Energy Demand Research Articles

Improving solar HVAC performance by incorporating sutterby SiO2–Cu/C3H8O2 hybrid nanofluid flow via a collocation based Gegenbauer wavelets technique: Effects of electromagnetic control and Smoluchowksi's temperature slippage

AbstractThe utilization of solar energy in heating, ventilation, and air conditioning (HVAC) systems has gained significant attention as a sustainable and environmentally friendly solution to meet the increasing energy demands. Therefore, this research work introduces a novel engineering study that explores solar‐HVAC systems. However, the study utilized hybrid nanofluids (HNFs) consisting of silicon dioxide and copper nanoparticles, with propylene glycol serving as the base fluid. Furthermore, this exploration features a magnetohydrodynamics (MHD) driven rotating flow with activation energy to enhance the heat transfer performance of solar‐HVAC systems. To increase the model novelty, 3D mathematical models were developed to characterize boundary conditions, which include the speed slippery and Smoluchowski temperature slippage. The partial differential equations in the model are transformed into ordinary differential equations (ODEs) through the use of similarity transformations. The Wavelets and Gegenbauer wavelets method in Mathematica was utilized to solve ODEs and investigate physical attributes such as plate friction, Nusselt number, Sherwood number, and mass flux. The findings show that the solar thermal radiation and magnetic field improve the thermal transfer efficiency of solar‐HVAC. Therefore, the findings of the research have practical applications in solar energy for HVAC systems and can contribute to technological improvements in the manufacturing industry.

Unravelling the molecular regulation network of carbon metabolism and lipid metabolism during seed development in Akebia trifoliata via integrated multi-omics analysis

Akebia trifoliata is a medicinal plant with high oil content and broad pharmacological effects. To investigate the regulatory mechanisms of key metabolic pathways during seed development, we conducted an integrated multi-omics analysis, including transcriptomics, proteomics, and metabolomics, exploring the dynamic changes in carbon and lipid metabolism. Metabolomics analysis revealded that glucose and sucrose levels decreased, while glycolytic intermediate phosphoenolpyruvate and fatty acids increased with seed development, indicating a shift in carbon flux towards fatty acid synthesis. Integrated transcriptomic and proteomic analyses showed that 70 days after flowering, the expression levels of genes and proteins associated with carbon and fatty acid metabolism were upregulated, suggesting an increased energy demand. Additionally, LEC2, LEC1, WRI1, FUS3, and ABI3 were identified as vital regulators of lipid synthesis. By constructing a multi-omics co-expression network, we identified hub genes such as aroE, GAPDH, KCS, TPS, and hub proteins like PGM, PDH, ENO, PFK, PK, ACCase, SAD, PLC, and OGDH that play critical regulatory roles in seed lipid synthesis. This study provides new ideas for the molecular basis of lipid synthesis in Akebia trifoliata seeds and can facilitate future research on the genetic improvement through molecular-assisted breeding.

Convolutional Neural Networks for Real Time Classification of Beehive Acoustic Patterns on Constrained Devices.

Recent research has demonstrated the effectiveness of convolutional neural networks (CNN) in assessing the health status of bee colonies by classifying acoustic patterns. However, developing a monitoring system using CNNs compared to conventional machine learning models can result in higher computation costs, greater energy demand, and longer inference times. This study examines the potential of CNN architectures in developing a monitoring system based on constrained hardware. The experimentation involved testing ten CNN architectures from the PyTorch and Torchvision libraries on single-board computers: an Nvidia Jetson Nano (NJN), a Raspberry Pi 5 (RPi5), and an Orange Pi 5 (OPi5). The CNN architectures were trained using four datasets containing spectrograms of acoustic samples of different durations (30, 10, 5, or 1 s) to analyze their impact on performance. The hyperparameter search was conducted using the Optuna framework, and the CNN models were validated using k-fold cross-validation. The inference time and power consumption were measured to compare the performance of the CNN models and the SBCs. The aim is to provide a basis for developing a monitoring system for precision applications in apiculture based on constrained devices and CNNs.

A Short term Electricity Load Forecasting for Community Residents Based on Federated Learning and Considering Privacy Protection

INTRODUCTION: As the penetration rate of renewable energy increases and patterns of energy demand evolve, fluctuations on both the supply and demand sides of electricity are becoming more pronounced. Consequently, accurate forecasting of community residential electrical loads has become crucial. OBJECTIVES: Although the widespread adoption of smart meters among residents provides abundant data for model training, strict challenges arise during the training process due to the need for privacy protection and data security. METHODS: This paper proposes a privacy-preserving community residential short-term electric load forecasting method based on federated learning. Initially, the method applies shared random masking encryption to the sensitive data of community residents, ensuring data privacy while maintaining consistency with the original data after preprocessing. Subsequently, a private data aggregation scheme is established to perform dynamic clustering of the community’s electrical load. RESULTS: The clustered model then serves as the basis for establishing individual load forecasting models for each category of community residents to predict short-term electrical loads. Finally, an empirical analysis is performed using the electrical load data from 120 households across 6 communities in a city in Southern China. CONCLUSION: The analysis demonstrates that the proposed method can achieve the prediction of community residential electrical loads without sharing residents’ data, thus verifying the effectiveness of this approach.

Analysis of differences in fossil fuel consumption in the world based on the fractal time series and complex network

Fossil fuels remain indispensable energy resources despite their non-renewable nature. Understanding the patterns of global fossil fuel consumption is essential for energy security and policy-making. This study employs complex network theory and fractal time series analysis to explore the underlying dynamics and patterns of fossil fuel consumption globally, with a focus on coal, oil, and gas consumption.The study applies the Hurst index to raw fossil fuel consumption data to identify fractal characteristics. Additionally, the visibility graph method is used to convert time series data into complex networks, allowing further analysis of consumption patterns. The study examines fossil fuel consumption in 38 countries to assess global trends and differences. The analysis reveals that global fossil fuel consumption follows a fractal time series pattern, with Hurst index values exceeding 0.9, indicating long-term memory characteristics. The application of the visibility graph method demonstrates variations in the Hurst index of degree distribution, enabling the differentiation of consumption patterns across regions. The method also uncovers distinct features of coal, oil, and gas consumption when viewed from a network perspective. The findings suggest that fossil fuel consumption has predictable long-term patterns, which are crucial for assessing future energy demands. The study highlights the importance of legislative measures to safeguard fossil fuel resources, especially for countries like China, where energy security and international competitiveness are paramount. Understanding these consumption patterns could guide future energy policies aimed at managing non-renewable resources more effectively.

Comparative analysis of direct coupling and MPPT control in standalone PV systems for solar energy optimization to meet sustainable building energy demands

Solar energy, a prominent renewable source, has reached an installed capacity of 71.78 GW in India. This research explored the load demands of the computer center at an engineering college in Tanjore, Tamil Nadu, India. The computer center at the engineering college has an annual energy requirement of 260,552 kWh/Year. Consequently, the research focused on the planning and implementation of a standalone photovoltaic (SAPV) system, assessing it against the institution's total annual energy consumption. The performance ratio and losses of the SAPV system with both direct coupling and an MPPT charge controller was compared. This comparative analysis aimed to evaluate the efficacy of two solar photovoltaic control methods—SAPV direct coupling and Maximum Power Point Tracking control—in optimizing energy harvesting from solar panels.

Empowering energy management in smart buildings: A comprehensive study on distributed energy storage systems for Sustainable consumption

The increment of photovoltaic generation in smart buildings and energy communities makes the use of energy storage systems desired to increase the self-consumption efficiency. This paper proposes and explores a model for energy storage systems management that considers local renewable generation, local demand, and retailer energy prices. The proposed model was tested at both energy community-level and the smart building level, demonstrating their capabilities of deployment. To validate the proposed model, a case study with two scenarios, including a 251 members energy community, was executed. The results demonstrate significant cost reductions for community members when adopting energy storage systems and the proposed management model. Regarding the smart building application four scenarios were tested, it is demonstrated that the demand for energy from the retailer could be set to zero during periods of time to enable its participation in demand response events. Overall, this paper contributes to the state-of-the-art by identifying and evaluating a model that manages the energy storage systems charge and discharge operation to actively reduce energy costs at the community-level (19.26%) and building level (11.75%) and to demonstrate that part of the loads can be optimized to understand if the building can be energy net-zero.

Particle swarm optimization for performance enhancement of cadmium telluride thin film solar cells by embedded nano-grating structures with a plasmonic metal coating layer

As the demand for energy in world is increasing rapidly and there is pursuit for renewable energy sources which is cheap, easy to generate and requires low maintenance, solar energy is a top contender. The world is in dire need of photovoltaic solar cells that can aid in keeping up with the hiking energy demands. However, thin film solar cells (TFSCs) which are developed for cost effectivity, suffer in performance due to reduced thickness. Therefore, this computational study uses Finite-Difference Time-Domain (FDTD) and delves into the scope of using a 1-D nano-grating structure embedded into absorbing layer of cadmium telluride TFSC to enhance the optoelectronic performance. A unique nano-grating structure with SiO2 at its core and aluminium coating aims to enhance the performance of CdTe TFSC with cost effectivity and ease of fabricability as the main motifs. Additionally, an evolutionary computational technique, Particle Swarm Optimization (PSO), was used to determine the optimal parameters of nano-gratings on CdTe TFSCs, i.e., nano-grating height, angle, duty cycle, aluminium coating thickness and grating substrate thickness. The PSO algorithm resulted in a nano-grating structure that yielded an efficiency increase of 52.86% and increase in short-circuit current density of 25.45% with respect to bare CdTe TFSCs. Subsequently, the performance of the optimal nano-grating structure was also observed to be insensitive to variation of wide range of incidence angle of light, a key feature preferred for versatile application of TFSCs.

Environmental performance of a multi-energy liquid air energy storage (LAES) system in cogeneration asset – A life cycle assessment-based comparison with lithium ion (Li-ion) battery

Increase in energy demand is shaping both developed and developing countries globally. As a result, the endeavour to reduce carbon emissions also encompasses electrical energy storage systems to ensure environmentally friendly power production and distribution. Currently, the scientific community is actively exploring and developing new storage technologies for this purpose. The focus of this work is to compare the eco-friendliness of a relatively novel technology such as liquid air energy storage (LAES) with an established storage solution such as Li-Ion battery (Li-ion). The comparison is carried out through Life Cycle Assessment (LCA) methodology which aims to assess the environmental impacts from each life stage, according to different impact categories. In particular, the study refers to the unitary storage and the delivery of electricity as well as cooling energy, considering all the inefficiencies and limits of each technology. The results show that in the full electric case study Li-ion battery environmentally outperform LAES due to (1) the higher round trip efficiency and (2) the significantly high environmental impact of the diathermic oil utilized by LAES, accounting for 92 % of the manufacture and disposal phase. Conversely, the cogeneration case study highlights that the “flexibility” and “dualism” of LAES, capable to efficiently deliver both electricity and cooling, offset the impact related to the higher electricity consumption during the use phase. Notably in energy mix frameworks with high share of primary energy source from fossil fuels, cogenerative LAES demonstrates superior environmental performance compared to Li-ion battery (i.e. 1302 kgCO2eq/MWhe vs 1140 kgCO2eq/MWhe for Singapore energy mix), attributable to its reduced electricity consumption.

Multi-swarm multi-tasking ensemble learning for multi-energy demand prediction

The increasing uptake of renewable energy sources leads to more uncertainties of energy scheduling, resulting in more difficulties of energy demand management in smart grid. Accurate forecasting plays a significant role in energy management, and in some cases it may involve predictions of different consumers or geographic regions. Traditionally, these prediction problems have been solved independently, ignoring the potential shared knowledge among them that may help facilitate the overall performance. In this manuscript, we propose a multi-swarm multi-tasking ensemble learning (MSMTEL) framework for solving the energy demand forecasting across multiple cities. The proposed method comprises single-task pretraining, multi-task optimization (MTO), and ensemble learning (EL). For each prediction task, several subtasks are generated based on predefined parameters in the forecasting model. Each subtask utilizes an independent deep neural network (DNN) as the predictor, which is pretrained individually. Subsequently, we modify the dynamic multi-swarm particle swarm optimization (DMS-PSO) as a customized multi-swarm PSO (CMS-PSO) algorithm for implementing MTO. Each subswarm in CMS-PSO focuses on finding the optimal model knowledge (pretrained DNN weights and biases) of source tasks (all subtasks) to be reused by the target subtask. Finally, instead of choosing the best forecasting among the subtasks, results of subtasks over the same energy prediction problem are combined to yield the EL result, wherein weight coefficients determining the contributions of subtasks to EL are optimized by PSO. MSMTEL is evaluated against single-task learning (STL) and MTO to demonstrate its superior performance.

Characterization of Municipal Solid Waste Based on Seasonal Diversity as Energy Potential Materials

Abstract Interrelated problems related to resource availability and population growth rates, such as energy demand and waste generation, have become pressing global concerns. Municipal Solid Waste (MSW) poses a significant challenge due to its impact on society and the environment, including the associated hazards and threats. This study was conducted at the Piyungan Integrated Santary Landfill (TPST Piyungan) in Yogyakarta, Indonesia, from June to November 2022. The annual waste generation in the city is 586,267.37 tons per year in 2021, with a daily per capita waste generation rate of 0.43 kg/person. Two types of MSW samples were collected during the dry and rainy season to assess their composition. The collected samples were sorted, sun-dried, quartered, and reduced to 1mm particles for proximate analysis. The results of MSW characterization revealed that non-organic combustible materials accounted for a higher percentage during the dry season (62.7%) compared to the rainy season (53.1%). Conversely, organic matter constituted a higher proportion during the rainy season (36.7%) compared to the dry season (30.4%). The moisture content ranged from 4 to 11%, volatile matter content varied between 60% and 66%, and fixed carbon content ranged from 19 to 31%. The energy content of MSW during the dry season was measured at 20.67 MJ/kg, while in the rainy season, it yielded 14.99 MJ/kg. These findings indicate that due to the substantial energy content and waste generation rates, waste-to-energy (WtE) technologies can be effectively applied to manage MSW in Yogyakarta, simultaneously reducing waste volumes and recovering energy.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

Modeling of Energy Usage Intensity Based on West Java Region Conditions Using Dynamic Systems Approach

Abstract West Java is a region in Indonesia experiencing significant population growth and economic activity, leading to a substantial increase in energy demand and presenting challenges for sustainable energy resource management. Energy is fundamental to economic and social activities, and efficient, sustainable management is essential for balancing economic growth with environmental preservation. Modeling energy usage intensity is crucial for understanding the dynamics of energy consumption and its influencing factors. This study aims to develop a model that accurately represents energy consumption patterns in West Java using a dynamic systems approach. The study utilizes Causal Loop Diagrams (CLD) and Stock Flow Diagrams (SFD) to systematically map and model the intricate dynamics of the energy system, providing a comprehensive understanding of the interactions and influences among various factors. The model was validated by comparing its outputs with historical data and calculating a Mean Absolute Percentage Error (MAPE). This enhances confidence in the analyses and recommendations derived from the model, making it a valuable tool for understanding and influencing energy usage patterns in West Java. In conclusion, this study not only provides deep insights into energy consumption in West Java but also offers practical approaches for improved energy management, supporting sustainable development and environmental protection in the region.

ANN-based Maximum Power Point Tracking Technique for PV Power Management under Variable Conditions

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.

Large-scale energy storage for carbon neutrality: thermal energy storage for electrical vehicles

AbstractThermal Energy Storage (TES) systems are pivotal in advancing net-zero energy transitions, particularly in the energy sector, which is a major contributor to climate change due to carbon emissions. In electrical vehicles (EVs), TES systems enhance battery performance and regulate cabin temperatures, thus improving energy efficiency and extending vehicle range. The enhanced efficiency reduces overall energy consumption in EVs. Consequently, this reduction in energy demand can lead to decreased infrastructure needs, minimising the scale and investment required in energy production and distribution systems. Furthermore, the integration of TES with existing infrastructure allows for the simultaneous charging of thermal and electrical energy, leveraging waste heat or renewable energy sources. This not only cuts costs by optimizing resource use but also bolsters sustainability by minimising reliance on non-renewable energy sources. The widespread adoption of TES in EVs could transform these vehicles into nodes within large-scale, distributed energy storage systems, thus supporting smart grid operations and enhancing energy security. Strategic investments and regulatory updates are essential to realise a sustainable, carbon-neutral transportation future, underpinned by robust, cost-efficient infrastructure.

Predictive building energy management with user feedback in the loop

Retrofitting buildings with predictive control strategies can reduce their energy demand and improve thermal comfort by considering their thermal inertia and future weather conditions. A key challenge is minimizing additional infrastructure, such as sensors and actuators, while ensuring user comfort at all times. This study focuses on retrofitting with intelligent software, incorporating the users’ feedback directly into the control loop. We propose a predictive control strategy using an optimisation-based energy management system (EMS) to control thermal zones in an office building. It uses a physically motivated grey-box model to predict and adjust thermal demand, with individual zones modelled using an RC-approach and parameter estimation handled by an unscented Kalman filter (UKF). This reduces deployment effort as the parameters are learned from historical data. The objective function ensures user comfort, penalises undesirable behaviour and minimises heating and cooling costs. An internal comfort model, automatically calibrated with user feedback by another UKF, further improves system performance. The practical case study is an office building at the ”Innovation District Inffeld”. Operation of the system for one year yielded significant results compared to conventional control. Thermal comfort was improved by 12% and thermal energy consumption for heating and cooling was reduced by about 35%.

A hybrid renewable energy system for Hassi Messaoud region of Algeria: modeling and optimal sizing

The growing global energy demand and the need to mitigate greenhouse gas emissions have driven the exploration of sustainable and efficient energy solutions. In Algeria, where the energy sector relies heavily on fossil fuels, integrating renewable energy systems is essential for enhancing energy security and reducing environmental impacts. This study focuses on optimizing a hybrid renewable energy system (HRES) for off-grid applications in the Hassi Messaoud region of Algeria to balance technical performance, economic viability, and environmental sustainability.A hybrid system consisting of photovoltaic (PV) panels, wind turbines (WT), fuel cells (FC), and diesel generators (DG) was modeled and optimized using a genetic algorithm (GA). The optimization process aims to minimize the annual cost of the system while ensuring high reliability, as measured by the loss of power supply probability (LPSP), and maximizing the use of renewable energy. A particle swarm optimization (PSO) approach was also implemented for comparison, highlighting the advantages of the GA in terms of cost distribution and system reliability. The optimized HRES demonstrated that renewable sources (PV and WT) provided 77% of the total energy demand, with an overall system cost of 0.18080$×kWh−1, significantly lower than recent studies, which reported costs between $0.213 and 0.609$×kWh−1. FCs contributed 14% to the load, whereas DGs were limited to 8% to minimize emissions, resulting in annual CO2 emissions of 10,865 kg and a relative emission rate of 3.608 gCO2eq×kWh−1. Economic analysis showed that DGs and FCs accounted for 44% and 24% of the annual cost, respectively, highlighting the impact of backup systems in ensuring reliability.Sensitivity analysis under varying load demands and renewable energy availability confirmed the robustness of the system, and the GA approach was found to be more effective than PSO in maintaining cost efficiency and reliability. Additionally, the social analysis highlighted a renewable fraction (RF) of 91.5%, emphasizing the contribution of the system to sustainable energy practices. These findings validate GA-based optimization as a superior method for designing cost-effective, reliable, and environmentally sustainable HRES, offering significant potential to reduce fossil fuel dependency in industrial applications. These results not only support the broader adoption of renewable energy systems in similar regions but also contribute valuable insights for future research and policy development in the field of energy sustainability.

Retrofitting of the Italian precast industrial building stock. LCA analysis as decision-making tool

The challenging European goal concerns the achievement of a carbon-free economy by 2050 must necessarily consider the improvement of the construction sector along with the redevelopment of the existing buildings. In fact, according to a European report concerning existing buildings, 80% of the built environment will remain the same by 2050. Nowadays buildings are responsible for 30% of the global final energy consumption and 37% of global energy and process emissions. As regards industrial buildings, they account for 33% of global final energy demand. The research aims to define some effective retrofitting measures to be applied in the existing Italian industrial facilities to improve their both energy and environmental performance. Several redevelopment scenarios and their possible combinations related to external envelope technological solutions and systems are analysed. The different interventions are applied to an existing Italian industrial building chosen as a case study and are compared by Life Cycle Assessment (LCA) analysis considering different environmental indexes (GWP, AP, PERT and PENRT). The calculation of the primary energy demand is also performed. Phase B6 accounts for about 80% of the whole environmental impact calculated considering 20 years as building lifetime. The redevelopment scenarios that foresee the intervention on the external envelope are the most impactful ones in terms of the production phase (A1-A3). Those related to the existing roof are characterised by a significant impact due to the demolition, disposal and landfill of finishing panels made of asbestos. The configurations that include the substitution of the existing heating system with an air-to-air heat pump coupled with renewables to produce electrical energy on-site are the most advantageous ones. The latter make the industrial building carbon-zero thanks to the considerable amount of surplus electrical energy produced on-site. In this case, phase D mostly recovers the environmental impact of the whole life cycle considering the different indexes.

An overview on building-integrated photovoltaics: technological solutions, modeling, and control

The advancement of renewable and sustainable energy generation technologies has been driven by environment-related issues, energy independence, and high costs of fossil fuels. Building-integrated photovoltaic systems have been demonstrated to be a viable technology for the generation of renewable power, with the potential to assist buildings in meeting their energy demands. This work reviews the current status of novel PV technologies, including bifacial solar cells and semi-transparent solar cells. This review discusses the various constructions of PV technologies, recent advances in these products, the influence of key design factors on electrical and thermal performance, and their potential in the design of energy-efficient smart buildings. The attention is focused on bifacial and semi-transparent PV systems, given the high level of interest of the scientific community in their current and potential applications.Focus is also devoted to the analysis of the electrical, optical, and thermal modeling procedures developed for sizing, designing, and integrating photovoltaics into larger building simulations. The development of these models has a positive impact on the implementation of next-generation smart buildings. The latest innovative developments and key issues in the application of bifacial PV solutions in buildings are also summarized and analyzed. Special attention is paid to rear side electrical performance, which can be evaluated by means of illuminance/optical backside modeling. Finally, energy management and control of PV-equipped buildings via both model-based and data-driven approaches are discussed, as well as the integration of electric storage systems in a multi-building context.

Fiscal decentralisation and renewable energy development: Inhibition or promotion?

In line with global green development and decarbonisation initiatives, China has prioritized renewable energy advancement in recent decades. Given the critical role of financial support, scholarly attention has increasingly focused on the impact of fiscal decentralisation (FD) within this sector. This study investigates FD's effect on renewable energy development, analyzing data from 30 Chinese provinces between 2008 and 2022. Findings reveal that FD tends to impede renewable energy progress. Specifically, a one-standard-deviation increase in FD leads to a decrease of 23.1775 % of a standard deviation in renewable energy development. However, this hindrance can be mitigated by foreign direct investment and central transfer payments, while research and development intensity exacerbates the negative impact. Notably, regions with limited natural resources, weaker environmental regulations, fiscal transparency, high marketisation, energy demand, and vertical fiscal imbalances experience a more pronounced decentralisation-renewable-energy relationship. Anti-corruption campaigns in China can alleviate but not reverse this effect. Furthermore, the diffusion of technology and cooperative efforts in nearby regions positively influence renewable energy development. Nonetheless, overall effects still turn out negative due to competition among local governments under the FD framework. Based on these findings, policy recommendations for accelerating renewable energy development are summarized.