Energy Systems: Design, Management, Control and Monitoring

Topic Information

Dear Colleagues,

We are delighted to invite you to contribute to our forthcoming Topic entitled “Energy Systems: Design, Management, Control and Monitoring”.

The present issue welcomes researchers and authors to submit research and review articles exploring stationary and mobile applications of energy systems including, but not limited to, the following:

• Integrated design, control, and operational management of multi-vector energy systems and energy community/valley/regions;
• Electrical and thermal design and management: simulation, control, and measurement of energy storage, energy conversion, and energy generation systems;
• Smart grid-ready distributed generation systems’ performance, stability, resilience, and reliability;
• Applications of machine learning, AI, forecasting, and optimization techniques in energy systems;
• Peer-to-peer energy sharing, consumer-centric energy systems, and market pricing analysis;
• Advanced measuring and monitoring systems and methods for green energy applications;
• Vehicle to grid integrations: V2G and V1G management, charging scheduling, technology, and protocols;.
• Automotives and energy management: electric, hybrid, and plug-in hybrid vehicles;
• Self-driving and autonomous vehicles: route planning, control, energy management, and socio-economic explorations of connected and non-connected platoons of vehicles;
• Energy and buildings, thermal networks, HVAC, and indoor air quality management;
• Applications of Internet of Things (IoT) in smart energy management and control;
• Interoperability and flexibility service design, development, and optimization;
• Muti-criteria decision making and stakeholder engagement for energy sectors;
• Decarbonization, life cycle assessment, and sustainability management.

Dr. Majid Vafaeipour
Dr. Danial Karimi
Dr. Christoph Bergmeir
Prof. Dr. Farivar Fazelpour
Dr. Michela Longo
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Batteries batteries	4.8	6.6	2015	18.5 Days	CHF 2700	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.2 Days	CHF 2600	Submit
Machines machines	2.5	4.7	2013	16.9 Days	CHF 2400	Submit
Smart Cities smartcities	5.5	14.7	2018	26.8 Days	CHF 2000	Submit
Sustainability sustainability	3.3	7.7	2009	19.3 Days	CHF 2400	Submit

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Published Papers (3 papers)

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All Batteries Electronics Energies Machines Smart Cities Sustainability

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55 pages, 15873 KB  

Open AccessArticle

Optimal µ-PMU Placement and Voltage Estimation in Distribution Networks: Evaluation Through Multiple Case Studies

by Asjad Ali, Noor Izzri Abdul Wahab, Mohammad Lutfi Othman, Rizwan A. Farade, Husam S. Samkari and Mohammed F. Allehyani

Sustainability 2025, 17(24), 11036; https://doi.org/10.3390/su172411036 - 9 Dec 2025

Viewed by 379

Abstract

This study optimizes the placement of μ-PMUs using the BPSO and BGWO algorithms for the IEEE 33-bus and 69-bus systems, with a focus on minimizing deployment costs while ensuring robust system observability. Three case studies are analysed: Case 1 (normal conditions), Case 2 [...] Read more.

This study optimizes the placement of μ-PMUs using the BPSO and BGWO algorithms for the IEEE 33-bus and 69-bus systems, with a focus on minimizing deployment costs while ensuring robust system observability. Three case studies are analysed: Case 1 (normal conditions), Case 2 (single μ-PMU outage), and Case 3 (Zero Injection Buses, ZIBs). In Case 1, both algorithms identified 24 μ-PMUs as the optimal placement for the IEEE 69-bus system, achieving the minimum PMUs required for full observability. For Case 2, redundancy requirements increased the μ-PMU count to 24 μ-PMUs for the IEEE 33-bus system and 51 μ-PMUs for the IEEE 69-bus system, ensuring full observability even under a single μ-PMU failure. Case 3, leveraging Zero Injection Buses (ZIBs), reduced the μ-PMU count to 20 μ-PMUs for both BPSO and BGWO, optimizing the system configuration while maintaining observability. A trade-off analysis was performed to examine the trade-off between redundancy and PMU count, showing that increasing the number of μ-PMUs improves system resilience. Voltage and current channels were measured from the optimized placements to ensure accurate voltage measurement in all case studies. Subsequently, the Weighted Least Squares algorithm was applied for voltage estimation, serving as a peripheral to the main objective of the optimal μ-PMU placement. Voltage estimation was conducted under three noise levels: 0.01 STD for basic analysis and 0.02 and 0.04 STD to observe the impact of varying measurement noise. The results highlight that higher μ-PMU placements improve voltage estimation accuracy, particularly under higher noise levels. Statistical analysis confirms that BGWO outperforms BPSO in terms of computational efficiency, stability, and convergence, especially in large-scale systems. By enhancing grid monitoring and state estimation, this research directly contributes to the development of more resilient and efficient power networks, which is a fundamental prerequisite for integrating renewable energy sources and advancing overall power system sustainability. This research emphasizes the balance between cost and reliability in μ-PMU placement and provides a comprehensive methodology for state estimation in modern power systems. Full article

(This article belongs to the Topic Energy Systems: Design, Management, Control and Monitoring)

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32 pages, 17410 KB  

Open AccessArticle

An Improved Black-Winged Kite Algorithm for High-Accuracy Parameter Identification of a Photovoltaic Double Diode Model

by Quanru Chen, Kun Ding, Xiang Chen, Zenan Yang, Mingkang Xu and Fei Teng

Machines 2025, 13(8), 706; https://doi.org/10.3390/machines13080706 - 9 Aug 2025

Viewed by 603

Abstract

This study proposes an improved Black-Winged Kite Algorithm (SRQ-BKA) for accurate parameter identification of the photovoltaic (PV) double diode model (DDM). The proposed method integrates three key mechanisms: specular reflection learning (SRL) to improve initial population diversity, preventing premature convergence and enabling a [...] Read more.

This study proposes an improved Black-Winged Kite Algorithm (SRQ-BKA) for accurate parameter identification of the photovoltaic (PV) double diode model (DDM). The proposed method integrates three key mechanisms: specular reflection learning (SRL) to improve initial population diversity, preventing premature convergence and enabling a more comprehensive exploration of the solution space for optimal parameters; soft rime search (SRS) to balance global exploration and local exploitation, ensuring efficient identification by dynamically adjusting the search focus; and quadratic interpolation (QI) to accelerate convergence by fine-tuning the search toward optimal parameters, enhancing accuracy and speeding up the identification process. The root mean square error (RMSE) is employed as the objective function to minimize the error between the measured and predicted I-V characteristics of the PV module. Experimental results demonstrate that the SRQ-BKA outperforms other algorithms, achieving a minimum RMSE of 0.00262 A for the DDM and exhibiting strong stability, as evidenced by an average RMSE of 0.00278 A across 1000 runs. The method also demonstrates excellent parameter identification accuracy for both the single diode model (SDM) and triple diode model (TDM), further validating its robustness and practical applicability. Full article

(This article belongs to the Topic Energy Systems: Design, Management, Control and Monitoring)

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26 pages, 8900 KB  

Open AccessArticle

Common Rail Injector Operation Model and Its Validation

by Karol Dębowski and Mirosław Karczewski

Energies 2025, 18(9), 2271; https://doi.org/10.3390/en18092271 - 29 Apr 2025

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Abstract

The aim of this study was to develop and subsequently validate a simulation model of a Common Rail (CR) system injector. The study includes a description of simulation and experimental tests conducted under various injector operating conditions. Experimental tests were performed using the [...] Read more.

The aim of this study was to develop and subsequently validate a simulation model of a Common Rail (CR) system injector. The study includes a description of simulation and experimental tests conducted under various injector operating conditions. Experimental tests were performed using the STPiW-2 test bench. The operating conditions of the injector were varied in terms of injection pressure and injector opening time. The injector model was developed using the Amesim software, where simulation studies were also conducted. The simulations focused on generating injection characteristics, specifically the volume of fuel injected per injection at pressures ranging from 20 MPa to 140 MPa in 10 MPa increments. Four such injection characteristics were obtained during both experimental and simulation studies, corresponding to injector opening times of 500 µs, 1000 µs, 1500 µs, and 2000 µs. Additionally, volume characteristics were generated under the same conditions. The validation demonstrated a high level of accuracy for the developed model. The obtained injection characteristics exhibited a correlation coefficient exceeding 90% in all four cases. The most accurately replicated injection characteristic was for the 500 µs injector opening time, achieving a correlation coefficient of 99%. Meanwhile, the simulation-derived overflow volume characteristic matched the experimental results with a correlation of 98%. For longer injector opening times, the correlation coefficients were slightly lower but remained satisfactory. The study concluded that for short injector opening times, the assumed model simplifications had minimal impact on the injected fuel volume at a given pressure. However, for longer opening times, discrepancies between simulation and experimental results became more pronounced. This divergence could be attributed to pressure variability within the injector during operation and associated hydraulic phenomena. Full article

(This article belongs to the Topic Energy Systems: Design, Management, Control and Monitoring)

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