Journal Description
Electricity
Electricity
is an international, peer-reviewed, open access journal on electrical engineering published quarterly online by MDPI.
- Open Access—free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within ESCI (Web of Science), Scopus, EBSCO and other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 26 days after submission; acceptance to publication is undertaken in 5.5 days (median values for papers published in this journal in the first half of 2025).
- Journal Rank: CiteScore - Q2 (Electrical and Electronic Engineering)
- Recognition of Reviewers: APC discount vouchers, optional signed peer review, and reviewer names published annually.
- Extra Benefits: no space constraints, no color charges.
- Journal Cluster of Energy and Fuels: Energies, Batteries, Hydrogen, Biomass, Electricity, Wind, Fuels, Gases, Solar, ESA and Methane.
Impact Factor:
1.8 (2024);
5-Year Impact Factor:
1.9 (2024)
Latest Articles
Techno-Economic Analysis of Peer-to-Peer Energy Trading Considering Different Distributed Energy Resources Characteristics
Electricity 2025, 6(4), 57; https://doi.org/10.3390/electricity6040057 - 4 Oct 2025
Abstract
Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic
[...] Read more.
Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks.
Full article
(This article belongs to the Special Issue Advancing Energy Systems for a Decarbonized Future: Renewable Integration, Smart Grids, and Optimization Strategies)
Open AccessArticle
A Cooperative Game Theory Approach to Encourage Electric Energy Supply Reliability Levels and Demand-Side Flexibility
by
Gintvilė Šimkonienė
Electricity 2025, 6(4), 56; https://doi.org/10.3390/electricity6040056 - 3 Oct 2025
Abstract
►▼
Show Figures
Electrical energy supply services are characterised by unpredictable risks that affect both distribution network operators (DSOs) and electricity consumers. This paper presents an innovative cooperative game theory (GT) framework to enhance electric energy supply reliability and demand-side flexibility by aligning the interest of
[...] Read more.
Electrical energy supply services are characterised by unpredictable risks that affect both distribution network operators (DSOs) and electricity consumers. This paper presents an innovative cooperative game theory (GT) framework to enhance electric energy supply reliability and demand-side flexibility by aligning the interest of DSOs and consumers. The research investigates the performance of the proposed GT model under different distribution network (DN) topologies and fault intensities, explicitly considering outage durations and restoration times. A cooperation mechanism based on penalty compensation is introduced to simulate realistic interactions between DSOs and consumers. Simulation results confirm that adaptive cooperation under this framework yields significant reliability improvements of up to 70% in some DN configurations. The GT-based approach supports informed investment decisions, improved stakeholder satisfaction, and reduced risk of service disruptions. Findings suggest that integrated GT planning mechanisms can lead to more resilient and consumer-centred electricity distribution systems.
Full article

Figure 1
Open AccessArticle
A Hierarchical RNN-LSTM Model for Multi-Class Outage Prediction and Operational Optimization in Microgrids
by
Nouman Liaqat, Muhammad Zubair, Aashir Waleed, Muhammad Irfan Abid and Muhammad Shahid
Electricity 2025, 6(4), 55; https://doi.org/10.3390/electricity6040055 - 1 Oct 2025
Abstract
Microgrids are becoming an innovative piece of modern energy systems as they provide locally sourced and resilient energy opportunities and enable efficient energy sourcing. However, microgrid operations can be greatly affected by sudden environmental changes, deviating demand, and unexpected outages. In particular, extreme
[...] Read more.
Microgrids are becoming an innovative piece of modern energy systems as they provide locally sourced and resilient energy opportunities and enable efficient energy sourcing. However, microgrid operations can be greatly affected by sudden environmental changes, deviating demand, and unexpected outages. In particular, extreme climatic events expose the vulnerability of microgrid infrastructure and resilience, often leading to increased risk of system-wide outages. Thus, successful microgrid operation relies on timely and accurate outage predictions. This research proposes a data-driven machine learning framework for the optimized operation of a microgrid and predictive outage detection using a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architecture that reflects inherent temporal modeling methods. A time-aware embedding and masking strategy is employed to handle categorical and sparse temporal features, while mutual information-based feature selection ensures only the most relevant and interpretable inputs are retained for prediction. Moreover, the model addresses the challenges of experiencing rapid power fluctuations by looking at long-term learning dependency aspects within historical and real-time data observation streams. Two datasets are utilized: a locally developed real-time dataset collected from a 5 MW microgrid of Maple Cement Factory in Mianwali and a 15-year national power outage dataset obtained from Kaggle. Both datasets went through intensive preprocessing, normalization, and tokenization to transform raw readings into machine-readable sequences. The suggested approach attained an accuracy of 86.52% on the real-time dataset and 84.19% on the Kaggle dataset, outperforming conventional models in detecting sequential outage patterns. It also achieved a precision of 86%, a recall of 86.20%, and an F1-score of 86.12%, surpassing the performance of other models such as CNN, XGBoost, SVM, and various static classifiers. In contrast to these traditional approaches, the RNN-LSTM’s ability to leverage temporal context makes it a more effective and intelligent choice for real-time outage prediction and microgrid optimization.
Full article
(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)
►▼
Show Figures

Figure 1
Open AccessArticle
Investigating Small-Scale DER Impact on Fault Currents and Overcurrent Protection Coordination in Distribution Feeders Under Brazilian Technical Standards
by
Murillo Cobe Vargas, Mariana Altoé Mendes, Oureste Elias Batista and Yongheng Yang
Electricity 2025, 6(3), 54; https://doi.org/10.3390/electricity6030054 - 18 Sep 2025
Abstract
This paper investigates the impacts of small-scale distributed energy resources (DERs) on fault currents and overcurrent protection (OCP) coordination in distribution feeders, considering the Brazilian regulatory framework. Changes in fault current levels and OCP coordination are analyzed by focusing on the relationships between
[...] Read more.
This paper investigates the impacts of small-scale distributed energy resources (DERs) on fault currents and overcurrent protection (OCP) coordination in distribution feeders, considering the Brazilian regulatory framework. Changes in fault current levels and OCP coordination are analyzed by focusing on the relationships between DER location, output power, and OCP positioning. Simulations were conducted in Simulink/MATLAB using the IEEE 13-Node Distribution Test Feeder as a case study, considering various DER integration scenarios. The DER model adheres to the Brazilian standard NBR 16149:2013, which governs fault current injection and voltage ride-through behavior. The results indicate that DER integration can disrupt OCP coordination and significantly affect fault current levels, despite their relatively small current contributions during faults. In one scenario, OCP coordination was lost, while in others, coordination time intervals decreased. The findings show that DER location has a minimal influence on fault current changes, whereas output power plays a more critical role. Faults occurring farther from the substation cause greater current variation in installed relays, with deviations nearing ±10%. Additionally, reverse fault currents through relays are identified as a key concern for protection engineers.
Full article
(This article belongs to the Special Issue Advances in Operation, Optimization and Control of Smart Grids: 2nd Edition)
►▼
Show Figures

Figure 1
Open AccessArticle
Electro-Thermal Modeling and Thermal Analysis of High-Inertia Synchronous Condenser Converters
by
Jinxin Ouyang, Yaowei Lin, Zhiqi Ye and Yanbo Diao
Electricity 2025, 6(3), 53; https://doi.org/10.3390/electricity6030053 - 15 Sep 2025
Abstract
►▼
Show Figures
High-inertia energy storage synchronous condenser (HI-ES-SC) is operated through rotor-excited variable-speed mechanisms to provide grid power support. Power devices are exposed to alternating electro-thermal stresses, with significant implications for system reliability. Therefore, an electro-thermal modeling approach is developed for the converter of HI-ES-SC
[...] Read more.
High-inertia energy storage synchronous condenser (HI-ES-SC) is operated through rotor-excited variable-speed mechanisms to provide grid power support. Power devices are exposed to alternating electro-thermal stresses, with significant implications for system reliability. Therefore, an electro-thermal modeling approach is developed for the converter of HI-ES-SC during power support operation. Switching dynamics and conduction states are incorporated in the model. A theoretical framework is established to analyze loss mechanisms and junction temperature evolution. A coupled electro-thermal model is constructed, accounting for temperature-dependent thermal network parameters. A numerical solution is proposed to enable co-simulation of condenser–converter systems. The simulation results indicate that the error in thermal parameter estimation remains below 10%. Key findings are summarized as follows: Under active power support, the peak junction temperature is observed to reach 81.69 °C during synchronous speed crossing, accompanied by notable low-frequency thermal accumulation. The derived operational-thermal correlation provides critical guidance for optimal thermal design and device selection.
Full article

Figure 1
Open AccessArticle
Controller Hardware-in-the-Loop Validation of a DSP-Controlled Grid-Tied Inverter Using Impedance and Time-Domain Approaches
by
Leonardo Casey Hidalgo Monsivais, Yuniel León Ruiz, Julio Cesar Hernández Ramírez, Nancy Visairo-Cruz, Juan Segundo-Ramírez and Emilio Barocio
Electricity 2025, 6(3), 52; https://doi.org/10.3390/electricity6030052 - 6 Sep 2025
Abstract
►▼
Show Figures
In this work, a controller hardware-in-the-loop (CHIL) simulation of a grid-connected three-phase inverter equipped with an LCL filter is implemented using a real-time digital simulator (RTDS) as the plant and a digital signal processor (DSP) as the control hardware. This work identifies and
[...] Read more.
In this work, a controller hardware-in-the-loop (CHIL) simulation of a grid-connected three-phase inverter equipped with an LCL filter is implemented using a real-time digital simulator (RTDS) as the plant and a digital signal processor (DSP) as the control hardware. This work identifies and discusses the critical aspects of the CHIL implementation process, emphasizing the relevance of the control delays that arise from sampling, computation, and pulse width modulation (PWM), which also adversely affect system stability, accuracy, and performance. Time and frequency domains are used to validate the modeling of the system, either to represent large-signal or small-signal models. This work shows multiple representations of the system under study: the fundamental frequency model, the switched model, and the switched model controlled by the DSP, are used to validate the nonlinear model, whereas the impedance-based modeling is followed to validate the linear representation. The results demonstrate a strong correlation among the models, confirming that the delay effects are accurately captured in the different simulation approaches. This comparison provides valuable insights into configuration practices that improve the fidelity of CHIL-based validation and supports impedance-based stability analysis in power electronic systems. The findings are particularly relevant for wideband modeling and real-time studies in electromagnetic transient analysis.
Full article

Figure 1
Open AccessArticle
Optimized Economic Dispatch and Battery Sizing in Wind Microgrids: A Depth of Discharge Perspective
by
Muhammad Mukit Hosen, Md Shafiul Alam, Shaharier Rashid and S. M. G. Mostafa
Electricity 2025, 6(3), 51; https://doi.org/10.3390/electricity6030051 - 4 Sep 2025
Cited by 1
Abstract
►▼
Show Figures
This article presents an optimized approach to battery sizing and economic dispatch in wind-powered microgrids. The primary focus is on integrating battery depth of discharge (DoD) constraints to prolong battery life and ensure cost-effective energy storage management. Because of the intermittent nature of
[...] Read more.
This article presents an optimized approach to battery sizing and economic dispatch in wind-powered microgrids. The primary focus is on integrating battery depth of discharge (DoD) constraints to prolong battery life and ensure cost-effective energy storage management. Because of the intermittent nature of wind energy, wind-powered microgrids require sophisticated energy storage systems to ensure stable operation. This study develops a metaheuristic optimization method that balances power supply, battery lifespan, and economic dispatch in a microgrid. The proposed method optimizes both battery size and dispatch strategy while considering wind energy variability and the impact of DoD on battery lifespan. Case studies conducted on a wind-powered microgrid under varying load conditions show that the developed approach achieves a 40 to 50% reduction in operating costs and cost of electricity (CoE) compared to other approaches. The results also reveal that the inclusion of DoD constraints enhances battery lifespan. The proposed method offers a practical solution for improving the economic and operational efficiency of wind-powered microgrids, providing valuable understanding for energy planners and grid operators in renewable energy systems.
Full article

Figure 1
Open AccessArticle
Design and Techno-Economic Feasibility Study of a Solar-Powered EV Charging Station in Egypt
by
Mahmoud M. Elkholy, Ashraf Abd El-Raouf, Mohamed A. Farahat and Mohammed Elsayed Lotfy
Electricity 2025, 6(3), 50; https://doi.org/10.3390/electricity6030050 - 2 Sep 2025
Abstract
►▼
Show Figures
This research focused on determining the technical and economic feasibility of the design of a solar-powered electric vehicle charging station (EVCS) in Cairo, Egypt. Using HOMER Grid, hybrid system configurations are assessed technically and economically to reduce costs and ensure reliability. These systems
[...] Read more.
This research focused on determining the technical and economic feasibility of the design of a solar-powered electric vehicle charging station (EVCS) in Cairo, Egypt. Using HOMER Grid, hybrid system configurations are assessed technically and economically to reduce costs and ensure reliability. These systems incorporate photovoltaic (PV) systems, lithium-ion battery energy storage systems (ESS), and diesel generators. A comprehensive analysis was conducted in Cairo, Egypt, focusing on small vehicle charging needs in both grid-connected and generator-supported scenarios. In this study, a 468 kW PV array integrated with 29 units of 1 kWh lithium-ion batteries and supported by time-of-use (TOU) tariffs, were used to optimize energy utilization. This study demonstrated the feasibility of the system in a case of eight chargers of 150 kW each and forty chargers of 48 kW. Conclusions suggest that the PV + ESS has the lowest pure power costs and reduced carbon emissions compared to traditional network-dependent solutions. The optimal configuration of USD 10.23 million over 25 years, with lifelong savings, results in annual savings of tool billing of around USD 409,326. This study concludes that a solar-powered EVC in Egypt is both technically and economically attractive, especially in the light of increasing energy costs.
Full article

Figure 1
Open AccessArticle
A Reinforcement Learning Approach Based on Group Relative Policy Optimization for Economic Dispatch in Smart Grids
by
Adil Rizki, Achraf Touil, Abdelwahed Echchatbi and Rachid Oucheikh
Electricity 2025, 6(3), 49; https://doi.org/10.3390/electricity6030049 - 1 Sep 2025
Abstract
►▼
Show Figures
The Economic Dispatch Problem (EDP) plays a critical role in power system operations by trying to allocate power generation across multiple units at minimal cost while satisfying complex operational constraints. Traditional optimization techniques struggle with the non-convexities introduced by factors such as valve-point
[...] Read more.
The Economic Dispatch Problem (EDP) plays a critical role in power system operations by trying to allocate power generation across multiple units at minimal cost while satisfying complex operational constraints. Traditional optimization techniques struggle with the non-convexities introduced by factors such as valve-point effects, prohibited operating zones, and spinning reserve requirements. While metaheuristics methods have shown promise, they often suffer from convergence issues and constraint-handling limitations. In this study, we introduce a novel application of Group Relative Policy Optimization (GRPO), a reinforcement learning framework that extends Proximal Policy Optimization by integrating group-based learning and relative performance assessments. The proposed GRPO approach incorporates smart initialization, adaptive exploration, and elite-guided updates tailored to the EDP’s structure. Our method consistently produces high-quality, feasible solutions with faster convergence compared to state-of-the-art metaheuristics and learning-based methods. For instance, in the case of the 15-unit system, GRPO achieved the best cost of USD 32,421.67/h with full constraint satisfaction in just 4.24 s, surpassing many previous solutions. The algorithm also demonstrates excellent scalability, generalizability, and stability across larger-scale systems without requiring parameter retuning. These results highlight GRPO’s potential as a robust and efficient tool for real-time energy scheduling in smart grid environments.
Full article

Figure 1
Open AccessArticle
Hybrid SDE-Neural Networks for Interpretable Wind Power Prediction Using SCADA Data
by
Mehrdad Ghadiri and Luca Di Persio
Electricity 2025, 6(3), 48; https://doi.org/10.3390/electricity6030048 - 1 Sep 2025
Abstract
Wind turbine power forecasting is crucial for optimising energy production, planning maintenance, and enhancing grid stability. This research focuses on predicting the output of a Senvion MM92 wind turbine at the Kelmarsh wind farm in the UK using SCADA data from 2020. Two
[...] Read more.
Wind turbine power forecasting is crucial for optimising energy production, planning maintenance, and enhancing grid stability. This research focuses on predicting the output of a Senvion MM92 wind turbine at the Kelmarsh wind farm in the UK using SCADA data from 2020. Two approaches are explored: a hybrid model combining Stochastic Differential Equations (SDEs) with Neural Networks (NNs) and Deep Learning models, in particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and the Combination of Convolutional Neural Networks (CNNs) and LSTM. Notably, while SDE-NN models are well suited for predictions in cases where data patterns are chaotic and lack consistent trends, incorporating stochastic processes increases the complexity of learning within SDE models. Moreover, it is worth mentioning that while SDE-NNs cannot be classified as purely “white box” models, they are also not entirely “black box” like traditional Neural Networks. Instead, they occupy a middle ground, offering improved interpretability over pure NNs while still leveraging the power of Deep Learning. This balance is precious in fields such as wind power prediction, where accuracy and understanding of the underlying physical processes are essential. The evaluation of the results demonstrates the effectiveness of the SDE-NNs compared to traditional Deep Learning models for wind power prediction. The SDE-NNs achieve slightly better accuracy than other Deep Learning models, highlighting their potential as a powerful alternative.
Full article
(This article belongs to the Topic Recent Advances in Smart Grid and Energy Storage Applications)
►▼
Show Figures

Figure 1
Open AccessArticle
Modeling the Tripping Behavior of Fuses Based on Data Sheet Characteristics and Conductor Material Properties
by
Manuel Seidenath and Martin Maerz
Electricity 2025, 6(3), 47; https://doi.org/10.3390/electricity6030047 - 31 Aug 2025
Abstract
Accurately simulating fuses is challenging because the fuse behavior is affected by a variety of thermal and electrical factors. This paper presents a SPICE fuse model and its parameterization procedure. The model mimics the physical behavior of the time–current characteristic including the transition
[...] Read more.
Accurately simulating fuses is challenging because the fuse behavior is affected by a variety of thermal and electrical factors. This paper presents a SPICE fuse model and its parameterization procedure. The model mimics the physical behavior of the time–current characteristic including the transition region. For the parameterization only, the time–current characteristic of the fuse, its resistance at room temperature and the melting temperature of the conducting material are needed. The novelty of this SPICE fuse model is the mathematical derivation of a physically based correction factor that considers the temperature dependence of the electrical fuse conductivity. The correction factor is applied to the inverted time–current characteristic. A third-order Foster thermal equivalent network is fitted to the adapted fuse characteristic using a least square algorithm. After a Foster–Cauer transformation, the thermal equivalent network is integrated into the SPICE model. Exemplary LTSpice is used to show and validate the model’s wiring diagram. Comparisons show a very good agreement with data sheet characteristics for a variety of fuse types and current ratings. In the adiabatic and transition region—i.e., at low tripping times—the maximum relative error between the data sheet characteristic and the simulated characteristic was consistently below 15% and thus within the production parameter spread.
Full article
(This article belongs to the Topic Power System Protection)
►▼
Show Figures

Figure 1
Open AccessArticle
Techno-Economic Assessment of Fixed and Variable Reactive Power Injection Using Thyristor-Switched Capacitors in Distribution Networks
by
Oscar Danilo Montoya, César Leonardo Trujillo-Rodríguez and Carlos Andrés Torres-Pinzón
Electricity 2025, 6(3), 46; https://doi.org/10.3390/electricity6030046 - 11 Aug 2025
Abstract
This paper presents a hybrid optimization framework for solving the optimal reactive power compensation problem in medium-voltage smart distribution networks. Leveraging Julia’s computational environment, the proposed method combines the global search capabilities of the Chu & Beasley genetic algorithm (CBGA) with the local
[...] Read more.
This paper presents a hybrid optimization framework for solving the optimal reactive power compensation problem in medium-voltage smart distribution networks. Leveraging Julia’s computational environment, the proposed method combines the global search capabilities of the Chu & Beasley genetic algorithm (CBGA) with the local refinement efficiency of the interior-point optimizer (IPOPT). The objective is to minimize the annualized operating costs by reducing active power losses while considering the investment and operating costs associated with thyristor-switched capacitors (TSCs). A key contribution of this work is the comparative assessment of fixed and time-varying reactive power injection strategies. Simulation results on the IEEE 33- and 69-bus test feeders demonstrate that the proposed CBGA-IPOPT framework achieves annualized cost reductions of up to 11.22% and 12.58% (respectively) under fixed injection conditions. With variable injection, cost savings increase to 12.43% and 14.08%. A time-domain analysis confirms improved voltage regulation, substation reactive demand reductions exceeding 500 kvar, and peak loss reductions of up to 32% compared to the uncompensated case. Benchmarking shows that the hybrid framework not only consistently outperforms state-of-the-art metaheuristics (the sine-cosine algorithm, the particle swarm optimizer, the black widow optimizer, and the artificial hummingbird algorithm) in terms of solution quality but also demonstrates high solution repeatability across multiple runs, underscoring its robustness. The proposed method is directly applicable to real-world distribution systems, offering a scalable and cost-effective solution for reactive power planning in smart grids.
Full article
(This article belongs to the Collection Optimal Operation and Planning of Smart Power Distribution Networks)
►▼
Show Figures

Figure 1
Open AccessArticle
A Tuned Parallel Population-Based Genetic Algorithm for BESS Operation in AC Microgrids: Minimizing Operational Costs, Power Losses, and Carbon Footprint in Grid-Connected and Islanded Topologies
by
Hugo Alessandro Figueroa-Saavedra, Daniel Sanin-Villa and Luis Fernando Grisales-Noreña
Electricity 2025, 6(3), 45; https://doi.org/10.3390/electricity6030045 - 9 Aug 2025
Abstract
►▼
Show Figures
The transition to decentralized renewable energy systems has highlighted the role of AC microgrids and battery energy storage systems in achieving operational efficiency and sustainability. This study proposes an improved energy management system for AC MGs based on a tuned Parallel Population-Based Genetic
[...] Read more.
The transition to decentralized renewable energy systems has highlighted the role of AC microgrids and battery energy storage systems in achieving operational efficiency and sustainability. This study proposes an improved energy management system for AC MGs based on a tuned Parallel Population-Based Genetic Algorithm for the optimal operation of batteries under variable generation and demand. The optimization framework minimizes power losses, emissions, and economic costs through a master–slave strategy, employing hourly power flow via successive approximations for technical evaluation. A comprehensive assessment is carried out under both grid-connected and islanded operation modes using a common test bed, centered on a flexible slack bus capable of adapting to either mode. Comparative analyses against Particle Swarm Optimization and the Vortex Search Algorithm demonstrate the superior accuracy, stability, and computational efficiency of the proposed methodology. In grid-connected mode, the Parallel Population-Based Genetic Algorithm achieves average reductions of 1.421% in operational cost, 4.383% in power losses, and 0.183% in CO2 emissions, while maintaining standard deviations below 0.02%. In islanded mode, it attains reductions of 0.131%, 4.469%, and 0.184%, respectively. The improvement in cost relative to the benchmark exact methods is 0.00158%. Simulations on a simplified 33-node AC MG with actual demand and generation profiles confirm significant improvements across all performance metrics compared to previous research works.
Full article

Figure 1
Open AccessArticle
Transient Stability Analysis for the Wind Power Grid-Connected System: A Manifold Topology Perspective on the Global Stability Domain
by
Jinhao Yuan, Meiling Ma and Yanbing Jia
Electricity 2025, 6(3), 44; https://doi.org/10.3390/electricity6030044 - 1 Aug 2025
Abstract
►▼
Show Figures
Large-scale wind power grid-connected systems can trigger the risk of power system instability. In order to enhance the stability margin of grid-connected systems, this paper accurately characterizes the topology of the global boundary of stability domain (BSD) of the grid-connected system based on
[...] Read more.
Large-scale wind power grid-connected systems can trigger the risk of power system instability. In order to enhance the stability margin of grid-connected systems, this paper accurately characterizes the topology of the global boundary of stability domain (BSD) of the grid-connected system based on BSD theory, using the method of combining the manifold topologies and singularities at infinity. On this basis, the effect of large-scale doubly fed induction generators (DFIGs) replacing synchronous units on the BSD of the system is analyzed. Simulation results based on the IEEE 39-bus system indicate that the negative impedance characteristics and low inertia of DFIGs lead to a contraction of the stability domain. The principle of singularity invariance (PSI) proposed in this paper can effectively expand the BSD by adjusting the inertia and damping, thereby increasing the critical clearing time by about 5.16% and decreasing the dynamic response time by about 6.22% (inertia increases by about 5.56%). PSI is superior and applicable compared to traditional energy functions, and can be used to study the power angle stability of power systems with a high proportion of renewable energy.
Full article

Figure 1
Open AccessArticle
Multi-Objective Optimization for Economic and Environmental Dispatch in DC Networks: A Convex Reformulation via a Conic Approximation
by
Nestor Julian Bernal-Carvajal, Carlos Arturo Mora-Peña and Oscar Danilo Montoya
Electricity 2025, 6(3), 43; https://doi.org/10.3390/electricity6030043 - 1 Aug 2025
Cited by 1
Abstract
This paper addresses the economic–environmental dispatch (EED) problem in DC power grids integrating thermoelectric and photovoltaic generation. A multi-objective optimization model is developed to minimize both fuel costs and CO2 emissions while considering power balance, voltage constraints, generation limits, and thermal line
[...] Read more.
This paper addresses the economic–environmental dispatch (EED) problem in DC power grids integrating thermoelectric and photovoltaic generation. A multi-objective optimization model is developed to minimize both fuel costs and CO2 emissions while considering power balance, voltage constraints, generation limits, and thermal line capacities. To overcome the non-convexity introduced by quadratic voltage products in the power flow equations, a convex reformulation is proposed using second-order cone programming (SOCP) with auxiliary variables. This reformulation ensures global optimality and enhances computational efficiency. Two test systems are used for validation: a 6-node DC grid and an 11-node grid incorporating hourly photovoltaic generation. Comparative analyses show that the convex model achieves objective values with errors below 0.01% compared to the original non-convex formulation. For the 11-node system, the integration of photovoltaic generation led to a 24.34% reduction in operating costs (from USD 10.45 million to USD 7.91 million) and a 27.27% decrease in CO2 emissions (from 9.14 million kg to 6.64 million kg) over a 24 h period. These results confirm the effectiveness of the proposed SOCP-based methodology and demonstrate the environmental and economic benefits of renewable integration in DC networks.
Full article
(This article belongs to the Topic Energy Systems Planning, Operation and Optimization in Net-Zero Emissions)
►▼
Show Figures

Figure 1
Open AccessArticle
Fault Location on Three-Terminal Transmission Lines Without Utilizing Line Parameters
by
Hongchun Shu, Le Minh Tri Nguyen, Xuan Vinh Nguyen and Quoc Hung Doan
Electricity 2025, 6(3), 42; https://doi.org/10.3390/electricity6030042 - 10 Jul 2025
Abstract
Transmission lines are constantly exposed to changes in climatic conditions and aging which affect the parameters and change the characteristics of the three-terminal circuit over time. In this paper we propose a fault location algorithm for three-terminal transmission lines to solve this problem.
[...] Read more.
Transmission lines are constantly exposed to changes in climatic conditions and aging which affect the parameters and change the characteristics of the three-terminal circuit over time. In this paper we propose a fault location algorithm for three-terminal transmission lines to solve this problem. The algorithm utilizes the positive components of the voltage and current signals measured synchronously from the terminals. In this work no prior knowledge of the line parameters was required when calculating the fault location and the use of fault classification algorithms was not necessary. In addition, the proposed method determines the parameters of the line segment and fault location based on a solid mathematical basis and has been verified through simulation results using SIMULINK/MATLAB R2018a software. The fault location results demonstrate the high accuracy and efficiency of the algorithm. Moreover, this method can estimate the characteristic impedance and propagation constants of the transmission lines and determine the location of the fault, which is not affected by different fault parameters including fault location, and fault resistance.
Full article
(This article belongs to the Topic Power System Protection)
►▼
Show Figures

Figure 1
Open AccessArticle
Novel Methodology for Determining Necessary and Sufficient Power in Integrated Power Systems Based on the Forecasted Volumes of Electricity Production
by
Artur Zaporozhets, Vitalii Babak, Mykhailo Kulyk and Viktor Denysov
Electricity 2025, 6(3), 41; https://doi.org/10.3390/electricity6030041 - 4 Jul 2025
Abstract
►▼
Show Figures
This study presents a novel methodology for determining zonal electricity generation and capacity requirements corresponding to forecasted annual production in an integrated power system (IPS). The proposed model combines the statistical analysis of historical daily load patterns with a calibration technique to translate
[...] Read more.
This study presents a novel methodology for determining zonal electricity generation and capacity requirements corresponding to forecasted annual production in an integrated power system (IPS). The proposed model combines the statistical analysis of historical daily load patterns with a calibration technique to translate forecast total demand into zonal powers (base, semi-peak and peak). A representative reference daily electrical load graph (ELG) is selected from retrospective data using least squares criteria, and a calibration factor α = Wx/Wie scales its zonal outputs to match the forecasted annual generation Wx. The innovation lies in this combination of historical ELG identification and calibration for accurate zonal power prediction. Applying the model to Ukrainian IPS data yields high accuracy: a zonal power error below 1.02% and a generation error below 0.39%. Key contributions include explicitly stating the research questions and hypotheses, providing a schematic procedural description and discussing model limitations (e.g., treatment of renewable variability and omission of meteorological/astronomical factors). Future work is outlined to incorporate unforeseen factors (e.g., post-war demand shifts, electric vehicle adoption) into the forecasting framework.
Full article

Figure 1
Open AccessArticle
Improving the Operation of Transmission Systems Based on Static Var Compensator
by
Kelly M. Berdugo Sarmiento, Jorge Iván Silva-Ortega, Vladimir Sousa Santos, John E. Candelo-Becerra and Fredy E. Hoyos
Electricity 2025, 6(3), 40; https://doi.org/10.3390/electricity6030040 - 4 Jul 2025
Cited by 1
Abstract
This study evaluates and compares centralized and distributed reactive power compensation strategies using Static Var Compensators (SVCs) to enhance the performance of a high-voltage transmission system in the Caribbean region of Colombia. The methodology comprises four stages: system characterization, assessment of the uncompensated
[...] Read more.
This study evaluates and compares centralized and distributed reactive power compensation strategies using Static Var Compensators (SVCs) to enhance the performance of a high-voltage transmission system in the Caribbean region of Colombia. The methodology comprises four stages: system characterization, assessment of the uncompensated condition under peak demand, definition of four SVC-based scenarios, and steady-state analysis through power flow simulations using DIgSILENT PowerFactory. SVCs were modeled as Thyristor-Controlled Devices (“SVC Type 1”) operating as PV nodes for voltage regulation. The evaluated scenarios include centralized SVCs at the Slack node, node N4, and node N20, as well as a distributed scheme across load nodes N51 to N55. Node selection was guided by power flow analysis, identifying voltage drops below 0.9 pu and overloads above 125%. Technically, the distributed strategy outperformed the centralized alternatives, reducing active power losses by 37.5%, reactive power exchange by 46.1%, and improving node voltages from 0.71 pu to values above 0.92 pu while requiring only 437 MVAr of compensation compared to 600 MVAr in centralized cases. Economically, the distributed configuration achieved the highest annual energy savings (36 GWh), the greatest financial return (USD 5.94 M/year), and the shortest payback period (7.4 years), highlighting its cost-effectiveness. This study’s novelty lies in its system-level comparison of SVC deployment strategies under real operating constraints. The results demonstrate that distributed compensation not only improves technical performance but also provides a financially viable solution for enhancing grid reliability in infrastructure-limited transmission systems.
Full article
(This article belongs to the Special Issue Innovations in Smart Grid Technologies and Sustainable Energy Solutions)
►▼
Show Figures

Figure 1
Open AccessArticle
MI-Convex Approximation for the Optimal Siting and Sizing of PVs and D-STATCOMs in Distribution Networks to Minimize Investment and Operating Costs
by
Oscar Danilo Montoya, Brandon Cortés-Caicedo, Luis Fernando Grisales-Noreña, Walter Gil-González and Diego Armando Giral-Ramírez
Electricity 2025, 6(3), 39; https://doi.org/10.3390/electricity6030039 - 3 Jul 2025
Cited by 1
Abstract
The optimal integration of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in electrical distribution networks is important to reduce their operating costs, improve their voltage profiles, and enhance their power quality. To this effect, this paper proposes a mixed-integer convex (MI-Convex)
[...] Read more.
The optimal integration of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in electrical distribution networks is important to reduce their operating costs, improve their voltage profiles, and enhance their power quality. To this effect, this paper proposes a mixed-integer convex (MI-Convex) optimization model for the optimal siting and sizing of PV systems and D-STATCOMs, with the aim of minimizing investment and operating costs in electrical distribution networks. The proposed model transforms the traditional mixed-integer nonlinear programming (MINLP) formulation into a convex model through second-order conic relaxation of the nodal voltage product. This model ensures global optimality and computational efficiency, which is not achieved using traditional heuristic-based approaches. The proposed model is validated on IEEE 33- and 69-bus test systems, showing a significant reduction in operating costs in both feeders compared to traditional heuristic-based approaches such as the vortex search algorithm (VSA), the sine-cosine algorithm (SCA), and the sech-tanh optimization algorithm (STOA). According to the results, the MI-convex model achieves cost savings of up to 38.95% in both grids, outperforming the VSA, SCA, and STOA.
Full article
(This article belongs to the Special Issue Recent Advances in Power and Smart Grids)
►▼
Show Figures

Figure 1
Open AccessArticle
Secure Optimization Dispatch Framework with False Data Injection Attack in Hybrid-Energy Ship Power System Under the Constraints of Safety and Economic Efficiency
by
Xiaoyuan Luo, Weisong Zhu, Shaoping Chang and Xinyu Wang
Electricity 2025, 6(3), 38; https://doi.org/10.3390/electricity6030038 - 3 Jul 2025
Abstract
►▼
Show Figures
Hybrid-energy vessels offer significant advantages in reducing carbon emissions and air pollutants by integrating traditional internal combustion engines, electric motors, and new energy technologies. However, during operation, the high reliance of hybrid-energy ships on networks and communication systems poses serious data security risks.
[...] Read more.
Hybrid-energy vessels offer significant advantages in reducing carbon emissions and air pollutants by integrating traditional internal combustion engines, electric motors, and new energy technologies. However, during operation, the high reliance of hybrid-energy ships on networks and communication systems poses serious data security risks. Meanwhile, the complexity of energy scheduling presents challenges in obtaining feasible solutions. To address these issues, this paper proposes an innovative two-stage security optimization scheduling framework aimed at simultaneously improving the security and economy of the system. Firstly, the framework employs a CNN-LSTM hybrid model (WOA-CNN-LSTM) optimized using the whale optimization algorithm to achieve real-time detection of false data injection attacks (FDIAs) and post-attack data recovery. By deeply mining the spatiotemporal characteristics of the measured data, the framework effectively identifies anomalies and repairs tampered data. Subsequently, based on the improved multi-objective whale optimization algorithm (IMOWOA), rapid optimization scheduling is conducted to ensure that the system can maintain an optimal operational state following an attack. Simulation results demonstrate that the proposed framework achieves a detection accuracy of 0.9864 and a recovery efficiency of 0.969 for anomaly data. Additionally, it reduces the ship’s operating cost, power loss, and carbon emissions by at least 1.96%, 5.67%, and 1.65%, respectively.
Full article

Figure 1
Highly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Energies, Sensors, Electronics, Modelling, Electricity
EMC and Reliability of Power Networks
Topic Editors: Antonella Ragusa, Alistair DuffyDeadline: 31 October 2025
Topic in
Algorithms, Applied Sciences, Electricity, Energies, Forecasting
Short-Term Load Forecasting—2nd Edition
Topic Editors: Antonio Gabaldón, María Carmen Ruiz-Abellón, Luis Alfredo Fernández-JiménezDeadline: 31 December 2025
Topic in
Electronics, Energies, Processes, Smart Cities, Sustainability, Electricity, Inventions, Batteries
Recent Advances in Smart Grid and Energy Storage Applications
Topic Editors: Alfredo Alcayde, John Alexander Taborda-GiraldoDeadline: 31 January 2026
Topic in
Energies, Sustainability, Electricity
Advanced Technology of Smart Battery and Energy Management System of Transportation Electrification
Topic Editors: Longxing Wu, Zhiqiang Lyu, Renjing Gao, Muyao WuDeadline: 30 March 2026

Conferences
Special Issues
Special Issue in
Electricity
Fault Diagnosis of Clean Energy Equipment
Guest Editors: Qi Xiao, Senxiang Lu, Yu Yao, Wenhui LiDeadline: 25 October 2025
Special Issue in
Electricity
Innovations in Smart Grid Technologies and Sustainable Energy Solutions
Guest Editor: Farhad ShahniaDeadline: 20 November 2025
Special Issue in
Electricity
Advancing Energy Systems for a Decarbonized Future: Renewable Integration, Smart Grids, and Optimization Strategies
Guest Editors: Changgi Min, Heejin KimDeadline: 30 November 2025
Special Issue in
Electricity
Enhancing Flexibility and Security in Net-Zero Integrated Energy Systems Through Emerging Technologies
Guest Editors: Shaohua Yang, Jianwei Li, Tao Chen, Sheng WangDeadline: 31 December 2025
Topical Collections
Topical Collection in
Electricity
Optimal Operation and Planning of Smart Power Distribution Networks
Collection Editor: Pavlos S. Georgilakis