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Sustainability
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Modeling, Control, and Optimization of Hybrid Energy Systems
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Modeling, Control, and Optimization of Hybrid Energy Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 5 October 2025 | Viewed by 4540

Special Issue Editors

Dr. Ayman Al-Quraan

E-Mail Website
Guest Editor
Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan
Interests: wind energy; renewable energy; solar energy; PV systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ala'aldeen Al-Halhouli

E-Mail Website
Guest Editor
Mechatronics Engineering Department, German Jordanian University, Amman 11180, Jordan
Interests: inkjet printed electronics; nanomaterials for future energy; hydrogen technology
Dr. Ahmad M. A. Malkawi

E-Mail Website
Guest Editor
Mechatronics Engineering Department, School of Engineering, The University of Jordan, Amman 11942, Jordan
Interests: power electronics and drive; renewable energy systems; micro-grid and smart-grids; hybrid energy storage systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modeling renewable and hybrid energy systems is a critical aspect of modern energy engineering, focusing on optimizing the integration and utilization of various renewable energy sources, such as solar, wind, hydro, and biomass. These models are essential for predicting energy production, assessing system performance, and planning for future energy needs. By leveraging advanced computational techniques and simulation tools, individuals can analyze the behavior of these systems under different conditions and scenarios. For instance, modeling can help determine the optimal configuration of solar panels and wind turbines in a hybrid system to maximize efficiency and reliability. Additionally, these models can incorporate real-time data and predictive analytics to enhance energy management and sustainability.

Hybrid energy systems, which combine multiple renewable sources, offer significant advantages in terms of energy stability and resilience. Modeling these systems involves a comprehensive approach that considers various factors, such as resource availability, grid integration, storage solutions, and economic feasibility. By simulating the interactions between different energy sources, engineers can develop strategies to mitigate intermittency and ensure a consistent energy supply. For example, a hybrid system might use solar energy during the day and wind energy at night, supported by battery storage to balance supply and demand. Moreover, these models can aid in identifying potential challenges and opportunities, guiding policymakers and stakeholders in making informed decisions to promote the adoption of renewable energy technologies. Overall, the modeling of renewable and hybrid energy systems is pivotal in advancing towards a sustainable and resilient energy future.

We encourage researchers and colleagues to submit both their critical review papers and original, distinct works. These are just a few examples of possible models and topics which can be submitted to this issue:

  • Model of renewable energy;
  • Hybrid renewable energy systems using biomass and biofuel;
  • Energy systems;
  • Energy conservation;
  • Energy-saving technologies;
  • Applications of robotics in renewable energy systems in energy policy;
  • Electric automobiles;
  • Applications of power electronics in renewable energy;
  • Pollution in the atmosphere;
  • Solar systems;
  • Wind systems;
  • Artificial intelligence applications in the energy system; 
  • Urban wind energy and its aerodynamic effect.

Dr. Ayman Al-Quraan
Prof. Dr. Ala'aldeen Al-Halhouli
Dr. Ahmad M. A. Malkawi
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. Sustainability 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

  • renewable energy
  • solar energy
  • wind energy systems
  • biomass and biofuels
  • geothermal energy
  • tidal power
  • wave energy
  • photosynthetic process
  • hydro-power

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Published Papers (4 papers)

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Research

23 pages, 1734 KiB  
Article
A Comparative Modeling Framework for Forecasting Distributed Energy Resource Adoption Under Trend-Based and Goal-Oriented Scenarios
by Zheng Grace Ma, Magnus Værbak and Bo Nørregaard Jørgensen
Sustainability 2025, 17(12), 5283; https://doi.org/10.3390/su17125283 - 7 Jun 2025
Viewed by 323
Abstract
Accurate forecasting of Distributed Energy Resource (DER) adoption is essential for decarbonization, effective policy, and infrastructure planning. This paper develops a comparative framework integrating trend-based and goal-oriented approaches using the logistic growth and Bass diffusion models. Using Danish household data for electric vehicles [...] Read more.
Accurate forecasting of Distributed Energy Resource (DER) adoption is essential for decarbonization, effective policy, and infrastructure planning. This paper develops a comparative framework integrating trend-based and goal-oriented approaches using the logistic growth and Bass diffusion models. Using Danish household data for electric vehicles (EVs), heat pumps (HPs), and rooftop photovoltaics (PVs), we evaluate four logistic-growth-based and two Bass-diffusion-based methods. Each method supports standard curve-fitting (trend-based) or incorporates explicit policy goals (goal-based), such as reaching a specified adoption threshold by a target year. An integrated flow diagram visually summarizes the decision process for method selection based on data availability, market maturity, and policy targets. Results show that Bass diffusion excels in early-stage or policy-driven markets like EVs, while logistic approaches perform better for PVs after subsidies are removed, with HP adoption falling in between. A key innovation is integrating future adoption targets into parameter estimation, enabling stakeholders to assess the required acceleration in adoption rates. The findings highlight the need to align model choice with data, market conditions, and policy objectives, offering practical guidance to accelerate DER deployment. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Hybrid Energy Systems)
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22 pages, 5928 KiB  
Article
A Method for Calculating the Optimal Size of Energy Storage for a GENCO
by Marin Mandić, Tonći Modrić and Elis Sutlović
Sustainability 2025, 17(5), 2278; https://doi.org/10.3390/su17052278 - 5 Mar 2025
Viewed by 677
Abstract
Market liberalization and the growth of renewable energy sources have enabled the rise of generation companies (GENCOs) managing diverse generation portfolios, creating a dynamic market environment that necessitates innovative energy management strategies to enhance operational efficiency and economic viability. Investing in the energy [...] Read more.
Market liberalization and the growth of renewable energy sources have enabled the rise of generation companies (GENCOs) managing diverse generation portfolios, creating a dynamic market environment that necessitates innovative energy management strategies to enhance operational efficiency and economic viability. Investing in the energy storage system (ESS), which, in addition to participating in the energy and ancillary services markets and in joint operations with other GENCO facilities, can mitigate the fluctuation level from renewables and increase profits. Besides the optimal operation and bidding strategy, determining the optimal size of the ESS aligned with the GENCO’s requirements is significant for its market success. The purpose of the ESS impacts both the sizing criteria and the sizing techniques. The proposed sizing method of ESS for a GENCO daily operation mode is based on the developed optimization operation model of GENCO with utility-scale energy storage and a cost-benefit analysis. A GENCO operates in a market-oriented power system with possible penalties for undelivered energy. The proposed method considers various stochastic phenomena; therefore, the optimization calculations analyze the GENCO operation over a long period to involve multiple potential combinations of uncertainties. Numerical results validate the competencies of the presented optimization model despite many unpredictable parameters. The results showed that both the battery storage system and the pumped storage hydropower plant yield a higher net income for a specific GENCO with a mixed portfolio, regardless of the penalty clause. Considering the investment costs, the optimal sizes for both types of ESS were obtained. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Hybrid Energy Systems)
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41 pages, 101624 KiB  
Article
Power Demand Patterns of Public Electric Vehicle Charging: A 2030 Forecast Based on Real-Life Data
by Marco Baronchelli, Davide Falabretti and Francesco Gulotta
Sustainability 2025, 17(3), 1028; https://doi.org/10.3390/su17031028 - 27 Jan 2025
Cited by 1 | Viewed by 1604
Abstract
As the adoption of electric vehicles accelerates, understanding the impact of public charging on the power grid is crucial. However, today, a notable gap exists in the literature regarding approaches capable of accurately estimating the expected influence of e-mobility power demand on electrical [...] Read more.
As the adoption of electric vehicles accelerates, understanding the impact of public charging on the power grid is crucial. However, today, a notable gap exists in the literature regarding approaches capable of accurately estimating the expected influence of e-mobility power demand on electrical grids, especially at medium and low voltage levels. To fill this gap, in this study, a procedure is proposed to estimate the power demand patterns of public car parks in a 2030 scenario. To this end, data collected from real-life car parks in Italy are used in Monte Carlo simulations, where probabilistic daily power demand curves are created with different maximum charging powers (from 7.4 kW to ultra-fast charging). The results highlight high variability in the power demand depending on the location and type of car park. City center car parks exhibit peak demand during morning hours, linked to commercial activities, while car parks near railway stations and hospitals show demand patterns aligned with transportation and healthcare needs. Business area car parks, in contrast, have a more pronounced demand during work hours on weekdays, with much lower activity during weekends. This study also demonstrates that, in some situations, ultra-fast charging can increase peak power demand from the grid by up to 210%. Given their contribution to the existing literature, the power demand patterns from this research constitute a valuable starting point for future studies aimed at quantitatively assessing the impact of e-mobility on the power system. In addition, they can effectively support decision-makers in optimally designing the e-mobility recharge infrastructure. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Hybrid Energy Systems)
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29 pages, 18077 KiB  
Article
Efficiency Maximization of Stand-Alone HRES Based on Tri-Level Economic Predictive Technique
by Ayman Al-Quraan, Ibrahim Athamnah and Ahmad M. A. Malkawi
Sustainability 2024, 16(23), 10762; https://doi.org/10.3390/su162310762 - 8 Dec 2024
Cited by 1 | Viewed by 1118
Abstract
Renewable energy has been widely used in grid-connected and standalone hybrid renewable energy systems. These systems require a hybrid energy storage system due to the unpredictable climate and the inequality between the produced energy and the consumed energy. In this paper, a tri-level [...] Read more.
Renewable energy has been widely used in grid-connected and standalone hybrid renewable energy systems. These systems require a hybrid energy storage system due to the unpredictable climate and the inequality between the produced energy and the consumed energy. In this paper, a tri-level optimization method is used to optimize the sizing and the energy management of a standalone HRES, simplify the proposed optimization problem, and speed up the convergence process. Horizon prediction and weighting factor strategies are combined with the tri-level technique to find the most appropriate quantity of each element in the project and find the best energy management strategy. The objective function of the proposed methodology aims to minimize the total cost and improve the efficiency of the whole system. The proposed method was investigated on a standalone PV-WT with battery-hydrogen storage in different scenarios. The simulation results from the Matlab toolbox show that the performance indicators (cost and efficiency) are affected by the combination of the weighting factor and the forecasting index. The total productivity was improved by more than 2.5% in some scenarios while the investment cost and the running cost were reduced by values of 49.3% and 28.6%, respectively. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Hybrid Energy Systems)
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