Sustainable Development Of Renewable Energy Research Articles

Physical–Chemical–Biological Pretreatment for Biomass Degradation and Industrial Applications: A Review

Lignocellulosic biomass, including agricultural, forestry, and energy crop waste, is one of Earth’s most abundant renewable resources, accounting for approximately 50% of global renewable resources. It contains cellulose, hemicellulose, and lignin, making it crucial for biofuels and bio-based chemicals. Due to its complex structure, single-pretreatment methods are inefficient, leading to the development of combined pretreatment technologies. These methods enhance cellulose accessibility and conversion efficiency. This paper analyzes the principles, advantages, and disadvantages of various combined pretreatment methods and their practical benefits. It highlights recent research achievements and applications in biofuel, biochemical production, and feed. By integrating multiple pretreatment methods, biomass degradation efficiency can be significantly improved, energy consumption reduced, and chemical reagent use minimized. Future advancements in combined physical, chemical, and biological pretreatment technologies will further enhance biomass utilization efficiency, reduce energy consumption, and protect the environment, providing robust support for sustainable renewable energy development and ecological protection.

A New Grey Prediction Model and Its Application in Renewable Energy Consumption

Renewable energy is an energy resource that can be used continuously. At present, international oil prices continue to rise, the problem of global climate change is becoming increasingly prominent, and renewable energy and clean energy have ushered in a new round of development opportunities. Based on the new gray prediction model, this paper forecasts the consumption of renewable energy and further analyzes the sustainable development of renewable energy. In this paper, the combinatorial optimization method of cumulative order, background value coefficient, and initial conditions, parameter optimization combination, parameter combinatorial optimization process of the gray prediction model, and parameter optimization mechanism based on the PSO algorithm are proposed, and the reduction error analysis is carried out. The consumption of wind power and photovoltaic renewable energy is forecasted, and three different forecasting methods as exponential smoothing method, time series analysis method, and new gray forecasting method are compared, and the wind speed, irradiation intensity, and load are forecasted by these three different forecasting methods. Compared with the time series analysis method and the exponential smoothing method, the RMSE of the new grey prediction method is reduced by 127.12% and 160.59%, and the error rate is reduced by 3.16% and 4%, respectively. Based on the consumption forecast of renewable energy, this paper analyzes its sustainability from three directions economy, resource supply, and environment, and finally gives energy policy recommendations.

Photovoltaic Solar Farms Site Selection through “Policy Constraints–Construction Suitability”: A Case Study of Qilian County, Qinghai

The scientific selection of photovoltaic (PV) sites is essential for achieving sustainable development of renewable energy and ensuring regional ecological security. In western China, extensive land resources coexist with a fragile ecological environment. To this end, we propose a PV siting framework based on policy restrictions and construction suitability. This paper evaluated the PV construction suitability index (CSI) from four dimensions of topography, climate, location, and ecology and proposed typical “PV+” models. Then, Qilian County was selected as a case study. The results showed the following: (1) In Qilian, 59.97% (8333.18 km2) of the area is unsuitable for development due to policy restrictions, leaving 40.03% (5563.02 km2) available for PV construction. (2) The most suitable areas are approximately in the western and southern areas, where there is a lot in common with the reported PV sites under construction. (3) Three distinct PV development models are proposed according to policy guidelines and local circumstances, including the PV + pastoralism model, PV + mine rehabilitation model, and PV + hydropower model. The results can be used to determine the suitable areas for solar PV farms and the appropriate development model, as well as promote the sustainable development of renewable energy.

A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting

Ultra-short-term photovoltaic (PV) power forecasting is crucial in the scheduling and functioning of contemporary electrical systems, playing a key role in promoting renewable energy integration and sustainability. In this paper, a novel hybrid model, termed AI_VMD-HS_CNN-BiLSTM-A, is introduced to tackle the challenges associated with the volatility and unpredictability inherent in PV power output. Firstly, Akaike information criterion variational mode decomposition (AI_VMD) integrates the Akaike information criterion with variational mode decomposition (VMD) and reduces data complexity, enhancing grid optimization and energy efficiency. The adaptive selection of optimal parameters enhances VMD decomposition performance, supporting sustainable energy management. Secondly, the hierarchical scale-transform convolutional architecture (HS_CNN) supplements the traditional convolutional neural network (CNN) with two channels featuring distinct dilation rates, thereby extracting dual levels of time-scale information for a more comprehensive data representation. Finally, a bidirectional long short-term memory neural network (BiLSTM) with an attentional mechanism combines past and future data to enable more accurate forecasts, aiding in carbon reduction and smart grid advancements. Experimentation with data from the Alice Springs PV plant in Australia demonstrates that the proposed AI_VMD-HS_CNN-BiLSTM-A model exhibits superior adaptability and accuracy in multiple time-scale forecasting compared to the baseline models. This approach is important for decision-making and scheduling in grid-connected photovoltaic systems, enhancing energy resilience and promoting the sustainable development of renewable energy.

Determinants of Carbon Emission- A Study in South Asian Countries

The rapid economic growth of South Asian countries, coupled with rising populations, has led to an increasing demand for energy, thereby posing significant challenges for sustainable development. This paper explores the importance of sustainable energy development in South Asia, highlighting its critical role in ensuring economic stability, environmental protection, and social equity. The United Nations has underscored this through its UNFCCC 2016 targets, transitioning from the Millennium Development Goals to the broader Sustainable Development Goals, with a heightened focus on mitigating climate change. In this context sustainable energy development is very important. This study focuses on the sustainable energy development status of South Asian Association for Regional Cooperation (SAARC) countries—Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. Utilizing 23 years of data (2000-2022), we employed Panel Data Analysis to investigate the determinants influencing carbon intensity in these nations. Our findings reveal that energy intensity, per capita renewable energy consumption and access to electricity significantly increase carbon intensity. Conversely, higher per capita income, a greater share of renewable energy in total consumption and increased per capita energy generation from fossil fuels correlate with a significant reduction in carbon intensity. The results suggest that enhanced collective efforts among SAARC countries are imperative for advancing sustainable renewable energy development.

Capacity optimization of independent hybrid renewable energy system under different operational strategies based on improved gray wolf algorithm

Renewable energy sources such as wind and solar power exhibit strong stochasticity and volatility, resulting in decreased power supply security and sustainability. A strategically optimized hybrid renewable energy system (HRES) is crucial for maintaining stable load operations and achieving sustainable energy development. This paper introduces an energy optimization management model for an independent HRES consisting of wind turbines, photovoltaic systems, diesel generators, and energy storage units. Operational strategies focus on energy storage-led loads following diesel generator-led load prioritizations. The model aims to optimize objectives to include economic, environmental, and power supply reliability indices. A dynamic adaptive parameter approach balances the parameters of the objective function at various instances. The optimal capacity allocation of the model is solved using the improved gray wolf optimization (IGWO) algorithm. This approach incorporates the golden sine strategy, the levy flight strategy, and the dynamic inverse learning strategy into the traditional GWO algorithm. Analyzing different test functions, evaluation metrics, and actual load data indicates that the proposed algorithm excels in global optimization capabilities and search speeds. The model significantly reduces the economic and environmental costs of the HRES microgrids and improves the sustainable development of renewable energy in various scenarios.

Eco-Energetical analysis of circular economy and community-based virtual power plants (CE-cVPP): A systems engineering-engaged life cycle assessment (SE-LCA) method for sustainable renewable energy development

Amidst the escalating complexity of energy demands and climate concerns, natural calamities, and the Black Swan phenomenon have all presented formidable obstacles to the advancement and implementation of renewable energy sources. In light of the present backlog of studies in energy technology, the three primary issues identified and resolved in this study are inadequate analysis of the actual situation, absence of evidence of sustainable development, and flawed systematic methodology. This study proposes a novel renewable energy development initiative known as the circular economy and community-based virtual power plant (CE-cVPP). By integrating community participation and circular economy principles, this initiative aims to foster sustainable development and facilitate resilient progress. The principal focuses of the model are community renewable energy supply, energy demand, and community participation management. The sustainable framework is constructed upon the principles of circular economy theory. The systems engineering-based life cycle assessment (SE-LCA) method is proposed as an innovative approach to whole life cycle assessment that is grounded in systems engineering. Its purpose is to facilitate a systematic implementation and empirical evaluation of the sustainability of energy, the environment, and the economy. On the basis of disparities in renewable energy sources and a variety of spatiotemporal dimensions, CE-cVPP is categorised into four representative groups. We present a business community model for energy and environmental analysis that is based on a hospital in Harbin. The results suggest that the overall lifecycle primary energy conservation rate is 67.21%, which is an exceptional outcome that significantly promotes sustainable development in comparison to conventional methods. This improves environmental and human health protection as the full lifecycle emission reduction ratios for atmospheric pollutants are 53.50% (SO2-eq.), 51.69% (PM2.5-eq.), and 74.85% (CO2-eq.). Consideration is given to raw materials, equipment construction, equipment operation, tariffs on six pollutants, and material recycling when calculating the total cost. The annual total cost savings ratio for the full lifecycle is 54.21% when compared to the traditional system. This research possesses methodological and theoretical applicability to the energy and power sectors.

A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting

Accurate photovoltaic (PV) power forecasting allows for better integration and management of renewable energy sources, which can help to reduce our dependence on finite fossil fuels, drive energy transitions and climate change mitigation, and thus promote the sustainable development of renewable energy sources. A convolutional neural network (CNN) forecasting method with a two-input, two-scale parallel cascade structure is proposed for ultra-short-term PV power forecasting tasks. The dual-input pattern of the model is constructed by integrating the weather variables and the historical power so as to convey finer information about the interaction between the weather variables and the PV power to the model; the design of the two-branch, two-scale CNN model architecture realizes in-depth fusion of the PV system data with the CNN’s feature extraction mechanism. Each branch introduces an attention mechanism (AM) that focuses on the degree of influence between elements within the historical power sequence and the degree of influence of each meteorological variable on the historical power sequence, respectively. Actual operational data from three PV plants under different meteorological conditions are used. Compared with the baseline model, the proposed model shows a better forecasting performance, which provides a new idea for deep-learning-based PV power forecasting techniques, as well as important technical support for a high percentage of PV energy to be connected to the grid, thus promoting the sustainable development of renewable energy.

The Statutory Interpretation of Renewable Energy Based on Syllogism of British Government Constitutional Forms towards Sustainable Development Goals

The current production for energy consumption generates harmful impacts of carbon dioxide to the environment causing instability to sustainable development goals. The constitutional reforms of British Government serve to be an important means of resolving any encountered incompatibilities to political environment. This study aims to evaluate green economy using developed equation for renewable energy towards political polarization of corporate governance. The Kano Model Framework is used to measure the equivalency of 1970 Patents Act to UK Intellectual Property tabulating the criteria for the fulfillment of sustainable development goals in respect to the environment, Artificial Intelligence, and dynamic dichotomy of administrative agencies and presidential restriction, as statutory interpretation development to renewable energy. The constitutional forms of British Government satisfy the sustainable development goals needed to fight climate change, advocate healthy ecosystem, promote leadership of magnates, and delegate responsibilities towards green economy. The presidential partisanship must be observed to delineate parties of concerns and execute the government prescriptions in equivalence to the dichotomous relationship of technology and the environment in fulfilling the rights and privileges of all citizens. Hence, the Political Elites can execute corporate governance towards sustainable development of renewable energy promoting environmental parks and zero emission target of carbon dioxide discharges. The economic theory developed in statutory interpretation for renewable energy serves as a tool to reduce detrimental impacts of carbon dioxide to the environment, mitigate climate change, and produce artefacts of bioenergy and Artificial Intelligence promoting sustainable development. It is suggested to explore other vulnerabilities of Artificial Intelligence to prosper economic success.

Opportunity Assessment of Virtual Power Plant Implementation for Sustainable Renewable Energy Development in Indonesia Power System Network

Renewable energy sources have become one of the important roles for sustainable energy development. One of the promising mechanisms to deploy renewable energy is through a Virtual Power Plant (VPP), which can integrate various distributed renewable energy resources into a single controllable and deployable entity. This paper examines the opportunity for VPP adoption in Indonesia, which investigates the minimum implementation criteria, provides a gap analysis for VPP implementation, and proposes recommendations for VPP implementation in Indonesia. The implementation criteria are obtained from the literature review, including the lessons learned from other countries, and categorized into four aspects: regulatory, technical, economic, and social. The gap analysis is performed by evaluating the current state of Indonesia’s utility network in correlation with the VPP minimum implementation criteria and then provides a scoring matrix for each criterion. Lastly, the recommendations are arranged to narrow these gaps, organized into ten key focus points, and divided into four phases, initiation, preparation, piloting, and deployment, across a ten-year timeframe.

The Statutory Interpretation of Renewable Energy Based on British Government Constitutional Forms towards New Public Management of Sustainable Development for Corporate Governance Using Artificial Intelligence

Purpose: The current production for energy consumption generates harmful impacts of carbon dioxide (CO2) to the environment causing instability to sustainable development goals. The constitutional reforms of British government serve to be an important means of resolving any encountered incompatibilities to political environment. This study aims to evaluate green economy using developed equation for renewable energy towards political polarization of corporate governance.
 Methodology: The Kano Model Assessment is used to measure the equivalency of 1970 Patents Act to UK Intellectual Property tabulating the criteria for the fulfillment of sustainable development goals in respect to the environment, artificial intelligence, and dynamic dichotomy of administrative agencies and presidential restriction, as statutory interpretation development to renewable energy. 
 Findings: The constitutional forms of British government satisfy the sustainable development goals needed to fight climate change, advocate healthy ecosystem, promote leadership of magnates, and delegate responsibilities towards green economy. The presidential partisanship must be observed to delineate parties of concerns and execute the government prescriptions in equivalence to the dichotomous relationship of technology and the environment in fulfilling the rights and privileges of all citizens. Hence, the political elites can execute corporate governance towards sustainable development of renewable energy promoting environmental parks and zero emission target of carbon dioxide (CO2) discharges.
 Implications to Theory, Practice and Policy: The economic theory developed in statutory interpretation for renewable energy serves as a tool to reduce detrimental impacts of carbon dioxide (CO2) to the environment, mitigate climate change, and produce artefacts of bioenergy and artificial intelligence promoting sustainable development. It is suggested to explore other vulnerabilities of artificial intelligence to prosper economic success.

The Statutory Interpretation of Renewable Energy Based on British Government Constitutional Forms towards New Public Management of Sustainable Development for Corporate Governance Using Artificial Intelligence

Purpose: The current production for energy consumption generates harmful impacts of carbon dioxide (CO2) to the environment causing instability to sustainable development goals. The constitutional reforms of British government serve to be an important means of resolving any encountered incompatibilities to political environment. This study aims to evaluate green economy using developed equation for renewable energy towards political polarization of corporate governance.
 Materials and Methods: The Kano Model Assessment is used to measure the equivalency of 1970 Patents Act to UK Intellectual Property tabulating the criteria for the fulfillment of sustainable development goals in respect to the environment, artificial intelligence, and dynamic dichotomy of administrative agencies and presidential restriction, as statutory interpretation development to renewable energy. 
 Findings: The constitutional forms of British government satisfy the sustainable development goals needed to fight climate change, advocate healthy ecosystem, promote leadership of magnates, and delegate responsibilities towards green economy. The presidential partisanship must be observed to delineate parties of concerns and execute the government prescriptions in equivalence to the dichotomous relationship of technology and the environment in fulfilling the rights and privileges of all citizens. Hence, the political elites can execute corporate governance towards sustainable development of renewable energy promoting environmental parks and zero emission target of carbon dioxide (CO2) discharges.
 Implications to Theory, Practice and Policy: The economic theory developed in statutory interpretation for renewable energy serves as a tool to reduce detrimental impacts of carbon dioxide (CO2) to the environment, mitigate climate change, and produce artefacts of bioenergy and artificial intelligence promoting sustainable development. It is suggested to explore other vulnerabilities of artificial intelligence to prosper economic success.

Short-term wind speed prediction based on improved Hilbert–Huang transform method coupled with NAR dynamic neural network model

Wind energy, as a renewable energy source, offers the advantage of clean and pollution-free power generation. Its abundant resources have positioned wind power as the fastest-growing and most widely adopted method of electricity generation. Wind speed stands as a key characteristic when studying wind energy resources. This study primarily focuses on predictive models for wind speed in wind energy generation. The intense intermittency, randomness, and uncontrollability of wind speeds in wind power generation present challenges, leading to high development costs and posing stability challenges to power systems. Consequently, scientifically forecasting wind speed variations becomes imperative to ensure the safety of wind power equipment, maintain grid integration of wind power, and ensure the secure and stable operation of power systems. This holds significant guiding value and significance for power production scheduling institutions. Due to the complexity of wind speed, scientifically predicting its fluctuations is crucial for ensuring the safety of wind power equipment, maintaining wind power integration systems, and ensuring the secure and stable operation of power systems. This research aims to enhance the accuracy and stability of wind speed prediction, thereby reducing the costs associated with wind power generation and promoting the sustainable development of renewable energy. This paper utilizes an improved Hilbert–Huang transform (HHT) using complementary ensemble empirical mode decomposition (CEEMD) to overcome issues in the traditional empirical mode decomposition (EMD) method, such as component mode mixing and white noise interference. Such an approach not only enhances the efficiency of wind speed data processing but also better accommodates strong stochastic and nonlinear characteristics. Furthermore, by employing mathematical analytical methods to compute weights for each component, a dynamic neural network model is constructed to optimize wind speed time series modeling, aiming for a more accurate prediction of wind speed fluctuations. Finally, the optimized HHT-NAR model is applied in wind speed forecasting within the Xinjiang region, demonstrating significant improvements in reducing root mean square errors and enhancing coefficient of determination. This model not only showcases theoretical innovation but also exhibits superior performance in practical applications, providing an effective predictive tool within the field of wind energy generation.

The Statutory Interpretation of Renewable Energy Based on Syllogism of British Government Constitutional Forms towards Sustainable Development Goals

The current production for energy consumption generates harmful impacts of carbon dioxide to the environment causing instability to sustainable development goals. The constitutional reforms of British Government serve to be an important means of resolving any encountered incompatibilities to political environment. This study aims to evaluate green economy using developed equation for renewable energy towards political polarization of corporate governance. The Kano Model Assessment is used to measure the equivalency of 1970 Patents Act to UK Intellectual Property tabulating the criteria for the fulfillment of sustainable development goals in respect to the environment, artificial intelligence, and dynamic dichotomy of administrative agencies and presidential restriction, as statutory interpretation development to renewable energy. The constitutional forms of British government satisfy the sustainable development goals needed to fight climate change, advocate healthy ecosystem, promote leadership of magnates, and delegate responsibilities towards green economy. The presidential partisanship must be observed to delineate parties of concerns and execute the government prescriptions in equivalence to the dichotomous relationship of technology and the environment in fulfilling the rights and privileges of all citizens. Hence, the political elites can execute corporate governance towards sustainable development of renewable energy promoting environmental parks and zero emission target of carbon dioxide discharges. The economic theory developed in statutory interpretation for renewable energy serves as a tool to reduce detrimental impacts of carbon dioxide to the environment, mitigate climate change, and produce artefacts of bioenergy and artificial intelligence promoting sustainable development. It is suggested to explore other vulnerabilities of artificial intelligence to prosper economic success.

Stakeholder mapping in sustainable renewable energy development in West Sumatra Province, Indonesia

This study investigates the players involved in developing renewable energy in West Sumatra Province, Indonesia, considering the insufficient contribution of renewable energy in 2021, which stood at 11.5% in contrast to the government’s set objectives of 23% by 2025 and 31% by 2050. The research methodology encompasses primary data gathering methods, namely interviews conducted with stakeholders who have been selected using the Delphi Stakeholders process. Additionally, secondary data will be incorporated into the study. The findings revealed a disparity in the influence and significance of stakeholders, wherein four primary stakeholders, namely the Ministry of Energy and Mineral Resources, West Sumatra Provincial Energy and Mineral Resources Agency, PLN, and Pertamina, exhibited substantial influence and high levels of importance. Conversely, most other stakeholders demonstrated relatively low levels of importance. In summary, it is imperative to enhance involvement, transparency, and collaboration to effectively tackle these disparities and foster the advancement of sustainable renewable energy initiatives within the area.

Development and Prospects of China's Renewable Energy Export Trade under the Belt and Road Policy

Under the trend of economic globalization, the cost of renewable energy development has been decreasing globally, and renewable energy development has become the focus. But since 2018 the trade war between China and the United States has been escalating, and China's renewable energy development has been hindered by the United States because China's core technology is still not fully mastered. How to deal with related issues is crucial for China's development. The article uses literature analysis to analyze the impact of the US-China trade war and China's own overcapacity problem on China's renewable energy development, and to provide countermeasures to address the corresponding problems. This paper argues that the sustainable development of China's renewable energy industry faces three basic problems, which are restricted by the trade barriers of the US-China trade war, the lack of some core technologies, and the potential overcapacity problem in the future. In order to achieve sustainable development of renewable energy in China, it is suggested seizing the opportunity of the Belt and Road policy development with energy cooperation as the key area of development. By analyzing the various factors affecting the development of China's renewable energy industry, and by giving suggestions and countermeasures, this paper hopes to give a comprehensive understanding of the development history and prospects of the renewable energy industry in China and provide reliable suggestions for the development of related industries in China.

Policy Capacity Challenges for Renewable Energy Development in West Sumatra, Indonesia

West Sumatra Province already has an energy development planning document, one of which is renewable energy development. Unfortunately, four years on, the policy’s realization has not followed existing plans. Thus, this study aims to analyze the challenges of policy capacity in developing renewable energy in West Sumatra Province. This research method is qualitative with a case study approach. Furthermore, data was collected from interviews with resource persons from various parties. The results show that low analytical capacity is the main factor affecting the formulation of evidence-based policies. Low operational capacity also hinders the effectiveness of renewable energy policy implementation. Political challenges, such as changing priorities and lack of commitment, also affect renewable energy development. Lack of transparency in policy formulation leads to public resistance to renewable energy development. The study concludes that it is crucial to strengthen policy capacity and engage actors from various sectors to achieve the goal of sustainable renewable energy development and contribute to climate change mitigation globally. This article can serve as a guide for policymakers and industry in achieving ambitious renewable energy goals in the future.

Renewable energy development efficiency: Spatial dynamic evolution and influencing factors

AbstractThis paper aims to estimate the spatial dynamic evolution of renewable energy development efficiency and justify the dimensions that impact renewable energy development efficiency. The study applies the following methods: the ultraefficient slack‐based model (SBM) (to measure the efficiency of renewable energy development); the Dagum‐Gini coefficient decomposition process (to measure the interregional differences in the development of renewable energy efficiency); nuclear density estimation (to measure the dynamic distribution); the Markov model (to forecast renewable energy development efficiency); and the Tobit model (to justify the influencing factors of renewable energy development efficiency). The empirical findings confirm that the overall regional gaps in renewable energy development efficiency in China are widening year by year. The average value of renewable energy development efficiency increased from 0.932 in 2006 to 1.078 in 2020. The mean Gini coefficient increased gradually from 0.028 in 2006 to 0.174, with mean differences exceeding the average growth trend after 2011 and slowly decreasing post‐2016. There is polarization in the eastern region, while there is no polarization in the northeast. The overall level of renewable energy development efficiency in the middle and western areas is improving and showing a trend of absolute difference narrowing. In addition, economic development, green finance, technological progress, urbanization rate, and economic openness are conducive to renewable energy development efficiency, and renewable energy development efficiency is in a rapid development trend. Considering the findings, China should implement targeted regional development strategies, enhance green finance mechanisms, promote technological innovation, and align urbanization policies with renewable energy goals to reduce regional disparities and accelerate sustainable renewable energy development.

Optimizing pumped-storage power station operation for boosting power grid absorbability to renewable energy

Optimizing peak-shaving and valley-filling (PS-VF) operation of a pumped-storage power (PSP) station has far-reaching influences on the synergies of hydropower output, power benefit, and carbon dioxide (CO2) emission reduction. However, it is a great challenge, especially considering hydro-wind-photovoltaic-biomass power inputs. This study proposed a novel optimization operation framework for a PSP station driven by the PS-VF operation for boosting power grid absorbability to renewable energy inputs. An optimization operation model based on a grasshopper optimization algorithm was developed to minimize the residual load volatility. A PSP station in the Hunan Province of China constituted the case study, and the practical operation scheme formed the benchmark. The findings underscore the effectiveness of the proposed approach in fostering remarkable synergy, evident in substantial improvement rates of 61% for power output, 58% for power benefit, and 62% for CO2 emission reduction compared to the practical operational scheme. This study not only furnishes valuable scientific and technical backing for the PS-VF operation of the PSP station, enhancing the interplay between hydropower generation, financial benefits, and CO2 reduction, but also provides policymakers with well-informed strategies that consider potential load volatilities and advantages. These strategies are geared towards enhancing the power grid's capacity to assimilate hydro-wind-photovoltaic-biomass power inputs, aligning with the goals of sustainable renewable energy development.

Development of a Procedure for Torsion Measurement Using a Fan-Shaped Distance Meter System.

Maximising the efficiency of wind turbines is crucial for sustainable development of renewable energy. In this context, monitoring and optimising rotor blade performance is becoming increasingly important, especially rotor blade deformation and torsion. We developed an approach for marker-free and contactless measurement of rotor blades during operation. Deformations of rotor blades can be recorded, with focus on torsion measurement. An innovative measuring system, named the fan-shaped distance meter system (FDMS), uses a combination of multiple laser scanners and photogrammetry. The focus of this work is to analyse the suitability of the FDMS for torsion measurement. We designed a torsion simulator to assess the achievable accuracy. Computer simulations and initial laboratory tests have demonstrated precise torsion measurements are possible using this method with an accuracy of 0.3°. Measurements can be carried out during operation of the wind turbine without the need to apply markers or sensors on rotor blades. By precisely recording the deformation and, in particular, torsion of rotor blades, targeted optimisation measures can be obtained in order to maximise performance of wind turbines. This innovative approach to measure the torsion of rotor blades in operation might offer great potential to increase the efficiency and life cycle of wind turbines.