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Processes
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Research Advances in Modeling, Optimization, and Control of Distributed Energy...
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Research Advances in Modeling, Optimization, and Control of Distributed Energy Systems

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 322

Special Issue Editors

Prof. Dr. Yskandar M. Hamam

E-Mail Website
Guest Editor
1. The Department of Electrical Engineering, Tshwane University of Technology, Pretoria, South Africa
2. The Ecole Superieure of Electronique and Electrotechnique, Noisy le Grand, France
Interests: electrical power systems; water distribution networks; optimal planning and optimisation of networks
Prof. Dr. Adedayo Ademola Yusuff

E-Mail Website
Guest Editor
Department of Electrical and Smart Systems Engineering, University of South Africa, Corner of Christiaan de Wet Road & Pioneer Avenue, 1709 Gauteng, FL, South Africa
Interests: computational intelligence; adaptive networks; network characterization optimization; systems modeling tools

Special Issue Information

Dear Colleagues,

This Special Issue addresses problems related to the planning and operation of distributed energy systems. It seeks high-quality papers that focus on the planning, management and control of these systems.

With the increasing integration of small renewable energy resources, these issues have become very important. Several aspects have to be considered, such as the following:

  • Optimal operation that takes into account the contribution of small production units.
  • Demand-side management and load shedding that favors the use of intermittent production units at the time of their availability.
  • Accounting for instability problems due to the variable production of units.
  • In the case of available storage units, managing the use of the energy stored and used in the network.

In this Special Issue, papers addressing these challenges and others related to the modeling, optimization, and control of distributed energy systems will be considered.

Prof. Dr. Yskandar M. Hamam
Prof. Dr. Adedayo Ademola Yusuff
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 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. Processes is an international peer-reviewed open access monthly 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

  • renewable energy
  • optimal planning
  • optimal operation and control
  • distributed energy management
  • mixed energy sources
  • energy storage management.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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27 pages, 5818 KiB  
Article
Scenario-Based Stochastic Optimization for Renewable Integration Under Forecast Uncertainty: A South African Power System Case Study
by Martins Osifeko and Josiah Munda
Processes 2025, 13(8), 2560; https://doi.org/10.3390/pr13082560 - 13 Aug 2025
Viewed by 174
Abstract
South Africa’s transition to a renewable-powered grid faces critical challenges due to the inherent variability of wind and solar generation as well as the need for economically viable and reliable dispatch strategies. This study proposes a scenario-based stochastic optimization framework that integrates machine [...] Read more.
South Africa’s transition to a renewable-powered grid faces critical challenges due to the inherent variability of wind and solar generation as well as the need for economically viable and reliable dispatch strategies. This study proposes a scenario-based stochastic optimization framework that integrates machine learning forecasting and uncertainty modeling to enhance operational decision making. A hybrid Long Short-Term Memory–XGBoost model is employed to forecast wind, photovoltaic (PV) power, concentrated solar power (CSP), and electricity demand, with Monte Carlo dropout and quantile regression used for uncertainty quantification. Scenarios are generated using appropriate probability distributions and are reduced via Temporal-Aware K-Means Scenario Reduction for tractability. A two-stage stochastic program then optimizes power dispatch under uncertainty, benchmarked against Deterministic, Rule-Based, and Perfect Information models. Simulation results over 7 days using five years of real-world South African energy data show that the stochastic model strikes a favorable balance between cost and reliability. It incurs a total system cost of ZAR 1.748 billion, with 1625 MWh of load shedding and 1283 MWh of curtailment, significantly outperforming the deterministic model (ZAR 1.763 billion; 3538 MWh load shedding; 59 MWh curtailment) and the rule-based model (ZAR 1.760 billion, 1.809 MWh load shedding; 1475 MWh curtailment). The proposed stochastic framework demonstrates strong potential for improving renewable integration, reducing system penalties, and enhancing grid resilience in the face of forecast uncertainty. Full article
(This article belongs to the Special Issue Research Advances in Modeling, Optimization, and Control of Distributed Energy Systems)
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