Solar Wind Energy Research Articles

Islanded micro-grid under variable load conditions for local distribution network using artificial neural network

ABSTRACT This research proposes an intelligent artificial neural network (ANN) controller designed for a micro-grid featuring solar photovoltaic, wind, and Battery Energy Storage (BES) systems, integrated with a shunt voltage source converter. The primary goal of this proposed method is to minimize Total Harmonic Distortion (THD) and ensure a stable Distribution Line Commutation Voltage (DLCV) amidst load variations, achieving a rapid settling period. The STF not only facilitates phase synchronization but also effectively separates harmonic and fundamental components. This research contributes to advancing micro-grid control strategies, particularly in enhancing stability and reducing distortion during variable operating conditions. Based on an analysis of the four test cases, it is evident that the proposed strategy lowered the THDs to 4.63%, 3.45%, 3.05%, and 4.70% for each test case, as well as the PF to a level that was similar. The proposed controller shows better results compared with other controllers like Sliding Mode Controller (SMC) and the Proportional-Integral Controller (PIC).

Simulation study of a novel methanol production process based on an off-grid Wind/Solar/Oxy-fuel power generation system

Power-to-methanol technology represents a promising energy storage solution to manage the fluctuating supply and demand of renewable energy effectively. A novel methanol production process based on an off-grid wind/solar/Oxy-fuel power plant is presented in the paper, which consists of solar-wind energy, oxy-fuel combined cycle, proton exchange membrane electrolyzer, and carbon dioxide hydrogenation to methanol unit. The hydrogen produced by the proton exchange membrane electrolyzer is used to synthesize methanol, and the by-product oxygen is used in the oxy-fuel combined cycle. At the same time, low-cost carbon capture is achieved with liquefied natural gas cold energy utilization, and the collected carbon dioxide is used to synthesize methanol. The system carbon emission is negative, about −0.344 kg carbon dioxide/kg methanol, the system has no other products except methanol. The structure of renewable energy has a significant impact on the technical and economic performance of the system. Therefore, a comparative analysis of different wind/photovoltaic installed capacity ratios is carried out. The case study shows that the proposed system has the best performance when the wind/photovoltaic installed capacity ratio is 25%/75 %, with a renewable energy utilization of 97.62 %, methanol production cost of 1.34 $/kg methanol, and an energy efficiency of 63.5 %. The proposed system may be a practical solution for methanol production from renewable energy.

Maximizing self-sufficiency and minimizing grid interaction: Combining electric and molecular energy storage for decentralized balancing of variable renewable energy in local energy systems

The increase of renewable electricity from variable sources, such as solar PV and wind turbines, leads to increasing need for energy storage to maintain balance between demand and supply, and decrease peak load on electricity grids. Energy storage options to achieve this include electric energy storage such as batteries, and molecular energy storage such as hydrogen. In this research, we use a model to simulate the behavior of a local residential grid in the Netherlands supplied exclusively by decentralized solar PV and wind energy. Our model includes combinations of batteries and hydrogen as energy storage options to investigate synergies between them. The simulation results indicate that combining the characteristics of battery and hydrogen energy storage in a single grid leads to a more resilient and flexible system compared to one in which only one of the two technologies is deployed. In our simulations of combined storage options, local demand can be met with less installed capacity, and less energy exchange with external grid takes place. However, we also conclude that diversity of storage options alone is insufficient to reduce the highest peak load on external grids, and additional measures are required to ensure net residual load remains within specific bandwidths.

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

Research on technical cooperation path of renewable energy between China and South Africa

South Africa possesses extremely rich renewable energy resources, including solar energy and wind energy, in spite of this, South Africa traditionally relies on coal as its primary energy source. Recently, South Africa confronted with severe electricity shortage. With pressure of power shortage and the transition to low-carbon energy, South Africa has been actively promoting the development of renewable energy, aiming to diversify its energy structure. China, as the world’s leading country in the development and utilization of renewable energy, has long been committed to helping South Africa achieve renewable energy development. This article reviews the cooperation progress between China and South Africa in renewable energy, and provides an in-depth analysis of the challenges currently faced by both sides. To address such challenges, it is suggested to strengthen the design of top-level cooperation mechanisms, create diverse collaboration platforms, prioritize the exchange and collaboration of scientific talents. This paper aims to provide a typical reference for deepening renewable energy cooperation between China and South Africa.

Investigating the impact of solar PV and wind energy systems on the strength of a longitudinal power grid

The integration of inverter-based generation (IBG), such as solar photovoltaic (PV) and wind systems, presents unique challenges for power grid strength, especially in regions with limited conventional generation capacity. This study investigates the impact of such integration on the strength of Nigeria's power grid, focusing on sparsely connected northern region with abundant renewable resources. Using the network response short-circuit ratio (NRSCR) metric, which accounts for interactions between IBGs, we assess grid strength in three case studies. Simulations were conducted with the Power System Simulation for Engineering (PSS®E) software. Static analysis reveals inherent weakness in the northern region, characterized by low short-circuit capacity. Integrating IBGs at nine strategic locations shows varying responses: strong buses (NRSCR > 10) adapt well, while weak buses (NRSCR < 10), particularly in Kano and Katsina, constrain the operational capacity of the IBGs. Furthermore, close interaction among IBGs at these locations significantly impacts the system's dynamic response, although planned addition of conventional generators offers marginal improvement. This work highlights the potential of renewable resources to address energy shortages but also underscores the crucial need for detailed design studies at each point of interconnection (PoI), considering the longitudinal grid layout in the northern region and potential limitations imposed by IBG interactions. The findings also offer a structured approach that can be applied to power grids with comparable IBG integration levels, similar to the energy landscape of Nigeria.

Evidence of Kelvin-Helmholtz and tearing mode instabilities at the magnetopause during space weather events

Introduction: Kelvin-Helmholtz (KH) and tearing mode (TM) instabilities are one of the most important mechanisms of solar wind energy, momentum and plasma transport within the magnetosphere.Methods: To investigate the conditions under which KHTM instabilities occur in the Earth environment it is fundamental to combine simultaneous multipoint in situ measurements and MHD simulations. We analyzed data from the THEMIS and Cluster spacecraft considering two Space Weather (SWE) events starting with an M2.0 flare event (hereafter Case-1) that occurred on 21 June 2015 and the most-intensive flare (X9.3) of solar cycle 24 that occurred on 6 September 2017 (hereafter Case-2).Results: Our analysis utilized a 2D MHD model for incompressible and viscous flow. The results from Case-1 indicate the presence of KH and TM instabilities, suggesting existence of observed low-amplitude oscillations at the nose of the magnetopause. However, the MHD simulations for Case-2 did not show any evidence of KH vortices, but did reveal the presence of “magnetic island” structures during a low-shear condition. The reconnection rate derived from the observations is compared with the computed one in the presence of developed instabilities inside the Earth’s magnetopause.

A Combined Ranking and Sensitivity Analysis of Power Generation Using Multi-Criteria Decision-Making and Monte-Carlo Simulation

This research examined energy sources that can be employed in a region to assist policymakers in determining energy priorities. Three key components were analyzed in this research to rank these energy sources: Levelized Cost of Energy (LCOE), CO2 emissions, and power density. A combination of multi-criteria decision-making (MCDM) methods, namely the Analytical Hierarchy Process (AHP)-Entropy-the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), was used to assess these criteria, which had not been previously applied to rank energy sources. Additionally, the Monte-Carlo method was utilized to detect changes in sensitivity throughout the rankings. Results of the study indicated that gas energy topped the list, followed by Solar Photovoltaic (PV)-crystalline, geothermal, wind, nuclear, Solar PV Commercial &amp; Industrial (C&amp;I), Solar Thermal Tower with Storage, and residential PV rooftop solar. Moreover, nuclear energy ranked the highest when looking at the sensitivity of parameters, while utility-scale Solar PV and wind energy ranked the next highest. Thus, this research can be used to increase objectivity in the assessment and selection of power generation technology to be implemented.

Enhancement of Power Quality through the Implementation of a Hybrid Artificial Intelligence Model within Solar-Wind Energy Systems

The generation of electricity is significantly assisted by hybrid solar-wind power systems, which also play a crucial role in the advancement of intelligent grids. Furthermore, the integration of wind and solar energy storage control systems, in conjunction with energy markets, has rendered the electricity grid more economically feasible. To meet the growing demand for electricity, hybrid renewable energy systems connected to microgrids consist of substantial identifying elements. The issue of harmonic distortion in microgrids arising from nonlinear loads is a fundamental area of research. Understanding the impact of microgrids on power quality is also of great importance. This study focuses on enhancing power quality in solar-wind hybrid systems by incorporating emerging Artificial Intelligence (AI) methodologies. The proposed approach comprises two main components: (i) detection of distortions caused by voltage fluctuations and (ii) improvement of power quality through an AI-based control system. Initially, power distortions are identified and eliminated using the Enhanced Kalman Filter (EKF) algorithm. A Hybrid Artificial Neural Network with Fuzzy Intelligence (HANFI) model is then developed based on this power data. This model integrates two prominent neural network architectures with fuzzy systems. Experimental validation of the system is conducted using MATLAB, which facilitates the simulation of hybrid energy systems.

Application of Renewable Energy in Green Buildings and Energy Consumption Optimization

INTRODUCTION: With the increasing global awareness of sustainable development and environmental protection, green building has become one of the important development directions in the construction industry. The application of sustainable type energy in the construction industry is of great significance in reducing building energy consumption and environmental pollution. This study aims to explore the application of sustainable types of energy and conduct research on energy consumption optimization. OBJECTIVE: The aim of this study is to analyze the current situation of the application of sustainable types of energy in the construction industry, to explore its impact on the energy consumption of buildings, and to propose corresponding optimization strategies in order to achieve the goal of sustainable development of green buildings in China. METHODS: This study adopts a combination of literature review and case study; firstly, a literature review on the application of sustainable types of energy, sorting out its technical characteristics and application effects; then, several typical cases are selected to analyze its energy application and energy consumption in buildings; finally, relevant strategies and suggestions for optimizing the energy consumption are put forward by combining the results of the literature review and the case study. RESULTS: Through the literature review and case analysis, it is found that sustainable types of energy, such as solar energy and wind energy, have been widely used in buildings and achieved certain energy-saving effects. However, there are also some problems, such as inefficient energy utilization and high cost. To address these problems, this study proposes a series of optimization strategies, including suggestions for optimizing energy system design, improving energy utilization efficiency, and reducing energy costs. CONCLUSION: This study concludes that the application of sustainable types of energy in green buildings is an important way to optimize building energy consumption and sustainable development. Through measures such as optimizing energy system design and improving energy utilization efficiency, building energy consumption can be further reduced, environmental pollution can be reduced, and the development of the construction industry can be promoted. However, further research and practice are still needed to continuously improve relevant technologies and policies to promote the application and development of sustainable types of energy in buildings.

Experimental Characterization of Commercial Scroll Expander for Micro-Scale Solar ORC Application: Part 1

In order to reduce greenhouse gas emissions and achieve global decarbonisation, it is essential to find sustainable and renewable alternatives for electricity production. In this context, the development of distributed generation systems, with the use of thermodynamic and photovoltaic solar energy, wind energy and smart grids, is fundamental. ORC power plants are the most appropriate systems for low-grade thermal energy recovery and power conversion, combining solar energy with electricity production. The application of a micro-scale ORC plant, coupled with Parabolic Trough Collectors as a thermal source, can satisfy domestic user demand in terms of electrical and thermal power. In order to develop a micro-scale ORC plant, a commercial hermetic scroll compressor was tested as an expander with HFC-245fa working fluid. The tests required the construction of an experimental bench with monitoring and control sensors. The aim of this study is the description of the scroll performances to evaluate the application and develop optimization strategies. The maximum isentropic effectiveness is reached for an expansion ratio close to the volumetric expansion ratio of the scroll, and machine isentropic effectiveness presents small variations in a wide range of working conditions. The filling factor is always higher than one, due to leakage in the mechanical seals of the scroll or other inefficiencies. This study demonstrates that using a commercial scroll compressor as an expander within an ORC system represents a valid option for such applications, but it is necessary to improve the mechanical seals of the machine and utilize a dedicated control strategy to obtain the maximum isentropic effectiveness.

Triple-Level Single-Ended Main Inductor Converter (SeMLC) with regard to Wind-Solar Hybrid Energies

An output from a DC-to-DC converter that can be more or less than its input is called a Single-Ended Main Inductor Converter (SeMLC). Nevertheless, there is more switching stress in this dual-level SEMIC, which raises switching losses. This rise in switching loss causes the power converter's efficiency to drop. This research suggests a Triple stage SeMIC with lower switching losses to get over this drawback. Using a lower rated switch, Triple level SEMIC increases power efficiency. There is a description of a control method that balances the voltage of the capacitor to avoid damaging the power switch. The suggested converter also lessens the ripple in inductor current in the output inductor. Triple level SeMIC uses a hybrid wind-solar energy system as a source. The benefits of the suggested converter are emphasized by discussing the simulation results of both dual- and Triple-level SeMIC.

Magnetosheath Ion Field‐Aligned Asymmetry and Implications for Ion Leakage to the Foreshock

AbstractThe ion foreshock is highly dynamic, disturbing the bow shock and the magnetosphere‐ionosphere system. To forecast foreshock‐driven space weather effects, it is necessary to model foreshock ions as a function of upstream shock parameters. Case studies in the accompanying paper show that magnetosheath ions sometimes exhibit strong field‐aligned asymmetry toward the upstream direction, which may be responsible for enhancing magnetosheath leakage and therefore foreshock ion density. To understand the conditions leading to such asymmetry and the potential for enhanced leakage, we perform case studies and a statistical study of magnetosheath and foreshock region data surrounding ∼500 Time History of Events and Macroscale Interactions during Substorms mission bow shock crossings. We quantify the asymmetry using the heat flux along the field‐aligned direction. We show that the strong field‐aligned heat flux persists across the entire magnetosheath from the magnetopause to the bow shock. Ion distribution functions reveal that the strong heat flux is caused by a secondary thermal population. We find that stronger asymmetry events exhibit heat flux preferentially toward the upstream direction near the bow shock and occur under larger IMF strength and larger solar wind dynamic pressure and/or energy flux. Additionally, we show that near the bow shock, magnetosheath leakage is a significant contributor to foreshock ions, and through enhancing the leakage the magnetosheath ion asymmetry can modulate the foreshock ion velocity and density. Our results imply that likely due to field line draping and compression against the magnetopause that leads to a directional mirror force, modeling the foreshock ions necessitates a more global accounting of downstream conditions.

Vanadium redox flow battery: Characteristics and application

Renewable energy such as solar energy and wind energy will enter a new period of development. However, the output power of photovoltaic power generation has great randomness. In order to ensure the normal operation of the whole photovoltaic power generation system, it is particularly important to introduce smooth power dynamics of energy storage system (ESS) to ensure electrical stability. As an energy storage device, flow batteries will develop in the direction of large-scale and modularization in the future. The flow battery system can easily realize computer automatic control and is an ideal smart battery. In addition, the combination of flow batteries with photovoltaic cells, wind power stations, tidal power stations, biogas power stations and other renewable energy systems is an important category and content in developing the application fields of flow batteries. As a new type of green battery, Vanadium Redox Flow Battery (VRFB) has the advantages of flexible scale, good charge and discharge performance and long life. It is suitable for large-scale electric energy storage and has attracted wide attention in recent years. In this paper, the characteristics and applications of liquid flow battery and VRFB are summarized. This paper starts from introducing ESS, analyzing several types of flow batteries, and finally focusing on VRFB to analyze its technical characteristics and application market.

Decarbonising mining of Australia's critical mineral deposits: Opportunities for sustainable mining through solar photovoltaics and wind energy integration

Critical minerals are essential in numerous industrial applications, particularly those related to green energy production. Nonetheless, the mining and processing of these minerals are severely energy-intensive, relying predominantly on diesel, coal, and gas as sources of power in Australia. Consequently, the use of diesel, coal, and gas as power sources results in a substantial carbon footprint. An important research gap that remains to be explored in the Australian mining sector is investigating the potential for decarbonising the mining of critical minerals through the implementation of renewable energy to attain sustainable mining. This study examines the potential of using solar photovoltaics (PV) and wind energy towards the decarbonisation of critical mineral deposits, taking into account both the intrinsic mineral characteristics such as ore grade, ore depth, and co-existing mineral clusters, as well as external factors such as the capacity factor (CF) and the periods of low energy generation (lull times) of solar PV and wind, which are crucial in assessing the availability of the renewable energy sources explored. The results from this study indicate that a considerable number of critical mineral deposits in Australia are remotely located from the existing national transmission line infrastructure. These deposits often form geographical clusters and typically have favourable conditions for solar PV and wind energy implementation, characterised by high capacity factors and minimal lull times in solar PV/wind. This clustering presents a promising opportunity for powering mining activities with solar PV and wind energy, thus facilitating the decarbonisation of mining operations in Australia through the utilisation of shared renewable energy infrastructures. Moreover, the study identifies specific mineral deposits that are most suitable for decarbonisation with solar PV and wind energy integration. The research method used in this study proposes an evaluative framework that correlates the capacity factor of solar PV and wind with lull times from these sources, as well as with mineral intrinsic characteristics within deposits. This framework enables a comparative analysis of different deposits, assessing their relative suitability for the integration of solar PV and wind energy technologies. This framework is versatile and can be employed to assess different renewable energy sources, making it applicable to various global contexts. The findings from this study underscore the transformative potential of renewable energy sources, indicating that solar PV and wind energy offer new opportunities for attaining decarbonised and sustainable mining in Australia.

Hybrid wind-solar energy resources mapping in the European Atlantic

The complementarity of offshore wind and solar resources can enhance the energy output of a hybrid farm and reduce its variability relative to a stand-alone, conventional offshore wind farm. In this work offshore wind and solar resources are characterised and mapped in a large study area covering the European Atlantic, the North and Baltic Seas, and the Canary Islands. The intra-annual and overall variabilities of wind power density and solar irradiance are investigated, and their complementarity is evaluated on the basis of their correlation. Negatively correlated regions include the seas around Ireland and Great Britain, with vast wind resources (mean wind power density ~1500 Wm−2 off W Ireland) and comparatively limited solar resources (mean solar irradiance ~100 Wm−2). Positively correlated regions include notably the Canary Islands, with the highest values of solar irradiance in the study area (mean values of ~280 Wm−2). Two study sites are chosen for more detailed investigation – one with a negative correlation, off W Ireland; the other with a positive correlation, off the Canary Islands. Even in the positively correlated regions, it is found that the correlation coefficient is never large (always under 0.2), which signals an opportunity for reducing power output variability through hybrid or co-located wind-solar farms. This, along with the other advantages of hybrid or co-located wind-solar farms (optimised use of scarce marine space, shared electrical infrastructure, shared O&M crews and vessels, etc.), attests to their potential in the European Atlantic. This potential could be realised through new hybrid or co-located wind-solar farms, or by retrofitting floating solar PV into existing offshore wind farms.

An Empirical Study on Wind-solar Energy Reserve in Inner Mongolia in the Context of Carbon Neutrality

This study explores the empirical research of wind and solar energy reserves in the Inner Mongolia region within the broader context of carbon neutrality. Through detailed surveys and data analysis of wind and solar energy resources, we comprehensively assess their potential, reserves, and sustainability. Our findings reveal Inner Mongolia’s abundant wind and solar resources, pivotal in advancing carbon neutrality strategies. However, overcoming challenges such as cost, efficiency, sustainability, and grid coordination of energy storage technologies is imperative to fully leverage this potential. Therefore, this paper proposes suggestions for improvement, including technological innovation, planning optimization, and energy coordination measures. These recommendations aim to address current challenges and enhance the utilization of wind and solar resources in Inner Mongolia, providing valuable insights for future energy planning and policy formulation. This study is a guiding framework for promoting sustainable energy development in the region.

A comprehensive optimization mathematical model for wind solar energy storage complementary distribution network based on multi-regulatory devices under the background of renewable energy integration

In the context of global energy transformation and sustainable development, integrating and utilizing renewable energy effectively have become the key to the power system advancement. However, the integration of wind and photovoltaic power generation equipment also leads to power fluctuations in the distribution network. The research focuses on the multifaceted challenges of optimizing the operation of distribution networks. It explores the operation and control methods of active distribution networks based on energy storage and reactive power compensation equipment. The stable operation of the distribution network is analyzed under the conditions of wind and photovoltaic integration, with a particular focus on precise regulation to address the limitations of existing methods. Afterwards, the study proposes an improvement plan that combines on load tap changer transformers and reactive power compensation equipment to solve complex power balance problems through second-order cone programming relaxation method. The results of numerical analysis show that the constructed mathematical model maintains a stable voltage of 1 to 1.1 pu at distribution network nodes within 24 h. Especially during peak hours from 15:00 to 24:00, it remains normal without any abnormal fluctuations when the control equipment is not added. These results confirm that precise regulation of multiple devices ensures voltage stability and avoids low or high voltage issues.

Recognition of Energy Harvesting Techniques for Enhanced Operation in WSN Using IoT with Deep Learning

The recognition of energy harvesting techniques for enhanced operation in Wireless Sensor Networks (WSN) using IoT and deep learning entails a novel approach to improving the energy efficiency and operational durability of sensor nodes deployed in various environments. Energy harvesting refers to the process of collecting energy from diverse sources such as solar power, thermal energy, wind energy, and mechanical vibrations, and converting it into electrical energy to power electronic devices, including sensor nodes in a WSN. The integration of the Internet of Things with WSNs allows for interconnectivity, enabling enhanced communication, data exchange, and remote management capabilities. This integration facilitates the deployment of more intelligent and adaptive sensor networks, capable of making decisions based on collected information. Deep learning, a subset of machine learning, plays a vital role in enhancing the operation of WSNs by providing advanced data analysis and predictive modeling capabilities. Through the utilization of deep learning algorithms, the proposed system can forecast energy consumption patterns, identify optimal periods for energy harvesting, and optimize the energy efficiency of sensor nodes. This includes adjusting operational parameters such as data communication rates or sleep cycles based on the predicted availability of harvested energy. Deep learning also aids in identifying and prioritizing the most viable energy sources in the deployment environment of the WSN. By employing appropriate energy harvesting techniques based on environmental conditions, such as solar energy harvesting in summer or mechanical vibration harvesting in industrial settings, the proposed method ensures more efficient operation and sustainability of sensor networks across various applications.

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.