Development Of Energy Systems Research Articles

Experimental investigation of a hybrid photovoltaic-thermal energy system for hot air production

Solar energy as a non-fossil alternative energy source has become the best choice to overcome the problem of energy demand in most countries in the world. There are two different techniques to convert solar energy: photovoltaic (PV) panels to produce electricity and thermal collectors to generate heat. The two technologies can be combined to provide electrical and thermal energy either simultaneously or separately. In order to optimize the performance of a hybrid photovoltaic-thermal (PVT) solar air heater, it is necessary to collect experimental data on solar irradiation and temperature. This paper emphasized the development of a PVT energy system for hot air production in a temperature range of 50-55 °C. Additionally, experiments were constructed to monitor the information acquired from the proposed PVT solar air heater and the environment, such as hot air temperature, ambient temperature, and solar irradiation. The real-time monitoring system was set for five sample days. A microcontroller unit was used to control the hot air temperature and save the measurement data into memory. The experimental results showed that the proposed PVT solar air heater is capable of maintaining a certain level of hot air temperature throughout the day and night.

Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events

Because the global climate change intensifies as well as the natural disasters frequently occur, extreme events have caused serious impacts on the energy system in urban areas, and at the same time, they have brought great challenges to the supply and scheduling of urban energy systems. Therefore, in order to better integrate and manage various energy resources in urban areas, a Deep Q-Leaning Network-Quasi Upper Confidence Bound model is innovatively constructed using deep reinforcement learning technology to learn the state and behavior mapping relationship of energy system. Use deep learning to fit complex nonlinear models to optimize the entire energy system. Compare and verify the experiment with the real energy system. The improved Deep reinforcement learning algorithm is compared with Q-learning model, PDWoLF PHC algorithm model, Quasi Upper Confidence Bound algorithm model and deep Q-Leaning Network algorithm model. The results show that the research algorithm has the smallest instantaneous error value and absolute value of frequency deviation for area control, and the average value of the research algorithm in the absolute value of the frequency deviation is reduced by 45%–73% compared to other algorithms; over time, the unit output power of the research algorithm is able to flexibly track the stochastic square wave loads. Therefore, the proposed system strategies can provide feasible solutions to meet the challenges of extreme events and promote the sustainable development and safe operation of urban energy systems.

Pumped-storage hydropower and hydrogen storage for meeting water and energy demand through a hybrid renewable energy system

AbstractThe majority of the Greek islands have autonomous energy stations, which use fossil fuels to produce electricity in order to meet electricity demand. Also, the water in the network is not fit for consumption. In this paper, the potential development of a hybrid renewable energy system is examined to address the issue of generating drinking water (desalination) and electricity while releasing zero pollutants into the atmosphere. Wind turbines supply wind energy, while an additional amount of energy is stored using pumped-storage hydropower and green hydrogen tanks. These two storage options are investigated for the purpose of storing and distributing clean wind energy in a controlled manner. Three scenarios are investigated. The first scenario only relies on the pumped-storage hydroelectricity technology (88% of the total annual power demand is covered), the second scenario investigates hydrogen storage technology (83% of the total annual electricity demand is covered), and the third scenario uses a hybrid storage solution consisting of pumped-storage hydropower and green hydrogen tanks (95% coverage).

Exploring the dynamics of urban energy efficiency in China: A double machine learning analysis of green finance influence

Analysing the impact of green finance policies on urban energy efficiency contributes to finding solutions to promote the development of China's low-carbon energy system and the global reduction of greenhouse gas emissions. Utilizing Chinese urban data from 2006 to 2021, the study employs an enhanced non-radial distance function (NDDF) model to evaluate the energy efficiency of 282 prefecture-level cities. It examines the 'Green Finance Reform and Innovation Pilot Zone' policy as a quasi-natural experiment. It uses a double machine learning model to analyze its effect on urban energy efficiency and its mechanisms. The research demonstrates that green finance policies significantly improve energy efficiency, mainly through strengthening environmental regulation, fostering green technology innovation, and optimizing industrial structure. Additionally, an in-depth analysis of regional heterogeneity reveals variations in the policy's effectiveness across eastern, central, and western regions and between resource-rich and resource-poor cities. The findings suggest that the efficacy of green finance policies, while generally positive, is influenced by regional economic development and resource endowment differences. The study suggests targeted strategies to improve the green finance system, customize policies according to regional specifics, incentivize corporate green innovation, and foster cross-sectoral collaboration. By offering novel insights into sustainable development and providing actionable policy recommendations, this research contributes to advancing Chinese urban centres' economic and environmental sustainability through the strategic application of green finance.

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs.

Demand flexibility and its impact on a PEM fuel cell-based integrated energy supply system with humidity control

The development of efficient energy systems and renewable alternative energy sources has attracted substantial attention owing to the rise in environmental deterioration and energy usage. A combined cooling, heating, and power (CCHP) system can fully use different types of energy with low emissions and offers a viable solution to address these issues. Demand flexibility is a key issue in collaborating with different types of supplies and demands for optimal system operation. In this study, a proton-exchange membrane fuel cell driven CCHP system with humidity control is proposed to fully investigate the impact of demand flexibility on system operation. A novel flexible load regulation strategy was designed for the proposed system. The results show that flexible load regulation can reduce the thermal storage capacity by 25%. Supplement heating and cooling do not need operate in winter and summer. With flexible regulation, the heating load can be reduced by 15.69% in winter, and the cooling and dehumidification loads can be reduced by 30.71% and 6.09% in summer. The reductions in hydrogen consumption can reach 1.98% and 7.51% during winter and summer. The proposed method can simultaneously increase the exergy efficiency and reduce carbon emission, as well as the economy cost.

An artificial intelligence study on energy, exergy, and environmental aspects of upcycling face mask waste to a hydrogen-rich syngas through a thermal conversion process

Studies in the field of gasification process for face mask wastes play a significant role in addressing the environmental issues associated with these wastes and harnessing their potential as an important energy source. On the other hand, machine learning techniques offer a promising approach to predict the environmental, energy, and exergy performances of the gasification process. This study addressed the utilization of machine learning techniques on the gasification process of face mask wastes. Various polynomial regression machine learning algorithms are developed for syngas lower heating value, exergy and cold gas efficiencies, and emission level and their performances are checked. Remarkable precision of machine learning algorithms in forecasting the response variables is evidenced by the impressive outcomes. In fact, the majority of cases yield R-sq values surpassing 98%, thus highlighting their effectiveness. Specifically, when it comes to exergy and cold gas efficiencies, and emission, the machine learning algorithms achieve exceptional R-sq values of 99.85%, 98.93%, and 99.33%, respectively. These exceptional results confirm the credibility and accuracy of these algorithms in providing precise predictions. Leveraging the power of artificial intelligence not only contributes to the development of more sustainable energy systems but also helps in making informed decisions regarding process design and operation.

LVRT Enhancement of DFIG-based WECS using SVPWM-based Inverter Control

Abstract: Across many countries, wind turbine generation systems (WTGS) have been established over the past few decades. In this paper, we augment the low voltage ride-through (LVRT) enrichment facility of driving a DFIG-based wind energy conversion system (WECS) using space vector pulse width modulation (SVPWM)-based inverter control. The proposed technique employs an SVPWM-based control algorithm to regulate the voltage and frequency of the output power during grid faults, thereby enhancing the WECS's ability to remain connected to the grid and provide power. The study focuses on decreasing transient current throughout the instant of fault. Modeling and control approaches were also discussed in this study. The performance of the proposed technique is evaluated using MATLAB/Simulink simulations, and the results demonstrate that the technique effectively improves the LVRT capability of the DFIG-based WECS. Background: Due to the variation in wind speed, the power generated by wind turbines is inconsistent. The power generated and the losses in wind turbines change correspondingly with changes in wind speed. The only type of machine that can generate power at speeds below the fixed speed is the doubly-fed induction generator (DFIG). But DFIG is oversensitive to network faults, which makes the bidirectional converters and DC link capacitor fail due to high inrush current and over-voltage. Methods: The converters connected to DFIG consist of an AC-to-DC converter, a boost converter, and a space vector pulse width modulation (SVPWM)-based DC-AC converter. The performance of the SVPWM controller is analyzed during symmetrical and unsymmetrical fault conditions. Results: The anticipated control provides adequate reactive power support to the network through the time of the fault and improves voltage and current waveform. The reactive power flow is also analyzed, and the effectiveness of the proposed controller is verified using MATLAB and Simulink. Conclusion: SVPWM (Space Vector Pulse Width Modulation)-based inverter control is an effective technique for wind energy conversion systems (WECS). The use of SVPWM can provide accurate and precise control of the AC voltage generated from the DC voltage source, resulting in improved system efficiency and reduced harmonic distortion in the output waveform. : The comparative analysis of THD suggests that SVPWM is a superior technique compared to other inverter control techniques such as sine-triangle pulse width modulation (SPWM) and carrier-based pulse width modulation (CPWM). SVPWM can help to reduce the distortion in the output waveform, leading to improved system efficiency, reduced wear on the system components, and overall better performance of the WECS. Furthermore, SVPWM offers several advantages over other inverter control techniques, including better utilization of DC voltage, improved voltage control, and better utilization of switching devices. These advantages make SVPWM a valuable tool for optimizing the operation of WECS and improving the reliability and performance of renewable energy systems. The value of THD for SVPWM inverter control in WECS is 1.53 under symmetrical fault and 1.34 for unsymmetrical fault, respectively. In summary, the use of SVPWM-based inverter control for WECS is an effective way to improve the efficiency and performance of the system while reducing the distortion in the output waveform and providing adequate reactive power support. The advantages of SVPWM over other inverter control techniques make it a valuable tool for the development and optimization of renewable energy systems.

Hydrogen production by water electrolysis driven by a photovoltaic source: A review.

The integration of water electrolyzers and photovoltaic (PV) solar technology is a potential development in renewable energy systems, offering new avenues for sustainable energy generation and storage. This coupling consists of using PV-generated electricity to power water electrolysis, breaking down water molecules into hydrogen and oxygen. While oxygen is a useful byproduct, the created hydrogen is used as a clean, storable energy carrier or feedstock for numerous businesses. It is possible to operate the device with or without battery storage. When solar energy is combined with batteries, excess solar energy may be stored for later use, maximizing energy efficiency and guaranteeing a steady supply of electricity even in the absence of direct sunlight. On the other hand, battery-free systems depend on the electrolyzer’s continuous power generation to convert solar energy into hydrogen during the day. In addition to allowing for the production of renewable hydrogen,this hybrid PV-solar and water electrolyzer setup contributes to grid stability by offering demand-side flexibility. Moreover, the modularity of these systems enables scalability to meet diverse energy requirements, spanning from residential to industrial applications, thereby fostering a cleaner and more sustainable energy landscape. This review delves into various topologies for PV-driven electrolysis and conducts a thorough exploration of the dynamics of low-temperature water electrolyzers. Specifically, it examines their integration with three primary technologies: Proton Exchange Membrane, Alkaline, and Anion Exchange Membrane, shedding light on their implications for the broader integration landscape. Through detailed analysis and insights, this study enriches the understanding of the potential and challenges inherent in the convergence of PV solar, water electrolysis, and renewable energy systems.

An Efficient and Interpretable Stacked Model for Wind Speed Estimation Based on Ensemble Learning Algorithms

Wind energy has gained tremendous attention recently due to its abundance potential and to obviate the adverse impacts of fossil fuel consumption. However, the efficient development and operation of wind energy systems require precise and accurate wind speed prediction. To fulfill this aim, machine learning algorithms have shown its prowess in accurately predicting the wind speed and this study proposes an interpretable stacked ensemble with ridge regressor utilizing ML algorithms such as catboost (CB), gradient boost (GB), multilayer perceptron (MLP), and random forest (RF) as a base model. The base models’ estimations are integrated by applying a ridge regressor to enhance the accuracy of wind speed estimation. The dataset consists of 87 600 samples from which 80% samples are used for training. The proposed approach provides interpretability of the prediction results through Shapley additive explanations and feature importance analysis. The result of the proposed method is calculated by comparing the estimation outcomes with each model such as CB, GB, MLP, and RF in terms of error metrics such as root mean squared error (RMSE) and mean absolute error (MAE). The proposed model outperforms the others due to a reduction of error in RMSE by 19.89% and MAE by 24% respectively.

Characteristic Investigation of a Novel Aircraft SOFC/GT Hybrid System under Varying Operational Parameters

In this study, the implementation of a solid oxide fuel cell–gas turbine hybrid engine for primary propulsion and electric power generation in aircraft is investigated. The following three parameters, which are crucial in attaining optimal performance at any point in the flight profile, were identified: the oxygen-to-carbon ratio of the catalytic partial oxidation reformer, the fuel utilization factor of the fuel cell, and the airflow split ratio at the outlet of the high-pressure compressor. The study assesses the impact of varying these parameters within specified ranges on the performance of the hybrid system. At the design point, the system yielded a total power output of 1.96 MW, with 102.5 kW of electric power coming from the fuel cell and 7.9 kN (1.86 MW) of thrust power coming from the gas turbine. The results indicate that varying the oxygen-to-carbon ratio affected the fuel cell’s fuel utilization and resulted in a slight decrease in gas turbine thrust. The fuel utilization factor primarily affected the power output of the fuel cell stack, with a minor impact on thrust. Notably, varying the airflow split ratio showed the most significant influence on the overall system performance. This analysis provides insights into the system’s sensitivities and contributes to the development of more sustainable aircraft energy systems.

Energy efficiency security in urban areas: Challenges and implementation

In this paper, we explore the transformative potential of data-driven methodologies in the intricate landscape of urban energy transactions, with a keen focus on enhancing decision-making intelligence, amplifying the integration of renewable energy sources, and fostering active community engagement. A pivotal contribution to this research lies in the introduction of an innovative Intrusion Detection System (IDS) fortified by the robust capabilities of deep evolving convolutional Generative Adversarial Networks (GANs). This novel security measure is designed to fortify urban energy transactions against potential cyber threats, ensuring the integrity and reliability of the energy infrastructure. Furthermore, our study introduces a groundbreaking hybridization of the Bat Algorithm (BA) and Teaching-Learning-Based Optimization (TLBO) for the optimization of GAN model training. This fusion of optimization techniques enhances the efficiency and accuracy of the training process, elevating the reliability of the generated models within the context of urban energy management. To validate and demonstrate the practical applicability of these innovations, we deploy them on the real-time dataset of a smart city's digital twin. This implementation serves as a tangible proof of concept, offering valuable insights into the performance and adaptability of the proposed approaches within real-world urban energy scenarios. As the findings unfold, it becomes evident that our research significantly contributes to the evolution of urban energy management, providing a robust foundation for the development of secure, resilient, and community-centric smart city energy systems. Through these advancements, we pave the way for a sustainable urban future where energy transactions are not only efficient but also secure and inclusive, aligning with the broader goals of smart city development.

Development and performance assessment of a novel multigeneration clean energy system based on biomass-LNG using a dynamic model and supervised learning algorithms

Combining process integration mechanisms with artificial intelligence (AI) can be used as a tool for maximum use of biomass capacity and energy production. This study proposes a continuous multi-generation hybrid energy production system based on biomass-LNG using the carbon capture, utilization, and storage (CCUS) method. In this system, using the Gibbs free energy minimization approach, a process based on the fluidized bed gasifier was designed, which in addition to producing hydrogen and electricity from biomass, also produces liquid CO2 and precipitated calcium carbonate (PCC) from liquid natural gas and limestone for the first time. This process was also examined from thermal (HHV) and thermodynamic (Exergy) standpoints. The simulation of the gasification unit was able to predict the percentage of exhaust gases from the gasifier with errors of RMSE = 3.8 and RMSE = 3.2 for almond shell and rice shell biomass, respectively. Following that, a dataset with more than 14 million records was generated. Then, machine learning methods were employed to investigate and improve the process's simulation characteristics. The evaluation of 15 machine learning algorithms in the construction of the predictive model showed that ANN, Extra Tree Regressor, and Random Forest are the most accurate algorithms in predicting the desired values (such as hydrogen, work, PCC, liquid CO2, exergy, and HHV) with R2 > 0.99. The dataset analysis showed that the current process could produce 130 kg/h of hydrogen, 375 kWh of work, 2600 kg/h of PCC, and 531 kg/h of liquid carbon dioxide on average. As a final step, a WPF desktop application was developed that simplifies the use of ML models so that the impact of changing the variables can easily be calculated.

An integrated energy system day-ahead scheduling method based on an improved dynamic time warping algorithm

With the construction and development of the new energy system, the integrated energy system (IES) has garnered significant attention as a crucial energy carrier in recent years. Therefore, to address the scheduling challenges of IES influenced by uncertainty in source load and mitigate the conservatism of scheduling schemes while enhancing clustering accuracy, a method for day-ahead top-note scheduling of IES is proposed. First, by improving dynamic time warping (DTW) for hierarchical clustering of wind, solar, and load data in IES, typical scenarios of IES are derived. Second, using the interval method to model wind, solar, and load data in IES along with their coupled devices and considering the conservatism issue of interval optimization, the established IES interval model undergoes affine processing. Finally, with the goal of minimizing the operating costs of IES, a day-ahead interval affine scheduling model is established, which is solved using the CPLEX Solver and INTLAB toolbox, and scheduling schemes for all typical scenarios are provided. Through comparative analysis of calculation examples, it is found that the method proposed in this paper can enhance clustering accuracy and reduce the conservatism of system scheduling schemes.

Active Fault-Tolerant and Attack-Resilient Control for a Renewable Microgrid Against Power-Loss Faults and Data Integrity Attacks.

The next-generation power grid evolves from the development of fundamental cyber-physical energy systems called smart microgrids. In order to improve the reliability, safety, and security of smart microgrids and achieve a more cost-effective operation, innovative approaches for physical fault diagnosis and fault-tolerant control (FTC) as well as intrusion detection and attack-resilient control (ARC) should be investigated. Given that, this article considers a smart hybrid renewable-based microgrid with different types of distributed generation units, including solar photovoltaic (PV) array, wind turbines, and battery energy storage system. Novel active FTC and ARC strategies are designed for pulse-width modulation (PWM) converters at microgrid level. The proposed fault-tolerant controller is based on an optimal fuzzy gain-scheduling technique that is used to accommodate the adverse impacts of PV power-loss faults. Also, the proposed attack-resilient controller relies on the estimated values of sensor measurements during the occurrence of data integrity cyber-attacks. To access and evaluate the microgrid's real-time health status, both FTC and ARC strategies employ an integrated model-based intrusion detection and fault diagnosis (IDFD) system that is designed using a fuzzy modeling and identification technique. Finally, the effectiveness of the proposed solutions is demonstrated via a series of simulations in MATLAB/Simulink using an advanced microgrid benchmark.

Energy innovation funding and social equity: Mediating role of just energy transition

Public investments in research, development, demonstration, and deployment in energy systems are crucial for accelerating the adoption of new energy technologies that can support a low-carbon just energy transition and promote the fair and equitable distribution of benefits and costs, improving social equity. However, the impact of energy innovation funding on social equity through progressing the just energy transition has not been explored in prior research for advanced economies. Thus, this research quantitively examines how energy innovation funding affects social equity in 23 developed economies for the period of 1995–2020. Employing a fixed effect, augmented mean group, and a system-generalized method of moments technique along with a mediating model, this study finds that increasing energy innovation funding significantly improves social equity directly as well as indirectly via accelerating energy justice in advanced economies. Therefore, proper allocation and utilization of public energy innovation budgets are necessary to promote clean energy technologies, advance the just energy transition, and improve social equity, inclusion, and community engagement. This study emphasizes the importance of policy interventions that promote a just energy transition and energy innovation funding to build resilient and inclusive energy systems that improve social equity while achieving Sustainable Development Goal 7, affordable and clean energy.

Entropy and energy transfer analysis of a Maxwell thin‐film fluid over an inclined surface with viscous dissipation effect

AbstractNon‐Newtonian fluid plays a vital role in the field of manufacturing and engineering sector, because of its immense heat transfer rate. The two‐dimensional incompressible unsteady Maxwell nanofluid thin‐film flow with MHD and viscous dissipation over an inclined surface is investigated. One of the mechanisms in the second law of thermodynamics, that is irreversibility which also analyzed. The Maxwell nanofluid thin film energy and motion equations in the additional information have been turned into a coupled differential system of the third order. The transformed ODE equations are further evaluated by using the Homotopy Analysis Method (HAM). The variations of entropy rate, velocity, Bejan number and temperature field with the various emerging parameters are analyzed. This could potentially lead to the development of solar energy systems that are both affordable and highly efficient, so enabling the more efficient use of renewable energy sources and reducing our dependence on fossil fuels.

Exploring the effects of TiO2${\rm TiO}_2$–Ag/water hybrid nanofluid on MHD flow over a permeable exponentially stretching–shrinking sheet with radiative heat transfer and slip conditions

AbstractThe objective of this article is to study the importance of velocity and thermal slip in the exponentially stretching or shrinking sheet with the megnetohydrodynamic (MHD) hybrid nanofluid flow along with magnetic field, radiative, heat generation effects. This study has practical importance in enhancing heat transfer efficiency, improving energy conversion and optimizing thermal management systems. The findings can also contribute to the development of sustainable energy systems and aid in assessing the stability and safety of nanofluid applications. The governing partial differential equations are transformed into nonlinear ordinary differential equations using similarity transformation. The ODEs are solved using bvp4c solver in MATLAB. The dual solutions are achieved for specific range of the stretching or shrinking parameter and mass suction parameter. The first solution is physically significant after considering stability analysis. The hybrid nanofluid has numerous real‐life and industrial applications, such as microelectronics, manufacturing, naval structures, nuclear system cooling, biomedical, and drug reduction. The skin‐friction increases but Nusselt number decreases over the stretching sheet whereas the opposite effect shows over shrinking sheet for the both solution with the increasing velocity slip parameter. On the other hand, The Nusselt number decreases over the shrinking or stretching sheet for both solutions with the increasing in the thermal slip parameter. The skin friction increases over shrinking sheet but decreases over stretching sheet for the first solution with increasing silver volume fraction parameter and porosity parameter (). Enhancing magnetic field parameter leads to the skin friction increases over the shrinking sheet but decreases over stretching sheet but Nusselt number shows opposite trend. The Nusselt number decreases over shrinking sheet but increases over stretching sheet for the first solution with increasing porosity parameter (). The Nusselt number decreases over shrinking or stretching sheet as the increases heat generation () and variable thermal conductivity parameter () but Nusselt number shows opposite trend as the increases radiative parameter ().

Development of a new renewable energy system for clean hydrogen and ethanol production

The present research work aims to present a uniquely designed renewable energy-based integrated system along with an equilibrium model for the processing of feedstock by following a hybrid route of thermochemical and biochemical ways. In this regard, Canadian maple leaves and plastic wastes are selected as potential feedstocks for co-pyrolysis and syngas fermentation. The influence of co-pyrolysis process parameters on the overall system performance is investigated and assessed. Also, several sensitivity analyses are performed to determine the optimal operating parameters that can generate maximum yields of hydrogen and ethanol. The present system is further investigated thermodynamically in terms of energetic and exergetic approaches and efficiencies. The present study shows that a molar flow ratio of 1:1 for maple leaves to plastic wastes, a temperature of 1000°C temperature, and a pressure of 1 bar appear to be the most suitable operating conditions with the net production capacities of 7.43 tons/day for hydrogen and 8.72 tons/day for ethanol. The cold gas efficiency and LHV of the syngas produced are found to be 57.23% and 19.96 MJ/kg, respectively. The overall energetic and exergetic efficiencies of the present system are found to be 30.98% and 26.88%, respectively.

Blockchain-Enabled Integrated Energy System Trading Model for CCS-P2G-Coupled Operation: Enhancing Energy Trading Efficiency and Carbon Emission Reduction

The need for sustainable integrated energy systems to mitigate environmental impact is hindered by challenges in fluctuating demand, trading reliability, and trustworthiness. This paper proposes an innovative approach to tackle these challenges by introducing a blockchain-based integrated energy system trading model with smart contracts. It is intricately linked with the operation of carbon capture and storage (CCS) technology and power-to-gas (P2G) equipment. The CCS-P2G-coupled operation principle is first outlined, followed by the presentation of a comprehensive system model. The peer-to-peer (P2P) energy trading algorithm is enhanced using the Bloom filtering technique. Leveraging smart contracts, a distributed energy trading mechanism is employed, resulting in a meticulously constructed integrated energy system trading model for CCS-P2G-coupled operation. The proposed model is rigorously evaluated for energy trading efficiency and system performance, revealing significant improvements compared to prior studies and showcasing substantial cumulative benefits. Three operation scenarios are examined, with experimental results highlighting the model’s superior carbon emission reduction capacity. This study introduces an innovative paradigm for trading and managing integrated energy systems, holding potential implications for the sustainable development and decarbonization transition of future energy systems.