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Renewable Sources and Storage: Grid Impact, Modeling and Integration Strategies: 2nd Edition

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 3395

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Prof. Dr. Sebastijan Seme

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Faculty of Energy Technology, University of Maribor, Hočevarjev trg 1, 8270 Krško, Slovenia
Interests: modeling and optimization; energy efficiency; power electronics; photovoltaic systems; renewable energy sources
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Dr. Klemen Sredenšek

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Dear Colleagues,

As the integration of renewable energy sources and energy storage systems becomes increasingly crucial to achieving the sustainability and resilience of modern electrical networks, understanding and optimizing their penetration are essential. This transition towards a more sustainable energy system presents significant challenges, as well as opportunities, in terms of the modeling, analysis, and effective management of these resources within electrical networks.

This Special Issue aims to present and disseminate the latest research findings and advancements related to the modeling and analysis of the penetration of renewable energy sources and storage systems in electrical networks. We seek the submission of contributions that explore innovative approaches, methodologies, and technologies that can enhance the efficiency, reliability, and stability of electrical networks incorporating renewable energy and storage solutions.

Topics of interest for this Special Issue include, but are not limited to, the following:

Modeling techniques for renewable energy integration in electrical networks;
Analysis of the impact of renewable energy sources on grid stability and reliability;
The role of storage systems in alleviating the demand on electrical networks;
Smart grid technologies for renewable energy and storage management;
Grid modernization to accommodate high levels of renewable penetration;
Techniques for optimizing renewable energy systems for low-voltage network usage;
Economic and environmental impacts of renewable energy and storage systems;
Electricity management systems in low-voltage networks (domestic use);
Policy and regulatory aspects of renewable energy and storage penetration;
Market impacts on the integration of renewable energy sources;
Hydrogen technologies and their integration into energy systems;
Electrification of heating and cooling systems and their impact on the grid;
Wind and small hydropower energy systems and their interaction with the grid;
Smart buildings, smart cities, and intelligent energy networks;
Climate-related aspects and resilience of energy infrastructure;
Modeling and operation of electricity distribution tariffs and network charges;
Integration of electrical machines and devices in renewable-based networks;
Electricity market design and flexibility mechanisms for RES integration;
Agrivoltaics and floating photovoltaic systems.

Prof. Dr. Sebastijan Seme
Dr. Klemen Sredenšek
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-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 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

renewable energy sources
electrical network
energy storage systems
modeling and analysis
optimization

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Related Special Issue

Renewable Sources and Storage: Grid Impact, Modeling, and Integration Strategies in Energies (6 articles)

Published Papers (4 papers)

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Research

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22 pages, 4101 KB

Open AccessArticle

Enhancing Peak Shaving Efficiency in Small Hydro Power Plants Through Machine Learning-Based Predictive Control

by Francesca Mangili, Marco Derboni, Lorenzo Zambon, Vincenzo Giuffrida and Matteo Salani

Energies 2026, 19(4), 985; https://doi.org/10.3390/en19040985 - 13 Feb 2026

Viewed by 425

Abstract

Small hydropower plants (HPPs) equipped with water storage play an important role in managing fluctuating energy demand. This article presents a real-world case study in which model predictive control (MPC), driven by energy-demand and water-inflow forecasts produced using the Light Gradient Boosting Machine (LGBM), is applied to optimize the operation of a small hydropower plant for peak shaving. A comparative analysis is conducted between the current non-predictive control strategy, which relies on operator decisions for peak shaving, and a fully automatic controller that optimally schedules the utilization of available water resources based on ML predictions. Results show that the MPC can outperform the operator-based scheduling and that this has the potential to improve the peak shaving capabilities of small HPPs. Unlike previous studies that predominantly focus on large and complex hydropower systems or introduce new control formulations evaluated under idealized assumptions, this work offers a pragmatic solution to the underexplored context of peak shaving for small HPPs operated with limited data and resources, that small utilities can adopt with minimal effort using their own data. We show that even these small-scale hydropower operations have room for improvement through optimal scheduling. Full article

(This article belongs to the Special Issue Renewable Sources and Storage: Grid Impact, Modeling and Integration Strategies: 2nd Edition)

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24 pages, 5597 KB

Open AccessArticle

Transformation of the Network Tariff Model in Slovenia: Impact on Prosumers and Other Network Users

by Klemen Sredenšek, Jernej Počivalnik, Domen Kuhar, Eva Simonič and Sebastijan Seme

Energies 2026, 19(2), 567; https://doi.org/10.3390/en19020567 - 22 Jan 2026

Viewed by 654

Abstract

The aim of this paper is to present the transformation of the network tariff system in Slovenia using a comprehensive assessment methodology for the techno-economic evaluation of electricity costs for households. The novelty of the proposed approach lies in the combined assessment of the previous and new network tariff systems, explicitly accounting for power-based network tariff components, time-block-dependent charges, and different support schemes for household photovoltaic systems, including net metering and credit note-based schemes. The results show that the transition from an energy-based to a more power-based network tariff system, introduced primarily to mitigate congestion in distribution networks, is not inherently disadvantageous for consumers and prosumers. When tariff structures are appropriately designed, the new framework can support efficient grid utilization and maintain favorable conditions for prosumers, particularly those integrating battery storage systems. Overall, the proposed methodology provides a transparent and robust framework for evaluating the economic impacts of network tariff reforms on residential consumers and prosumers, offering relevant insights for tariff design and the development of future low-carbon household energy systems. Full article

(This article belongs to the Special Issue Renewable Sources and Storage: Grid Impact, Modeling and Integration Strategies: 2nd Edition)

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19 pages, 734 KB

Open AccessArticle

Optimization of an Off-Grid PV System with Respect to the Loss of Load Probability Value

by Zvonimir Šimić, Marinko Barukčić, Goran Knežević and Danijel Topić

Energies 2025, 18(19), 5174; https://doi.org/10.3390/en18195174 - 29 Sep 2025

Viewed by 1163

Abstract

In this paper, a method for finding the optimal size of an off-grid photovoltaic (PV) system regarding the Loss of Load Probability (LOLP) value is proposed. The proposed method is applied to an off-grid PV system in a scenario where an electricity supply needs to be provided during three summer months. According to the simulation results, 11 PV modules and 11 batteries are required with 0% LOLP. An increase in LOLP to 1% results in 10 PV modules and 7 batteries, and a 24.9% cost reduction. With 5% LOLP, the cost reduction is 39.3%, and with 10% LOLP, it is 49.5%. The use of less expensive batteries also contributes to cost reduction. With the modification of electricity consumption, one combination can be suitable for 4% lower LOLP, and the cost can be reduced to up to 7%. It can be concluded that the required increase in LOLP value leads to a decrease in the number of required PV modules and batteries and to the use of less expensive battery technologies, which then leads to cost reduction. Additionally, with the modification of electricity consumption, the amount of power deficit can be reduced, which makes one combination suitable for lower LOLP and also leads to a further system cost decrease. Lower system costs can encourage more people to invest in an off-grid PV system in locations with occasional consumption or consumption over only a few months. The cost reduction strongly depends on how willing users are to not have all their electricity demands met. Full article

(This article belongs to the Special Issue Renewable Sources and Storage: Grid Impact, Modeling and Integration Strategies: 2nd Edition)

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Review

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39 pages, 524 KB

Open AccessReview

The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets

by Ciaran O’Connor, Mohamed Bahloul, Steven Prestwich and Andrea Visentin

Energies 2026, 19(8), 1929; https://doi.org/10.3390/en19081929 - 16 Apr 2026

Viewed by 670

Abstract

Electricity price forecasting has become a critical tool for decision-making in energy markets, particularly as the increasing penetration of renewable energy has introduced greater volatility and uncertainty. Historically, research in this field has been dominated by point forecasting methods, which provide single-value predictions but fail to quantify uncertainty. However, as power markets evolve due to renewable integration, smart grids, and regulatory changes, the need for probabilistic forecasting has become more pronounced, offering a more comprehensive approach to risk assessment and market participation. This paper presents a review of probabilistic forecasting methods, tracing their evolution from Bayesian and distribution based approaches to quantile regression techniques to recent developments in conformal prediction. Particular emphasis is placed on advancements in probabilistic forecasting, including validity-focused methods that address key limitations in uncertainty estimation. Additionally, this review extends beyond the day-ahead market to include the intra-day and balancing markets, where forecasting challenges are intensified by higher temporal granularity and real-time operational constraints. We examine state-of-the-art methodologies, key evaluation metrics, and ongoing challenges, such as forecast validity, model selection, and the absence of standardised benchmarks, providing researchers and practitioners with a comprehensive and timely resource for navigating the complexities of modern electricity markets. Full article

(This article belongs to the Special Issue Renewable Sources and Storage: Grid Impact, Modeling and Integration Strategies: 2nd Edition)

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