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Energies
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Optimization and Machine Learning Approaches for Power Systems
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Optimization and Machine Learning Approaches for Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2024

Special Issue Editors

Dr. Spyros Giannelos

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, London, UK
Interests: optimization; machine learning; power systems; smart technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Danny Pudjianto

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Imperial College London, London, UK
Interests: integration of renewable energy; optimization in energy system planning and operation; smart grid; economics and regulations in power systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, “Optimization and Machine Learning Approaches for Power Systems”, aims to spotlight cutting-edge methodologies and the modeling of problems at the intersection of machine learning and optimization, tailored for enhancing the efficiency, reliability, and sustainability of modern power systems. With the increasing complexity and variability of energy systems due to renewable integration, electric vehicle adoption, and decentralised energy resources, traditional power system management faces substantial challenges. This Special Issue invites researchers and practitioners to explore how advanced optimization techniques and models, as well as machine learning models, can address these emerging complexities.

Key areas of interest include optimization, stochastic optimization, load and price forecasting, renewable energy forecasting, and novel applications of machine learning, such as deep learning, reinforcement learning, and federated learning, for dynamic and real-time decision making within the grid. Contributions that combine optimization algorithms—such as linear and nonlinear programming, stochastic optimization, and metaheuristic methods—with machine learning models are also valued for their potential to solve complex, high-dimensional problems like grid reconfiguration, unit commitment, and energy dispatch.

By highlighting novel approaches and applications, this Special Issue aims to provide insights into how optimization and machine learning can collaboratively transform power system planning, operation, and control. Ultimately, it seeks to pave the way toward a resilient, sustainable, and intelligent energy future, equipping the industry with advanced tools to meet the demands of an evolving energy landscape.

Dr. Spyros Giannelos
Dr. Danny Pudjianto
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. 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

  • stochastic optimization
  • machine learning
  • deep learning
  • smart grid technologies
  • power system economics

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Published Papers (3 papers)

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Research

25 pages, 7974 KiB  
Article
A Multimodal Interaction-Driven Feature Discovery Framework for Power Demand Forecasting
by Zifan Ning, Min Jin and Pan Zeng
Energies 2025, 18(11), 2907; https://doi.org/10.3390/en18112907 - 1 Jun 2025
Viewed by 398
Abstract
Power demand forecasting is a critical and challenging task for modern power systems and integrated energy systems. Due to the absence of well-established theoretical frameworks and publicly available feature databases on power demand changes, the known interpretable features of power demand fluctuations are [...] Read more.
Power demand forecasting is a critical and challenging task for modern power systems and integrated energy systems. Due to the absence of well-established theoretical frameworks and publicly available feature databases on power demand changes, the known interpretable features of power demand fluctuations are primarily derived from expert experience and remain significantly limited. This substantially hinders advancements in power demand forecasting accuracy. Emerging multimodal learning approaches have demonstrated great promise in machine learning and AI-generated content (AIGC). In this paper, we propose, for the first time, a textual-knowledge-guided numerical feature discovery (TKNFD) framework for short-term power demand forecasting by interacting text modal data—a potentially valuable yet long-overlooked resource in the field of power demand forecasting—with numerical modal data. TKNFD systematically and automatically aggregates qualitative textual knowledge, expands it into a candidate feature-type set, collects corresponding numerical data for these features, and ultimately constructs four-dimensional multivariate source-tracking databases (4DM-STDs). Subsequently, TKNFD introduces a two-stage quantitative feature identification strategy that operates independently of forecasting models. The essence of TKNFD lies in achieving reliable and comprehensive feature discovery by fully exploiting the dual relationships of synonymy and complementarity between text modal data and numerical modal data in terms of granularity, scope, and temporality. In this study, TKNFD identifies 38–50 features while further interpreting their contributions and dependency correlations. Benchmark experiments conducted in Maine, Texas, and New South Wales demonstrate that the forecasting accuracy using TKNFD-identified features consistently surpasses that of state-of-the-art feature schemes by up to 36.37% MAPE. Notably, driven by multimodal interaction, TKNFD can discover previously unknown interpretable features without relying on prior empirical knowledge. This study reveals 10–16 previously unknown interpretable features, particularly several dominant features in integrated energy and astronomical dimensions. These discoveries enhance our understanding of the origins of strong randomness and non-linearity in power demand fluctuations. Additionally, the 4DM-STDs developed for these three regions can serve as public baseline databases for future research. Full article
(This article belongs to the Special Issue Optimization and Machine Learning Approaches for Power Systems)
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14 pages, 1597 KiB  
Article
Optimal Power Flow for High Spatial and Temporal Resolution Power Systems with High Renewable Energy Penetration Using Multi-Agent Deep Reinforcement Learning
by Liangcai Zhou, Long Huo, Linlin Liu, Hao Xu, Rui Chen and Xin Chen
Energies 2025, 18(7), 1809; https://doi.org/10.3390/en18071809 - 3 Apr 2025
Viewed by 603
Abstract
The increasing integration of renewable energy sources (RESs) introduces significant uncertainties in both generation and demand, presenting critical challenges to the convergence, feasibility, and real-time performance of optimal power flow (OPF). To address these challenges, a multi-agent deep reinforcement learning (DRL) model is [...] Read more.
The increasing integration of renewable energy sources (RESs) introduces significant uncertainties in both generation and demand, presenting critical challenges to the convergence, feasibility, and real-time performance of optimal power flow (OPF). To address these challenges, a multi-agent deep reinforcement learning (DRL) model is proposed to solve the OPF while ensuring constraints are satisfied rapidly. A heterogeneous multi-agent proximal policy optimization (H-MAPPO) DRL algorithm is introduced for multi-area power systems. Each agent is responsible for regulating the output of generation units in a specific area, and together, the agents work to achieve the global OPF objective, which reduces the complexity of the DRL model’s training process. Additionally, a graph neural network (GNN) is integrated into the DRL framework to capture spatiotemporal features such as RES fluctuations and power grid topological structures, enhancing input representation and improving the learning efficiency of the DRL model. The proposed DRL model is validated using the RTS-GMLC test system, and its performance is compared to MATPOWER with the interior-point iterative solver. The RTS-GMLC test system is a power system with high spatial–temporal resolution and near-real load profiles and generation curves. Test results demonstrate that the proposed DRL model achieves a 100% convergence and feasibility rate, with an optimal generation cost similar to that provided by MATPOWER. Furthermore, the proposed DRL model significantly accelerates computation, achieving up to 85 times faster processing than MATPOWER. Full article
(This article belongs to the Special Issue Optimization and Machine Learning Approaches for Power Systems)
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20 pages, 872 KiB  
Article
Energy Hub Operation Under Uncertainty: Monte Carlo Risk Assessment Using Gaussian and KDE-Based Data
by Spyros Giannelos, Danny Pudjianto, Tai Zhang and Goran Strbac
Energies 2025, 18(7), 1712; https://doi.org/10.3390/en18071712 - 29 Mar 2025
Cited by 2 | Viewed by 576
Abstract
Energy hubs integrating onsite renewable generation and battery storage provide cost-efficient solutions for meeting building electricity requirements. This study presents methods for modeling uncertainties in load demand and solar generation, ranging from normal distribution assumptions to distributions sourced from CityLearn 2.3.0. We also [...] Read more.
Energy hubs integrating onsite renewable generation and battery storage provide cost-efficient solutions for meeting building electricity requirements. This study presents methods for modeling uncertainties in load demand and solar generation, ranging from normal distribution assumptions to distributions sourced from CityLearn 2.3.0. We also implement kernel density estimation (KDE) to represent the non-parametric distribution characteristics of actual data. Through Monte Carlo simulation, we emphasize the value of robust, data-driven methodologies in optimizing energy hub operations under realistic uncertainty conditions and effectively conducting risk assessment. The CityLearn real-world data confirms that the non-Gaussian nature of building-level energy demand and solar PV electricity output is most accurately represented through KDE, leading to more precise cost projections for the considered energy hub. Full article
(This article belongs to the Special Issue Optimization and Machine Learning Approaches for Power Systems)
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