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Analysis and Control of New Power System with Multiple Types...
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Analysis and Control of New Power System with Multiple Types of Flexible Resources

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 12917

Special Issue Editors

Dr. Qianyi Liu

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha 410083, China
Interests: new distribution system; power quality; distributed generation; energy storage; harmonic suppression
Special Issues, Collections and Topics in MDPI journals
Dr. Jiayan Liu

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: power system planning modeling; electric vehicle charging station planning and optimal operation; active distribution network stability and economic analysis; integrated energy system coupling operation research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Distributed generation, multi-type load, distributed energy storage, and other types of flexible resource access have greatly changed the structure and operation mode of the power grid, which is mainly manifested in the change from a passive network to an active network and from single-direction power flow to two-way power flow. Faced with a series of problems, such as strong power/voltage fluctuation and strong power impact, as well as low local absorption rate of distributed generation, it is difficult to realize high-quality power regulation of the new power system, which brings great challenges to the safe, stable, and efficient operation of the power system.

The Special Issue targets the analysis and control of new power systems with multiple types of flexible resources.

Topics of interest include, but are not limited to, the following：

  • Power quality control with energy storage;
  • Energy management strategies for energy storage;
  • Active coordination control on power grid (voltage support and frequency regulation);
  • Power efficiency optimization and improvement methods;
  • Topology of new compensation systems;
  • Coordination control of multiple types of flexible resources;
  • Advanced control strategies of grid power electronic systems;
  • Power quality issues of aggregated grid power electronics systems;
  • Power regulation applications in T&D.

 

Dr. Qianyi Liu
Dr. Jiayan Liu
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. Processes 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

  • new power systems
  • power quality
  • distributed generation
  • energy storage
  • flexible resources

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Published Papers (5 papers)

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Research

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25 pages, 1672 KB  
Article
Capacity Regression and Temperature Prediction for Canada’s Largest Solar Facility, Travers Solar, Alberta
by Zhensen Gao, Yutong Chai, Anthony Thai, Tayo Oketola, Geoffrey Bell, Walter Schachtschneider and Shunde Yin
Processes 2026, 14(7), 1078; https://doi.org/10.3390/pr14071078 - 27 Mar 2026
Viewed by 378
Abstract
Utility-scale photovoltaic (PV) plants rely on supervisory control and data acquisition (SCADA) streams for performance verification, yet high-frequency measurements are routinely affected by sensor dropouts, intermittency, and operating-state transitions that bias regression-based capacity estimates. This study evaluates a reproducible SCADA processing workflow for [...] Read more.
Utility-scale photovoltaic (PV) plants rely on supervisory control and data acquisition (SCADA) streams for performance verification, yet high-frequency measurements are routinely affected by sensor dropouts, intermittency, and operating-state transitions that bias regression-based capacity estimates. This study evaluates a reproducible SCADA processing workflow for capacity-style reporting and a complementary soiling–clean temperature prediction model using data from a documented October 2022 test window (5 s SCADA aggregated to 1 min). The following three filtering approaches are compared: (i) naïve thresholds (Baseline A), (ii) deterministic stability screening using ramp-rate and rolling-variability constraints (Baseline B), and (iii) an optional residual-based outlier trimming step (Method C). Capacity is estimated via a multivariate regression evaluated on a fixed-size reporting-condition subset (RC197) with day-coverage constraints. All methods achieved high fit quality on RC197 (R2≈0.99), with Baseline B improving error and uncertainty over Baseline A (RMSE 2.05 vs. 2.18 MW; U95 0.97% vs. 1.03%) while preserving day coverage; Method C yielded the lowest in-sample RMSE (1.89 MW) but reduced day coverage. For temperature prediction, a baseline-plus-residual learning formulation substantially improved leave-one-day-out performance, reducing MAE/RMSE from 2.99/3.76 °C to 1.43/1.80 °C and increasing R2 from 0.60 to 0.91. The results highlight trade-offs between fit tightness and representativeness in capacity-style filtering and demonstrate residual learning is an effective approach for SCADA-based thermal characterization. Full article
(This article belongs to the Special Issue Analysis and Control of New Power System with Multiple Types of Flexible Resources)
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24 pages, 1985 KB  
Article
Planning Method for Power System Considering Flexible Integration of Renewable Energy and Heterogeneous Resources
by Yuejiao Wang, Shumin Sun, Zhipeng Lu, Yiyuan Liu, Yu Zhang, Nan Yang and Lei Zhang
Processes 2026, 14(6), 984; https://doi.org/10.3390/pr14060984 - 19 Mar 2026
Viewed by 364
Abstract
The large-scale grid integration of distributed renewable energy enhances the flexible regulation capacity of the power system. However, the inherent randomness and volatility of its output, coupled with weak coupling access characteristics, pose severe challenges to the safe and stable operation of the [...] Read more.
The large-scale grid integration of distributed renewable energy enhances the flexible regulation capacity of the power system. However, the inherent randomness and volatility of its output, coupled with weak coupling access characteristics, pose severe challenges to the safe and stable operation of the power system. To address these issues, this paper proposes a power system planning method suitable for urban power grids. To accurately characterize the uncertainty of renewable energy output, the method incorporates the concept of multi-scenario stochastic optimization and introduces a dynamic scenario generation method for wind and solar power based on nonparametric kernel density estimation and standard multivariate normal distribution sequence sampling. This method generates a set of typical daily dynamic output scenarios for wind and solar power that closely match actual output characteristics. Considering the spatiotemporal response characteristics of flexible resources, the Soft Open Point (SOP) DC link enables flexible cross-node power transmission and spatiotemporal coupling regulation of flexible resources. Therefore, this paper constructs a mathematical model for the grid integration of flexible resources based on the SOP DC link. By integrating operational constraints such as power flow constraints in the power grid and source-load uncertainty constraints, a power system planning model is established. However, traditional convex optimization methods require approximate simplifications of the model, which can easily lead to a loss of accuracy. Although the Particle Swarm Optimization (PSO) algorithm is suitable for nonlinear optimization, it is prone to getting trapped in local optima. Therefore, this paper introduces an improved PSO algorithm based on refraction opposite learning, which enhances the algorithm’s global optimization capability by expanding the particle search space and increasing population diversity. Finally, simulation verification is conducted based on an improved IEEE-39 bus test system, and the results show that the proposed scenario generation method achieves a sum of squared errors of only 4.82% and a silhouette coefficient of 0.94, significantly improving accuracy compared to traditional methods such as Monte Carlo sampling. Full article
(This article belongs to the Special Issue Analysis and Control of New Power System with Multiple Types of Flexible Resources)
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12 pages, 397 KB  
Article
Physics-Informed Neural Networks for Parameter Identification of Equivalent Thermal Parameters in Residential Buildings During Winter Electric Heating
by Sijia Liu, Qi An, Ziyi Yuan and Pengchao Lei
Processes 2025, 13(9), 2860; https://doi.org/10.3390/pr13092860 - 7 Sep 2025
Cited by 1 | Viewed by 2121
Abstract
Accurate identification of equivalent thermal parameters (ETPs) is crucial for optimizing energy efficiency in residential buildings during winter electric heating. This study proposes a physics-informed neural network (PINN) approach to estimate ETP model parameters, integrating physical constraints with data-driven learning to enhance robustness. [...] Read more.
Accurate identification of equivalent thermal parameters (ETPs) is crucial for optimizing energy efficiency in residential buildings during winter electric heating. This study proposes a physics-informed neural network (PINN) approach to estimate ETP model parameters, integrating physical constraints with data-driven learning to enhance robustness. The method is validated using real-world measurements from seven rural residences, with indoor and outdoor temperatures and heating power sampled every 15 min. The PINN is compared with linear regression (LR), heuristic methods (GA, PSO, TROA), and data-driven methods (RF, XGBoost, LSTM). The results show that the PINN reduces MAE by over 90% compared to LR, 42% compared to heuristic methods, and 75% compared to pure data-driven methods, with similar improvements in RMSE and MAPE, while maintaining moderate computational time. This work highlights the potential of PINNs as an efficient and reliable tool for building energy management, offering a promising solution for parameter identification within the specific context of the studied residences, with future work needed to confirm scalability across diverse climates and building types. Full article
(This article belongs to the Special Issue Analysis and Control of New Power System with Multiple Types of Flexible Resources)
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23 pages, 5996 KB  
Article
Cooperative Operation Optimization of Flexible Interconnected Distribution Networks Considering Demand Response
by Yinzhou Yao, Ziruo Wan, Ting Yang, Zeyu Yang, Haoting Xu and Fei Rong
Processes 2025, 13(9), 2809; https://doi.org/10.3390/pr13092809 - 2 Sep 2025
Cited by 2 | Viewed by 860
Abstract
The integration of renewable energy into distribution networks has led to voltage violations and increased network losses. Traditional control devices, with slow response, struggle to precisely control power flow in active distribution networks (ADNs). Optimizing from both supply and demand sides, an ADN [...] Read more.
The integration of renewable energy into distribution networks has led to voltage violations and increased network losses. Traditional control devices, with slow response, struggle to precisely control power flow in active distribution networks (ADNs). Optimizing from both supply and demand sides, an ADN power flow optimization method is proposed for accurate and dynamic power flow regulation to address these issues. On the demand side, the peak, valley, and flat periods are divided by the fuzzy transitive closure method. Balancing user satisfaction maximization and load fluctuation minimization, time-of-use (TOU) prices are solved by the non-dominated sorting genetic algorithm II (NSGA-II). On the supply side, operating cost and voltage deviation minimization are objectives, with a proposed optimization method coordinating precise continuous regulation devices and low-cost discrete ones. After second-order cone programming and linearization, the multi-objective model is solved via the normalized normal constraint (NNC) algorithm to get a solution set, from which the optimal solution is selected using Entropy Weight and Technique for Order Preference by Similarity to an Ideal Solution (EW-TOPSIS). The results indicate that, in comparison with the proposed method, ADN not implementing demand-side TOU pricing strategies exhibits an increase in operating costs by 13.83% and a rise in voltage deviation by 4.14%. Meanwhile, ADN utilizing only traditional discrete control devices demonstrates more significant increments, with operating costs increasing by 182.40% and voltage deviation rising by 113.02%. Full article
(This article belongs to the Special Issue Analysis and Control of New Power System with Multiple Types of Flexible Resources)
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Review

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39 pages, 3301 KB  
Review
A Systematic Review and Meta-Analysis of Model Predictive Control in Microgrids: Moving Beyond Traditional Methods
by Elnaz Yaghoubi, Elaheh Yaghoubi, Mohammad Reza Maghami, Javad Rahebi, Mehdi Zareian Jahromi, Raheleh Ghadami (Melisa Rahebi) and Ziyodulla Yusupov
Processes 2025, 13(7), 2197; https://doi.org/10.3390/pr13072197 - 9 Jul 2025
Cited by 13 | Viewed by 8153
Abstract
Microgrids are gaining considerable attention as a promising solution for integrating distributed energy resources and enhancing grid resilience. Model predictive control (MPC) has emerged as a powerful control strategy for microgrids due to its ability to handle complex dynamics and optimization problems. This [...] Read more.
Microgrids are gaining considerable attention as a promising solution for integrating distributed energy resources and enhancing grid resilience. Model predictive control (MPC) has emerged as a powerful control strategy for microgrids due to its ability to handle complex dynamics and optimization problems. This study aims to conduct a comprehensive assessment of MPC applications and evaluate their overall effectiveness across various microgrid functionalities. Previous studies have not collectively examined MPC and have not explored its advantages and disadvantages in the microgrid. This study systematically categorizes and addresses this gap in the existing literature. An extensive list of suitable research papers was compiled from the Web of Science and analyzed, considering the method of the studies, main focus and objectives, publication year, and findings. Moreover, this research incorporates co-occurrence keyword analysis, covering MPC applications, systematic reviews, microgrids, and review articles. The visualization and analysis of the data obtained from the Web of Science database were conducted using VOS viewer. This discussion includes approaches that help electrical engineers evaluate the benefits and disadvantages of MPC within the microgrid setup. This knowledge enables electrical practitioners to select the appropriate methods for providing a resilient and reliable ecosystem. Full article
(This article belongs to the Special Issue Analysis and Control of New Power System with Multiple Types of Flexible Resources)
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