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Energies
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Optimization and Control of Smart Energy Systems
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Optimization and Control of Smart Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (5 May 2026) | Viewed by 1757

Special Issue Editor

Dr. Adib Allahham

E-Mail Website
Guest Editor
Department of Mathematics, School of Engineering, Physics and Mathematics, Northumbria University, Newcastle upon Tyne NE1 8SA, UK
Interests: smart grids; electricity distribution; power sector; multi-vector energy systems

Special Issue Information

Dear Colleague,

The accelerating digitalisation and decarbonisation of the global energy sector are reshaping traditional energy networks into highly interconnected smart energy systems that integrate electricity, heating, cooling, transport, and hydrogen infrastructures. Managing these multi-vector systems efficiently requires advanced optimisation and control strategies that address increasing complexity, uncertainty, and the intermittent nature of renewable energy resources.

This Special Issue aims to gather original research and comprehensive reviews focusing on innovative optimisation algorithms, real-time control frameworks, and decision-support models that enhance the flexibility, reliability, and sustainability of smart energy systems. The issue seeks contributions that advance both theoretical and applied aspects of system operation, emphasising interdisciplinary approaches that combine control theory, data science, and energy system modelling.

This Special Issue welcomes submissions covering a broad range of topics related to the modelling, optimisation, and control of smart and integrated energy systems, including but not limited to:

  • Advanced optimisation techniques for multi-vector energy systems;
  • Optimal power flow and energy management strategies;
  • Model predictive control and distributed control approaches;
  • Data-driven and AI-based control for smart grids and microgrids;
  • Integration of renewable generation, energy storage, and hydrogen systems;
  • Game-theoretic and cooperative control for multi-agent energy systems;
  • Demand response and flexibility optimisation;
  • Virtual power plants and decentralised energy coordination;
  • Robust and stochastic optimisation under uncertainty;
  • Decision-support systems for decarbonization and energy resilience.

This Special Issue aims to connect researchers, industry experts, and policymakers to share methods, applications, and case studies that support the development of intelligent, sustainable, and resilient energy infrastructures.

Dr. Adib Allahham
Guest Editor

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

  • smart energy systems
  • multi-vector energy systems
  • optimisation and control
  • renewable integration
  • microgrids and virtual power plants
  • demand-side management
  • energy storage optimisation
  • AI and data-driven control
  • game theory and coordination mechanisms
  • decarbonisation and energy resilience

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

Published Papers (3 papers)

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Research

31 pages, 2002 KB  
Article
Coordinated Optimal Configuration for Hybrid Energy Storage System Involving Differentiated Requirements from Supply-Side and Demand-Side in Microgrid
by Jiyuan Zhang, Yang Liu and Huaqiang Li
Energies 2026, 19(10), 2410; https://doi.org/10.3390/en19102410 - 17 May 2026
Viewed by 159
Abstract
To address the challenges of power fluctuations caused by the integration of distributed generation (DG) and the difficulty in simultaneously managing peak-valley load regulation due to diverse user energy demands in a microgrid system, this paper presents a coordinated optimal configuration method for [...] Read more.
To address the challenges of power fluctuations caused by the integration of distributed generation (DG) and the difficulty in simultaneously managing peak-valley load regulation due to diverse user energy demands in a microgrid system, this paper presents a coordinated optimal configuration method for serving a hybrid energy storage system (HESS), which explicitly considers the differentiated requirements from both the supply-side and the demand-side. In the presented method, an improved empirical mode decomposition (EMD) method is first presented to decompose the DG power into high-frequency, medium-frequency, and low-frequency bands. Based on the complementary technical and economic characteristics of different energy storage types, a coordinated regulation strategy for HESS in the multiple time-frequency domains is developed. Second, a coordinated optimal configuration model for HESS is further established. This model integrates key performance indicators, including maximum fluctuation and renewable energy utilization rate on the supply-side and the peak-valley difference reduction rate on the demand-side. Finally, a distributed optimization algorithm based on an improved alternating direction method of multipliers (ADMM) is developed to solve the coordinated configuration model. The experimental results demonstrate that the presented method can effectively smooth the DG power fluctuations and reduce the load peak-valley difference. The renewable energy utilization rate reaches 100%, and the peak-valley difference reduction rate reaches approximately 80%. The presented method successfully achieves the coordinated optimal configuration of HESS on both the supply and demand sides, providing a theoretical underlying infrastructure for the configuration of energy storage in the microgrid system with high penetration of renewable energy. Full article
(This article belongs to the Special Issue Optimization and Control of Smart Energy Systems)
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30 pages, 1869 KB  
Article
A Cooperative Planning Framework for Hydrogen Blending in Great Britain’s Integrated Energy System
by Mohamed Abuella, Adib Allahham and Sara Louise Walker
Energies 2026, 19(9), 2018; https://doi.org/10.3390/en19092018 - 22 Apr 2026
Viewed by 389
Abstract
Achieving Great Britain’s 2050 net-zero target requires strategic integration of hydrogen into the national energy system. This study evaluates the system-wide impacts of hydrogen blending (0–100%) using a bi-level optimisation framework that combines long-term cooperative investment planning with short-term operational Optimal Power and [...] Read more.
Achieving Great Britain’s 2050 net-zero target requires strategic integration of hydrogen into the national energy system. This study evaluates the system-wide impacts of hydrogen blending (0–100%) using a bi-level optimisation framework that combines long-term cooperative investment planning with short-term operational Optimal Power and Gas Flow (OPGF) simulation. The strategic layer models infrastructure investment decisions under a cooperative game-theoretic structure, where system value is allocated among electricity, hydrogen production, and storage technologies using the Shapley-value payoff mechanism. Contrary to traditional centralised cost-minimisation models, our findings demonstrate that a cooperative planning structure identifies superior transition pathways. Comparative results reveal that at 100% hydrogen penetration, the cooperative framework reduces total system CO2 emissions by 31%, lowers operational costs by 26%, and decreases total electricity supply requirements by 8% relative to centralised planning. Furthermore, the cooperative approach significantly enhances economic resilience, yielding a more robust Net Present Value (NPV) across all blending levels compared to centralised planning, while ensuring project profitability at lower blending thresholds (20%) where traditional models remain loss-making. Simulation results indicate that hydrogen blending up to 20% maintains operational stability with manageable increases in operational cost. Full hydrogen conversion (100%) increases peak electricity supply requirements by approximately 30% relative to low-blending scenarios due to electrolysis-driven load expansion and conversion losses. The findings demonstrate that hydrogen blending represents a viable transitional pathway when supported by integrated infrastructure development and cooperative stakeholder coordination, enabling a more efficient and economically sustainable phased progression towards Great Britain’s 2050 net-zero target. Full article
(This article belongs to the Special Issue Optimization and Control of Smart Energy Systems)
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49 pages, 14161 KB  
Article
SMARGE: An AI–Blockchain Smart EV Charging Platform with Cryptocurrency-Based Energy Transactions
by Al Mothana Al Shareef and Serap Ulusam Seçkiner
Energies 2026, 19(4), 992; https://doi.org/10.3390/en19040992 - 13 Feb 2026
Viewed by 854
Abstract
The accelerating adoption of electric vehicles (EVs) is intensifying pressure on urban power grids, particularly during evening peak hours. Existing smart-charging frameworks remain constrained by centralized control, static pricing, and limited integration of predictive intelligence. This study presents SMARGE, a hybrid AI–Blockchain smart [...] Read more.
The accelerating adoption of electric vehicles (EVs) is intensifying pressure on urban power grids, particularly during evening peak hours. Existing smart-charging frameworks remain constrained by centralized control, static pricing, and limited integration of predictive intelligence. This study presents SMARGE, a hybrid AI–Blockchain smart charging platform that combines load forecasting, dynamic pricing, and cryptocurrency-based incentives to enhance decentralized EV energy management in Gaziantep Province. An ensemble of forecasting models (SARIMA, LightGBM, N-BEATS, and TFT) predicts 2026 hourly electricity demand, while an adaptive inverse-sigmoid pricing mechanism generates real-time incentives and disincentives for EV charging behavior. A fuzzy logic-based behavioral model simulates both unmanaged and managed charging across three scenarios. Results show that managed charging reduces peak load by 22.43%, shifts 67.45% of energy demand to off-peak periods, and achieves 94.86% charging fulfillment under constrained grid conditions. The blockchain layer—implemented through a custom ERC-20 token (SMARGE) on the Ethereum Sepolia testnet—enables secure, transparent, and low-cost microtransactions with an average confirmation time of 0.63 s. These findings demonstrate that tightly coupling AI forecasting with tokenized blockchain incentives can improve grid stability, lower operational costs, and enhance user autonomy in a scalable and decentralized manner. While promising, the study is limited by assumptions of synthetic user behavior and ideal communication conditions; future work will validate the platform in real-world pilot deployments and across different urban regions. Full article
(This article belongs to the Special Issue Optimization and Control of Smart Energy Systems)
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