Renewable Energy Research Articles

Load Frequency Control Design for Complex Power Systems Implementing Integral Single-Phase Sliding Mode Control

This paper proposes a Decentralized Integral Single-phase Sliding Mode Control (DISSMC) for Load Frequency Control (LFC) in Complex Power Systems with Multi-Source generation (CPSMS), integrating reheat, hydro, gas, and wind turbines. A generalized structure is employed to model Multi-Area Linked Power Systems (MALPS), providing a realistic representation of diverse power plants. The proposed method formulates an integral Sliding Surface (SS) to mitigate the chattering phenomena and ensures finite-time stability through a continuous control law. Additionally, a single-phase technique eliminates the reaching phase, ensuring immediate trajectory control. The MATLAB/Simulink simulations validate the DISSMC's effectiveness in stabilizing frequency fluctuations and managing load variations across three interconnected regions, each hosting different power plant types. The results highlight the proposed controller's robustness and adaptability to dynamic load and renewable energy source fluctuations.

Pengaruh Penambahan Doping Cerium terhadap Nilai Bandgap CuO dengan Metode Sol-Gel

Copper(II) oxide (CuO) has been recognized as a promising semiconductor material for various applications, such as photocatalysis, sensors, and renewable energy devices. However, its efficiency is often limited by a suboptimal bandgap value. This study aims to analyze the effect of cerium doping on the properties of CuO nanoparticles synthesized through the sol-gel process. The sol-gel method ensures a homogeneous doping distribution and produces nanoparticles with a stable structure. The bandgap energy of CuO nanoparticles was determined through characterization using UV-DRS. Cerium was introduced as a dopant in CuO at a concentration of 0.4 mmol, resulting in a bandgap value of 1.26 eV, whereas undoped CuO exhibited a bandgap of 1.35 eV. The analysis indicates that the Ce doping concentration significantly affects the bandgap of CuO nanoparticles, with a reduction observed at 0.4 mmol compared to undoped CuO. This decrease is attributed to symmetry disruption caused by doping, including oxygen vacancies, structural defects, and the presence of impurities that create additional energy levels within the bandgap. Furthermore, uniform microstrain and a smaller particle size contribute to structural disturbances that also influence the bandgap.

Design and optimization of distributed energy management system based on edge computing and machine learning

With the continuous growth of global energy demand and the rapid development of renewable energy, traditional energy management systems are facing enormous challenges, especially in the scheduling and optimization of distributed energy. In order to meet these challenges, edge computing and machine learning technology are widely used in the design and optimization of distributed energy management systems. This paper proposes a design scheme of distributed energy management system based on edge computing and machine learning, and optimizes it. The system reduces data transmission latency and improves energy scheduling efficiency by performing real-time data processing and analysis on edge devices. The experimental results show that the proposed system performs outstandingly in optimizing energy allocation, reducing energy consumption, and improving system response speed. Specifically, by using machine learning algorithms for dynamic scheduling of distributed energy resources, the system can achieve an energy utilization rate 12% higher than traditional scheduling methods, and reduce energy waste by 18% in the event of fluctuations in energy demand. In addition, the system response time has been improved by 30% compared to traditional cloud-based solutions. These optimizations not only reduce energy costs, but also effectively enhance the sustainability and intelligence level of distributed energy systems. The contribution of this research lies in the combination of edge computing and machine learning technology to achieve real-time optimal control of the distributed energy system, reduce the system’s computing load and delay, and improve the accuracy and flexibility of energy management through data-driven methods. Future research can further explore how to integrate multiple machine learning algorithms to optimize energy scheduling strategies and improve the system’s adaptability in complex environments.

Improving photovoltaic water pumping system performance with ANN-based direct torque control using real-time simulation

Photovoltaic Water Pumping Systems (PVWPS) have become increasingly important as a renewable energy solution in rural areas, providing energy independence, cost savings, and environmental friendliness. This system has two main controllers. The first controller is employed to maximize power extraction from the PV array by controlling the duty ratio of the DC-DC boost converter. The second controller is responsible for regulating the operation of the induction motor through the switching pulses of the Voltage Source Inverter (VSI). These two controllers play an essential role in the system, which increases efficiency and performance. Therefore, the innovative aspect of this work consists of introducing Artificial Neural Networks (ANNs) based on each PVWPS controller. On the one hand, ANN-based MPPT is implemented to ensure optimal performance of the PV array under varying irradiation levels. On the other hand, to overcome the defects and problems caused by Direct Torque Control (DTC), such as flux and torque ripples, high switching frequency, and challenges at low speeds, an ANN-based DTC is proposed in which each of the hysteresis comparators, switching table, and speed controller in the DTC are replaced by ANN controllers. The PVWPS based on the proposed controls is thoroughly modeled and simulated using MATLAB/Simulink software and validated using dSPACE DS1104 Board. The results demonstrate significant improvements, including a 75.51% reduction in flux ripples, a 77.5% reduction in torque ripples, a 44.79% improvement in response time, and an increase in the water quantity. Furthermore, the Real-Time simulation and visualization obtained are consistent with the simulation outcomes.

Modeling and Optimization of Hydropower Plant Operations in the Northern Vietnam Power System by 2030

The Northern Vietnam Power System (NVPS) has a long history, with over a century and a quarter of development. The NVPS has experienced a significant expansion in its total power generation capacity. In 1954, the total capacity was 31.5 MW. By the end of 2023, the total capacity of the NVPS reached 29,537 MW, with hydropower accounting for 9,764 MW, representing 33.06%. In 2023, the NVPS generated 90.380 billion kWh of electricity. Over the past decade, the annual growth rate of commercial electricity has averaged between 10% and 12%. Northern Vietnam is endowed with vast hydropower potential, particularly across its four major river basins: the theoretical maximum capacity of the Bang Giang-Ky Cung, Red River, Ma River, and Ca River basins is 20,598 MW. Hydropower plays a significant role in ensuring energy security due to its low production cost, capacity to rapidly meet peak demand, and status as a clean energy source. This study proposes an optimal operational model for hydropower plants in the NVPS for the period from 2025 to 2030. The results demonstrate that hydropower will continue to be crucial for meeting the growing energy demand while minimizing operational costs. Additionally, it supports the integration of renewable energy sources into the power grid, thereby underscoring the strategic importance of hydropower in maintaining system stability and promoting sustainable development in the region.

Application of the PSI Method in Selecting Sustainable Energy Development Technologies

The development of renewable energy is not only an urgent solution for addressing climate change but also a driving force for sustainable economic growth. The transition to clean, inexhaustible energy sources not only helps to reduce greenhouse gas emissions and protect the environment but also ensures national energy security, creates employment opportunities, and enhances the quality of life for individuals. Presently, various technologies exist for sustainable energy development, each characterized by multiple criteria, complicating the evaluation of their performance. This study presents a straightforward method for identifying the best option among eight sustainable energy development alternatives: hydropower, geothermal, biomass, wind, solar, concentrated solar power, coal technology, and oil-fired power plants, each of which is characterized by 17 distinct criteria. The simple method utilized is the Preference Selection Index (PSI) method, which eliminates the need for criteria weighting. This absence of criteria weight calculation in the PSI method distinguishes it from other ranking techniques that typically require such calculations. Therefore, the PSI method significantly simplifies the comparison of the available options compared to other ranking methods, as it bypasses the need for criteria weight calculations. The optimal option identified through the PSI method was also compared with the optimal option identified using 6 other methods: Multi Atributive Ideal Real Com parative Analysis (MAIRCA), Evaluation Based on Distance from Average Solution (EDAS), COmplex PRroportional ASsessment (COPRAPS), Multiobjective Optimization On the basis of Ratio Analysis (MOORA), Proximity Indexed Value (PIV), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Notably, all employed methods consistently identified geothermal energy as the optimal choice.

Greenhouse Gases and their Role in Air Pollution and Global Warming

Today, one of the most significant global challenges is the increase in climate change due to the excessive emission of greenhouse gases. Carbon dioxide gas, resulting from the combustion of fossil fuels, and methane are recognized as the primary greenhouse gases and the foremost contributors to climate change. Population density, increased vehicular traffic, industrial factories, and neglect of environmental concerns are major factors influencing the concentration of greenhouse gases in the atmosphere. Recent global studies indicate that since the onset of the Industrial Revolution—a period marked by a significant rise in fossil fuel consumption—human activity has played a crucial role in the process of climate change and global warming through the production and emission of greenhouse gases. Understanding how these types of pollution evolve requires attention to the various factors affecting their emission. Accordingly, this study collects and examines data obtained from library-based research using a descriptive-analytical method. Consequences of the greenhouse effect include flooding, reduction in potable water and agricultural products, increased soil erosion, the extinction of some plant and animal species, and the migration of certain population groups. These consequences underscore the necessity and importance of focusing on the use of renewable energy sources.

Paper Waste Co-Digestion, Combined Milling, and Hydrothermal Pretreatment for Improved Biogas Production

This research, conducted in two experiments, focused on the effect of co-digestion and combined hydrothermal with milling pretreatment of paper waste (PW) and chicken manure (CM) on biogas production. In the first experiment, three reactors labeled B-D contained varying ratios of the feedstock (4:1, 3:2, 1:1) with control reactors A and E respectively containing milled paper waste (MPW) and chicken manure (CM) alone were prepared and assessed for biogas production. In the second experiment, the optimum co-digested mixture (reactor D) with a ratio of 1:1 and cumulative biogas volume (633.5mL/gVS) was selected from the first experiment and duplicated (reactor F) for the hydrothermal pretreatment. All digesters were operated at a temperature of 45oC for 32 days. The results revealed that reactor F, which contained the hydrothermally pretreated paper waste, showed a significant cumulative biogas yield of 1095.5 mL/gVS which was 1.7 times higher, surpassing control reactor D with a yield of 633.5 mL/gVS. Bacterial isolates present in the substrates and inoculum belonged to the following genera: Staphylococcus (50%), Lactobacillus (22.2%), Micrococcus (16.7%), and Bacillus (11.1%). The research outcome showed that co-digestion combined with hydrothermal pretreatment method led to enhanced biogas production. These results highlight how creative pretreatment methods can optimize waste-to-energy operations, supporting both renewable energy production and sustainable waste management.

Direct Load Control Strategy of Centralized Chiller Plants for Emergency Demand Response: A Field Experiment

As the penetration rate of renewable energy in the power grid increases, the imbalance between power supply and demand has become one of the key issues. Buildings and their heating, ventilation, and air conditioning (HVAC) systems are considered excellent flexible demand response (DR) resources that can reduce peak loads to alleviate operational pressures on the power grid. Centralized chiller plants are regarded as flexible resources with large capacity and rapid adjustability. The direct load control of chiller plants can respond to the power grid within minutes, making them highly suitable for participation in emergency DR. However, existing studies are generally based on simulations and lack experimental research in actual large-scale buildings to demonstrate the effectiveness of this method and provide related lessons learned. This study conducted field experiments on a centralized chiller plant within an industrial building in Guangdong, China. The results indicate that the strategy of shutting down chiller plants is an effective DR measure. It can complete the load reduction process within 15 min, rapidly decreasing the system power by 380~459 kW, with a maximum duration of up to 50 min, without significantly affecting the thermal comfort of indoor occupants. Additionally, the impact of existing control logic on the participation of chiller plants in the DR process is also discussed.

The Impact of Integrating Variable Renewable Energy Sources into Grid-Connected Power Systems: Challenges, Mitigation Strategies, and Prospects

Although the impact of integrating solar and wind sources into the power system has been studied in the past, the chaos caused by wind and solar energy generation has not yet had broader mitigation solutions notwithstanding their rapid deployment. Many research efforts in using prediction models have developed real-time monitoring of variability and machine learning predictive algorithms in contrast to the conventional methods of studying variability. This study focused on the causes and types of variability, challenges, and mitigation strategies used to minimize variability in grids worldwide. A summary of the top ten cases of countries that have successfully managed variability in their electrical power grids has been presented. Review shows that most of the success cases embraced advanced energy storage, grid upgrading, and flexible energy mix as key technological and economic strategies. A seven-point conceptual framework involving all energy stakeholders for managing variability in power system networks and increasing variable renewable energy (VRE)-grid integration has been proposed. Long-duration energy storage, virtual power plants (VPPs), smart grid infrastructure, cross-border interconnection, power-to-X, and grid flexibility are the key takeaways in achieving a reliable, resilient, and stable grid. This review provides a useful summary of up-to-date research information for researchers and industries investing in a renewable energy-intensive grid.

ICT-Driven Strategies for Enhancing Energy Efficiency in G20 Economies: Moderating the Role of Governance in Achieving Environmental Sustainability

Achieving environmental sustainability has become a global priority, with energy efficiency (EE) emerging as a critical pathway. This study examines the influence of information and communication technology service exports (ICT) on EE by integrating the moderating role of regulatory quality. We employ a super-slack-based measure (Super-SBM) and generalized least squares models in G20 economies throughout 2001–2023. The findings show that the average EE is 0.855, which indicates a potential for further improvement of 14.50%. The findings further show that ICT is positively related to EE, and regulatory quality delivers a conducive environment for the adoption of technologies to optimize energy usage. The findings also indicate a synergistic effect between ICT and regulatory quality, which can lead to substantial improvements in EE, emphasizing the importance of governance in facilitating technological advancements. The findings highlight the role of renewable energy and economic openness in shaping EE. Furthermore, Argentina and South Africa achieved the highest EE, reflecting their proximity to the efficient frontier. In robust tests, this study verifies its results using the generalized method of moments, panel-corrected standard error, and feasible generalized least squares models. The findings suggest that ICT and governance perspectives can provide valuable insights for policymakers aiming to enhance energy sustainability through digital transformation and institutional reforms.

A2AlInI6 (A = K, Rb, Cs) Double Perovskite Halides for Renewable Energy Applications: A DFT Study on Stability, Light Absorption, and Thermoelectric Performance

A2AlInI6 (A = K, Rb, Cs) Double Perovskite Halides for Renewable Energy Applications: A DFT Study on Stability, Light Absorption, and Thermoelectric Performance

In Search of "Fertile Ground": How Territorial Characteristics Influence the Social Acceptability of Renewable Energy Projects.

Renewable energy (RE) projects are vital to achieving sustainable development in the context of the pressing challenges of climate change and the energy transition. However, despite the global consensus on their importance, RE projects often encounter significant resistance at the local level. This article investigates how territorial characteristics influence the social acceptability of RE projects and explores the concept of fertile ground as a framework for understanding the conditions that foster or hinder local support for such projects. Drawing on theories of regional path dependency and leadership agency, we identify the historical, socioeconomic, cultural and governance factors shaping local attitudes towards RE projects. By employing qualitative methods, including semi-structured interviews with diverse stakeholders and the analysis of press articles, we develop a nuanced approach that incorporates key territorial dynamics. Our findings reveal the critical role of historical industrial conflicts, political leadership, place attachment and perceptions of fairness in shaping local acceptability. They also highlight the influence of habituation and saturation effects, which shows that the acceptability of RE projects is not static; rather, it evolves over time in response to local conditions and stakeholder engagement. By situating the fertile ground concept within broader frameworks of social acceptability, this study provides actionable insights to policymakers and project developers seeking to align RE initiatives with local contexts and values.

Decision optimization in the linked electricity market at multi-timescale: Integrating coupled tradable green certificate and green power trading.

Achieving the national climate target would depend on national actions. China has implemented important market mechanisms for a green and low-carbon energy transition, including the Renewable Portfolio Standard (RPS), the Tradable Green Certificate (TGC) market, the green power trading market, and so on. However, how to effectively integrate coupled TGC and green power trading to achieve a balance between maximizing economic benefits and environmental friendliness remains to be explored. Therefore, this study extends prior research by establishing a bi-level decision optimization model to explore market participants' decision making from the perspective of energy supply and economic value, and analyzes the impact of the RPS, TGC price, and the penetration of renewable energy (RE) in the electricity market, the green power market, and the trading strategy of market participants in a multi-market equilibrium state. The feasibility and effectiveness of the bi-level decision optimization model and algorithm are verified using the example of IEEE14 node and historical data of Elia Energy. The results show that: (1) Under the market equilibrium condition, the clearing price in the day-ahead RE market is equal to the clearing price in the day-ahead traditional energy market plus the TGC price. (2) When green power participates in the spot market, there is a complementary relationship between RE generation and traditional energy generation. (3) A shift from decreasing to increasing costs for electricity consumers when RE penetration is above a certain threshold (α>30%) and the TGC price is 100 CNY/MWh, and a decrease in the growth rate of RE generators' profits when RE penetration is above a certain threshold (α>35%). (4) Traditional energy generators with small installed capacities adopt riskier market behaviors to declare more electricity and try to obtain higher profit in market transactions. These insights can make up for the research gap of decision optimization in multi-timescale electricity market, achieve energy optimization allocation and environmental sustainability.

Advancing Renewable Energy Transition for Economic Sustainability: Insights from Global Practices and Local Adaptation Strategies

Renewable energy in Ukraine accounts for 9 per cent. Renewable energy is planned to be developed in the post-war reconstruction. The Swedish experience is suitable for this purpose and it is advisable to apply it. The biggest problem is the conversion of old enterprises to renewable energy sources. The article presents a methodology for assessing the possibility of transition to renewable energy sources Keywords: energy, renewable energy, economic sustainability, energy system energy strategy

Renewable Energy and Socio-Economic Transformation: Three Case Studies

Portugal’s renewable energy transition plays a pivotal role in addressing climate change, enhancing energy security, and promoting sustainable development. This study examines the socio-economic impacts of renewable energy policies in Portugal from 2014 to 2022, with a focus on financial performance, employment trends, and gender equity. Utilizing financial assessments from the Orbis database and input–output analysis, the research examines three companies—EDP, E-Redes, and Logical Gravity—classified under NACE Rev. 2 code 3513. The study finds that investments in renewable energy improved profitability and solvency, especially during recovery phases, reflecting the sector’s financial resilience. Larger firms like EDP experienced employment growth, while smaller firms faced more volatility, highlighting the need for targeted support. Despite progress, gender disparities and pay gaps persist, underscoring the need for gender-sensitive policies. This research provides valuable insights for policymakers seeking to ensure a just and sustainable energy transition, emphasizing strategic investments, workforce adaptation, and inclusivity.

The role of taxation in environmental sustainability in G-20 economies: A double dividend theoretical assessment.

This study analyses the influence of environmental taxes, renewable energy, economic growth, green innovation and financial development on environmental sustainability in G-20 countries from 1990 to 2022. To this end, the Method of Moments Quantile Regression (MMQR) was applied to obtain the reference results, complemented with Fully Modified Ordinary Least Square (FMOLS) and Driscoll-Kraay techniques to perform a comparative analysis. Our results confirm a negative relationship between environmental taxes and sustainability in all quantiles, although this relationship is only significant in the middle and upper quantiles. Furthermore, it is evident that economic growth significantly improves environmental sustainability, supporting the "double dividend" hypothesis, which argues that revenues generated by environmental taxes can be used to finance tax reductions in other areas while contributing to the regulation of environmental degradation. Our findings also show that renewable energy and green innovations play a key role in improving environmental sustainability, underlining the relevance of such variables as fundamental pillars for the fulfilment of the Sustainable Development Goals (SDGs), in particular SDG-7. On the other hand, a positive relationship between financial development and environmental sustainability is identified in the lower quantiles. In contrast, in the upper quantiles, this relationship becomes negative, although not significant. These findings are consistent with the robustness tests performed, which incorporate the use of FMOLS and Driscoll-Kraay standard error estimators.

Chinese power structure in 2050 considering energy storage and demand response under high renewable power penetration ratio.

Energy storage and demand response offer critical flexibility to support the integration of intermittent renewable energy and ensure the stable operation of the power system. Using the ERA5 dataset and hourly power load data, this study develops an hourly-based dynamic optimization model to assess the roles of energy storage and demand response in Chinese (2050) power structure under high renewable energy penetration. The results revea; that: (1) Energy storage and demand response significantly contribute to reducing power transition cost, carbon emission, and power curtailment. Specifically, 2h storage duratin and 10% demand response capacity are found to reduce transition costs by 6.07 trillion CNY, carbon emissions by 11.38 billion tons, and annual power curtailment by 2.21 PWh. (2) By 2050, Chinese power structure will be dominated by wind and PV, with installed capacity exceeding 7000GW. Regional differences will be evident, and energy storage will account for 8%-20% of the total installed renewable power capacity, while facilitating the retirement of over 250GW of thermal power plants. (3) Complex substitution effects between energy storage, demand response, CCS, and renewable energy are observed, varying according to the duration of energy storage and the capacity of transmission lines across regions. (4) The operational mechanisms of energy storage and demand response align closely with PV generation patterns, showing high utilization from Feb to May. In contrast, thermal power generation and CCS mainly complement renewable power generation during the peak power demand period of Jul to Sep.

The effect of digital technology innovation on low carbon investment in renewable energy enterprises

This study focuses on examining the impact of digital technology innovation (DIT) on low carbon investment(LCI) in renewable energy enterprises, and empirically tests the sample of listed renewable energy companies to obtain the following conclusions. Firstly, DIT significantly promotes the LCI of listed renewable energy companies. Secondly, DIT will suppress LCI by increasing production costs, and promote LCI by promoting environmental information disclosure and internal control management quality. Thirdly, DIT has an inverted U-shaped impact on LCI, with an impact that is first promoted and then suppressed. Fourthly, in terms of heterogeneity. DIT has a stronger promoting effect on the LCI of large-scale enterprises, state-owned enterprises, and eastern enterprises. Fifthly, intellectual property protection, digital infrastructure construction, and government's digital orientation will all promote the positive impact of DIT on LCI. This study aims to provide insights on digital construction for promoting LCI in renewable energy enterprises.

From pollution to sustainable environment: unlocking the role of energy intensity and renewable energy in highly pollutant nations

Abstract Environmental degradation is among one of the most critical issues of recent times. To address this critical challenge in highly pollutant nations, this research inquires about the role of Energy Intensity (EI) along with Renewable Energy Consumption (REC) and non-REC as controlling indicators in the carbon emissions’ model. The research is significant because it provides potential information to energy policymakers for mitigating the adverse effects of environmental degradation. The Cross-Sectional Dependent (CSD) test is utilized to authenticate the CSD in the variables and the Slope Heterogeneity (SH) test is applied to verify the heterogeneous relationships across the polluted economies. Moreover, CSD-based unit root tests are applied to confirm the stationarity of the series. The CSD-based cointegration test is applied, which cares for both CSD and SH in analysis. Lastly, CSD-based regressions are applied to find the long and short relationships. A period of 1990–2021 is utilized for data analyses as per data availability of all analyzed countries. The results demonstrate significantly increasing effects of EI on emissions leading to mitigated environmental quality. A 1% increase in EI increases emissions by 0.6227%. However, renewable energy significantly mitigates carbon emissions while non-REC is found to significantly escalate carbon emissions. A 1% increase in REC mitigates emissions by 0.2458% and a 1% increase in non-REC raises emissions by 0.65%. This research further confirms the evidence of the Environmental Kuznets Curve (EKC) with an income elasticity of 13.1393 and income square elasticity of -0.6209. The results of EI, REC, and non-REC are similar in all estimations while the validity of the EKC is witnessed in two out of three models. The causal relation suggests feedback between emissions and economic progress, EI, REC, and non-REC, which highlights the interdependence of the energy sector and environmental outcomes. This research suggests that governments of the investigated polluted economies should impose taxes on non-renewable energy consumption. The revenues from these taxes should be utilized to subsidize renewable energy consumption. Both initiatives would help reduce energy intensity in these polluted economies to condense the environmental consequences of the energy sector.