Wind-solar Hybrid Research Articles

Optimizing Solar-Wind Hybrid Microgrid Designs with Particle Swarm Techniques for Sustainable Energy Integration

Optimizing Solar-Wind Hybrid Microgrid Designs with Particle Swarm Techniques for Sustainable Energy Integration

Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

This study investigates the optimization of the size of a solar-wind hybrid microgrid using Particle Swarm Optimization (PSO) to improve energy production efficiency, economic feasibility, and overall sustainability. By using past solar and wind resource data, load demand profiles, and system component specifications, the PSO algorithm effectively maximized the capabilities of solar panels and wind turbines. The findings indicate a significant rise in daily energy production, with a 15% enhancement in solar panel capability and a 12% boost in wind turbine capability. The increased energy production plays a crucial role in dealing with the natural irregularity of renewable resources, hence enhancing the resilience and self-reliance of the microgrid. The economic calculations demonstrate significant improvements in the economic feasibility of the microgrid designs. The Levelized Cost of Energy (LCOE) undergoes a significant 10% decrease, suggesting a more economically efficient energy generation. Moreover, the payback time for the original expenditure is reduced by 15%, indicating faster returns on investment. The economic improvements highlight the practical advantages of using PSO for microgrid size, in line with the goal of creating sustainable energy solutions while minimizing economic costs. The improved performance of Particle Swarm Optimization (PSO) is shown by a thorough comparison study with other optimization approaches, such as Genetic Algorithms (GA) and Simulated Annealing (SA). The superior convergence rate of PSO, together with a 15% enhancement in solution quality relative to GA and SA, underscores the efficiency and efficacy of PSO in traversing the complex solution space associated with microgrid size. PSO’s comparative advantage makes it an effective tool for tackling the intricacies of integrating renewable energy, highlighting its potential for extensive use in microgrid design and optimization. The sensitivity evaluations demonstrate that the solutions optimized by the PSO are resilient even when important parameters vary, thereby highlighting the stability and dependability of the approach. In addition to technical and economic factors, the study evaluates the environmental consequences and social aspects of the optimum microgrid designs. The land use efficiency has seen a 10% enhancement, demonstrating the optimum application of area for renewable energy infrastructure. In addition, there is a 7% improvement in community approval, which demonstrates the algorithm’s ability to effectively handle social aspects and promote a comprehensive and socially acceptable approach to renewable energy projects.

Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

Particle Swarm Optimization for Sizing of Solar-Wind Hybrid Microgrids

Performance estimation technique for solar-wind hybrid systems: A machine learning approach

This study introduces a novel method to assess solar-wind hybrid energy systems, utilizing data analysis for key metrics (Performance Yields YR, Capacity Factor CF, and Performance Ratio PR), focused on their uncertainties estimation. Employing a bootstrapped-based K-means algorithm on features from a local weather station in Universidad de La Salle, Bogotá, the approach evaluates the technical feasibility of a photovoltaic system on the campus. It discusses the clustering algorithm advantages, emphasizing its suitability for modeling continuous processes in renewable energy systems. The hypothesis assumes independence and identically distributed centroid coordinates, aligning with large-scale energy system characteristics. Methods include data normalization, feature selection, statistical calculations, bootstrapping, and Probability Density Functions (PDF) estimation through a Maximum Likelihood Estimator (MLE) to assess uncertainties. Results suggest the unfeasibility of a wind energy system due to technological constraints. The study evaluates system flexibility, and design considerations, providing valuable decision-making insights. The conclusion highlights the potential use for accurate estimations, enhancing the system reliability.

Research on the Hybrid Wind–Solar–Energy Storage AC/DC Microgrid System and Its Stability during Smooth State Transitions

The hybrid AC/DC microgrid is an independent and controllable energy system that connects various types of distributed power sources, energy storage, and loads. It offers advantages such as a high power quality, flexibility, and cost effectiveness. The operation states of the microgrid primarily include grid-connected and islanded modes. The smooth switching between these two states is a key technology for ensuring the flexible and efficient operation of the microgrid. In this paper, the typical structure of an AC–DC hybrid microgrid and its coordination control strategy are introduced, and an improved microgrid model is proposed. Secondly, an adaptive current–voltage–frequency integrated control method based on signal compensation is proposed to solve the impulse current and voltage generated during the switching between a grid-connected state and an off-grid state. Finally, in response to unplanned grid-connected scenarios, an improved pre-synchronization control strategy based on BP neural networks is introduced to rapidly restore stable operation. The proposed control strategies enhanced the steady-state and transient stability of the hybrid wind–solar–energy storage AC/DC microgrid, achieving seamless grid-connected and islanded transitions without disturbances. The simulation and experimental results validated the correctness and effectiveness of the proposed theories.

Hybrid Power Generation: Wind and Solar Energy Collaboration: Review

Abstract: The challenge of providing electricity to non-electrified rural areas, while discouraging the extension of traditional electrical grids due to impracticality and environmental concerns, has led to the development of a forward-looking solution: a Solar-Wind Hybrid Power Plant. This innovative system combines solar panels and wind turbines to harness complementary energy sources, ensuring a reliable and uninterrupted power supply. Solar panels capture sunlight during the day, while wind turbines operate continuously, even at night, utilizing wind energy. This integration significantly reduces dependence on fossil fuels, mitigates greenhouse gas emissions, and promotes sustainability. The system's adaptability to various geographical locations and environmental conditions makes it a versatile and scalable solution suitable for both urban and remote areas. In addition to addressing rural energy needs, it contributes significantly to environmental conservation and sustainable development.

Multiobjective and Simultaneous Two-Problem Allocation of a Hybrid Solar-Wind Energy System Joint with Battery Storage Incorporating Losses and Power Quality Indices

In this paper, a multiobjective and simultaneous two-problem allocation of a hybrid distributed generation (HDG) system comprises of solar panels, wind turbines, and battery storage is proposed in a 33-bus unbalanced distribution network which can decrease total losses and improve power quality (PQ). The PQ indices are defined as voltage swell, total harmonic distortion, voltage sag, and voltage unbalance. In this study, the two problems of hybrid system design and its allocation in the distribution network are solved simultaneously. In the allocation problem, the HDG is placed ideally in the network to reduce energy losses and enhance PQ indices. The HDG is measured to minimize the cost of energy generation, including the initial investment, maintenance, and operation costs. The decision variable including the size of HDG components and its location is optimally determined via escaping bird search (EBS) algorithm which is inspired by the maneuvers of the swift bird to avoid predation. The results cleared that the proposed methodology using the wind and solar resources integrated with battery storage reduced the losses, voltage swell, total harmonic distortion, voltage sag, and voltage unbalance by 34.31%, 49.60%, 0.25%, 40.19%, and 2.18%, respectively, than the base network via the EBS and the results demonstrated the better network performance using all renewable resources against wind or solar application only. The outcomes demonstrated the superiority of the EBS in achieving the highest improvement of the different objectives compared with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO). Moreover, the superior capability of the EBS-based methodology is proved in comparison with previous studies.

Optimal design of standalone hybrid solar-wind energy systems for hydrogen-refueling station Case study

The optimization of renewable energy power plants (REPPs) to provide electricity and hydrogen for charging Electric Vehicles (EVs) and Fuel Cells Vehicles (FCVs) permits to achieve the sustainability goals in the near future. Indeed, REEPs, EVs, and FCVs have zero greenhouses gas emission. The optimization of the REPPs according to renewable energy sources potential and load requirements, results in costs minimization and increase in the REPPs efficiency. In this context, this paper presents the optimization and the analysis of four standalone REPPs providing electricity required for charging EVS and producing green hydrogen for charging FCVs. The analyzed REPPs consist of wind turbines, photovoltaic panels, power converters, fuel cell, electrolyzers, and hydrogen tanks. The obtained results of each technical solution are analyzed apart and compared with other results to identify the system having the lowest Net Present Cost (NPC), and the lowest levelized electricity and hydrogen costs. The renewable energy potential shows a wind average speed, and average solar radiation of 5 m/s and 6 kWh/m2/day, respectively. The optimization results show that the PV-battery system has a lowest NPC of 11 M€. The PV-wind fuel cell system has the highest NPC due to the high price of wind turbines and fuel cells. The minimum and maximum levelized cost of hydrogen are 4.78 €/kg and 5.55 €/kg, respectively. The optimized configuration produces around 11,756,589 kWh/year of electricity and 177,607 kg of green hydrogen. The total required capacity is 6 MWp PV system with 1800 kWh of battery for the storage of electricity.

The potential impact of climate change on European renewable energy droughts

The daily, seasonal, and interannual variability of solar and wind resources is well-documented, based on evidence from multi-decadal meteorological time series. However, with the growing share of non-dispatchable renewable-based power sources (e.g., wind and solar power), the stable operation of the power system could be undermined by prolonged periods of low availability of these resources. Consequently, this may result in extremely high prices in the energy market or even a power system blackout. This study analyzes the performance of solar, wind, and solar-wind hybrid systems in Europe based on eight regional climate models, considering two possible climate change projections. The resource availability has been evaluated based on the energy drought concept. The total duration of droughts is calculated using daily capacity factors covering the years 1970–2020 (reference period) and 2048–2098 (future period), considering sub-national regions across the whole of Europe. In general, the chosen climate models show a more significant agreement in the occurrence of energy droughts for northern and southern Europe compared to its central part. Assessing the potential for renewable energy droughts is critical to maintaining secure and reliable power system operation in both the present and future climate.

Study on the structure of offshore wind and solar hybrid power generation device under the background of green energy

There are sufficient solar and wind energy in the sea, which can be used as a good power generation energy and obtain great energy value. Therefore, the development of offshore green energy has become the mainstream trend. This paper mainly focuses on the study of offshore wind solar energy generation structures. This paper first introduces the principle of wind power generation and photovoltaic power generation and the existence of a large amount of energy offshore, and then leads to the basic structure of wind-solar hybrid power generation structure. Then it further studies the power generation, wind load, wave load, initial stability and economic type. Finally, the advantages and future environmental benefits of the structure are summarized, and the structure is reformed reasonably. The results show that the structure has good stability, load bearing capacity and economy, and the construction difficulty of the improved structure has been reduced.

Analysis and Integration of Renewable Energy Resources based Microgrid using Smart Inverter

Nowadays the world is depending on renewable energies like solar energy and wind energy as they are the natural resources to generate power. By combining both the renewable energies, it produces a system named as solar wind hybrid system as solar and wind energy are tremendous in nature and together they increase the reliability of the system. In this research paper, solar energy and wind turbine are used as the main sources, along with solar panel a buck boost converter is used to maintain the voltage to a rated value and with the wind turbine a rectifier circuit is used to convert AC voltage into DC. The characteristics of RES with an AC grid are also observed using solar and wind energy. With the aid of a multi-level inverter, solar and wind energy are converted from DC to AC at a predetermined voltage in order to produce electricity. It can be stated that, in comparison to traditional energy sources, renewable energy sources are currently quite useful.

Solar Electricity Energy: Utilization of Renewable Energy Sources to Realize a Sustainable Campus at UIN Raden Fatah Palembang

Universities play an essential role in achieving Indonesia's 2060 carbon-neutral target. Emissions in the campus are mainly sourced from its electrical energy, transportation, and water use, along with other wastes. Electrical energy from solar is renewable energy, free from pollutants and noise pollution. This paper examines the sustainable energy performance of solar energy at the Universitas Islam Negeri Raden Fatah Palembang (UINRFP) and its development efforts. From August 2022 to March 2023, a solar power plant (SPP) operation with a capacity of 7.74 kWp has generated 6,281.5 kWh of electricity (28.55 kWh/day). Using a radiation factor of 0.8 and an equivalent sun hour of 4.61 (3-5 hours/day), the rooftop capacity of all buildings on Jakabaring Campus is 2,000 kWp, the potential of SPP electricity of UINRFP will exceed 39.3% compared to the 2022 electricity demand. If the capacity is, half the electricity generated from renewable energy meets 70% of the demand. Gradually UINRFP continues to develop its energy sources towards sustainable energy. The development of other research-scale renewable energy with hybrid solar energy and wind power for the aesthetic lighting function on the rectorate building has the potential to generate 290 kWh/year. In addition, micro-hydro energy sources from campus canals (water volume ±9,400 m3) and biomass are potential renewable energy sources at UINRFP for a sustainable campus in the future and contributing to reducing the impact of climate change.

Energy storage system based on hybrid wind and photovoltaic technologies

Clean energy sources like wind and solar have a huge potential to lessen reliance on fossil fuels. Due to the stochastic nature of various energy sources, dependable hybrid systems have recently been developed. This paper's major goal is to use the existing wind and solar resources to provide electricity. A 6 kWp solar-wind hybrid system installed on the roof of an educational building is studied and optimized using HOMER (Hybrid Optimization of Multiple Energy Resources) software at different levels of reliability. At an average annual Cost of Energy (COE) of $1.156 per kWh, the system generates 1996 kWh of power overall. Investigations are made on the techno-economic characteristics of real and ideal hybrid system topologies with maximum capacity shortfalls of 0 %, 5 %, 10 %, and 20 %. The hybrid system's sensitivity analysis looks at how a capacity gap affects overall net present costs and excess power generation. A 2 kWp PV system with one string of ten 12V batteries is shown to be more cost-effective than the existing system with a COE of $0.575/kWh. The most effective configuration for utilizing the site's solar and wind resources is demonstrated to be a 5 kWp wind turbine, a 2 kWp PV system, and battery storage. A wind-solar hybrid system is more expensive than the current system. Despite this, an additional 1 kWp solar PV system may be added to the current system due to the reduction in the limit deficit from 22.3 % to 3.1 %. The findings show that solar-wind hybrid energy systems may efficiently use renewable energy sources for dispersed applications. Additionally, new research directions are noted.

Rural electrification and optimization of biogas–solar–wind hybrid system for decentralized energy generation in India: a case study of Ringhim village, Sikkim

Rural electrification and optimization of biogas–solar–wind hybrid system for decentralized energy generation in India: a case study of Ringhim village, Sikkim

Site Selection for Sustainable Wind-Solar Hybrid Energy Harvesting Plant

The world’s energy landscape is undergoing rapid transformation as societies strive towards renewable and sustainable power sources in response to the need for energy security and climate change. Depletion of fossil fuels and increasing focus on environmental sustainability have been significant drivers in this global shift towards renewables. Among the promising solutions, solar-wind hybrid energy systems have gained attention, capitalizing on the complementary nature of solar and wind resources. However, selecting the optimal site for these hybrid plants is crucial for their successful implementation and efficiency. This research introduces a comprehensive methodology for site selection, seamlessly integrating the Delphi method and Analytic Hierarchy Process (AHP). The Delphi method harnesses expert consensus to distil essential criteria, while AHP offers a structured framework to evaluate and prioritize these criteria. The results showed successful utilization of the AHP and Delphi techniques for evaluating the optimal location selection among different cities for solar-wind hybrid energy harnessing in the northwest region of Pakistan. The key factors for selection of optimal location were identified through expert feedback and analysis. Among the selected key main criteria, resource availability and infrastructure and socio-economic factors emerged as the top-ranked criteria in this study. The results of this study hold significant relevance and should be duly considered for solar-wind power project site selection, while also offering opportunities for further enhancements in the future.

Leveraging Green Ammonia for Resilient and Cost-Competitive Islanded Electricity Generation from Hybrid Solar Photovoltaic-Wind Farms: A Case Study in South Africa.

Hybrid solar photovoltaic (PV) and wind generation in combination with green ammonia as a seasonal energy storage vector offers an excellent opportunity to decrease the levelized cost of electricity (LCOE). In this work, an analysis is performed to find the most cost-effective configuration of power-to-ammonia-to-power (P2A2P). In P2A2P, wind and solar resources are combined with energy storage to design a resilient electricity grid. For daily generation, batteries are utilized for energy storage, whereas ammonia is employed to cope with seasonal fluctuations. The costs of energy storage capacity have a significant influence on the LCOE. Therefore, this work studies the effect of solar/wind hybrid generation systems and energy storage capacity on the LCOE. A base case of the region of De Aar in South Africa was selected because this inland location has excellent wind and solar resources. The optimized battolyzer and Haber-Bosch design capacity led to an overall load factor of 20-30%. At a 30% load factor, a hybrid system with 37% wind-based and 63% solar-based energy generation capacity was the most cost-effective configuration, resulting in a LCOE of 0.15 USD/kWh at a 5% annual discount rate. In an optimistic scenario for PV costs, the LCOE achieved is essentially unaltered (0.14 USD/kWh), while the contribution of wind and PV changes to 25 and 75%, respectively. This analysis indicates that appropriate designing of hybrid energy solutions will play a key role in determining the final energy storage capacities needed to reduce the LCOE. While these costs for LCOE are above those reported for coal-powered electricity in South Africa (e.g., 0.072 USD/kWh for businesses and 0.151 USD/kWh for households), a carbon tax of 50 USD/ton of CO2 can increase these costs to 0.102 and 0.191 USD/kWh, rendering a more promising outlook for the P2A2P concept.

Capacity configuration optimization for green hydrogen generation driven by solar-wind hybrid power based on comprehensive performance criteria

Green hydrogen generation driven by solar-wind hybrid power is a key strategy for obtaining the low-carbon energy, while by considering the fluctuation natures of solar-wind energy resource, the system capacity configuration of power generation, hydrogen production and essential storage devices need to be comprehensively optimized. In this work, a solar-wind hybrid green hydrogen production system is developed by combining the hydrogen storage equipment with the power grid, the coordinated operation strategy of solar-wind hybrid hydrogen production is proposed, furthermore, the NSGA-III algorithm is used to optimize the system capacity configuration with the comprehensive performance criteria of economy, environment and energy efficiency. Through the implemented case study with the hydrogen production capacity of 20,000 tons/year, the abandoned energy power rate will be reduced to 3.32% with the electrolytic cell average load factor of 64.77%, and the system achieves the remarkable carbon emission reduction. In addition, with the advantage of connect to the power grid, the generated surplus solar/wind power can be readily transmitted with addition income, when the sale price of produced hydrogen is suggested to 27.80 CNY/kgH2, the internal rate of return of the system reaches to 8% which present the reasonable economic potential. The research provides technical and methodological suggestions and guidance for the development of solar-wind hybrid hydrogen production schemes with favorable comprehensive performance.

A Hybrid Solar Photovoltaic and Wind Turbine Power Generation for Stand-Alone System with IoT-Based Monitoring and MPPT Control

The goal of this effort is to monitor and manage a hybrid stand-alone photovoltaic (PV) and wind energy system (WES) using the Internet of Things (IoT). The suggested hybrid system uses Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) and Perturb and Observe (P&O)-based Sliding Mode Control (SMC) approaches. The cost-effective Arduino Uno board in Matlab is used to analyze the P&O and INC MPPT methods. Energy from renewable sources is stored in a battery, and the output voltage of each source is monitored using the blynk app and GSM 800c module. Simulation results validate the controller’s operation, and the performance of both MPPT algorithms is compared. Experimental outcomes demonstrate the effectiveness of the IoT-based controller and its characteristic functionalities. Overall, this proposed hybrid PV and WES configuration offers advantages such as reduced human resources, cost-effectiveness, time savings, enhanced reliability, and improved data collection and control capabilities.

Research on key technologies of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization

Due to the uncertainty and randomness of large-scale wind and light, the output power of the power grid has great fluctuations. If it is directly connected to the grid, it will affect the main grid. In addition, when the grid switches between on-grid/off-grid operation modes, there will be power shortages, shocks and oscillations. The scientific and reasonable configuration of energy storage system capacity big data can reduce the load power shortage rate, improve the utilization rate of renewable energy, and ensure the reliable operation of the power grid. For this reason, the key technology of large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization is studied. A large-scale wind-solar hybrid grid energy storage structure is proposed, and the working characteristics of photovoltaic power generation and wind power generation are analyzed, and the probability model of photovoltaic power generation, wind power generation and load, as well as the charging and discharging model of battery and super capacitor are established accordingly. On this basis, the optimization objective function is set, the constraints are determined, and the large-scale wind-solar hybrid grid energy storage capacity big data configuration optimization model is constructed. And the PSO algorithm is used to solve the model to realize the big data configuration optimization of large-scale wind-solar hybrid grid energy storage capacity. The research results show that the proposed method of large-scale wind-solar hybrid grid energy storage system has good power supply reliability and economy, and can effectively improve the utilization rate of renewable energy.

Resilience and reliable integration of PV-wind and hydropower based 100% hybrid renewable energy system without any energy storage system for inaccessible area electrification

Achieving sustainable development goals requires off-grid electrification of remote areas using sustainable energy systems (SES). However, capacity planning for SES is challenging due to the long-term need to meet fluctuating demand and the intermittent and unpredictable nature of renewable energy (RE) sources. The best system size for resilience and reliable load servicing of a 100% hybrid renewable energy system has been investigated in this study. The ideal system configuration for a hybrid solar PV, wind, and hydro energy system has been achieved by applying the multi objective genetic algorithm (MOGA) optimization technique to assess optimal size of the renewable energy system. The PV/Wind/Hydro system has the lowest NPC and COE with the best target capabilities among all the communities examined, provided the system lasts 25 years, according to the NPC study. The most ideal configuration (PV 88 kW, wind 18 kW, Hydro1 215 kW, and Hydro2 197 kW) investigated for deployment of hybrid power system at the specified site has NPC, total system cost, and energy cost of $163,238 and $517,141, respectively, and $0.35/kWh. Finally, the implementation of this innovative proposal demonstrated that reliable load serving is possible with 100% renewable energy without the use of an energy storage device.