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Processes
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Control, Optimization and Scheduling of Smart Distribution Grids
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Control, Optimization and Scheduling of Smart Distribution Grids

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2869

Editors

Prof. Dr. Mauricio Eduardo Samper

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Guest Editor
Institute of Electrical Energy (IEE), Universidad Nacional de San Juan (UNSJ)-CONICET, San Juan 5400, Argentina
Interests: planning and operation of distribution and subtransmission systems; smart grids; distributed energy resources; modeling and simulation; optimization; power quality and reliability; investment assessment and risk analysis
Prof. Dr. Marcelo Gustavo Molina

E-Mail Website
Guest Editor
Institute of Electrical Energy (IEE), Universidad Nacional de San Juan (UNSJ)-CONICET, San Juan 5400, Argentina
Interests: modeling, simulation, and control of electric power systems; energy efficiency; power electronics and electric drives; smart grid and microgrid technologies; renewable energy and energy storage
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maximiliano Martinez

E-Mail Website
Guest Editor
Institute of Electrical Energy (IEE), Universidad Nacional de San Juan (UNSJ)-CONICET, San Juan 5400, Argentina
Interests: development of a computational model for the optimal sizing and placement of energy storage systems in power systems; with a focus on energy efficiency; load frequency reserve; smart grid and microgrid technologies; renewable energy integration
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gastón Orlando Suvire

E-Mail Website
Guest Editor
Institute of Electrical Energy (IEE), Universidad Nacional de San Juan (UNSJ)-CONICET, San Juan 5400, Argentina
Interests: simulation methods; power systems dynamics and control; power electronics modeling and design; the application of wind energy and energy storage in power systems

Special Issue Information

Dear Colleagues,

Power distribution systems play a crucial role in ensuring a secure, reliable, and adequate power quality and cost-effective electricity supply. Nowadays, challenges for designing and operating distribution grids arise due to the growing adoption of distributed energy resources (DERs) and there are several uncertainties involved; thus, there is a need for resilient operation.

The massive incorporation of DER, such as distributed generation, demand response, energy storage systems, plug-in electric vehicles, agregators and microgrids, brings significant changes to the paradigms of distribution grid planning and operation. As a result, distribution companies are undergoing profound transformations, including the implementation of new tools and planning methodologies designed to provide intelligent solutions for the coordinated, efficient scheduling, control, and optimization of the system. 

This Special Issue focuses on promising and dynamic research areas, aiming to gather high-quality contributions that highlight the challenges, recent advancements, and innovative solutions for renewable energy-based power distribution systems. Topics include, but are not limited to, the following:

  • Simulation techniques, software, algorithms, or other tools for modeling and simulation of distribution grids.
  • Design, analysis, control, optimization, operation, planning, or scheduling for more flexible and resilient distribution grids.  
  • Flexibility management in distribution networks with different devices, including energy storage systems.
  • Power electronics-based solutions for smart distribution systems.
  • Emerging technologies in smart grids. Operational management of smart grids, including microgrids.

Prof. Dr. Mauricio Eduardo Samper
Prof. Dr. Marcelo Gustavo Molina
Prof. Dr. Maximiliano Martinez
Prof. Dr. Gastón Orlando Suvire
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. Processes 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 2400 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

  • planning and operation
  • distribution networks
  • smart grids
  • microgrids
  • modeling and simulation
  • optimization
  • distributed energy resources
  • power electronics and electric drives
  • renewable energy and energy storage
  • electric vehicles
  • demand response
  • reliability and resilience

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Published Papers (3 papers)

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Research

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31 pages, 8584 KB  
Article
Load Profile Assignment for Planning and Operation Support in Distribution Networks Under Partial Smart Meter Penetration
by Jorge Lara, Mauricio Samper and Delia Graciela Colomé
Processes 2026, 14(10), 1505; https://doi.org/10.3390/pr14101505 - 7 May 2026
Viewed by 547
Abstract
The growing need to enhance observability in distribution networks has driven the development of load pseudomeasurement generation methods, particularly under partial smart meter (SM) penetration. This paper proposes a load pseudomeasurement framework that builds representative daily load profiles (load curves) from hourly SM [...] Read more.
The growing need to enhance observability in distribution networks has driven the development of load pseudomeasurement generation methods, particularly under partial smart meter (SM) penetration. This paper proposes a load pseudomeasurement framework that builds representative daily load profiles (load curves) from hourly SM time series using clustering techniques, with and without weather information. Markov chain models are then used to capture day-to-day dynamics by predicting the most likely next-day profile to be assigned to customers without SM. To enable this transfer, a hierarchical grouping scheme based on monthly energy consumption is introduced to map behaviors from SM-equipped customers to customers without SM measurement. The methodology is validated with real residential data from the Low-Carbon London project under multiple observability scenarios including different SM availability levels, where SM measurements are withheld from the inputs to emulate customers without SM measurement, and the resulting pseudomeasurements are benchmarked against the original measurements. The results show that the Euclidean representative curve method achieved the most robust overall performance, with a minimum MAE of 1.65 in the Reduced × 75% SM configuration. The best-performing configuration depended on the observability level: Reduced was the most robust option under medium-to-high observability, whereas Temp_reduced with a 21-day window performed best under the lowest-observability condition. In addition, the Euclidean method showed low practical deviation in the Reduced × 25% SM case, with a bias of 0.63 and Cohen’s d = 0.27. Overall, the proposed approach accurately reproduces the hourly load shape and captures inter-day variability under partial observability conditions. Full article
(This article belongs to the Special Issue Control, Optimization and Scheduling of Smart Distribution Grids)
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22 pages, 1742 KB  
Article
Robust Two-Stage Optimization Scheduling of TG-IES Considering Gas Thermal Dynamics
by Jin Wang, Tao Zhang, Wenli Liu and Min Chen
Processes 2025, 13(6), 1836; https://doi.org/10.3390/pr13061836 - 10 Jun 2025
Cited by 1 | Viewed by 887
Abstract
On the one hand, the dynamic characteristics of gas-heat flow in the IES (integrated energy system) are important in achieving multi-energy coupling, improving system scheduling flexibility, and increasing energy regulation potential. On the other hand, the uncertainty of new energy causes fluctuations in [...] Read more.
On the one hand, the dynamic characteristics of gas-heat flow in the IES (integrated energy system) are important in achieving multi-energy coupling, improving system scheduling flexibility, and increasing energy regulation potential. On the other hand, the uncertainty of new energy causes fluctuations in the interactive power between the upper TG (transmission grid) and the lower IES, and its coupling characteristics weaken the autonomy of each system. Therefore, this paper proposes a robust two-stage scheduling strategy for TG-IES, considering gas-heat dynamic characteristics. Firstly, according to the characteristic equation of gas-heat energy flow, the dynamic model of the gas-heat network is established and applied to system scheduling. Secondly, aiming at the uncertainty problem, a TG-IES two-stage robust model is constructed, and the ATC (analytical target cascading) method and the C&CG (column and constraint generation) algorithm are combined to realize the distributed solution of the non-convex coupling model. Finally, the effectiveness of the model and strategy is verified by using the IEEE-39 system as TG and the IEEE39 Grid-20 Gas Grid-6 Heat Network system as IES. The simulation results show that using a two-stage robust model and considering the dynamic characteristics of gas and heat can effectively reduce system operating costs and improve the environmental friendliness of system scheduling. Full article
(This article belongs to the Special Issue Control, Optimization and Scheduling of Smart Distribution Grids)
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Review

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30 pages, 858 KB  
Review
Review on Ansatz Architectures of Variational Quantum Algorithms for Continuous Optimization: From Fixed Structures to Adaptive Evolution
by Chuanzhou He, Qiang Li and Jun Zhang
Processes 2026, 14(13), 2095; https://doi.org/10.3390/pr14132095 - 27 Jun 2026
Viewed by 487
Abstract
Variational quantum algorithms (VQAs) are a leading framework for realizing quantum advantages in the Noisy Intermediate-Scale Quantum (NISQ) era, with applications spanning discrete combinatorial problems and continuous optimization. While the topologies of parameterized quantum circuits (ansatzes) fundamentally govern both expressibility and trainability in [...] Read more.
Variational quantum algorithms (VQAs) are a leading framework for realizing quantum advantages in the Noisy Intermediate-Scale Quantum (NISQ) era, with applications spanning discrete combinatorial problems and continuous optimization. While the topologies of parameterized quantum circuits (ansatzes) fundamentally govern both expressibility and trainability in continuous landscapes, existing reviews predominantly focus on static algorithmic classifications or discrete settings, leaving the structural evolution and practical limitations of ansatz architectures insufficiently explored. To address this gap, this review presents a systematic analysis of variational ansatz architectures, tracing their progression from static, pre-defined topologies to adaptive growth mechanisms. Beyond traditional gradient-driven and architecture-search paradigms, we evaluate supplementary strategies such as layerwise training and noise-adaptive construction, revealing inherent vulnerabilities such as local minima entrapment and the compilation overhead induced by calibration drift. The mathematical foundations of VQAs are outlined, and representative fixed ansatz architectures, including hardware-efficient, physics-inspired, and problem-specific designs, are characterized within continuous-domain mappings. Intrinsic limitations arising from barren plateaus (BPs) and noise-induced barren plateaus (NIBPs) are analyzed, revealing the fundamental coupling between circuit depth, parameter scaling, and trainability degradation. Furthermore, adaptive construction strategies and recent advances in automated variational quantum architecture search (VQAS) are examined. Through the synthesis of intrinsic limitations (BPs, NIBPs, and hardware-algorithm coupling) and the evaluation of standardized benchmarking protocols, this review rigorously assesses the resource trade-offs of current VQA frameworks. Ultimately, next-generation ansatz design will adopt hardware–software co-design principles grounded in physical constraints, enabling scalable and noise-resilient quantum optimization. Full article
(This article belongs to the Special Issue Control, Optimization and Scheduling of Smart Distribution Grids)
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