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Electronics
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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: 20 February 2026 | Viewed by 599

Special Issue Editors

Assoc. Prof. Dr. Saher Javaid

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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 (1 paper)

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Research

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
Viewed by 390
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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