • power electronic converters
  • renewable energy integration
  • protection schemes
  • fault diagnosis
  • state estimation
  • learning-based method
  • neural network models

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Published Papers (2 papers)

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Research

29 pages, 4666 KB  
Article
A Controlled Benchmark for Sampling-Based Monitoring of Compound Power Quality Disturbances in Nonlinear Three-Phase Systems: Trade-Offs Between Adaptive, Uniform, and Event-Triggered Strategies
by Cristian Cristobal Cuji, Edwin M. Garcia, Alexander Aguila Téllez and Milton Ruiz
Energies 2026, 19(15), 3578; https://doi.org/10.3390/en19153578 - 30 Jul 2026
Viewed by 330
Abstract
This paper presents a reproducible MATLAB R2025b benchmark for evaluating uniform, adaptive, and event-triggered sampling under a compound power-quality disturbance in a nonlinear three-phase system. The benchmark combines voltage swell, harmonic distortion, a damped transient, low-frequency oscillation, and phase unbalance within a finite [...] Read more.
This paper presents a reproducible MATLAB R2025b benchmark for evaluating uniform, adaptive, and event-triggered sampling under a compound power-quality disturbance in a nonlinear three-phase system. The benchmark combines voltage swell, harmonic distortion, a damped transient, low-frequency oscillation, and phase unbalance within a finite interval. Performance is assessed using phase-specific and aggregated three-phase indicators, including RMSE, maximum error, detection delay, reconstruction percentage, spectral deviation, critical-time error, symmetrical components, and voltage unbalance factor. To ensure methodological fairness, the strategies are compared using a common linear reconstruction method and an additional experiment with an equal sample budget. Robustness is evaluated for different disturbance severities and durations, as well as under 40 dB and 30 dB noise, using 30 Monte Carlo realizations. The Fault Detection Sensitivity Index (FDSI) is introduced as a benchmark-specific composite metric and examined through 1000 weight perturbations of ±20%. Under the natural acquisition configuration, event-triggered sampling achieved the lowest three-phase RMSE (0.0302 p.u.), the highest reconstruction percentage (96.149%), and the shortest detection delay (7.75 ms). Under equal-budget conditions, uniform sampling provided the lowest RMSE, whereas event-triggered sampling retained the fastest temporal response. The complete FDSI ranking remained stable in 100% of the sensitivity trials. The proposed framework provides a traceable pre-validation tool for sampling-based monitoring in industrial networks, microgrids, and converter-dominated electrical systems. Full article
(This article belongs to the Special Issue Modeling and Intelligent Control for Microgrids and Smart Grids)
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29 pages, 3662 KB  
Article
AMI-Informed Hierarchical Deep Reinforcement Learning–Model Predictive Control for Coordinated EV, PV, and Battery Energy Management in Campus Microgrids
by Mousa A. Aljabri, Mohammed O. Bahabri, Nasser A. Alakhrash, Fahd A. Hariri and Mohammad N. Ajour
Energies 2026, 19(13), 3210; https://doi.org/10.3390/en19133210 - 7 Jul 2026
Viewed by 519
Abstract
This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning [...] Read more.
This paper proposes an advanced metering infrastructure (AMI)-informed hierarchical energy management framework for coordinated operation of electric vehicles (EVs), photovoltaic (PV) systems, and battery energy storage systems (BESS) in campus microgrids. The proposed two-layer architecture integrates a soft actor–critic (SAC) deep reinforcement learning (DRL) agent in the upper layer with a receding horizon model predictive control (MPC) optimizer in the lower layer. The key novelty is an AMI-to-control pipeline that transforms historical 15 min smart-meter measurements into operational flexibility features and embeds them into a hierarchical SAC–MPC architecture, where the DRL layer provides adaptive coordination and the MPC layer enforces grid, storage, and EV-service constraints. The proposed framework using the real-world Pecan Street data (15 min resolution) of 73 homes across Austin, Texas and California (2014–2019) achieves a 53.1% cost reduction and a 25.7% peak demand reduction when compared with uncontrolled charging, and the proposed framework outperforms MPC-only (50.9%), DRL-only (−5.2%), and rule-based (5.1%) baselines. The statistically significant contributions of network-aware constraints, demand-response activation, and predictive look-ahead horizon are statistically significant (n = 10 independent runs) contributions (p = 0.001). The state representation informed by AMI offers directional cost improvement (+8.4%, p = 0.055) with 11% faster convergence of training. The zero network constraint violation is observed in all evaluation scenarios and the average MPC solve time is around 150 ms, which is much less than the 15 min sampling period. Sensitivity analyses show that the hierarchical DRL–MPC architecture remains computationally feasible across EV penetration, seasonal, and forecast-uncertainty scenarios. However, BESS provided no net economic benefit under the evaluated energy-only TOU tariff, increasing weekly cost by $15.25 and peak grid demand by 14.2 kW. Break-even analysis indicates that demand charges of approximately $9.9/kW per month are required for BESS to become cost-effective in the proxy system, highlighting that storage value depends strongly on tariff design and peak-demand objective formulation. Full article
(This article belongs to the Special Issue Modeling and Intelligent Control for Microgrids and Smart Grids)
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