Strategic Planning for Energy and the Environment

Optimised PIDN–FACTS Control for Frequency Stability in Multi-Area Systems with Fuel Cell Integration

2026-01-02T17:59:20+01:00

The growing induction of renewable and cleaner energy sources has made present-day interconnected power grids more complex, where frequency stability and tie-line power control are significant issues. This work presents a realistic two-area interconnected system modelled in MATLAB/Simulink comprising gas, reheat-thermal, and hydro units, with nonlinear consideration and a modified Proportional-Integral-Derivative-Filter (PIDN) controller design. The impact of the inclusion of a Fuel Cell (FC) unit with the grid on frequency stability is analysed, and a coordinated control strategy with Flexible AC Transmission System (FACTS) devices is developed. Several FACTS devices, namely Thyristor Controlled Series Capacitor (TCSC), Unified Power Flow Controller (UPFC), Interline Power Flow Controller (IPFC), and Static Synchronous Series Compensator (SSSC), are designed and integrated with the grid to improve frequency stability. Three population-based optimisation algorithms, viz, Grey Wolf Optimisation (GWO), Artificial Bee Colony (ABC), and Differential Evolution (DE), are used for minimising the cost function, Integral of Time multiplied by Absolute Error (ITAE), for optimal tuning of PIDN and FACTS controllers. A comprehensive study is performed across four scenarios, including single-area and multi-area Step Load Perturbations (SLP). The control performance of each strategy is evaluated based on the obtained ITAE, settling times, peak overshoots, peak undershoots, and rise times. Results demonstrate that GWO outperforms ABC and DE by achieving better control dynamics. Moreover, inclusion of FC achieves a substantial reduction in ITAE compared to conventional approaches, with values as low as 0.002044. FACTS devices further enhance performance, with IPFC consistently achieving the best damping, lowering ITAE to nearly half of the baseline PIDN–GWO values in multi-area disturbances. Overall, the study establishes that coordinated deployment of optimised controllers, FACTS technologies, and cleaner energy sources can significantly strengthen frequency regulation.

Optimized Design and Techno-Economic Analysis of a Renewable Energy-Based EV Charging Infrastructure for Residential Community in Arid Regions

2026-02-25T02:43:27+01:00

Off-grid power generation has become increasingly efficient for remote and developing regions with the advancement of renewable energy technologies. Areas with limited access to the conventional grid can now depend on self-sustaining hybrid systems that offer higher reliability and lower carbon emissions compared to traditional power sources. The main objective of this study is to propose an economically viable and optimally designed system model for electric vehicle (EV) charging infrastructure. The system aims to provide a practical and sustainable solution for EV charging, considering electric vehicles as a primary mode of transportation. The motivation behind this work is to promote the adoption of renewable energy in the transportation sector, thereby contributing to a cleaner and pollution-free environment. The selected location is the capital city of the central region of Oman. The electric load of a community was synthesized for 25 households, with 60% of them owning an electric vehicle. Comprehensive system modelling, optimization, and performance evaluation were carried out under varying operating conditions. The total Net Present Cost (NPC) and Cost of Electricity (COE) of the proposed system were estimated to be $7,11,333 and $28,394 respectively. In addition, a sensitivity analysis was conducted to study the impact of variations in average solar irradiance and diesel fuel prices in Oman, as these parameters significantly influence the overall production cost of the system.

Deep Learning Architectures for Advanced Anomaly Detection in Solar Photovoltaic Systems

2026-02-25T02:46:27+01:00

Solar photovoltaic (PV) system operation and maintenance are important for achieving global sustainability objectives. Also, the reliability of PV is one thing that has always prevented it from reaching the full product guarantee due to hotspot, diode degradation, shading, and cracks issues. Traditional inspection methods are time-consuming, expensive, and can contain errors, which justifies the development of automation systems. This paper proposes a deep learning-based framework for anomaly detection using high-resolution RGB images, which overcomes the drawbacks of low-resolution grayscale datasets. To improve robustness, a dataset consisting of 20,000 PV module images with eleven fault categories and normal modules was systematically preprocessed by resizing, augmentation, and quality assurance. Six models, namely, CNN model, AlexNet, VGG16, ResNet18, DenseNet, and EfficientNetV2B0, were compared with each other through the evaluation metrics of accuracy, precision, recall, and F1 score. The experimental results show that the RGB transform can greatly benefit feature learning and model generalization. Overall, ResNet18 had the highest accuracy (91.1%) while EfficientNetV2B0 had balanced performance overall metrics. The results highlight Deep-Learning applications with fine quality datasets (i.e., achieving high performances of Anomaly Detection, Predictive Maintenance, and Sustainable Energy Generation).

Hybrid Butterfly–Firefly Machine Learning Optimization for Enhanced Performance of IEEE Distribution Systems

2026-03-02T12:58:08+01:00

This paper suggests a new Hybrid Butterfly-Firefly Machine Learning Optimization (HBFL-OM) framework to enhance the operational performance of the IEEE distribution systems by optimizing the position and setting parameters of a Hybrid Static Compensator-Smart Voltage Stabilizer (HSVC). The HBFL-OM algorithm is a hybridization of Butterfly Optimization Algorithm (BOA) global exploration method and the Firefly Algorithm (FA) local refinement, where the Support Vector Regression (SVR) is added to learn the predictions and converge faster. They are optimized at the same time with multi-objective functions, such as minimization of power loss, power quality (THD), improvement of reliability (SAIFI/SAIDI), and balancing of loads. The AHP and TOPSIS are used to determine the best bus to place HSVC. The performances of the proposed HBFL-OM on IEEE 33-bus and 69-bus test systems prove that the given algorithm is better than GA and PSO algorithms with the subsequent results: 22.1% reduced power losses, 26% improved THD, and 20% increased indices of reliability with the preservation of balanced load distribution. The framework offers a strong and data driven solution that can be optimally utilized to optimize power systems in real-time.

Real Time Demand Driven Performance Assessment of Direct Coupled HESS for Electric Vehicles

2026-04-13T05:44:26+02:00

Multi-converter-based architectures are common in hybrid energy storage systems used in electric vehicles, increase a high cost, high power losses, and high weight. The current work presents a conceptual break to such traditional designs with a direct-coupled HESS that incorporates a proton exchange membrane fuel cell, a lithium-ion battery, and a supercapacitor into one DC bus so that the interconnection power converters are not required. This suggested configuration is based on the new demand-driven model, where the distribution of power is determined depending on the actual load requirements and without referring to predictive algorithms. The proposed system was modeled to evaluate the feasibility of this approach and was heavily simulated in an environment based on MATLAB/Simulink, with three standardized drive cycles FTP75, EPA Highway and WLTC Class. The performance is shown with EMS distribution efficiency of 94.8% and response time of 3.2 milliseconds. It also shows a more stress-free architecture of components, 55.7% decrease in peak current of the battery and better capacity retention. Through successful decoupling of power distribution and predictive models, the results demonstrate that a simplified, converter-free topology is a high-performance alternative providing a possible future, more solid, efficient, and commercially viable route to more robust and efficient powertrains in electric vehicles.

Enhanced Analysis of PV Array Operating in Partial Shading Conditions Using a Dispersed Illumination Technique

2025-11-28T08:54:00+01:00

Partial shading of a PV array leads to lower energy yield and results in multiple peaks in its P–V characteristics. The losses due to partial shading are not proportional to the shaded area but are dependent on the shading pattern, array configuration and the physical position of shaded modules in the array. This paper presents a novel Dispersed Illumination (DI) technique for mitigating partial shading effects in a Total Cross-Tied (TCT) photovoltaic (PV) array configuration. The proposed technique aims to maximize power output under various partial shading conditions by physically redistributing shaded modules across the array, thereby reducing the mismatch losses and suppressing the formation of multiple local maxima in the power–voltage characteristics. The simulation results have been compared to the Odd–Even and TCT configuration results. The proposed technique consistently outperforms both the Odd–Even and conventional TCT arrangements by delivering higher maximum power output, thereby demonstrating its superior effectiveness in mitigating partial shading losses. Owing to its simplicity, the proposed method offers a practical and efficient solution for improving PV array performance in real-world partially shaded environments.

Optimal Placement of Distributed Generators and Capacitors for Power Loss Reduction Using Crested Porcupine Algorithm

2026-06-12T05:20:26+02:00

In recent years, as the need for energy rises, the application of dispersed generation and shunt capacitors has become a more common solution to meet the growing demand of energy. The article presents a novel approach to optimise radial distribution networks, called the Crested Porcupine optimisation algorithm (CPOA), inspired by the behaviour of the Crested Porcupine. The proposed method is tested on the IEEE system, which has 33, 69 and 85 buses. The purpose of this work is to lower the cost and power loss while improving the voltage profile and voltage stability index (VSI) using the Capacitor Banks (CB) and Distributed Generator (DG) units at the correct location and of appropriate size. Inclusion of load models is also taken into consideration.

Unlocking Agro-residue Potential Via a Multi-product Integrated Biorefinery

2026-04-29T10:39:34+02:00

The intrinsic valorization of agro-industrial residues through integrated and sustainable strategies is gaining increasing attention due to its potential environmental, energetic, and economic benefits. The present study proposes a circular biorefinery model for the wine sector, aimed at converting grape pomace, wine lees, and pruning residues into biomethane and a crude bio-oil, through a synergistic combination of several processes. Both experimental and modelling results were combined to assess a novel process integration. Grape pomace and wine lees were considered as the substrate for the anaerobic digestion, biomethane was obtained from a pressure sorption adsorption process, while the off gases were used to sustain the energy demand of the digestate (hydrothermal liquefaction). Vine prunings were treated by pyrolysis and used as the adsorbent phase in the pressure sorption adsorption. The results highlight yields to gaseous, liquid, and solid products of approximately 10%, 30%, and 30%wt on dry raw material basis, respectively, demonstrating effective mass partitioning across product streams in the integrated biorefinery process. Through strategic energy integration – leveraging pyro-gas, hydro-gas, and off-gases from biomethane upgrading – the valorization of the residual digestate proved sufficient to sustain the HydroThermal Liquefaction (HTL) processing of approximately 80% of total digestate stream. This synergy not only achieves near-complete waste utilization, but also enhances overall process efficiency, reducing external energy inputs by over 70% while maintaining favourable technical viability.

Editorial

2026-07-22T04:54:08+02:00

Extensive researches are being performed on clean energy generation and integration into the power systems due to environmental friendliness and decreasing fossil resources. Innovative methods in clean energy generation and power electronics based integration into the power grids are rapidly becoming attractive. There are many applications to integrate clean energy resources to existing utility grid or microgrid or isolated load. These applica- tions require advanced control for which power electronics & drives plays an important role. The increased efficiency of power semiconductors devices and robustness of machines enable to improve many types of power conversion. On the other hand, alternate energy vehicles and energy storage systems are widely integrated with clean energy sources.

Urban Integrated Energy System Planning Integrating Temporal Characteristics and Two-Layer Optimization

2026-02-02T21:48:51+01:00

To address the problems of insufficient consideration of source-load temporal characteristics, weak coordination between site and equipment configuration, and low efficiency of multi-objective optimization in the planning of urban smart local energy systems, this paper constructs a system planning method that integrates temporal characteristic analysis and two-layer multi-objective optimization. By constructing a source-load temporal coupling model, the dynamic characteristics of combined heat and power loads and renewable energy outputs such as wind, solar, hydrogen, and storage are accurately characterized. The temporal model provides dynamic data input for the lower-layer equipment configuration optimization. A two-layer optimization framework based on Voronoi diagrams and genetic algorithms is designed. The upper layer uses Voronoi diagrams to partition the space and select station sites. The lower layer employs an improved NSGA-II algorithm to optimize equipment selection and capacity configuration. The two layers interact iteratively: the upper layer provides zoning and load information to the lower layer, and the lower layer feeds back configuration costs to update the upper-layer planning. The framework achieves collaborative site selection and zoned energy supply for multiple energy stations. A fuzzy membership decision mechanism is introduced to enhance the engineering applicability of the Pareto solution set. Experimental results show that, for economics, the total annual cost of the system is reduced by approximately 12% compared to the traditional step-by-step optimization method. In terms of environmental protection, carbon emissions are reduced by approximately 15%. In terms of robustness, even considering demand-side response and load fluctuations of up to 20%, the system can still maintain an energy supply reliability of over 88%. The core innovation lies in the deep integration of high-resolution temporal dynamics with spatial-device collaborative optimization through a closed-loop iterative mechanism, which significantly improves planning accuracy, efficiency, and system adaptability. This method provides a theoretical and technical support for the low-carbon and efficient layout of urban smart local energy systems.

Transient Voltage Control Strategy for Large-Scale Photovoltaic Access to Sending-End System

2026-03-12T18:58:21+01:00

This paper tackles the challenge of insufficient transient voltage support capability in sending-end systems following large-scale photovoltaic (PV) integration. A novel two-tier hierarchical control strategy is proposed, specifically directed toward the grid-connected point and the photovoltaic power station collection point. The grid-connected point control layer establishes a transient voltage security assessment index based on the voltage recovery boundary. It also presents an optimization method for SVC installation location, followed by a capacity optimization model that balances voltage security and investment costs, solved using the Dung Beetle algorithm. The collection point control layer improves transient voltage recovery by dynamically quantifying the distance to the recovery boundary and integrating it with fuzzy adaptive reactive power control for PV stations. Validation of the method’s efficacy is achieved via simulations conducted on an augmented Nordic test system.

Distributed Photovoltaic Hosting Capacity Evaluation for Distribution Networks Considering Medium-Voltage and Low-Voltage Interactions

2026-03-19T02:12:44+01:00

Under the “Dual Carbon” goals background, large-scale integration of distributed photovoltaics (PV) is transforming distribution networks from passive radial systems into active bidirectional interactive systems. To address this, this paper proposes a hosting capacity assessment method for high-penetration distributed PV in distribution networks that considers medium and low voltage levels (MV-LV) interactions. First, by analyzing the impacts of MV-LV grid interactions, four core interaction mechanisms are identified: considering voltage coupling under low-voltage saturation, current coupling under short-circuit superposition, and power coupling under photovoltaic concentration. An MV-LV interaction model is constructed based on these mechanisms. Subsequently, the concept of a Voltage Deviation Index (VDI) is introduced to quantify the voltage quality level of the system. An assessment model is then formulated with the dual objectives of maximizing PV hosting capacity and minimizing VDI, incorporating system-wide constraints, MV-specific and LV-specific constraints, as well as MV-LV interaction constraints. Finally, a Differential Evolution Non-dominated Sorting Whale Optimization Algorithm (DE-NSWOA) is employed to solve. The effectiveness of the proposed method is validated through simulations based on a 10 kV distribution network in a city in East China, providing a foundation for subsequent research.

Modeling and Optimization Method for Wind-Solar-Thermal Power Coupling Participating in the Joint Energy Spot and Frequency Regulation Market

2026-03-05T01:03:48+01:00

To address the challenges of absorption difficulty and insufficient economy caused by the grid connection of high-proportion renewable energy, this paper constructs a non-cooperative asymmetric game model between a wind-PV-thermal integrated energy system and external independent power generation units participating in the spot and frequency regulation coordinated market under the framework of Liaoning Province’s frequency regulation market rules. Taking the profit maximization of each participant as the core objective, a joint clearing model that incorporates the costs, compensations, and constraints of both the spot and regulation service markets are established, and the Nash equilibrium is solved through the application of the strategy iteration algorithm. Case studies verify that through internal resource coordination, the integrated energy collaborative system significantly reduces marginal costs and frequency regulation quotes, effectively improves its own market revenue while promoting renewable energy absorption, providing an economical and reliable operation support for the new power system.

Research on Optimal Trading Strategy and New Energy Consumption of Wind-Solar-Thermal Coupled System in Spot Market

2026-03-12T19:25:48+01:00

As the “dual-carbon” targets are set and the energy sector accelerates its shift toward green and low-carbon development, renewable sources such as wind power are being integrated at an unprecedented scale. This trend has imposed considerable strain on the stability and reliability of the evolving power system. To tackle this challenge, this study introduces a hybrid dynamic wind power forecasting approach that integrates machine learning with optimization algorithms. Furthermore, by incorporating a self-correcting parameter estimation process, a hybrid model is constructed. By continuously tuning the grid’s transmission capacity in real time, the proposed framework remains responsive to variations in wind power output. Based on observed fluctuation patterns, the framework continuously updates its system parameters, guaranteeing that the power grid operates optimally even under intricate and shifting environmental conditions. Using real-world data for validation, the proposed approach demonstrates clear strengths in both forecast precision and the operational efficiency of the power grid. By strengthening the grid’s resilience to wind power variability, this approach contributes to maintaining reliable and stable system operation. This technique offers tangible engineering backing for the seamless and efficient incorporation of wind energy into power grids.

Capacity Adequacy Performance Payment Strategy Considering Seasonal Fluctuations of Hydropower

2026-03-04T17:16:39+01:00

The long-term stable adequacy of power generation capacity and flexible adjustable resources is one of the keys to the stable operation of the power market. The uncertainty of new energy and the periodicity of hydropower output pose greater challenges to the adequacy of power market capacity and adjustable resources. To address this challenge, probabilistic modeling of error probabilities in different periods is conducted through the kernel density estimation method with separate parameters. Dynamic reserve capacity demand is calculated under confidence probability, and capacity adequacy performance payment is introduced. Finally, the incentive effects of the new method are compared with those of the traditional capacity payment system and market auction in the market, leading to the conclusion that the new method helps flexible adjustable units better recover costs.