Search Results (1,609)

The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by fluctuating climatic conditions, complicates system balancing processes and necessitates the reservation of capacities from conventional energy sources to ensure reliability. Under modern market conditions, the pricing of generated electricity is commonly based on day-ahead forecasts of day energy yield, which significantly affects the economic performance of solar power plants. Consequently, achieving high accuracy in day-ahead electricity production forecasting is a critical and highly relevant task. To address this challenge, a physico-statistical model has been developed, in which the analytical approximation of daily electricity generation is represented as a function of a random variable—cloud cover—modeled by a β-distribution. Analytical expressions were derived for calculating the mathematical expectation and variance of daily electricity generation as functions of the β-distribution parameters of cloudiness. The analytical approximation of daily generation deviates from the exact value, obtained through hourly integration, by an average of 3.9%. The relative forecasting error of electricity production, when using the mathematical expectation of cloudiness compared to the analytical approximation of daily generation, reaches 15.2%. The proposed forecasting method, based on a β-parametric cloudiness model, enhances the accuracy of day-ahead production forecasts, improves the economic efficiency of solar power plants, and contributes to strengthening the stability and reliability of power systems with a substantial share of solar generation. Full article

Demand electrification is creating new operating conditions in distribution networks—such as congestion, overloads, and voltage issues—that have traditionally been addressed through network expansion planning (NEP). As an alternative, this work proposes the use of non-wire alternatives (NWAs) based on hybrid photovoltaic–storage (ESS) plants and analyzes their siting and sizing under uncertainty conditions. To this end, a MINLP model with a DistFlow representation is formulated to determine generation and storage locations and capacities, minimizing investment while satisfying current and voltage limits. Different uncertainty management methodologies are compared: robust optimization, equivalent probabilistic profile, weighted multi-scenario, and multi-scenario with penalty. The results on the CIGRE MV network show that the robust approach guarantees feasibility in the worst case, albeit with a high investment cost. In contrast, methods based on averages or simple weightings fail to adequately capture adverse conditions, while the multi-scenario optimization with expected penalty emerges as the most effective option, balancing investment and overload reduction. In conclusion, the explicit consideration of uncertainty in NWA planning is essential to obtaining realistic and adaptable solutions, with the expected penalty formulation standing out as the most efficient alternative for network operators. Full article

To address the overvoltage problem in distribution networks with large-scale photovoltaic (PV) integration, this paper proposes an interactive game-based voltage optimization control strategy based on microgrid cluster partitioning. A multi-agent control architecture is constructed, including a dynamic partitioning layer, a parallel independent optimization layer, and an interactive game optimization layer. In the dynamic partitioning layer, microgrid clusters are formed considering coupling degree, voltage regulation capability, and cluster scale. In the parallel optimization layer, a network reconfiguration-based control model is established for utility-owned microgrids, and a PV active/reactive power regulation model is developed for PV microgrids, enabling independent cluster-level control. In the game optimization layer, a non-cooperative game model is formulated to coordinate voltage regulation among clusters. The effectiveness of the proposed method is demonstrated on an actual 10 kV feeder system. Full article

As power systems decentralize, Virtual Power Plants (VPPs) offer a promising approach to coordinate distributed energy resources (DERs) and enhance grid flexibility. However, real-world validation of VPP performance in Indonesia remains limited, especially regarding internationally aligned test standards. This study presents the design and experimental validation of a cluster-based VPP framework integrated with a centralized VPP Management System (VMS). Each cluster integrates solar photovoltaic (PV) system, battery energy storage system (BESS), and controllable load. A Local Control Unit (LCU) manages cluster operations, while the VMS coordinates power export–import dispatch, cluster-level aggregation, and grid compliance. The framework proposes a scalable VPP architecture and presents the first comprehensive experimental verification of key VPP performance indicators, including response time, adjustment rate, and accuracy, in the Indonesian context. Testing was conducted in alignment with the IEC TS 63189-1:2023 international standard. Results suggest real time responsiveness and indicate that, even at smaller scales, VPPs may contribute effectively to voltage control while exhibiting minimal influence on system frequency in interconnected grids. These findings confirm the capability of the proposed VPP framework to provide reliable real time control, ancillary services, and aggregated energy management. Its cluster-based architecture supports scalability for broader deployment in complex grid environments. Full article

The growing penetration of distributed photovoltaic (PV) systems presents significant operational challenges for power grids, driven by the scarcity of historical data and the high spatiotemporal variability of PV generation. To address these challenges, we propose Generative Reconstruction and Adaptive Identification via Latents (GRAIL), a unified, end-to-end framework that integrates generative modeling with adaptive clustering to discover latent structures and representative scenarios in PV datasets. GRAIL operates through a closed-loop mechanism where clustering feedback guides a cluster-aware data generation process, and the resulting generative augmentation strengthens partitioning in the latent space. Evaluated on a real-world, multi-site PV dataset with a high missing data rate of $45.4\%$, GRAIL consistently outperforms both classical clustering algorithms and deep embedding-based methods. Specifically, GRAIL achieves a Silhouette Score of 0.969, a Calinski–Harabasz index exceeding $4.132\times {10}^6$, and a Davies–Bouldin index of $0.042$, demonstrating superior intra-cluster compactness and inter-cluster separation. The framework also yields a normalized entropy of $0.994$, which indicates highly balanced partitioning. These results underscore that coupling data generation with clustering is a powerful strategy for expressive and robust structure learning in data-sparse environments. Notably, GRAIL achieves significant performance gains over the strongest deep learning baseline that lacks a generative component, securing the highest composite score among all evaluated methods. The framework is also computationally efficient. Its alternating optimization converges rapidly, and clustering and reconstruction metrics stabilize within approximately six iterations. Beyond quantitative performance, GRAIL produces physically interpretable clusters that correspond to distinct weather-driven regimes and capture cross-site dependencies. These clusters serve as compact and robust state descriptors, valuable for downstream applications such as PV forecasting, dispatch optimization, and intelligent energy management in modern power systems. Full article

The power system’s dynamic frequency stability is affected by common-mode ultra-low-frequency oscillation and differential-mode low-frequency oscillation. Traditional frequency control based on generators is facing the problem of capacity reduction. It is urgent to explore new regulation resources such as photovoltaics. To address this issue, this paper proposes a distributed active support method based on photovoltaic systems via state–disturbance observation and dynamic surface consensus control. A three-layer distributed control framework is constructed to suppress low-frequency oscillations and ultra-low-frequency oscillations. To solve the high-order problem of the regional grid model and to obtain its unmeasurable variables, a regional observer estimating both system states and external disturbances is designed. Furthermore, a distributed dynamic frequency stability control method is proposed for wide-area photovoltaic clusters based on the dynamic surface control theory. In addition, the stability of the proposed distributed active support method has been proven. Moreover, a parameter tuning algorithm is proposed based on improved chaos game theory. Finally, simulation results demonstrate that, even under a 0–2.5 s time-varying communication delay, the proposed method can restrict the frequency deviation and the inter-area frequency difference index to 0.17 Hz and 0.014, respectively. Moreover, under weak communication conditions, the controller can also maintain dynamic frequency stability. Compared with centralized control and decentralized control, the proposed method reduces the frequency deviation by 26.1% and 17.1%, respectively, and shortens the settling time by 76.3% and 42.9%, respectively. The proposed method can effectively maintain dynamic frequency stability using photovoltaics, demonstrating excellent application potential in renewable-rich power systems. Full article

Ternary hybrid nanofluid have been revealed to possess a wide range of application disciplines reaching from biomedical engineering, detection of cancer, over or photovoltaic panels and cells, nuclear power plant engineering, to the automobile industry, smart cells and and eventually to heat exchange systems. Inspired by the recent developments in nanotechnology and in particular the high potential ability of use of such nanofluids in practical problems, this paper deals with the flow of a three phase nanofluid of MWCNT-Au/Ag nanoparticles dispersed in blood in the presence of a bidirectional stretching sheet. The model derived in this study yields a set of linked nonlinear PDEs, which are first transformed into dimensionless ODEs. From these ODEs we get a dataset with the help of MATHEMATICA environment, then solved using AI-based technique utilizing Levenberg Marquardt Feedforward Algorithm. In this work, flow characteristics under varying physical parameters have been studied and analyzed and the boundary layer phenomena has been investigated. In detail horizontal, vertical velocity profiles as well as temperature distribution are analyzed. The findings reveal that as the stretching ratio of the surface coincide with an increase the vertical velocity as the surface has thinned in this direction minimizing resistance to the fluid flow. Full article

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

The equilibrium between electricity demand and consumption is vital to ensure the stability of the transmission and distribution systems grid (TS & DS) and to ensure the stable operation of the electrical system. The aim of this review study is to highlight the current legislative and technical situation and the possibilities for managing peak loads, decentralization, sharing, storage, and sale of electricity generated from renewable sources in Slovakia. The European Union′s (EU) goal of achieving carbon neutrality by 2050 and a minimum of 42.5% renewable energy consumption by 2030 brings with it obligations for individual member states. These are transposed into national strategies. The current share of renewable sources in Slovakia is approximately 24% and the EU target by 2030 is probably unrealistic. Water resources are practically exhausted; other possibilities for increasing the share of renewable energy sources (RES) are in photovoltaics, wind, and thermal sources. Due to long-term geographical and historical development, electricity production in Slovakia is based on large-scale solutions. The move towards decentralization requires legislative and technical support. The review article examines the possibilities of increasing the share of RES and energy sharing in Slovakia, and examines the legislative, economic, and social barriers to their wider application. At the same time as the share of renewable sources in electricity generation increases, the article examines and presents solutions capable of ensuring the stability of electricity networks across Europe. The study formulates diversified strategies at the distribution network level and the consumer and building levels, and identifies physical (various types of electricity storage, electromobility, electricity liquidators) and virtual (electricity sharing, energy communities, virtual batteries) solutions. In conclusion, it defines the necessary changes in the legislative, technical, social, and economic areas for the most optimal improvement of the situation in the area of increasing the share of RES, supporting the decentralization of the electric power industry, and sharing electricity in Slovakia, also based on experience and good examples from abroad. Full article

With the rapid development of renewable energy and the continuous pursuit of efficient energy utilization, distributed photovoltaic power generation has been widely used in village-level microgrids. As a key platform connecting distributed photovoltaics with users, energy storage systems play an important role in alleviating the imbalance between supply and demand in VMG. However, current energy storage systems rely heavily on lithium batteries, and their frequent charging and discharging processes lead to rapid lifespan decay. To solve this problem, this study proposes a hybrid energy storage system combining supercapacitors and lithium batteries for VMG, and designs a hybrid energy storage scheduling strategy to coordinate the “source–load–storage” resources in the microgrid, effectively cope with power supply fluctuations and slow down the life degradation of lithium batteries. In order to give full play to the active support ability of supercapacitors in suppressing grid voltage and frequency fluctuations, the scheduling optimization goal is set to maximize the sum of the virtual inertia time constants of the supercapacitor. In addition, in order to efficiently solve the high-complexity model, the reason for choosing the snow goose algorithm is that compared with the traditional mathematical programming methods, which are difficult to deal with large-scale uncertain systems, particle swarm optimization, and other meta-heuristic algorithms have insufficient convergence stability in complex nonlinear problems, SGA can balance global exploration and local development capabilities by simulating the migration behavior of snow geese. By improving the convergence effect of SGA and constructing a multi-objective SGA, the effectiveness of the new algorithm, strategy and model is finally verified through three cases, and the loss is reduced by 58.09%, VMG carbon emissions are reduced by 45.56%, and the loss of lithium battery is reduced by 40.49% after active support optimization, and the virtual energy inertia obtained by VMG from supercapacitors during the scheduling cycle reaches a total of 0.1931 s. Full article

In recent years, frequent typhoon-induced disasters have significantly increased the risk of power grid outages, posing severe challenges to the secure and stable operation of distribution grids with high penetration of distributed photovoltaic (PV) systems. Furthermore, during post-disaster recovery, the dual uncertainties of distributed PV output and the charging/discharging behavior of flexible resources such as electric vehicles (EVs) complicate the configuration and scheduling of mobile energy storage systems (MESS). To address these challenges, this paper proposes a two-stage robust optimization framework for dynamic recovery of distribution grids: Firstly, a multi-stage decision framework is developed, incorporating MESS site selection, network reconfiguration, and resource scheduling. Secondly, a spatiotemporal coupling model is designed to integrate the dynamic dispatch behavior of MESS with the temporal and spatial evolution of disaster scenarios, enabling dynamic path planning. Finally, a nested column-and-constraint generation (NC&CG) algorithm is employed to address the uncertainties in PV output intervals and EV demand fluctuations. Simulations on the IEEE 33-node system demonstrate that the proposed method improves grid resilience and economic efficiency while reducing operational risks. Full article

Basalt fiber-reinforced polymer (BFRP) composites are increasingly utilized in photovoltaic mounting systems due to their excellent mechanical properties and durability. Bolted connections, valued for their simplicity, ease of installation, and effective load transfer, are widely employed for joining composite components. An orthogonal experimental design was adopted to investigate the effects of key parameters—including bolt end distance, number of bolts, bolt material, bolt diameter, preload, and connection length—on the load-bearing performance of three bolted BFRP plate configurations: lap joint (DJ), single lap joint (DP), and double lap joint (SP). Test results showed that the DJ connection exhibited the highest average tensile load capacity, exceeding those of the SP and DP connections by 45.3% and 50.2%, respectively. This superiority is attributed to the DJ specimen’s longer effective shear length and greater number of load-bearing bolts. Conversely, the SP connection demonstrated the largest average peak displacement, with increases of 29.7% and 52.9% compared to the DP and DJ connections. The double-sided constraint in the SP configuration promotes more uniform preload distribution and enhances shear deformation capacity. Orthogonal sensitivity analysis further revealed that the number of bolts and preload magnitude significantly influenced the ultimate tensile load capacity across all connection types. Finally, a calculation model for the tensile load capacity of bolted BFRP connections was established, incorporating a friction decay coefficient (α) and shear strength (τ). This model yields calculated errors under 15% and is applicable to shear slip-dominated failure modes, thereby providing a parametric basis for optimizing the tensile design of bolted BFRP joints. Full article

Power outages in non-faulted zones caused by system failures significantly reduce the reliability of distribution networks. To address this issue, this paper proposes a fault self-healing technique based on the integration of soft open points (SOPs) and network reconfiguration. A mathematical model for power restoration is developed. The model incorporates SOP operational constraints and the stochastic output of photovoltaic (PV) distributed generation. And this formulation enables the determination of the optimal network reconfiguration strategy and enhances the restoration capability. The study first analyzes the operational principles of SOPs and formulates corresponding constraints based on their voltage support and power flow regulation capabilities. The stochastic nature of PV power output is then modeled and integrated into the restoration model to enhance its practical applicability. This restoration model is further reformulated as a second-order cone programming (SOCP) problem to enable efficient computation of the optimal network configuration. The proposed method is simulated and validated in MATLAB R2019a. Results demonstrate that combining the SOP with the reconfiguration strategy achieves a 100% load restoration rate. This represents a significant improvement compared to traditional network reconfiguration methods. Furthermore, the second-order cone programming (SOCP) transformation ensures computational efficiency. The proposed approach effectively enhances both the fault recovery capability and operational reliability of distribution networks with high penetration of renewable energy. Full article

With growing urban populations and rapid technological advancement, major cities worldwide are facing pressing challenges from surging energy demands. Interestingly, substantial unused space within residential buildings offers potential for installing renewable energy systems coupled with energy storage. This study innovatively proposes a grid-connected photovoltaic (PV) system integrated with pumped hydro storage (PHS) and battery storage for residential applications. A novel optimization algorithm is employed to achieve techno-economic optimization of the hybrid system. The results indicate a remarkably short payback period of about 5 years, significantly outperforming previous studies. Additionally, a threshold is introduced to activate pumping and water storage during off-peak nighttime electricity hours, strategically directing surplus power to either the pump or battery according to system operation principles. This nighttime water storage strategy not only promises considerable cost savings for residents, but also helps to mitigate grid stress under time-of-use pricing schemes. Overall, this study demonstrates that, through optimized system sizing, costs can be substantially reduced. Importantly, with the nighttime storage strategy, the payback period can be shortened even further, underscoring the novelty and practical relevance of this research. Full article

In laser wireless power transmission systems, the powersphere serves as a spherical enclosed receiver that performs photoelectric conversion, achieving uniform light distribution within the cavity through infinite internal light reflection. However, in practical applications, the high level of light absorption displayed by photovoltaic cells leads to significant disparities in light intensity between directly irradiated regions and reflected regions on the inner surface of the powersphere, resulting in poor light uniformity. One approach aimed at addressing this issue uses a spectroscope to split the incident beam into multiple paths, allowing the direct illumination of all inner surfaces of the powersphere and reducing the light intensity difference between direct and reflected regions. However, experimental results indicate that light transmission through lenses introduces power losses, leading to improved uniformity but reduced output power. To address this limitation, this study proposes a method that utilizes multiple incident laser beams combined with a centrally positioned spherical reflector within the powersphere. A wireless power transmission system model was developed using optical simulation software, and the uniformity of the intracavity light field in the system was analyzed through simulation. To validate the design and simulation accuracy, an experimental system incorporating semiconductor lasers, spherical mirrors, and a powersphere was constructed. The data from the experiments aligned with the simulation results, jointly confirming that integrating a spherical reflector and distributed incident lasers enhances the uniformity of the internal light field within the powersphere and improves the system’s efficiency. Full article