Search Results (346)

This study investigates the impact of Time-of-Use (TOU) scheduling and battery energy storage systems (BESS) on voltage stability in a typical Malaysian medium-voltage distribution network with high photovoltaic (PV) system penetration. The analyzed network comprises 110 nodes connected via eight feeders to a pair of 132/11 kV, 15 MVA transformers, supplying a total load of 20.006 MVA. Each node is integrated with a 100 kW PV system, enabling up to 100% PV penetration scenarios. A hybrid mitigation strategy combining TOU-based load shifting and BESS was implemented to address voltage violations occurring, particularly during low-load night hours. Dynamic simulations using DIgSILENT PowerFactory were conducted under worst-case (no load and peak load) conditions. The novelty of this research is the use of real rural network data to validate a hybrid BESS–TOU strategy, supported by detailed sensitivity analysis across PV penetration levels. This provides practical voltage stabilization insights not shown in earlier studies. Results show that at 100% PV penetration, TOU or BESS alone are insufficient to fully mitigate voltage drops. However, a hybrid application of 0.4 MWh BESS with 20% TOU load shifting eliminates voltage violations across all nodes, raising the minimum voltage from 0.924 p.u. to 0.951 p.u. while reducing active power losses and grid dependency. A sensitivity analysis further reveals that a 60% PV penetration can be supported reliably using only 0.4 MWh of BESS and 10% TOU. Beyond this, hybrid mitigation becomes essential to maintain stability. The proposed solution demonstrates a scalable approach to enable large-scale PV integration in dense rural grids and addresses the specific operational characteristics of Malaysian networks, which differ from commonly studied IEEE test systems. This work fills a critical research gap by using real local data to propose and validate practical voltage mitigation strategies. Full article

The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. Full article

This work proposes an effective control technique for enhancing the stability of Doubly Fed Induction Generator-Based Wind Turbines (DFIG-WTs) connected to the grid during voltage sag and swell events, ensuring the reliable and efficient operation of wind energy systems integrated with the grid. The proposed approach integrates a Dynamic Voltage Restorer (DVR) in series with a Wind Turbine Generator (WTG) output terminal to enhance the Fault Ride-Through (FRT) capability during grid disturbances. To develop a flexible control strategy for both unbalanced and balanced fault conditions, a combination of feedforward and feedback control based on a sliding mode control (SMC) for DVR converters is used. This hybrid strategy allows for precise voltage regulation, enabling the series compensator to inject the required voltage into the grid, thereby ensuring constant generator terminal voltages even during faults. The SMC enhances the system’s robustness by providing fast, reliable regulation of the injected voltage, effectively mitigating the impact of grid disturbances. To further enhance system performance, Model Predictive Control (MPC) is implemented for the Rotor-Side Converter (RSC) within the back-to-back converter (BTBC) configuration. The main advantages of the predictive control method include eliminating the need for linear controllers, coordinate transformations, or modulators for the converter. Additionally, it ensures the stable operation of the generator even under severe operating conditions, enhancing system robustness and dynamic response. To validate the proposed control strategy, a comprehensive simulation is conducted using a 2 MW DFIG-WT connected to a 120 kV grid. The simulation results demonstrate that the proposed control approach successfully limits overcurrent in the RSC, maintains electromagnetic torque and DC-link voltage within their rated values, and dynamically regulates reactive power to mitigate voltage sags and swells. This allows the WTG to continue operating at its nominal capacity, fully complying with the strict requirements of modern grid codes and ensuring reliable grid integration. Full article

The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration. Full article

With rising energy costs and the need for sustainable power solutions in urban South African settings, grid-tied renewable energy systems have become viable alternatives for reducing dependence on traditional grid supply. This study investigates the techno-economic feasibility of a grid-connected hybrid photovoltaic (PV) and battery storage system designed for a commercial facility located in Johannesburg, South Africa—an area characterized by a subtropical highland climate. We conducted the analysis using the HOMER Grid software and evaluated the performance of the proposed PV/battery system against the baseline grid-only configuration. Simulation results indicate that the optimal systems, comprising 337 kW of flat-plate PV and 901 kWh of lithium-ion battery storage, offers a significant reduction in electricity expenditure, lowering the annual utility cost from $39,229 to $897. The system demonstrates a simple payback period of less than two years and achieves a net present value (NPV) of approximately $449,491 over a 25-year project lifespan. In addition to delivering substantial cost savings, the proposed configuration also enhances energy resilience. Sensitivity analyses were conducted to assess the impact of variables such as inflation rate, discount rate, and load profile fluctuations on system performance and economic returns. The results affirm the suitability of hybrid grid-tied PV/battery systems for cost-effective, sustainable urban energy solutions in climates with high solar potential. Full article

The ambitious goals of decarbonization of the European economy by mid-century pose significant challenges, especially when relying heavily on resources whose nature is inherently intermittent, specifically wind and solar energy. The situation is even more serious in isolated regions with limited connections to larger power grids. Using EnergyPLAN software, three scenarios for 2023 were modeled: a diesel-only system, the current hybrid renewable system, and an optimized scenario. This paper evaluates the performance of the usual generation system existing in isolated systems, based on fossil fuels, and proposes an optimized system considering both the cost of the system and the penalties for emissions. All this is applied to the case study of the island of El Hierro, but the findings are applicable to any location with similar characteristics. This system is projected to reduce emissions by over 75% and cut costs by one-third compared to the current configuration. A system has been proposed that preserves the economic viability and reliability of diesel-based systems while achieving low emission levels. This is accomplished primarily through the use of renewable energy generation, supported by pumped hydro storage. The approach is specifically designed for remote regions with small isolated grids, where reliability is critical. Importantly, the system relies on appropriately sized renewable installations, avoiding oversizing, which—although it could further reduce emissions—would lead to significant energy surpluses and require even more efficient storage solutions. This emphasizes the importance of implementing high emission penalties as a key policy measure to phase out fossil fuel generation. Full article

This research focuses on optimizing a grid-connected hybrid microgrid system (HMGS) for The Neotia University (TNU), West Bengal, India, utilizing renewable energy sources to improve sustainability and energy efficiency. The system integrates solar panels, wind turbines, and an existing diesel generator (DG) to meet campus energy demands, including electric vehicle (EV) charging facilities for residents and staff. The pelican optimization algorithm (POA) is employed to determine the optimal capacity of PV and wind turbine units for reducing energy costs, enhancing reliability, and minimizing carbon emissions. The results reveal a substantial decrease in the cost of energy (COE) from INR 11.74/kWh to INR 5.20/kWh. Full article

This paper presents an adaptive Maximum Power Point Tracking (MPPT) strategy for grid-connected photovoltaic (PV) systems that uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Particle Swarm Optimization (PSO) to enhance energy extraction efficiency under diverse environmental conditions. The proposed ANFIS-PSO-based MPPT controller performs dynamic adjustment Pulse Width Modulation (PWM) switching to minimize Total Harmonic Distortion (THD); this will ensure rapid convergence to the maximum power point (MPP). Unlike conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, which struggle with tracking delays and local maxima in partial shading scenarios, the proposed approach efficiently identifies the Global Maximum Power Point (GMPP), improving energy harvesting capabilities. Simulation results in MATLAB/Simulink R2023a demonstrate that under stable irradiance conditions (1000 W/m², 25 °C), the controller was able to achieve an MPPT efficiency of 99.2%, with THD reduced to 2.1%, ensuring grid compliance with IEEE 519 standards. In dynamic irradiance conditions, where sunlight varies linearly between 200 W/m² and 1000 W/m², the controller maintains an MPPT efficiency of 98.7%, with a response time of less than 200 ms, outperforming traditional MPPT algorithms. In the partial shading case, the proposed method effectively avoids local power maxima and successfully tracks the Global Maximum Power Point (GMPP), resulting in a power output of 138 W. In contrast, conventional techniques such as P&O and INC typically fail to escape local maxima under similar conditions, leading to significantly lower power output, often falling well below the true GMPP. This performance disparity underscores the superior tracking capability of the proposed ANFIS-PSO approach in complex irradiance scenarios, where traditional algorithms exhibit substantial energy loss due to their limited global search behavior. The novelty of this work lies in the integration of ANFIS with PSO optimization, enabling an intelligent self-adaptive MPPT strategy that enhances both tracking speed and accuracy while maintaining low computational complexity. This hybrid approach ensures real-time adaptation to environmental fluctuations, making it an optimal solution for grid-connected PV systems requiring high power quality and stability. The proposed controller significantly improves energy harvesting efficiency, minimizes grid disturbances, and enhances overall system robustness, demonstrating its potential for next-generation smart PV systems. Full article

South Africa’s persistent energy shortages and high utility costs have led to increased interest in rooftop solar photovoltaic (PV) systems. However, understanding their economic and environmental viability in urban residential contexts remains limited. This study investigates the feasibility of integrating rooftop solar PV systems with local energy storage and grid electricity in residential housing complexes in Benoni, Gauteng Province. A hybrid energy system was proposed and modeled using detailed consumption data from a typical community in Benoni. The system includes rooftop PV installations, lithium-ion storage, and connection to the national grid. A techno-economic analysis was conducted over a 25-year project lifespan to evaluate energy cost, payback period, net present cost, and carbon dioxide emissions. The optimal system configuration—Solar PV + Storage + Grid—achieved average annual utility bill savings of USD 30,207, with a payback period of 1.0 year, a net present cost (NPC) of USD 40,782, and an internal rate of return (IRR) of 101.7%. Annual utility costs were reduced from USD 30,472 to USD 267, and the system resulted in a net reduction of 130 metric tons of CO₂ emissions per year. The levelized cost of energy (LCOE) was USD 0.0071/kWh. The integration of rooftop solar PV and energy storage with grid electricity presents a highly cost-effective and environmentally sustainable solution for residential communities in urban South Africa. The findings support policy initiatives aligned with Sustainable Development Goal (SDG) 7: “Affordable and Clean Energy”. Full article

The inherent randomness and intermittency of offshore renewable energy sources, such as wind and solar power, pose significant challenges to the stable and secure operation of the power grid. These fluctuations directly affect the performance of grid-connected systems, particularly in terms of harmonic distortion and load response. This paper addresses these challenges by proposing a novel harmonic control strategy and load response optimization approach. An integrated three-winding transformer filter is designed to mitigate high-frequency harmonics, and a control strategy based on converter-side current feedback is implemented to enhance system stability. Furthermore, a hybrid PI-VPI control scheme, combined with feedback filtering, is employed to improve the system’s transient recovery capability under fluctuating load and generation conditions. Experimental results demonstrate that the proposed control algorithm, based on a transformer-oriented model, effectively suppresses low-order harmonic currents. In addition, the system exhibits strong anti-interference performance during sudden voltage and power variations, providing a reliable foundation for the modulation and optimization of offshore wind–solar coupled hydrogen production power supply systems. Full article

Conventional approaches for distributed generation (DG) planning often fall short in addressing operational demands and regional control requirements within distribution networks. To overcome these limitations, this paper introduces a cluster-oriented DG planning method. In terms of cluster partitioning, this study breaks through the limitations of traditional methods that solely focus on electrical parameters or single functions. Innovatively, it partitions the distribution network by comprehensively considering multiple critical factors such as system grid structure, nodal load characteristics, electrical coupling strength, and power balance, thereby establishing a unique multi-level grid structure of **distribution network—cluster—node**. This partitioning approach not only effectively reduces inter-cluster reactive power transmission and enhances regional power self-balancing capabilities but also lays a solid foundation for the precise planning of subsequent distributed energy resources. It represents a functional expansion that existing cluster partitioning methods have not fully achieved. In the construction of the planning model, a two-layer coordinated siting and sizing planning model for distributed photovoltaics (DPV) and energy storage systems (ESS) is proposed based on cluster partitioning. In contrast to traditional models, this model for the first time considers the interaction between power source planning and system operation across different time scales. The upper layer aims to minimize the annual comprehensive cost by optimizing the capacity and power allocation of DPV and ESS in each cluster. The lower layer focuses on minimizing system network losses to precisely determine the PV connection capacity of each node within the cluster and the grid connection locations of ESS, achieving comprehensive optimization from macro to micro levels. For the solution algorithm, a two-layer iterative hybrid particle swarm algorithm (HPSO) embedded with power flow calculation is designed. Compared to traditional single particle swarm algorithms, HPSO integrates power flow calculations, allowing for a more accurate consideration of the actual operating conditions of the power grid and avoiding the issue in traditional methods where the current and voltage distribution are often neglected in the optimization process. Additionally, HPSO, through its two-layer iterative approach, is able to better balance global and local search, effectively improving the solution efficiency and accuracy. This algorithm integrates the advantages of the particle swarm optimization algorithm and the binary particle swarm optimization algorithm, achieving iterative solutions through efficient information exchange between the two layers of particle swarms. Compared with conventional particle swarm algorithms and other related algorithms, it represents a qualitative leap in computational efficiency and accuracy, enabling faster and more accurate handling of complex planning problems. Case studies on a real 10 kV distribution network validate the practicality of the proposed framework and the robustness of the solution technique. Full article

With the large-scale integration of renewable energy sources, power electronic components within power grids have surged. Traditional synchronous generator-based power generation is gradually transitioning to renewable energy generation integrated with grid-following (GFL) and grid-forming (GFM) inverters. Furthermore, power grid topology structures are evolving from traditional radial and ring-type configurations toward meshed-type architectures. The impact of grid topology structures on the stability of hybrid systems combining GFL and GFM inverters urgently requires systematic investigation. This paper establishes state-space models of GFM and GFL inverters under three typical grid topology structures and then compares the small signal stability of hybrid systems. First, mathematical models of inverters and transmission lines are established, and a full-order state-space model of the system is accordingly derived. Second, key stability indicators, including eigenvalues, damping ratio, participation factors, and sensitivity indices, are obtained by analyzing the system state matrix. Finally, simulation models for these grid topology structures are implemented in MATLAB/Simulink R2022b to validate the influences of grid topology structures on the stability related to inverters. The results demonstrate that GFL inverters are highly sensitive to grid topology structures, whereas GFM inverters are more influenced by their synchronization control capabilities. Smaller GFL inverters connection impedances and larger GFM inverters connection impedances can enhance system stability. Full article

This paper shows that using renewable energy sources in the power supply of transportation infrastructure is gradually becoming a new trend. Renewable energy systems are already valuable for railway and automotive infrastructure in various countries; however, this use is limited. This paper examines the improvement of control in a grid-connected, hybrid renewable energy system to meet the needs of a railway transportation infrastructure object by utilizing an additional diesel generator in autonomous mode. The aim is to reduce the depth of battery discharge and limit energy consumption from the grid during peak demand hours, considering the wide fluctuations in power consumption of the object and deviations in renewable energy generation relative to the forecast. Additionally, the task of ensuring long-term autonomous operation of the system is addressed. A control system is proposed based on the deviation of the battery’s state of charge relative to a set schedule, which is determined according to the forecast using an additional variable that sets the power consumption limit. This ensures the minimum possible depth of discharge and peak consumption, taking into account the generation of renewable energy sources, with a power-increase factor ranging from 1 to 1.5 relative to the calculated value. In autonomous mode, the task of minimizing energy consumption by the diesel generator is addressed. Solutions have been developed to implement control in grid and autonomous modes with the corresponding calculation algorithm. The system is not sensitive to the load schedule, and the battery’s depth of discharge limitations are maintained even when renewable energy generation is below the forecast by up to 20%. When generating renewable energy sources below the average monthly value in summer, it is possible to maintain a DoD of no less than 60%. Full article

With the progressive advancement of the energy transition strategy, wind–solar energy complementary power generation has emerged as a pivotal component in the global transition towards a sustainable, low-carbon energy future. To address the inherent challenges of intermittent renewable energy generation, this paper proposes a comprehensive energy optimization strategy that integrates coordinated wind–solar power dispatch with strategic battery storage capacity allocation. Through the development of a linear programming model for the wind–solar–storage hybrid system, incorporating critical operational constraints including load demand, an optimization solution was implemented using the Artificial Fish Swarm Algorithm (AFSA). This computational approach enabled the determination of an optimal scheme for the coordinated operation of wind, solar, and storage components within the integrated energy system. Based on the case study analysis, the AFSA optimization algorithm achieves a 1.07% reduction in total power generation costs compared to the traditional Simulated Annealing (SA) approach. Comparative analysis reveals that the integrated grid-connected operation mode exhibits superior economic performance over the standalone storage microgrid system. Additionally, we conducted a further analysis of the key factors contributing to the enhancement of economic benefits. The strategy proposed in this paper significantly enhances power supply stability, reduces overall costs and promotes the large-scale application of green energy. Full article

This paper proposes a hybrid synchronization control modular multilevel converter-based hybrid energy storage system (HSC-MMC-HESS) that innovatively integrates battery units within MMC submodules (SMs) while connecting a supercapacitor (SC) to the DC bus. The configuration synergistically combines the high energy density of batteries with the high power density of SCs through distinct energy/power pathways. The operational principles and control architecture are systematically analyzed, incorporating a hybrid synchronization control (HSC) strategy to deliver system inertia, primary frequency regulation, fault-tolerant mode transition capabilities, and isolation control. A hierarchical control framework implements power distribution through filtering mechanisms and state-of-charge (SOC) balancing control for battery management. Hardware-in-the-loop experimental validation confirms the topology’s effectiveness in providing inertial support, enabling flexible operational mode switching and optimizing hybrid energy storage utilization. The demonstrated capabilities indicate strong application potential for medium-voltage distribution networks requiring dynamic grid support. Full article