Solar Wind Power Research Articles

Comprehensive benefit evaluations for integrating off-river pumped hydro storage and floating photovoltaic

Integrating pumped hydro storage with wind-solar power is an effective method for large-scale integration of renewable energy. The integration of floating photovoltaics with pumped hydro storage solves the issues of unstable output from photovoltaic generation and limited land resources. However, traditional pumped hydro storage has limitations in terms of siting and structure, resulting in environmental issues and opposition when integrated with floating photovoltaics. To overcome these limitations, a novel integrated generation system that combines off-river pumped hydro storage and floating photovoltaic is proposed. Additionally, an optimized scheduling model is developed for the hybrid energy power system with the objective of minimizing operating costs. Finally, a case study of a large clean energy project in Qinghai, China is conducted to quantitatively evaluate the comprehensive benefits of this integrated system in hybrid renewable energy power generation from economic, technical, and environmental perspectives. The results indicate that the combination of off-river pumped hydro storage and floating photovoltaics provides improved technological, economic, and environmental benefits. Specifically, under the premise of meeting the power supply rate and ensuring power transmission reliability, the integration of off-river pumped hydro storage and floating photovoltaic generation system surpasses the traditional integration of pumped hydro storage and photovoltaics in multiple aspects. The integrated system can increase power transmission by 26%, decrease investment costs by 15.04%, lower operating costs by 31.75%, save land use by 4.78 km2, and evaporation reduction efficiency by 44.6%. The OFPHS + FPV operation scheme can provide a reference for the development of pumped hydro storage and photovoltaic industries in resource-constrained areas and provide technical guidance to improve the flexibility of regional power systems.

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.

Environmental Life Cycle Analysis and Energy Payback Period Evaluation of Solar PV Systems: The Case of Pakistan

This study employs a life cycle assessment (LCA) approach to investigate the environmental burden of photovoltaic power generation systems that use multi-crystalline silicon (multi-Si) modules in Pakistan. This study evaluates the energy payback time (EPBT) of this class of systems, and considers various environmental impacts, including climate change, acidification, and eutrophication. The assessment accounts for upstream, midstream, and downstream processes, including cell as well as module production. The critical stages in the production cycle were identified, including the metallic silicon transformation into solar silicon and the assembly of the panels, which involve energy-intensive materials such as aluminum frames and glass roofing. Despite using the most efficient conversion technology, the former stage consumes a significant amount of electricity. This study reveals that multi-Si PV systems in Pakistan have an EPBT that is considerably less than their lifespan, ranging from 2.5 to 3.5 years. These findings suggest that the development of PV systems in Pakistan is a very interesting option for energy production. Additionally, this study compares solar PV and wind power generation systems in various regions of Pakistan. The study outcomes can facilitate evidence-based decision-making processes in the renewable energy sector and contribute significantly to Pakistan’s endeavor to transition toward a sustainable energy system.

Deep Learning for Variable Renewable Energy: A Systematic Review

In recent years, both fields, AI and VRE, have received increasing attention in scientific research. Thus, this article’s purpose is to investigate the potential of DL-based applications on VRE and as such provide an introduction to and structured overview of the field. First, we conduct a systematic literature review of the application of Artificial Intelligence (AI), especially Deep Learning (DL), on the integration of Variable Renewable Energy (VRE). Subsequently, we provide a comprehensive overview of specific DL-based solution approaches and evaluate their applicability, including a survey of the most applied and best suited DL architectures. We identify ten DL-based approaches to support the integration of VRE in modern power systems. We find (I) solar PV and wind power generation forecasting, (II) system scheduling and grid management, and (III) intelligent condition monitoring as three high potential application areas.

How far is it from your home? Strategic policy and management to overcome barriers of introducing fuel-cell power generation facilities

The Korean government introduced the Hydrogen Portfolio Standard for expanding hydrogen fuel-cell power plants to complement the intermittency and volatility of solar photovoltaic and wind power. As for existing power plants, the public perceives fuel cell power plants as dangerous facilities. In this study, we examined the prevalence of the "Not in My Backyard (NIMBY)" phenomenon and the effect of information provision on public acceptance of power plants based on the choice experiment incorporating consumer behavior theory. Results showed that information regarding the construction and operation of power plants is essential for alleviating the NIMBY phenomenon. In addition, by reflecting on the expected separation distance for the power generation facility, loss avoidance behavior in which people become relatively more sensitive to having the plants closer to the residence was identified. Through various analysis results, this study presents policy and management strategies securing public acceptance toward expanding fuel cell power plants.

Maiden application of TIDμ1NDμ2 controller for effective load frequency control of non‐linear two‐area power system

AbstractIn the paper, the maiden application of Tilted Integral Fractional Derivative with Filter plus Fractional Derivative controller (), called TIFDNFD controller, is proposed to achieve load frequency control (LFC) of two area‐multi source power system. In the study, both areas include thermal power, hydropower, and gas turbine power systems. In this study, generation rate constraint and governor dead‐band are integrated into the power system to evaluate the performance of the controller in a non‐linear environment. The wild horse optimizer (WHO) is used for controller parameter tuning in the LFC problem for the first time. The performance of the proposed controller is demonstrated by comparing it with the recently published studies including different controllers. In this study, to obtain more realistic results, solar photovoltaic (PV) power, wind power, and load demand with stochastic form have been added to the power system. In addition, the dynamic effect of the hydrogen storage system, consisting of an electrolyser and a fuel cell, is considered a grid supporter. The superiority of the WHO algorithm in optimization has been verified by comparing it with three optimization algorithms recently proposed. Finally, the resilience and robustness of the power system are evaluated in perturbed system parameters.

Process simulation on the planning of wind-PV storage for near-zero carbon emission

The proposal of the dual carbon policy has put forward new requirements for the planning of wind power photovoltaic energy storage, which should not only meet economic requirements but also consider the timeliness of carbon reduction. Particularly, in recent years, there has been a contradiction between energy waste and power shortage in some regions of China, which needs to be resolved through reasonable planning of the capacity of multi energy systems. Based on the aforementioned background, this paper uses system dynamics simulation to analyze the efforts that should be made to maintain a stable energy balance between wind–solar power generation output and energy storage capacity when considering the cost of the power side and the demand response level of the load side. We assume a development plan for wind and solar energy, and optimize the allocation of energy storage capacity under these conditions to meet the balance requirements of carbon reduction and investment cost reduction. We use system dynamics simulation to simulate the energy storage demand under the demand response. In order to achieve the near-zero carbon goal, this paper discusses the reasonable state of a power energy structure based on different carbon emission reduction goals. Finally, we discovered the relationship between investment costs and carbon reduction targets as increasing carbon reduction targets will greatly increase investment costs and provide some suggestions for planning.

Meteorological conditions during periods of low wind speed and insolation in Germany: The role of weather regimes

AbstractRenewable power generation from wind and solar energy is strongly dependent on the weather. To plan future sustainable energy systems that are robust to weather variability, a better understanding of why and when periods of low wind and solar power output occur is valuable. We call such periods of low wind speed and insolation “Dunkelflauten”, the German word for “dark wind lulls”. In this article, we analyse the meteorological conditions during Dunkelflauten in Germany by applying the concept of weather regimes. Weather regimes are quasi‐stationary, recurrent and persistent large‐scale circulation patterns that explain multi‐day atmospheric variability (5–15 days). We use a regime definition that allows us to distinguish four different types of blocked regimes, characterized by high‐pressure situations in the North Atlantic‐European region. We find that Dunkelflauten in Germany occur mainly in winter when the solar power output is low due to the seasonal cycle of solar irradiance and wind power output drops for several consecutive days. A high‐pressure system over Germany, associated with the European Blocking regime, is responsible for most of the Dunkelflauten. Dunkelflauten during the Greenland Blocking regime are associated with colder temperatures than usual, causing higher electricity demand, and would present a particular challenge as space heating becomes electrified in the future. Furthermore, we show that Dunkelflauten occur predominantly when a weather regime is well established and persists longer than usual. Our study provides novel insight into the occurrence and meteorological characteristics of Dunkelflauten, which is essential for planning resilient energy systems and supporting grid operators to prepare for potential shortages in supply.

Analysis of Power Generation and Distribution of Hybrid Energy for Electricity Loads in Batakan Village

The need for electrical energy continues to increase over time. However, in Indonesia power plants are still dominated by fossil fuel power plants, and there are still many areas without access to electricity. The use of renewable energy is needed to replace fossil fuels considering that fossil fuels can run out one day. The coastal area of Batakan Village in Tanah Laut Regency, South Kalimantan Province, was chosen as the focus location for conducting hybrid power plant simulations because this village is located in a coastal area where wind and solar energy sources are abundant. Batakan Village is approximately 40 km from Pelaihari City. Medium-voltage network transmission system (JTM) is supplied from Pelaihari City, and it is almost certain that this village experiences large power losses over long distance. This power loss will be detrimental if an effort is not made to reduce it. The purpose of this research is first to determine the optimal hybrid power plant configuration design to reduce power loss in the electricity system in Batakan Village. Second, it will analyze the power loss of the hybrid power plant system in Batakan Village, and finally, this research is going to analyze the investment feasibility of the hybrid power plant system in Batakan Village. In this study, the design of renewable energy plants, such as solar power plants (PLTS) with a total capacity of 406.1 kW and wind power plants (PLTB) with a total capacity of 125 kW, and the electricity network (grid system) are used together in a hybrid power generation system. The ETAP software was used to analyze the power losses of the hybrid power generation system, while the HOMER software was used to determine the net present value (NPV) and cost of energy (COE) of the hybrid power generation system. The results show that the configuration of the solar, wind, and grid systems is the most optimal. It is obtained from the results of ETAP simulations that have been carried out during average load and peak load conditions that by including the Solar Power Plant and Wind Power Plant power losses in the electricity system in Batakan Village can be reduced from the previous one using the system configuration only connected to the PLN power grid (grid system only). The total power losses incurred was 269.1 kW of active power and 1613.5 kvar of reactive power at average load reduced to 266.9 kW of active power and 1568.9 kvar of reactive power. At peak load the total power losses were 423.4 kW of active power and 2573.0 kvar of reactive power and they deceased to 41.,5 kW of active power and 2510.5 kvar of reactive power. In terms of investment, the COE value decreased by IDR 111, and the NPC decreased by IDR 6,600,000,000 at the average load. At the peak load COE decreased by IDR 88, while NPC by IDR 7,000,000,000. The return of investment (ROI) value is 13.2%, which indicates that the investment is still in the profitable stage.

Improving the Efficiency of Photovoltaic Modules of a Solar Power Plant for Power Supply to Autonomous Rural Consumers

The article reviews one of the development stages of the hybrid wind-solar power plant for power supply of the autonomous rural consumers with a capacity up to 1 kW. The results of the photovoltaic modules efficiency during their heating and contamination analysis are presented. A model of a hybrid wind-solar power plant is proposed for consideration, where the wind wheel is combined with a frame for fixing the PVM. The blades are foldable and made of transparent plastic for reducing the resistance for the oncoming flow, which increases the speed of rotation of the wind wheel. An increase in the efficiency of photovoltaic modules and a reduction in their degradation is achieved due to their rotation around the axis of a truncated pyramid with an octagonal base. A laboratory model was constructed for conducting field experiments and testing the design of the security system. The obtained research results allow to further create an operating hybrid renewable energy source and search for the most rational design.

Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system

The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic nature of wind and solar energy resources is still a major challenge. These can affect the safety, stability, and reliability of the energy grid. In existing works of literature, the forecast and prediction of wind energy, solar power, wind power, and solar energy with various models have been considered independently. However, with the rise in solar power and wind power penetration, there exists a gap in literature on the development of models that can simultaneously forecast solar and wind power production. In this paper, two deep hybrid neural networks (DHN-Net) models are developed for the simultaneous forecast of wind and solar power. The novelty of this study is further strengthened as a minute-level timestep is considered for the application of the models developed. The models are trained and tested with data collected from Zone 1 of four different power system operators in the USA. The two DHN-Net models are built on the foundation of artificial, convolutional, and recurrent neural networks (ANN, CNN, and RNN). Results from this study show that the two DHN-Nets can accurately forecast solar and wind power with an R-squared (r2) value of 0.9915, RMSE of 0.01920, and MAE of 0.00736 for data collected from PJM_Zone1. The DHN-Net models recorded a better performance when compared to the benchmark results in literature.

Geographical balancing of wind power decreases storage needs in a 100% renewable European power sector

To reduce greenhouse gas emissions, many countries plan to massively expand wind power and solar photovoltaic capacities. These variable renewable energy sources require additional flexibility in the power sector. Both geographical balancing enabled by interconnection and electricity storage can provide such flexibility. In a 100% renewable energy scenario of 12 central European countries, we investigate how geographical balancing between countries reduces the need for electricity storage. Our principal contribution is to separate and quantify the different factors at play. Applying a capacity expansion model and a factorization method, we disentangle the effect of interconnection on optimal storage capacities through distinct factors: differences in countries' solar PV and wind power availability patterns, load profiles, as well as hydropower and bioenergy capacity portfolios. Results indicate that interconnection reduces storage needs by around 30% in contrast to a scenario without interconnection. Differences in wind power profiles between countries explain around 80% of that effect.

Scheduling optimization of wind-solar power generation system based on Power to Gas

Storage technology and consumption technology can reduce the amount of abandoned wind and sunlight, improve the utilization of new energy, and reduce cost. Firstly, this paper describes the role of P2G (Power to Gas) technology in the field of new energy consumption, then builds the economic cost-optimal day-ahead optimal dispatch model based on a microgrid, examining the ability of P2G devices to consume new energy. Finally, three different scenarios are applied in the example for verification. The calculation example proves that P2G devices have a good effect in the field of scenery consumption and reduce the cost of the microgrid.

Optimized Hybrid CNN-LSTM Based Islanding Detection of Solar-Wind Power System

The rising prevalence of dispersed energy-generating resources like wind and solar power systems degrades the quality of electricity. For the safety of maintenance staff and the equipment in such situations, detecting the power quality troubles in the power systems is highly significant. In order to cater for these issues, the stationary discrete wavelet transform (SDWT) and hybrid long short-term memory (LSTM) convolution neural network (CNN) based classifier is proposed in this work. Besides, the African vulture optimization algorithm (AVOA) is adopted for optimizing the weights of layers in hybrid LSTM-CNN (HLC). This work aims to detect the islanding operation under different faulty conditions in the network. The implementation results are analyzed for various conditions, showing the fidelity of a proposed method by detecting the islanding faults. Moreover, the proposed method is validated under different noise levels and shows better performance in noisy signal to noise ratio (SNR). The accuracy obtained for the proposed SDWT-HLC method without noise is 99.92%. The accuracy with 20, 30 and 40 dB noise for the proposed method is obtained as 99.84%, 99.87% and 99.89%, respectively. Furthermore, the accuracy, precision, recall and F1-score are 0.999, 0.995, 0.994 and 0.994, which shows the efficacy of the proposed method.

Renewable energy and electricity incapacitation in sub-Sahara Africa: Analysis of a 100% renewable electrification in Chad

Despite the global push for net-zero emission and the power system’s decarbonization, there are still a lot of developing countries (especially in sub-Saharan Africa) with electricity crises or inadequate electrification. Coincidentally, these countries have enormous renewable energy potential. Therefore, this paper presents a brief summary of the electricity incapacitation and renewable energy potential in many sub-Saharan African countries. This study is novel as it presents a techno-analysis of 100% renewable electrification in Chad which is one of the countries with the least electricity access globally. Also, the models developed are simulated using the EnergyPLAN tool with targeted implementation years set as 2030, 2040, and 2050 based on different global decarbonization targets. From the result of this study, the use of biomass power plants is the only scenario that does not have critical excess electricity production (CEEP) in the power system. Also, these scenarios require the least power capacities (270 MW for 2030, 650 MW for 2040, and 1600 MW for 2050) out of all the scenarios in this study. Solar PV and wind power can be categorized as the most probable options as they are matured technologies and in application in many countries, however, they will require pumped hydro storage integration.

Review of Hybrid Wind-Solar PV Technology in the Generation of Electricity

Achieving sustainability by utilizing alternative energy sources viable technological possibilities for creating sustainable energy, the sun, biomass, wind, geothermal resources, hydropower, and ocean resources are considered. Despite the fact that the total amount of energy produced by PV cells and wind turbines is still far less than that of fossil fuels, their ability to generate electricity has significantly expanded in recent years. This article provides an overview of the Solar-Wind hybrid power system, which generates electricity by combining the Sun and Wind, two renewable energy sources. Microcontrollers are widely used in the field of system management. We can maximize the utilization of those resources by employing this strategy, which takes into account the distinct production processes of each resource. Furthermore, it increases dependability and decreases reliance on any single input. This hybrid solar-wind power generation system is suitable for both industrial and residential applications.

Tatanan Indonesia Dalam Mewujudkan Pertumbuhan Ekonomi Berkelanjutan Melalui Green Economy

Green Economy is an economic approach that focuses on sustainable development and utilization of resources, by integrating economic, environmental, and social aspects by involving changes in mindset and lifestyle. With the expectation that people are expected to adopt sustainable behaviors and consumption such as reducing waste, supporting environmentally friendly products, and recycling. The main goals of a green economy are to reduce negative impacts on the environment, including reduction of greenhouse gas emissions, protection of biodiversity, conservation of water resources, and effective waste management. Green economy not only focuses on environmental protection, but also involves social and economic aspects. This includes improving social welfare and improving quality of life through sustainable job creation, increased access to basic services, and community empowerment in decision-making that impacts the environment. There are several important aspects in realizing a green economy through policies and regulations to encourage the transition to a sustainable economy that considers environmental and social aspects that support the green economy in realizing the transition to a sustainable economy. The second is investment in renewable energy that uses energy such as solar power, wind power, hydroelectricity, biomass and geothermal energy. The third is sustainable infrastructure development, the fourth is community empowerment and the fourth is collaboration between the public and private sectors.

Energy Management For Renewable Hybrid System Based On Artificial Neural Networks (ANN)

Abstract: It has become necessary in recent years to use renewable energy sources, such as wind and solar energy, to meet the load demand. Because of their endless existence and environmentally friendly nature, they are useful for power generation. The management of energy in hybrid wind-solar power sources is the topic of this article. Using a common current source interface multiple-input DC-DC converter, the photovoltaic (PV) array, wind turbine, and battery storage are all connected. The power control between intermittent renewable energy generation, energy storage, and the grid is maintained by the artificial neural network (ANN) controller. For wind turbines to produce the most electricity, variable speed control can be used. For solar systems, the Maximum Power Point Tracking (MPPT) algorithm is used. By keeping the common DC voltage constant, the grid interface inverter sends the energy drawn from the PV array and wind turbine into the grid. Using MATLAB Simulink, the complete control strategy is subjected to a simulation study. The results of the simulation show the photovoltaic/wind hybrid system's dynamic behavior and control effectiveness

Design and operational optimization of a methanol-integrated wind-solar power generation system

Wind and solar energy are rapidly being merged into electricity grids in China. High penetration of variable renewable electricity drives the development of energy storage with low cost, high flexibility and utility-scale. To this end, a methanol-based energy storage system is proposed to meet regional power demand by combining a hybrid wind-solar source. This work studies capacity configuration and logistics scheduling at the hourly level with the minimum power generation cost. The round-trip efficiency reaches 41.5%, and the levelized cost of electricity is 0.148 $/kWh. The wind-solar hybrid system improves the system efficiency and economy compared with separated wind or solar systems. Taking methanol as the hydrogen carrier significantly reduces the storage scale of hydrogen. In terms of regions with larger fluctuation of renewable energy, methanol-based energy storage is more cost-effective than hydrogen energy storage.

Symbiotic organisms search-based multi-objective optimal placement of distributed generators considering source and load uncertainty

Integration of Distributed Generation (DG) in the Distribution network will reduce the network expansion costs and increase the network reliability in terms of reduction in voltage magnitude deviations.This paper presents Symbiotic Organisms Search (SOS) based technique for finding out the best size and position of DG in Radial Distribution networks to improve the voltage profile and voltage security state of the Distribution network. The primary objectives of the paper are minimization of bus voltage variation and maximization of voltage stability index of the network as a multi objective optimization problem in presence of uncertainty of sources and loads. In this paper the uncertainty of solar power, wind power and load are modelled using 2m Point Estimate Method along with SOS algorithm.To show the effect of DG placement on voltage security state of Distribution system, the system is classified into three states based on the values of voltage stability index (VSI). Simulation results as obtained from two standard (IEEE) radial distribution networks prove the efficiency and accuracy of the proposed SOS method. The results of SOS based method are compared with some other techniques as found in recent literature, which shows that SOS algorithm outperforms other standard optimization techniques.