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Electronics
Special Issues
Smart Power System Optimization, Operation, and Control
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Smart Power System Optimization, Operation, and Control

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 1552

Special Issue Editors

Assoc. Prof. Dr. Saher Javaid

E-Mail Website
Guest Editor
1. Department of Computer Science, Kanazawa Gakuin University, 10 Suemachi, Kanazawa 920-1392, Ishikawa, Japan
2. School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi 923-1292, Ishikawa, Japan
Interests: smart home; home energy management system (HEMS); distributed energy resources; power flow control; power system stability and control; power flow coloring; demand response; energy on demand
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuto Lim

E-Mail Website
Guest Editor
School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi 923-1292, Ishikawa, Japan
Interests: predictive control; network coding; evolutionary multi-objective optimization; game theory; smart energy distribution; smart homes; wireless communications; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on advancing the theory and practice of optimization, operation, and control in modern power systems, particularly those undergoing transformation via the integration of distributed energy resources (DERs), renewable energy, smart grid technologies, and digital intelligence. The aim of this Special Issue is to provide a timely and cohesive collection of research that addresses the emerging challenges and opportunities in designing resilient, adaptive, and intelligent power systems.

(1) Focus, Scope, and Purpose:

  1. Focus:

This Special Issue will present innovative approaches to the optimization and control of smart power systems. It encompasses both theoretical advancements and practical implementations related to system efficiency, reliability, real-time operation, and the integration of intelligent technologies, including AI, machine learning, and advanced control frameworks.

  1. Scope:

We welcome the submission of original research articles, case studies, and review papers that explore the modeling, optimization, and control of power systems under uncertainty, with a focus on intelligent and decentralized solutions.

  1. Purpose:

This Special Issue aims to compile interdisciplinary research that addresses the evolving complexity of smart power systems. By highlighting innovative control mechanisms and optimization techniques, the Special Issue aims to bridge the gap between academic research and practical deployment in power grid applications. It also seeks to foster collaboration between engineers, researchers, and system operators to accelerate the development of resilient and efficient smart power infrastructures.

(2) Relation to Existing Literature:

The past decade has witnessed a significant increase in research on smart grid technologies and the integration of renewable energy systems. However, the existing literature often treats optimization, control, and operational management in isolation. This Special Issue aims to build upon foundational works in power system engineering, AI-driven optimization, and control theory by presenting a comprehensive and unified perspective on smart power system management. In doing so, it will offer new insights into how these domains intersect, particularly in scenarios involving distributed decision-making, uncertainty, and real-time system constraints. The Special Issue will serve as both a supplement to and an extension of the existing body of work, offering novel methodologies, scalable solutions, and practical applications for future energy systems.

Assoc. Prof. Dr. Saher Javaid
Prof. Dr. Yuto Lim
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. Electronics 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

  • smart grid optimization under uncertainty and volatility
  • power system control
  • distributed energy resources
  • renewable energy integration and dispatch
  • energy storage management, coordination, and control
  • artificial intelligence in power systems (AI, ML, and data-driven methods for power systems)
  • real-time operation
  • grid resilience
  • load forecasting and demand-side optimization
  • multi-agent and decentralized control strategies

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Published Papers (3 papers)

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Research

16 pages, 3795 KB  
Article
Cascaded Model Predictive Stabilization for DC Microgrids Without Weighting Factor Tuning
by Haiquan Cao, Renjian Zhang, Guo Wang, Ju He and Minrui Leng
Electronics 2026, 15(9), 1955; https://doi.org/10.3390/electronics15091955 - 5 May 2026
Abstract
The performance of a dc microgrid may be degraded when the tightly controlled electronic loads are connected, behaving as the constant power loads (CPLs) with negative impedances. In order to eliminate the oscillation in the microgrid, this paper proposes a simple model predictive [...] Read more.
The performance of a dc microgrid may be degraded when the tightly controlled electronic loads are connected, behaving as the constant power loads (CPLs) with negative impedances. In order to eliminate the oscillation in the microgrid, this paper proposes a simple model predictive stabilization method. This method is based on model predictive control, introducing a stabilization term into the cost function. By evaluating separate cost functions for the control objectives of the dc side and ac side in a cascaded way, the weigh factors are eliminated, which avoids tedious parameter tuning efforts, simplifying the design process. Moreover, current sensors in both the dc side and ac side are omitted, which are replaced by a full-order observer, reducing the size, cost, and isolation requirement of the system. The proposed method can stabilize a dc microgrid effectively by simple and cost-effective implementation. Meanwhile, the dynamic performance, such as small overshoot, fast and smooth response, is achieved. The effectiveness of the proposed stabilization method is validated by simulation results. Full article
(This article belongs to the Special Issue Smart Power System Optimization, Operation, and Control)
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24 pages, 8196 KB  
Article
A Dual-Phase Dual-Path Hybrid Buck-Boost Converter with Offset-Controlled Zero-Current Detection Achieving 95.88% Peak Efficiency
by Sungjun Moon, Jonghun Chae, Gyumin Kim, Junseong Hwang, Jieun Kim and Inho Park
Electronics 2026, 15(6), 1304; https://doi.org/10.3390/electronics15061304 - 20 Mar 2026
Viewed by 349
Abstract
This paper presents a dual-phase dual-path hybrid buck–boost (DPBB) converter with an offset-controlled zero-current detector for Li-ion battery applications. Compared with inductive buck–boost converters, the proposed hybrid converter has a continuous input current, reducing the input voltage (VIN) ripple, which [...] Read more.
This paper presents a dual-phase dual-path hybrid buck–boost (DPBB) converter with an offset-controlled zero-current detector for Li-ion battery applications. Compared with inductive buck–boost converters, the proposed hybrid converter has a continuous input current, reducing the input voltage (VIN) ripple, which is caused by the parasitic inductance of the bonding wire. The proposed switching operation of the DPBB topology shows a low inductor current ripple with the continuous output delivery current; therefore, the ripple of the output voltage (VOUT) is reduced with the efficiency improvement. Compared with the prior hybrid buck–boost converters, it supports the buck and boost modes only by adjusting the duty cycle, so this addresses the issues of mode transitions. The proposed work utilizes the dual-phase operation to lower the conduction loss and improve the dynamic range. The proposed offset-controlled zero-current detector compensates for the timing error owing to the propagation delay of the control signals to reduce the reverse current from the output. The chip is fabricated using a 180-nm BCD process. It regulates VOUT at 3.3 V with a wide VIN range of 2.8 V to 4.2 V. Peak efficiencies of 95.88% and 93.08% are achieved in the buck and boost modes, respectively, with 140 mΩ of inductor DC resistance. Full article
(This article belongs to the Special Issue Smart Power System Optimization, Operation, and Control)
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47 pages, 6988 KB  
Article
A Hierarchical Predictive-Adaptive Control Framework for State-of-Charge Balancing in Mini-Grids Using Deep Reinforcement Learning
by Iacovos Ioannou, Saher Javaid, Yasuo Tan and Vasos Vassiliou
Electronics 2026, 15(1), 61; https://doi.org/10.3390/electronics15010061 - 23 Dec 2025
Cited by 1 | Viewed by 847
Abstract
State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized [...] Read more.
State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized and computationally light but fundamentally reactive and limited, whereas model predictive control (MPC) is insightful but computationally intensive and prone to modeling errors. This paper proposes a Hierarchical Predictive–Adaptive Control (HPAC) framework for SoC balancing in mini-grids using deep reinforcement learning. The framework consists of two synergistic layers operating on different time scales. A long-horizon Predictive Engine, implemented as a federated Transformer network, provides multi-horizon probabilistic forecasts of net load, enabling multiple mini-grids to collaboratively train a high-capacity model without sharing raw data. A fast-timescale Adaptive Controller, implemented as a Soft Actor-Critic (SAC) agent, uses these forecasts to make real-time charge/discharge decisions for each BESS unit. The forecasts are used both to augment the agent’s state representation and to dynamically shape a multi-objective reward function that balances SoC, economic performance, degradation-aware operation, and voltage stability. The paper formulates SoC balancing as a Markov decision process, details the SAC-based control architecture, and presents a comprehensive evaluation using a MATLAB-(R2025a)-based digital-twin simulation environment. A rigorous benchmarking study compares HPAC against fourteen representative controllers spanning rule-based, MPC, and various DRL paradigms. Sensitivity analysis on reward weight selection and ablation studies isolating the contributions of forecasting and dynamic reward shaping are conducted. Stress-test scenarios, including high-volatility net-load conditions and communication impairments, demonstrate the robustness of the approach. Results show that HPAC achieves near-minimal operating cost with essentially zero SoC variance and the lowest voltage variance among all compared controllers, while maintaining moderate energy throughput that implicitly preserves battery lifetime. Finally, the paper discusses a pathway from simulation to hardware-in-the-loop testing and a cloud-edge deployment architecture for practical, real-time deployment in real-world mini-grids. Full article
(This article belongs to the Special Issue Smart Power System Optimization, Operation, and Control)
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