Sustainable Development Of Energy Systems Research Articles

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.

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 multi-input multi-output converter for decarbonization energy system

Decarbonization energy systems include a range of renewable energy sources, such as solar, wind, hydro, and geothermal. These sources generate electricity with minimal carbon emissions and can be used in various applications, including power generation, heating, and transportation. Therefore, the use of a multi-input multi-output (MIMO) converter is becoming increasingly important in decarbonization energy systems. This is because the integration of multiple renewable energy sources, requires a power converter that can manage multiple inputs and outputs efficiently. This paper presents the development of a MIMO converter using a single-phase matrix converter for decarbonization energy systems. The proposed converter is capable of integrating multiple renewable energy sources and can be used in a microgrid application, thus removing the need of multiple converters. The design and analysis of the converter are presented, including the selection of power switches and control strategy. The performance of the converter is evaluated through experimental results, demonstrating its effectiveness in managing multiple inputs and outputs using a single power converter circuit. The results indicate that the proposed converter is a promising solution for decarbonization energy systems, contributing to the development of sustainable energy systems.

Parametric and sensitive analysis of Pd-Ag membrane reactor performance in biogas reforming to generate decarbonized hydrogen by Computational Fluid Dynamic-Response Surface Methodology

The efficient production of hydrogen from renewable sources is pivotal for the development of sustainable energy systems. Biogas steam reforming (BSR) conducted in palladium-based membrane reactors (MRs) offers a pathway for high-purity H2 generation at lower temperatures compared to traditional processes. In this study, a computational fluid dynamics (CFD) model was developed to simulate the BSR process within a Pd-Ag MR. The CFD model has integrated comprehensive kinetics, mass transport, and hydrodynamics, and its accuracy was confirmed through validation against experimental data, demonstrating substantial agreement. As a novel approach, to optimize the BSR reaction conditions to get the best MR performance, the CFD model was coupled with response surface methodology (RSM), which has been employed to enable the identification of optimal values for key parameters such as reaction temperature, pressure, gas hour space velocity, feed molar ratio, and sweep gas ratio, where the temperature was found to have the most significant impact on the MR’s performance within the examined intervals of input parameters. The objective of RSM method applied to CFD modeling was to maximize with high accuracy CH4 conversion, H2 recovery, and reduce CO2 emissions. RSM models effectively established correlations between input parameters and responses. The determined optimal conditions for the BSR process were a temperature of 683 K, a reaction pressure of 6 bar, a GHSV of 3000 h−1, a H2O/CH4 ratio of 1.34, and a sweep ratio of 9. Under these conditions, the system achieved remarkable results with 100 % CH4 conversion, 43 % H2 recovery, and an 81 % reduction in CO2 emissions. This study underscores the effectiveness of integrating CFD with RSM for the precise modeling and optimization of MRs in BSR reactions. The synergy between these approaches provides a robust framework for advancing the efficiency and sustainability of hydrogen production processes from renewable sources.

Renewable energy resources utilization planning for sustainable energy system development on a stand-alone island

The potential of energy resources on islands that can be used for self-sufficiency and sustainable energy supply and demand systems has led to a challenge in energy planning in these areas. Due to Iran's dependence on fossil fuels, there are several challenges and concerns related to the reduction of fossil fuel reserves. Therefore, in order to transition from non-renewable to renewable energy sources, the maximum potential of the country should be utilized, which is one of the most promising areas of the islands. As a result, this study aims to provide approaches that reduce dependence on renewable energy sources in a short period of time and to create the conditions for the implementation of a 100 % renewable energy system. First of all, the basic energy supply system for the island in 2020 will be modeled in the EnergyPLAN software. As a next step, two different scenarios are presented for combining new renewable technologies with fossil fuels to meet the demands for electricity, heating, cooling, and transportation in 2030. As a result of this research, it was determined that by utilizing the potential of local renewable resources, the consumption of fossil fuels would be reduced by 36 %. Also, applying policies for the use of renewable resources can reduce carbon dioxide emissions by 0.7 million tons per year. Further, in the technical analysis of the scenarios, it was found that reducing the consumption of fossil fuels in the field of transportation was the most challenging aspect of achieving the goal of 100 % renewable islands.

Ultra-short-term solar forecasting with reduced pre-acquired data considering optimal heuristic configurations of deep neural networks

<abstract> <p>Forecasting solar irradiance, particularly Global Horizontal Irradiance (GHI), has drawn much interest recently due to the rising demand for renewable energy sources. Many works have been proposed in the literature to forecast GHI by incorporating weather or environmental variables. Nevertheless, the expensive cost of the weather station hinders obtaining meteorological data, posing challenges in generating accurate forecasting models. Therefore, this work addresses this issue by developing a framework to reliably forecast the values of GHI even if meteorological data are unavailable or unreliable. It achieves this by leveraging lag observations of GHI values and applying feature extraction capabilities of the deep learning models. An ultra-short-term GHI forecast model is proposed using the Convolution Neural Network (CNN) algorithm, considering optimal heuristic configurations. In addition, to assess the efficacy of the proposed model, sensitivity analysis of different input variables of historical GHI observations is examined, and its performance is compared with other commonly used forecasting algorithm models over different forecasting horizons of 5, 15, and 30 minutes. A case study is carried out, and the model is trained and tested utilizing real GHI data from solar data located in Riyadh, Saudi Arabia. Results reveal the importance of employing historical GHI data in providing precise forecasting outcomes. The developed CNN-based model outperformed in ultra-short-term forecasting, showcasing average root mean square error results across different forecasting horizons: 2.262 W/m<sup>2</sup> (5min), 30.569 W/m<sup>2</sup> (15min), and 54.244 W/m<sup>2</sup> (30min) across varied day types. Finally, the findings of this research can permit GHI to be integrated into the power grid and encourage the development of sustainable energy systems.</p> </abstract>

Robust Optimization of Large-Scale Wind–Solar Storage Renewable Energy Systems Considering Hybrid Storage Multi-Energy Synergy

With the rapid integration of renewable energy sources, such as wind and solar, multiple types of energy storage technologies have been widely used to improve renewable energy generation and promote the development of sustainable energy systems. Energy storage can provide fast response and regulation capabilities, but multiple types of energy storage involve different energy conversion relationships. How to fully utilize the advantages of multiple energy storage and coordinate the multi-energy complementarity of multiple energy storage is the key to maintaining a stable operation of the power system. To this end, this paper proposes a robust optimization method for large-scale wind–solar storage systems considering hybrid storage multi-energy synergy. Firstly, the robust operation model of large-scale wind–solar storage systems considering hybrid energy storage is built. Secondly, the column constraint generation (CCG) algorithm is adopted to transform the original problem into a two-stage master problem and sub-problem for solving to obtain the optimal strategy of system operation with robustness. Finally, the validity of the proposed method is verified through case tests. The results show that the proposed method can effectively coordinate the multi-energy complementary and coordinated operation of multiple hybrid energy storage, and the obtained operation strategy of large-scale wind–solar storage systems can well balance the economy and robustness of the system.

The Optimization of Steam Generation in a Biomass-Fired Micro-Cogeneration Prototype Operating on a Modified Rankine Cycle

According to the United Nations, one of the sustainable development goals is to ensure access to affordable, reliable, sustainable, and modern energy for all. Among other options, these goals can be achieved by developing and introducing micro-scale combined heat and power systems powered by renewable energy sources, including solar and biomass energy. Considering renewable energy-powered cogeneration technologies, the most promising are steam/vapor turbines, Stirling engines, and thermoelectric generators. This paper focuses on the selected operational aspects and retrofitting optimization of the prototypical micro-cogeneration system powered by a biomass-fired batch boiler and operating according to the modified Rankine cycle. The existing installation was tested, and the amount of energy transferred from the oil to the condensate and steam and the efficiency of the evaporator and the superheater were determined. A retrofitting optimization aimed at maximizing the piston engine’s power output was conducted based on the results. In particular, it was shown that the system’s power output might be as high as 9 kWe. Moreover, the analyzed system featured a high energy utilization factor of 97.9% at optimal operating conditions. In general, it was shown that the micro-scale steam Rankine system may successfully serve as an alternative technology for micro- and distributed cogeneration systems. As a technology supplied with renewable biomass energy and operating on a cheap and environmentally friendly working medium (water), it fits very well into the idea of sustainable energy system development.

Sustainable development pathways for energies in Yangtze River Delta urban agglomeration

The sustainable development of urban agglomerations plays a pivotal role in national and global efforts to reduce emissions. By focusing on the efficient exchange and optimization of energy consumption across various sectors, the sustainable development of energy systems within urban agglomerations can be achieved. However, the overall impact of the cross-sector energy optimization and complementarity has not been quantitatively analyzed. Here, we focused on the Yangtze River Delta (YRD) urban agglomeration in China and proposed an optimization framework for energy, environment, and economy. The framework considered four sectors: transportation sector, power sector, industry sector, and building sector, in order to determine the most sustainable development pathway for the urban agglomeration. The optimization model considers total costs and greenhouse gas emissions reduction as the objectives and utilizes technologies as constraints to optimize the pathway. We found that this optimization strategy resulted in a 53.1 billion tons increase in CO2 emissions reduction in the region. The results of emission reduction varied across sectors, ranging from 4.5 to 22.2 billion tons CO2 equivalent, and across cities, ranging from 7.1 to 4688.1 Mt. The results suggest that the core cities in the urban agglomeration can take on a leadership role. By promoting cross-sector collaboration and implementing energy recycling, the energy efficiency of surrounding cities can be greatly improved, leading to the sustainable development of the urban agglomeration.

A multidimensional factorial enviro-economic model: Approaches of retrospective decomposition and prospective projection for energy systems

Understanding the patterns of energy usage through simulation and prediction from multiple perspectives is crucial for alleviating energy-environmental conflicts and achieving sustainable energy system development. Despite their importance, multidimensional interactions among the production, demand, and supply sides have not been systematically characterized, thus representing a significant research gap. This paper aims to address this gap by developing a a multidimensional factorial enviro-economic (MFEE) model, which offers an in-depth investigation of Guangdong's energy system. This model, designed for retrospective decomposition (1997–2017) and prospective projections (2035), uniquely links historical and future analysis from systematic and sectoral perspectives through structural decomposition analysis, sectoral evolution analysis, and the Biproportional Scaling RAS method. Our findings reveal that the final demand level and Research & development (R & D) are the principal drivers of increased energy usage. We also found that the wholesale and retail sector have the most significant impact on demand and input sides, accounting for over 70 %. Furthermore, the interaction of key sectors from the production-based perspective notably influences system symbiosis. To effectively manage these dynamics, our study recommends regulating and controlling measures for the circulation sector that consider the benefits of both the energy sector and the accommodation and catering sector. This comprehensive analysis, therefore, offers scientifically-supported guidance for sustainable energy system development.

Research on the Dynamic Characteristics of Photovoltaic Power Production and Sales Based on Game Theory

This paper mainly studies the dynamic characteristics of production and sales of distributed photovoltaic power. This is beneficial for the construction and development of a green power system, and it promotes the sustainable development of the social economy. First, the production and sales laws of the photovoltaic power are analyzed, and the trade process between photovoltaic power producers (PVPs) and photovoltaic power demanders (PVDs) is modeled as a game. Different from the existing relevant literature, two kinds of payoff bimatrices are provided, which correspond to the PVD market and PVP market, respectively. Then, the dynamic characteristics of the model are revealed by using the semitensor product method. The results present a more reliable theoretical basis for the sustainable development of the green electricity market. Finally, through an illustrative example, it can be seen that the strategies of all participants are constantly changing to obtain more profits rather than stable at a traditional Nash equilibrium point. It is worth pointing out that the method and results are applicable to other distributed low-carbon energies, contributing to the development of sustainable energy systems.

Numerical and thermal analysis of a caloric refrigeration device operating near room temperature

The application of external stimuli such as the magnetic and electric field in magnetocaloric and electrocaloric materials, and stress and pressure in elastocaloric and barocaloric materials give rise to a new generation of a refrigeration technology based on caloric materials which are considered an emerging alternative to classical refrigeration. Active caloric regenerator (ACR) made in parallel plates is studied under a large number of materials with Comsol multiphysics for a 2D numerical model. In this work, we compare various types of caloric materials, in terms of their thermodynamic properties, working mechanisms, and potential applications as solid refrigerant on caloric refrigeration devices. For this purpose, the energy equation, Navier-Stocks equation, and continuity equation are considered to study the heat transfer phenomena in refrigerator. The water was used as a carrier fluid to transport the thermal energy from the solid refrigerants to heat exchanger. This study is performed at velocity 0.06 m/s and the frequency 2 Hz at room temperature. Among them, Gadolinium show the best results in term temperature span, coefficient of performance, and the cooling power, higher than every other caloric materials, conferring to magnetocaloric cooling globally the most promising system. Our analysis provides insights into the selection and optimization of caloric materials for caloric refrigeration, which can contribute to the development of sustainable energy systems.

Sustainable Intelligent Energy Management System for Microgrid Using Multi-Agent Systems: A Case Study

In this paper, a sustainable, intelligent energy management system for a microgrid based on a multi-agent system (MAS) is studied. The system is designed to address the challenges posed by the intermittence of renewable energy sources. Also, the system optimizes the use of available AC–DC renewable energy sources by utilizing load flexibility and the complementarity of renewable sources. To evaluate the effectiveness of this proposed multi-agent framework, a co-simulation using MATLAB and JADE platforms is conducted for a microgrid connected to the main grid. The results show that the proposed energy sharing system achieved over 82.34% of energy savings. This innovative solution has the potential to reduce the need for energy storage and improve energy efficiency while also reducing CO2 emissions. It offers a promising sustainable development solution for managing and controlling MG. The proposed system contributes to the development of sustainable energy systems based on artificial intelligence to meet the global goals.

Applications of Energy Storage Systems in Enhancing Energy Management and Access in Microgrids: A Review

As the world’s population continues to grow and the demand for energy increases, there is an urgent need for sustainable and efficient energy systems. Renewable energy sources, such as wind and solar power, have the potential to play a significant role in meeting this demand, but their intermittency can make integration into existing energy systems a challenge. Moreover, the development of sustainable energy systems has become even more critical in recent years, due to a confluence of events, including the decline in fuel prices, geopolitical conflicts, and the recent COVID-19 pandemic. The decrease in fuel prices has led to a decline in investment in renewable energy and has slowed the transition to sustainable energy systems. Additionally, geopolitical conflicts and pandemics have highlighted the need for resilient and self-sufficient energy systems that can operate independently of external factors. Also, energy storage technologies play a critical role in achieving this goal by providing reliable backup power and enabling microgrids to operate independently of the larger power grid. As such, developing efficient and effective energy storage technologies is essential for creating sustainable energy systems that can meet the demands of modern society while mitigating the impact of external factors. In this regard, this work provides an overview of microgrids’ latest energy storage technologies, including their applications, types, integration strategies, optimization algorithms, software, and uncertainty analysis. Energy storage technologies have a wide range of applications in microgrids, including providing backup power and balancing the supply and demand of energy. Different energy storage techniques have been discussed, including batteries, flywheels, supercapacitors, pumped hydro energy storage, and others. Moreover, integration strategies of energy storage in microgrids, models, assessment indices, and optimization algorithms used in the design of energy storage systems are presented in detail. The capabilities of software used in energy storage sizing are explored. Further, uncertainty analysis in modeling energy storage devices is presented and discussed. This state-of-the-art technology has been prepared to demonstrate the effectiveness of energy storage technologies in microgrids, providing valuable insights for future developments in the field.

Improving the economic and environmental benefits of the energy system: A novel hybrid economic emission dispatch considering clean energy power uncertainty

New energy power generation plays an important role in adjusting energy structure, reducing environmental pollution, and promoting sustainable social development. However, new energy power generation represented by solar and wind power has significant volatility, meanwhile, the wind and solar curtailment caused by uncertainty reduce utilization rate. In this context, considering the impact of uncertainty in new energy generation on system scheduling and developing new algorithms that can efficiently solves such complex scheduling problems are necessary. Therefore, this study constructed a hybrid dynamic economic emission model (HDEED) with the new energy generation penalty mechanism, and proposed a novel multi-objective tunicate swarm algorithm (MTSA)-based solution method. First, based on the unit operation characteristic equations, considering wind and solar power uncertainty, the HDEED model with the new energy generation penalty mechanism was constructed. Second, on the basis of the single objective tunicate swarm algorithm, MTSA algorithm was proposed by adding the running operators, and the feasibility of the proposed algorithm was verified. The test results showed that the Pareto fronts obtained by MTSA algorithm presented good distribution and diversity compared with existing algorithms. Finally, the proposed model and algorithm were proved by modified test system, meanwhile, the impacts of clean energy power on system operation stability, economic and environmental benefits were discussed. The results revealed that the penalty costs caused by uncertainty were higher than the operation and maintenance costs of new energy power stations, which accounted for about 2.1% of the total costs. The outcomes obtained in this study contribute to improving the utilization rate of clean energy and promoting the sustainable development of energy system.

Recent progress of the transition metal-based catalysts in the catalytic biomass gasification: A mini-review

Biomass is regarded as an important renewable resource, which plays a critical role in the development of sustainable energy systems. Biomass can be converted into energy or valuable chemicals by combustion, liquefaction, pyrolysis, and gasification. Among these processes, thermal gasification cracks biomass into light species at high temperatures, which can be applied to biomass with the greatest variety. As the target product of biomass gasification, syngas or H2-rich gas are very important intermediates in the petrochemical industry. However, the troublesome tar produced during gasification could bring a series of problems. Catalytic biomass gasification is a promising strategy to greatly alleviate these issues. The transition metal-based catalysts for catalytic biomass gasification have been studied in the last decades. Various transition metal catalysts for biomass gasification are reviewed in this article, including monometallic and bimetallic Ni-based catalysts, and other Fe-, Co-, and Pt-based catalysts. The performance of these catalysts is evaluated based on applied reaction parameters, the quality of produced syngas, and the performance of tar reduction. The applications of DFT calculation and AI for mechanism studies of biomass gasification have been summarized, and the cause of catalyst deactivation and regeneration of spent catalysts are also discussed. Therefore, the target of this article is to elucidate conventional biomass conversion pathways and review the recent progress on catalyst development in the biomass gasification process.

Performance investigation of hybrid photovoltaic thermal-heat with mini-channels for application in electric vehicles

The first autonomous car was developed in the 1980s, but it wasn't until the early 2010s that the technology began to gain significant attention and investment. In 2010, Google began testing self-driving cars on public roads, and since then, many other companies have joined the race to develop fully autonomous vehicles. Hybrid PVT (Photovoltaic Thermal) heat exchangers cooled with mini-channels find application in autonomous vehicles as a solution that enables efficient cooling of the vehicle's electronics and batteries. The solution involves using photovoltaic panels to generate electricity and removing the heat produced during the process using mini-channels is removed by mini-channels. Hybrid PVT heat exchangers cooled with mini-channels can help maintain appropriate temperatures inside autonomous vehicles that generate large amounts of heat from electronic systems and sensors. The setup can improve the performance and reliability of autonomous systems, increase energy efficiency, and reduce energy demands. The experimental setup includes two parallel mini-channel systems separated by a smooth copper plate. The study aims to determine local heat-transfer coefficients, with a cooled solar cell efficiency range of 10% to 14% compared to other research. The cooled PV temperature range achieved was from 19.6 to 22.4 degrees Celsius, which is favorable for photovoltaic panels' operation under approximate light intensity for Poland's latitude. Heat-transfer from hot surfaces to cold fluids is analyzed during single-phase convection using two calculation methods: one-dimensional and numerical simulations using Simcenter STAR CCM+. Cooling photovoltaic modules is critical for the photovoltaic and autonomous vehicle systems sector, making this research significant both theoretically and practically. The research and methods presented in the article on mini-channel cooling of photovoltaic systems and autonomous vehicle systems are innovative at a global scale, and are crucial for further development of sustainable energy systems and reduction of greenhouse gas emissions.

Combination of solar with organic Rankine cycle as a potential solution for clean energy production

The increasing requirement for energy, severe environmental problems, and the paucity of traditional sources of energy were among the most pressing global challenges which needed addressing immediately. Hence, the utilization of solar energy was such a vital tool for confronting critical issues including global warming, the depletion of fossil fuels as well as rising energy consumption owing to new lifestyle tendencies. More importantly, the organic Rankine cycle (ORC) was demonstrated to be a dependable technology capable of efficiently transforming low-to-medium-level heat sources into usable uses. Because of the great compatibility between the solar thermal-based collector technologies' working temperatures and the cycle’s required temperatures, using solar irradiation to drive an ORC was a viable technique based on renewable energy. The key significance of this review lies in summarizing the utilization of solar-driven ORC and its potential for the applications like water desalination, water pumping, and power generation. The key features like working fluid consideration and component selection are highlighted. Also, the challenges in the sector and the scope for further studies are discussed in detail. The final remarks show that solar ORC can be considered a promising avenue for the development of sustainable energy systems in a broader view.

Innovative Electrocatalysts for Fuel Cell and Battery Applications

The development of sustainable energy systems is essential to hinder global warming and environmental pollution emergencies [...]

Low-Carbon Economic Dispatch of Integrated Electricity-Gas Energy System Considering Carbon Capture, Utilization and Storage

With the rapid development of modern industry, while improving people’s living standards, the over-exploitation of coal, oil and natural gas has led to a shortage of fossil energy, global warming and an increasingly serious deterioration of the ecological environment. To mitigate the greenhouse effect caused by excessive carbon emissions, the vigorous development of integrated electricity-gas system (IEGS) dominated by clean energy is the future trend of sustainable development of energy systems. In this paper, a bi-level optimal scheduling model is proposed for an IEGS considering carbon capture, utilization and storage (CCUS), and the ladder carbon trading mechanism is introduced to convert carbon emissions into economic benefits. The upper model is an optimal distribution model of natural gas network, and the lower model is a day-ahead economic dispatch model of power system. Based on the Karush-Kuhn-Tucher (KKT) condition and strong duality theory of the lower model, the bi-level model is transformed into a mixed integer linear programming (MILP), which is solved by calling CPLEX through the Yalmip toolbox of the Matlab platform. Finally, the reasonableness and validity of the model are verified by three arithmetic simulations. The results show that the proposed bi-level model for low-carbon economic dispatch of IEGS considering CCUS can effectively reduce the operating costs and carbon emissions of the system.