Photovoltaic Energy Sources Research Articles

Research on Fault Diagnosis Method for Photovoltaic Array Based on XGBoost Algorithm

INTRODUCTION: Photovoltaic (PV) energy sources frequently experience issues, including fragmentation, open-circuit, short-circuiting, and other common and hazardous problems. The current focus of PV research is on fault detection within solar arrays. Traditional models encounter challenges in identifying errors due to uncertainties in panel settings and the complex nature of the actual PV structure.OBJECTIVES: This study aims to introduce a novel Extreme Gradient Boosting (XGBoost) approach for fault diagnosis in PV arrays.METHODS: The XGBoost algorithm is trained using collected PV array defect data samples. Data preprocessing is performed to manage missing values and remove noisy data. Feature extraction is conducted using Linear Discriminant Analysis (LDA) to improve detection accuracy. To further enhance XGBoost’s performance, the World Cup Optimization (WCO) approach is applied to select optimal features from the extracted data. Fault detection is then conducted using the XGBoost algorithm on the processed data. Various indicators are utilized for performance assessment within the Python environment.RESULTS: The comparative analysis demonstrates that this research improves fault detection efficiency in PV arrays compared to existing methodologies.CONCLUSION: The study presents an effective method for enhancing fault detection in PV systems, showcasing the advantages of the XGBoost and WCO-based approach over conventional methods.

Two-Stage Locating and Capacity Optimization Model for the Ultra-High-Voltage DC Receiving End Considering Carbon Emission Trading and Renewable Energy Time-Series Output Reconstruction

With the load center’s continuous expansion and development of the AC power grid’s scale and construction, the recipient grid under the multi-feed DC environment is facing severe challenges of DC commutation failure and bipolar blocking due to the high strength of AC-DC coupling and the low level of system inertia, which brings many complexities and uncertainties to economic scheduling. In addition, the large-scale grid integration of wind power, photovoltaic, and other intermittent energy sources makes the ultra-high-voltage (UHV) DC channel operation state randomized. The deterministic scenario-based timing power simulation is no longer suitable for the current complex and changeable grid operation state. In this paper, we first start with the description and analysis of the uncertainty in renewable energy (RE) sources, such as wind and solar, and reconstruct the time-sequence power model by using the stochastic differential equation model. Then, a carbon emission trading cost (CET) model is constructed based on the CET mechanism, and the two-stage locating and capacity optimization model for the UHV DC receiving end is proposed under the constraint of dispatch safety and stability. Among them, the first stage starts with the objective of maximizing the carrying capacity of the UHV DC receiving end grid; the second stage checks its dynamic safety under the basic and fault modes according to the results of the first stage and corrects the drop point and capacity of the UHV DC line with the objective of achieving safe and stable UHV DC operation at the lowest economic investment. In addition, the two-stage model innovatively proposes UHV DC relative inertia constraints, peak adjustment margin constraints, transient voltage support constraints under commutation failure conditions, and frequency support constraints under a DC blocking state. In addition, to address the problem that the probabilistic constraints of the scheduling model are difficult to solve, the discrete step-size transformation and convolution sequence operation methods are proposed to transform the chance-constrained planning into mixed-integer linear planning for solving. Finally, the proposed model is validated with a UHV DC channel in 2023, and the results confirm the feasibility and effectiveness of the model.

A frequency control strategy with DC microgrid systems for EV stations

ABSTRACT Fluctuations in power supply and demand lead to frequency deviations, which have detrimental effects on the operation of electrical devices and equipment. Therefore, the implementation of effective frequency control mechanisms is essential for enhancing the strength and dependability of microgrid systems. Research aims to develop a frequency control mechanism for an electrical grid system that integrates both wind and photovoltaic (PV) energy sources. The proposed system utilises a Doubly Fed Induction Generator (DFIG) system with frequency controller for regulating frequency and balancing source and demand of power in the grid. To optimise the solar power output, a Luo converter is implemented, which offers high efficiency with improved conversion of power. Additionally, an adaptive neuro fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller is engaged to ensure optimal regulation of photovoltaic system. DC link obtained from the PV system is then utilised for powering DC loads and charging electric vehicles (EVs) effectively. In order to accomplish this, a bidirectional converter is used, which permits power flow in both ways and facilitates effective EV charging from the microgrid system. The validation of research is conducted on MATLAB software, and the results proves greater converter efficiency of 94.93%, with reduced total harmonic distortion (THD) of 1.24%.

Assessing PV inverter efficiency degradation under semi-arid conditions: A case study in Morocco

The worldwide shift towards renewable energy sources, including solar and wind, is progressing rapidly. This change is driven by decreasing prices and technical advancements that are becoming more centralized. The objective is to develop a cutting-edge approach and technology that seamlessly incorporates photovoltaic (PV) energy sources into a power network while ensuring grid stability. This research evaluates the lifetime and degradation of PV inverters under real operating conditions, focusing on semi-arid climate scenarios. Current papers demonstrate a yearly failure rate of 1–15% for PV inverters, highlighting the need for a thorough reliability evaluation. This investigation research applied a unique technique that included continuous monitoring of PV inverter performance parameters by comparing the measured with the predicted normal operating output. The results show that Sandia's inverter performance model is highly suitable for accurately modeling normal PV inverter behavior. Furthermore, weighted efficiency dropped by 3.96%, 0.63% and 1.29% respectively for 7kW, 15kW and 20kW photovoltaic systems over the five years. These findings have implications for comprehensive maintenance strategies, including regular inspections, safety protocols, and remote monitoring solutions. Ultimately, this research paper sheds light on the causes of declining solar inverter performance and provides suggestions for enhancing PV plant maintenance and reliability. It also represents an outstanding reference given the literature shortage.

Stochastic Techno-Economic Optimization of Hybrid Energy System with Photovoltaic, Wind, and Hydrokinetic Resources Integrated with Electric and Thermal Storage Using Improved Fire Hawk Optimization

In this paper, a stochastic techno-economic optimization framework is proposed for three different hybrid energy systems that encompass photovoltaic (PV), wind turbine (WT), and hydrokinetic (HKT) energy sources, battery storage, combined heat and power generation, and thermal energy storage (Case I: PV–BA–CHP–TES, Case II: WT–BA–CHP–TES, and Case III: HKT–BA–CHP–TES), with the inclusion of electric and thermal storage using the 2m + 1 point estimate method (2m + 1 PEM) utilizing real data obtained from the city of Espoo, Finland. The objective function is defined as planning cost minimization. A new meta-heuristic optimization algorithm named improved fire hawk optimization (IFHO) based on the golden sine strategy is applied to find the optimal decision variables. The framework aims to determine the best configuration of the hybrid system, focusing on achieving the optimal size for resources and storage units to ensure efficient electricity and heat supply simultaneously with the lowest planning cost in different cases. Also, the impacts of the stochastic model incorporating the generation and load uncertainties using the 2m + 1 PEM are evaluated for different case results compared with the deterministic model without uncertainty. The results demonstrated that Case III obtained the best system configuration with the lowest planning cost in deterministic and stochastic models and. This case is capable of simply meeting the electrical and thermal load with the contribution of the energy resources, as well as the CHP and TESs. Also, the IFHO superiority is proved compared with the conventional FHO, and particle swarm optimization (PSO) achieves the lowest planning cost in all cases. Moreover, incorporating the stochastic optimization model, the planning costs of cases I–III are increased by 4.28%, 3.75%, and 3.57%, respectively, compared with the deterministic model. Therefore, the stochastic model is a reliable model due to its incorporating the existence of uncertainties in comparison with the deterministic model, which is based on uncertain data.

Optimal design of PV-SMES systems for power quality enhancement using pelicon optimized multi-level inverter model

In this article, a superconducting magnetic energy storage (SMES) system is integrated with a photovoltaic (PV) renewable energy source. The integrated system can achieve power balancing with voltage stability, high power quality, and reliability. Here, a Bonobo Optimization Algorithm (BOA)-based MPPT control has been developed to get the most electrical energy out of the solar PV cells under fluctuating weather conditions, including temperature and irradiance. The output voltage is greatly increased with minimal switching complexity by using the Zeta DC-DC converter, which helps to meet the energy requirements of load applications. Then, the Total Harmonics Distortions (THD) has been decreased with lower switching stress, power loss, and voltage ripples at the load using the sophisticated and simple five-level MLI circuit model. In addition, the performance of MLI has been greatly improved by tuning the controlling gain parameters with the use of Pelicon Optimization (PO) technique. Moreover, the Shunt Active Power Filter (SAPF) is used in order to achieve the best power quality results with the reduced overall harmonic distortions. The performance and results of the suggested circuit model are validated in this study through a complete simulation analysis using a variety of assessment indicators.

Management of a hybrid renewable energy storage system

The development of smart grids, made possible by advancements in computer technology, represents a transformative solution for electricity grids. These systems offer significant improvements in terms of security, reliability, and energy efficiency, while also promoting a low-carbon economy. Smart grids enable more efficient management of electricity supply and demand, as well as optimization of energy storage. However, despite these advantages, challenges persist, particularly in the areas of cybersecurity, and the reliability and speed of information processing. To address these challenges, we propose an innovative algorithm designed for energy management within electricity grids. This algorithm, specifically tailored for grids comprising hybrid storage systems, photovoltaic energy sources, and variable loads, aims primarily to balance supply and demand. This comprehensive approach not only ensures a continuous power supply but also reduces vulnerabilities associated with reliance on a single energy source. To evaluate the effectiveness of this algorithm, we conducted a series of rigorous simulation experiments using the MATLAB platform. These simulations meticulously tested the system's performance, providing substantial experimental evidence of its durability and viability in real-world scenarios. In summary, smart grids offer promising opportunities to enhance the security of electricity grids, maintain supply-demand balance, and promote sustainable energy storage practices. Despite persistent challenges in cybersecurity and information processing, our proposed algorithm represents a significant advancement in addressing these issues and strengthening the capabilities of smart grids.

Short-Term Forecasting of Photovoltaic Power Using Multilayer Perceptron Neural Network, Convolutional Neural Network, and k-Nearest Neighbors’ Algorithms

Governments and energy providers all over the world are moving towards the use of renewable energy sources. Solar photovoltaic (PV) energy is one of the providers’ favourite options because it is comparatively cheaper, clean, available, abundant, and comparatively maintenance-free. Although the PV energy source has many benefits, its output power is dependent on continuously changing weather and environmental factors, so there is a need to forecast the PV output power. Many techniques have been employed to predict the PV output power. This work focuses on the short-term forecast horizon of PV output power. Multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with success. The performances of these three algorithms have been compared with other algorithms singly or in a hybrid (with other methods) but not with themselves. This study aims to compare the predictive performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions and showcase their robustness in making predictions in this regard. The performance of MLPNN, CNN, and kNN are compared using solar PV (hourly) data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in South Africa that may be prone to extreme weather conditions. Our data does not have lots of missing data and many data spikes. The kNN algorithm was found to have an RMSE value of 4.95%, an MAE value of 2.74% at its worst performance, an RMSE value of 1.49%, and an MAE value of 0.85% at its best performance. It outperformed the others by a good margin, and kNN could serve as a fast, easy, and accurate tool for forecasting solar PV output power. Considering the performance of the kNN algorithm across the different seasons, this study shows that kNN is a reliable and robust algorithm for forecasting solar PV output power.

Tunnel oxide thickness-dependent dominant carrier transport in crystalline silicon solar cells

Silver consumption reduction is a current development in commercial tunnel passivated contact (TOPCon) crystalline silicon solar cell devices aimed at lowering the entire production cost of photovoltaic energy sources. It depends on the number of fingers and/or finger spacing (SP) on a cell area. In this paper, we analyze the possibility of minimizing silver use with respect to the dominant carrier transport mechanism. The carrier transporting mechanism, such as “pinhole” and/or “tunnel” models, is identified by examining temperature-dependent I–V characteristics of polysilicon passivating contact as a function of tunnel oxide (TO) thickness from 0.6 to 2.2 nm. Thermal oxidation was used to produce ultrathin TO films (0.6–2.2 nm) with temperature and gas ratio controlled. We find that the “pinholes” transport mechanism prevails when the TO thickness exceeds 1.6 nm, whereas the “tunnel” mode dominates when the TO thickness is less than 1.4 nm. The pinhole density is critical in pinhole mode for increasing SP. It is found that low pinhole densities and thick TO thickness (more than 1.6 nm) are two of the primary causes of narrow SP in TOPCon devices, which need a considerable quantity of silver. The experimental TOPCon devices as a function of TO thickness show a considerable trade-off between open circuit voltage (Voc) and fill factor (FF). While Voc rises, FF drops as TO thickness increases. The mechanism is described.

Integrating relational values in social acceptance of photovoltaic energy storage systems: A consumers' perspective assessment using structural equation modeling

Photovoltaic (PV) energy sources are considered potential sources of renewable energy for combating climate change. However, consumer acceptance of PV-based energy storage systems must be studied comprehensively and psychologically beyond mere awareness and affordability. This study explores consumer acceptance of PV energy storage systems, along with an added relational value context that demonstrates the conducive human-nature relationship among energy consumers. An online survey of 370 respondents was used to examine consumers’ willingness to prefer PV energy storage systems over non-renewable grid-connected energy storage systems. Results of the study were analyzed using a comprehensive structural equation model to estimate and test hypotheses. The findings indicate the favorable influence of several elements on PV energy system social acceptability, including PV energy system awareness, PV energy system beliefs, environmental knowledge, self-effective perception, and relational values. Conversely, the cost of PV energy systems has a negative effect. This study suggests that relational values may offset the negative impacts of energy costs. The inherent link between relational values and the prioritization of sustainable energy storage options serves as a source of information for energy policymakers and stakeholders to regulate energy consumption by influencing consumers to express their love for nature.

A new intelligent scheme for short-circuit detection, classification and location in power transmission lines with the PV generation presence

Transmission lines constitute critical energy infrastructure in an electrical installation, as they ensure a reliable and efficient energy transition. However, they are prone to breakdowns, due to their significant length and their continuous exposure to atmospheric conditions. In addition, the photovoltaic energy sources integration can modify their behavior, which contributes to a greater increase in the breakdowns probability. Short circuits are the most common and dangerous fault type, leading in the majority of cases to other fault types. This research describes the development of a new intelligent system that consists of three stages. The first stage aim is to identify whether there is a short circuit or not using a binary SVM. In the second stage, an innovative expert system is used to determine the short circuit type among 11 potential faults. The output of this system depends on the inputs obtained by four binary SVM, each of which determines the fault presence or absence in a phase. The third stage is responsible for estimating the exact fault position in a 100 km transmission line. This stage is made up of 11 universal regressors of type MLP. The proposed system was validated using signals, obtained from a modelled High-Voltage Line with a PV Energy Source (25 kV-50 Hz-100 Km) and processed by the Discrete Wavelet Transform. Statistical metrics (with all p-value < 0.001) validate the proposed system performance with: 100% sensitivity and specificity for the fault detector ; an average sensitivity, specificity, and false alarms of 99.38%, 99.94%, and 0.17%, respectively, for the fault discriminator ; and a 2.66% mean squared error and a 97.80% overall sensitivity for the fault location estimator. These results conclude the proposed approach reliability, demonstrating its potential effectiveness in real-world scenarios.

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms

In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler’s laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. The optimization problem is implemented in three optimization scenarios based on real and forecasted data as well as the investigation of the battery's depth of discharge in the HMG optimization in the distribution network and its effects on the different objectives. The results including energy losses, voltage deviations, and purchased power from the HMG have been presented. Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. Also, the outcomes proved that increasing the battery depth of discharge causes the BES to have more participation in the HMG effectiveness on the distribution network objectives and affects the network energy losses and voltage deviation reduction.

PV grid-connected information interaction methods based on public information modeling

The grid integration of large-scale photovoltaic and other distributed energy sources is an effective solution for addressing power supply shortages and environmental pollution. However, the widespread adoption of photovoltaics and grid integration presents various technological and management challenges. To fulfill the demands of grid management and ensure safe operations, the exchange of information between different terminals is continuously escalating. Use of diverse communication standards creates the problem of “information islands” among terminals. Hence, a standardized information model is crucial for describing the photovoltaic grid integration business and enhancing the efficiency of related software platform research and development. This study extends the IEC 61970/61968 standards and presents a common information model for the integration of photovoltaic systems into the grid. Initially, the operational procedures for integrating photovoltaic systems into the grid are analyzed, and UML modeling tools are employed for business modeling purposes. Subsequently, leveraging the outcomes of the business modeling and the content of the IEC 61970/61968 standards, the development of the common information model is executed. Lastly, causal analysis is conducted along with the modeling of communication standard extensions specifically targeted for the integration of photovoltaic systems into the grid, culminating in the finalization of the construction of the common information model for photovoltaic grid connection.

Steady state stability assessment of voltage source converter in a DC microgrid under weak grid conditions

Traditionally, steady-state assessment involves analyzing numerous variables using Eigen analysis. This paper presents a decision support application for diagnosing the steady-state assessment of droop-controlled voltage source inverters in islanded microgrid operations or weak grid operations with reduced input attributes. This paper proposes an approach using feature extraction from the state space variables of the droop-controlled voltage source inverter (VSI). Photovoltaic (PV) and wind energy sources are considered with their stipulated power-delivering capability considered. To improve the generalization of the predictive model, preprocessing techniques are employed to eliminate data distortions. Dimensionality reduction is achieved through principal component analysis (PCA) applied to the steady-state variables. The evaluation of the VSI's steady-state stability is conducted utilizing support vector classification algorithm. To ascertain the reliability of the steady-state stability classification, an assessment of the support vector machine (SVM) model's performance is carried out, which includes the examination of metrics like the area under the curve (AUC) and the receiver operating characteristics (ROC) curve. The findings from the assessment of VSI's steady-state stability indicate a commendable level of performance, achieving an accuracy rate of 93.5%.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

Hierarchical Coordinated Control Strategy for Active Power in Distribution Networks with Scaled Distributed Generation Access

Promoting scaled distributed wind turbines and photovoltaic, and other distributed generations access to the distribution network is an important measure for realize the goal of carbon peaking and carbon neutrality and to build new-type power systems. However, as the installed capacity of wind turbine, photovoltaic and other intermittent renewable energy sources climbs dramatically, the security and consumption problems of the distribution network are becoming increasingly prominent, and there is an urgent requirement to improve the active power control capability of distributed generation. To optimize the operation stability and consumption economy of the distribution network, a hierarchical coordinated control strategy for active power of scaled distributed generation is proposed based on the principle of unified control and hierarchical management, including peaking control, frequency control, mesh control and market trading control, to coordinate and optimize the system operation safety and consumption economy. The proposed strategy optimizes active power control instructions in the layer of distribution network layer, mesh layer, and functional area layer, thus fully expanding the depth and breadth of the consumption for distributed generation. The effectiveness of the proposed strategy is proved by the actual operation of Liaoning power grid.

Using Active Filter Controlled by Imperialist Competitive Algorithm ICA for Harmonic Mitigation in Grid-Connected PV Systems

Solar energy has been gaining momentum recently, with a focus on maximizing its investment potential due to its reputation as the most sustainable and efficient energy source. This shift towards solar power could potentially reduce the reliance on oil-based fuels in the future. As a result of the integration of photovoltaic (PV) energy sources into the grid, the reliability of power distribution and maintaining its quality in these systems has become increasingly important. The presence of non-linear loads in these grids causes distortion of both voltage and current waves on the grid side, so it is necessary to implement effective reduction techniques to reduce the distortions in these waves. The research contribution is TO introduce the integration of an active filter on the dc side of grid-connected PV systems, along with a control circuit for the filter switches. The control switches were operated using a Sinusoidal Pulse Width Modulation (SPWM) control scheme, while the controller parameters were tuned using the Imperialist Competitive Algorithm (ICA). The proposed system was simulated in the MATLAB/Simulink environment with variations in solar radiation and temperature. The simulation results demonstrated a reduction in the total harmonic distortion factor (THD) for voltage and current waveforms on the grid side, which are within the permissible limits. This confirms the effectiveness of the proposed filter and the efficiency of the control strategy and algorithm for parameter adjustment.

Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources.

Capacity Demand Analysis of Rural Biogas Power Generation System with Independent Operation Considering Source-Load Uncertainty

With the greatly increased penetration rate of wind power, photovoltaic, and other new energy sources in the power system, the proportion of controllable units gradually decreased, resulting in increased system uncertainty. The biogas power generation system can effectively alleviate the pressure caused by source-load uncertainty in such high-permeability systems of new energy sources such as wind power and photovoltaic. Hence, from the perspective of the power system, this paper introduces a capacity demand analysis method for a rural biogas power generation system capable of independent operation amidst source-load uncertainty. To enhance the depiction of pure load demand uncertainty, a scene set generation method is proposed, leveraging quantile regression analysis and Gaussian mixture model clustering. Each scene’s data and probability of occurrence elucidate the uncertainty of pure load demand. An integrated optimal operation model for new energy and biogas-generating units, free from energy storage capacity constraints, is established based on the generated scenario set. Addressing considerations such as biogas utilization rate and system operation cost, a biogas storage correction model, utilizing the gas storage deviation degree index and the cost growth rate index, is developed to determine biogas demand and capacity. The example results demonstrate the significant reduction in gas storage construction costs and charging and discharging imbalances achieved by the proposed model while ensuring systemic operational cost effectiveness.

Optimal allocation of solar PV and wind energy power for radial distribution system using spider monkey optimization

The integration of renewable energy sources, relatable as Solar Photovoltaic (PV) and Wind Power, into the radial distribution system has gained significant attention due to their eco-friendly and sustainable attributes. This article presents a narrative advent for achieving the finest share of Solar PV and Wind force power through a radial distribution system using the innovative Spider Monkey Optimization (SMO) algorithm. Multi-objective function for the minimization of distribution loss and voltage deviation with the constraints of power balance equation and boundary limits of voltage and power is considered. The Spider Monkey Optimization algorithm, stimulated via the community activities of spider monkeys, be employed to effectively search for the finest allotment of Solar PV and Wind Energy Power within the distribution network. The SMO algorithm exhibits robustness in handling non-linear and multi-dimensional optimization problems, making it suitable for this complex task. To authorize the usefulness and efficiency of the planned approach, it is functional to standard 33-bus radial division coordination. Comparative analyses of optimization techniques are reported and SMO reduces the losses to 104 KW and the voltage deviation is minimized to 0.0458 pu. The valuable perception is that incorporating Solar PV and Wind Energy sources into radial distribution systems improves the quality.