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The ever-increasing challenges related to water security and climate mitigation underscore the pressing need for forward-thinking systems planning tools to drive urban sustainability transitions. In this study, we propose a novel city-level multi-objective planning framework that explores the nexus between carbon trading and water management, with the aim of facilitating water-efficient urban industrial restructuring. The framework integrates cap-and-trade mechanisms into a two-layered optimization model, creating an integrated approach to optimizing carbon emissions and generating economic opportunities for improving water efficiency. To illustrate the potential application of this framework, we conducted a case study focusing on Dongguan City, a water-stressed industrial metropolis in southern China. The results illustrate potential synergies between water planning strategies and carbon trading schemes, which could be harnessed to enable targeted reductions in water usage and carbon emissions. Furthermore, our findings identify the textile, apparel, and paper products manufacturing industries as primary candidates for strategic production scale reduction, emphasizing the importance of sustained support for the development of the computer and electronic manufacturing sector as a catalyst for urban sustainability transitions. By pioneering this new nexus-based perspective, our study offers valuable insights into long-term strategic planning for a low-carbon and resource-efficient urban economy. • Synergizing carbon trading and water management within a novel integrated planning framework. • Implementing resource-efficiency-driven optimization for water- and carbon-intensive manufacturing sectors. • Gaining insights into the specific roles of key manufacturing sectors in water management and carbon trading. • Introducing carbon trading systems may help in promoting water-efficient industrial restructuring. [Display omitted] [ABSTRACT FROM AUTHOR]

Discrete manufacturing systems (MSs) are prevalent across various sectors such as electronic production, food processing, and apparel manufacturing. However, their operation raises critical concerns regarding significant energy consumption and carbon emissions. Implementing local electricity and carbon allowance sharing among MSs presents a potential solution to these issues, which is explored in this paper. The main contribution of this paper lies in achieving distributed electricity and carbon allowance sharing among MSs while addressing challenges related to algorithmic and physical feasibility. Firstly, a local electricity and carbon allowance sharing problem for MSs is formulated, which involves numerous binary decision variables due to the operation of discrete manufacturing facilities and energy storage, rendering the problem challenging to solve in a distributed manner. To tackle this challenge, we propose an alternating optimization procedure (AOP)-based distributed method to solve the problem while ensuring algorithmic feasibility. Secondly, the second-order cone relaxation program (SOCP)-based power flow model is identified cannot guarantee the exactness of the distribution system model when conducting local electricity sharing. We tackle this challenge by employing a convex-concave procedure (CCP)-based feasibility recovery procedure (FRP) to recover the exactness of the SOCP relaxation, thereby ensuring physical feasibility. The numerical results demonstrate that conducting local electricity and carbon allowance sharing can effectively reduce energy costs and carbon emissions for MSs. Moreover, compared with the alternating direction method of multipliers (ADMM), the proposed distributed method can guarantee both algorithmic and physical feasibility when solving the problem. • Enabling electricity and carbon allowance sharing among manufacturing systems. • A local electricity and carbon allowance sharing problem is formulated. • An alternating optimization procedure-based distributed method is proposed. • A convex-concave procedure-based feasibility recovery procedure is introduced. • Guaranteeing algorithmic and physical feasibility when solving the problem. [ABSTRACT FROM AUTHOR]

China's carbon emissions from households have increased with the growth of the consumption scale. To accomplish the ambitious goals of reducing emissions, carbon emission reduction on the household side should not be neglected. Based on the 2012–2018 CFPS database, This paper examines the relationship between household carbon emissions(HCEs) and the digital economy by analyzing data on household consumption expenditure and carbon emission coefficients. Utilize instrumental variables and the "Broadband China" natural experiment to conduct robustness tests, and further clarify how the digital economy influences direct and indirect household carbon emissions. The study finds that (1) Direct HCEs witness an increase while indirect HCEs experience a decrease due to the digital economy. (2) The digital economy increases direct HCEs by expanding the consumption scale, but surprisingly, the digital economy has not made a significant impact on enhancing residents' environmental awareness. (3) When analyzing indirect HCEs, we introduced a macro perspective, and it is found that the digital economy results in a decline in indirect HCEs primarily due to a reduction in the energy intensity of the whole industry. This paper provides new evidence on the digital economy and its impact on carbon emissions at the household level, which is instructive for understanding the interaction between the production and consumption sides in achieving carbon neutrality and how to promote emission reduction on the consumption side. • Household carbon emissions are measured from direct and indirect perspectives. • The impact of digital economy on household carbon emissions is heterogeneous. • Digital economy increases household carbon emissions through consumption scale. • Digital economy has no significant impact on environmental awareness. • Digital economy reduces indirect household carbon emissions through energy intensity. [ABSTRACT FROM AUTHOR]

Hydrogen Direct Reduction of Iron (HDRI) combined with renewable electricity is an attractive option for low-carbon steel production. In this paper, we present a novel and computationally efficient techno-economic power-to-x-plant optimization model which is then applied to an HDRI plant located in Finland. Plant dimensioning was carried out in several current and future power market and regulatory scenarios. We predict a production cost of 373 €/t for hot briquetted iron, and 351 €/t for a future scenario of 2025–2030. When the recently introduced EU rules for renewable fuels of non-biological origin are applied, the production costs increased by 30–46 €/t. The rules also have a significant increasing effect on the required hydrogen storage. The flexibility of the direct reduction shaft emerged as an important parameter affecting the required hydrogen storage as well as total production cost. The results of this paper hold significance for the optimal design of future low-carbon steel plants. • Computationally efficient optimization model for Power-to-X operations and dimensions. • Hydrogen Direct Reduction Ironmaking (HDRI) plant using wholesales and wind power. • Significant amounts of intermittent wind power can be absorbed by the HDRI plant. • HDRI/HBI production costs analyzed for EU regulatory scenarios on green hydrogen. • Emission reductions over 96% compared to CO 2 emissions of fossil steelmaking. [ABSTRACT FROM AUTHOR]

The carbon dioxide (CO 2) emissions have reached their highest levels since measurements began in recent years. This is widely regarded as one of the primary factors contributing to the increasing frequency of extreme climate events. Consequently, accurate and reliable CO 2 emission data becomes the benchmark in shaping policies and setting targets aimed at reducing emissions, which are essential for carbon emission-related research. Among all industries, the electric power sector takes the highest proportion of CO 2 emissions, making the carbon emission accounting for this sector a crucial component of overall carbon emissions accounting. Therefore, the objective of this paper is to provide a comprehensive review of the applied technologies and research progress in electricity carbon emissions accounting. The paper begins by introducing the fundamental concept of electricity carbon emission accounting. Electricity carbon emissions are categorized into two types: direct carbon emissions from electricity production and indirect carbon emissions from electricity consumption. Then, the current applied approaches for accounting for these two types of emissions are reviewed separately. After analyzing the shortages of existing applied carbon emission accounting approaches, the paper proceeds to review the research on the improved approaches of direct and indirect carbon emission accounting. Finally, the paper outlines the future development prospects of carbon emission accounting technology. • Application status of carbon emission accounting approaches in the electric power industry is analyzed. • Direct carbon emission accounting approaches based on big data technologies are introduced. • Research exploration in electricity indirect carbon emission accounting is reviewed and compared. [ABSTRACT FROM AUTHOR]

Blending hydrogen into the existing natural gas pipeline network is regarded as the most potential transportation mode on the utility-scale. However, the impact of hydrogen on the operation performance of the pipeline remained unclear. This paper analyzes the economic and environmental performance of natural gas pipeline under different hydrogen blending ratios and different hydraulic boundary conditions. In order to reduce the operation costs and carbon emissions in each condition, this paper establish a non-linear optimization model to get the new operation plan of the pipeline system. Further, in order to characterize different boundary conditions of the system, four scenarios, namely, the baseline scenario (BS), the setpoint of flow rate scenario (SF), the setpoint of pressure (SP), and the setpoint of heat flow rate (SH) are proposed in this paper. Two studied cases demonstrated that: (1) the original compressor for natural gas can adapt to a hydrogen blending without change the type of compressor. (2) The optimization model has significant potential in reducing the economic cost and carbon emissions of the system with an average decrease of 11.48%. (3) For every 1% of hydrogen added, the annual operating cost of the system is reduced by $0.73 million (3.29%) in SF, 0.017 million (0.08%) in SP, or increased by $1.67 million (7.50%) in SH. Moreover, the annual carbon emission is reduced by 0.38 kt (3.53%) in SF, 0.047 kt (0.44%) in SP, or increased by 0.76 kt (7.14%) in SH. However, when the hydrogen mixing ratio is more than 8%, the contrary trend may occur. (4) The maximum blending ratio at different points of the multi-source pipeline network are physically interdependent. The paper proposed an optimization model for the 2-E analysis of the pipeline and can provide theoretical guidance for the further application of this transportation mode. • Hydraulic impact of hydrogen on natural gas pipeline is estimated. • Factors that constrain further hydrogen blending are analyzed. • A power optimization model is proposed for the given hydrogen blending ratio. • Pareto set of hydrogen blending are obtained for multi-source pipeline. [ABSTRACT FROM AUTHOR]

To further explore the multi-energy complementary potential on multi-time scales under variable operating conditions, a refined modeling and collaborative configuration method for Electric-Hydrogen-Thermal-Gas Integrated Energy Systems (EHTG-IES) with hybrid energy storage system (HESS) is proposed in this paper. To commence with, an advanced operation model for the EHTG energy conversion equipment is formulated by incorporating their nonlinear operating characteristics under variable operating conditions. Next, the distinct features of HESS with different energy medium are accurately depicted with various effective linear-reduction and relaxation strategies. Then, the coupling design day method and the intra-day and inter-day state superposition strategy are combined to efficiently model the time horizon. Lastly, a co-optimal configuration model of the EHTG-IES is devised, with the aim of minimizing total annual cost. The case study validates that the refined modeling of coupled components leads to a 3.18% reduction in cost and 5.05% reduction in carbon emissions significantly. This paper also assessments the synergy and substitution benefits of multiple low-carbon technologies and finds that diurnal and seasonal hydrogen storage have a large part of overlapping roles, and hydrogen-based system contributes a deeper utilization of seasonal thermal storage. The sensitivity analysis indicates that the current carbon tax policy has a negligible impact on the carbon emissions, equivalent to about 1% of the gas price, but appropriately increasing attention on low carbon allows for an emission decrease of 28.62% at a cost increase of 4.38%. [Display omitted] • Various low-carbon technologies are finely modeled considering non-linear features. • Linear-reduction and relaxation strategies are developed for hybrid energy storage. • The complementary potential of low-carbon technologies is utilized and explained. • Cost and CO 2 emissions are reduced thanks to the refined model for the system. [ABSTRACT FROM AUTHOR]

Organic Rankine cycle (ORC) is considered to be a good solution to the high-efficient recovery of low-grade heat for saving energy and reducing carbon emissions. As the core component, the heat exchanger plays a significant role in the thermo-economic performance evaluation of an ORC plant and its heat transfer correlations greatly influence the cycle performance. In this paper, 8 two-phase heat transfer correlations (4 flow boiling ones and 4 flow condensation ones) are utilized to design the plate-type heat exchangers, and a comparative study among different correlations is carried out. The result shows the designed evaporators are finitely affected by heat transfer correlations whereas the situation with the designed condensers is the opposite. The net output power is slightly influenced with a relative difference within 1.1% but the system's economic performance is significantly affected with the maximum relative difference of electricity production cost up to 11.2%. The environmental performance is hardly affected by correlations with the maximum relative difference of total greenhouse gas emission within 0.96%. This paper analyzed the performance variation of the ORC under different correlations and provided a guideline for designing heat exchangers and evaluating the off-design performance of ORC systems. • 8 two-phase heat transfer correlations are compared for designing the heat exchangers. • Designed condensers are affected by the heat transfer correlations more significantly. • Net output power is slightly influenced with a relative difference within 1.1%. • Electricity production cost is affected with a relative difference 11.2%. • Total carbon emissions exhibit minimal variation with a relative difference of only 0.96%. [ABSTRACT FROM AUTHOR]

Solar energy-powered electrolytic water splitting represents a promising avenue for hydrogen production. However, current technologies for solar-driven hydrogen generation still face the challenges such as low efficiency and significant fluctuations in solar energy availability. This paper proposes a full-spectrum solar hydrogen production system integrated with spectral beam splitting technology and chemical energy storage to address these issues. The high-grade solar energy is allocated for generating electricity through photovoltaic cells, while the low-grade solar energy is utilized in the dry reforming of methane (DRM) process to produce syngas, which in turn is used for flexible electricity generation. Dispatchable electricity converting from syngas, along with intermittent electricity form photovoltaic cells, powers a solid oxide electrolysis cell (SOEC) to produce hydrogen. The results demonstrate that the energy efficiency is 32.08%. In addition, more than half (56.6%) of the electrolysis capacity can be utilized during night hours due to thermochemical energy storage (syngas). In addition, a year-long operation simulation showed that the system can diminish CO 2 emission by 25.7% to produce the same amount of hydrogen. The full-spectrum solar hydrogen production system provides a viable option for the transition from fossil energy to renewable energy. • A full-spectrum solar hydrogen production system integrated chemical energy storage is proposed. • Low-grade solar energy in long-wave spectrum is converted to high-grade chemical energy. • The hydrogen production efficiency is as high as 32.08%. • Full-spectrum solar energy provides both thermal and electrical energy for SOEC. • CO 2 emission is diminished by 25.7% to produce the same amount of hydrogen. [ABSTRACT FROM AUTHOR]

High-temperature aquifer thermal storage (HT-ATES) is an effective method to mitigate the increasing greenhouse gas emissions, and it is attracting industry attention as an alternative to traditional fossil fuels for heating and cooling. However, the uncertainty of exploration and long profit cycles impede the popularization of HT-ATES technology. In this paper, to optimize HT-ATES evaluation and make the results more convictive, we demonstrate a numerical study based on a real district and a proven aquifer. An integrated HT-ATES model includes the wellbore and aquifer is used to simulate the fluid flow and heat transfer. Moreover, a dynamic economic assessment is demonstrated depending on the HT-ATES fluctuation performance. A 30-year HT-ATES cycling simulation shows that the wellbore and aquifer have had a continuous heating loss since the operation started. Working well and balancing the well lost 2.7% and 2.2% of total energy through the wellbore. The aquifer lost 4.1% of total energy due to heating transfer to overburden and other layers. HT-ATES could recover around 90% of stored total energy. The HT-ATES economic performance is affected by the heating store and production cycling, the benefit mainly comes from the heating production season. The initial investment and heat exchange efficiency between the HT-ATES & end-application system determines the levelized heat (LCOH) cost and payback time, the optimist case still needs 3 years to be profitable. HT-ATES have considerable green benefits, it could reduce local CO 2 emissions 1937 t/year. • An integrated HT-ATES performance model considering the wellbore and reservoir is developed. • An economic assessment considering the system cost, fluctuation economic benefits, and CO 2 emissions is implemented. • HT-ATES system could recover around 90% of the total stored energy. • The long-term levelized cost of heat is lower than the current operating ATES systems. • HT-ATES shows considerable performance on net CO 2 emission reduction. ca. 1937 tons per year. [ABSTRACT FROM AUTHOR]

Under the trend of energy saving and carbon reduction, reducing household energy consumption, which is an important part of social energy consumption, has become a hot topic. In order to solve the problem of mismatch between supply and demand of household environment control systems as well as to better meet the individual needs of users, we innovatively designed a solar-driven domestic regional environment mobile robot as auxiliary heating and cooling method. In this paper, a simplified thermal network analysis of the thermal conditions of a dwelling is utilized to calculate the energy saving and carbon reduction potential on summer and winter reference days in Beijing and Guangzhou, China. The simulation results show that the system has good energy saving potential and carbon reduction effect. In particular, the single-day energy saving potential of the system on winter days in Beijing reaches to 583.6 kWh compared to the traditional method. Under the power supply mode, the single-day carbon reduction can reach 462 kg CO 2 on winter days in Guangzhou, and it can reach 333.2 kg CO 2 on summer days in Beijing. We also discuss the effect of regional space size and clothing thermal resistance. As the local space size and the clothing thermal resistance increases, the energy saving potential and carbon reduction benefits of the system decrease, but the overall impact is small. The proposed mobile robot following in the vicinity of home inhabitants to help maintain their thermal comfort, can improve the efficiency of dwellings heating and cooling systems and utilize the limited solar energy resources in family area. • We have proposed the regional household auxiliary heating and cooling method, which is more energy efficient; • Solar-driven domestic environment mobile robot (DREMR) is low-carbon and environmentally friendly; • TEC/THP components combined with human-machine interface can effectively meet the user's individual needs; • The energy demand and carbon emission analyses models can also be used in other household environment control systems. [ABSTRACT FROM AUTHOR]

With the widespread adoption of combined heat and power and power-to-heat technologies, multi-energy microgrids (MEMGs) have been garnering significant research attention from both industry and academia. However, dealing with uncertainties from renewable energy and load and coordinating multiple energy carriers are the main challenges for MEMG operation. In this regard, a two-stage robust operation method of electricity-gas-heat integrated MEMGs considering heterogeneous uncertainties is proposed in this paper. First, network models for an electricity-gas-heat-based distribution-level MEMG are formulated considering the dynamic characteristics of gas and heat networks. Then, the power-to‑hydrogen-and-heat unit and ladder-type carbon trading mechanism are introduced to reduce the curtailment of wind power and carbon emissions. Further, a two-stage robust optimization (TSRO) method is applied to tackle uncertainties of wind power and load under extreme scenarios in the MEMG operation by iteratively solving the operation problem with the column and constraint generation (C&CG) algorithm. Finally, case studies are conducted to verify our proposed method, demonstrating that it can reduce the multi-energy supply cost while the stepped carbon trading mechanism can also significantly reduce carbon emissions. • A coordinated operation model for a multi-energy microgrid (MEMG) integrated considering the P2HH devices is introduced. • The dynamic properties of the gas and heat systems are considered systematically for more flexible and economic operation. • To alleviate the negative effects of diverse uncertainties, a two-stage robust dispatch method for the MEMG is formulated. • The ladder-type carbon emission cost mechanism is proposed to reduce gas. [ABSTRACT FROM AUTHOR]

The design of a building's energy supply is a difficult task, where uncertainty in the requirements has to be taken into account and different conflicting objectives have to be optimized. To date, there exist various studies that consider different objective functions, different concepts of dealing with uncertainty, and different approximation techniques to find efficient solutions. However, there is no approach that gives the decision maker a complete overview of all trade-off solutions with their immediate consequences for the design configuration. This paper addresses the question of how much money and carbon emissions can be saved by allowing a certain amount of undersupply in the heating and cooling of an office building. To explore the trade-offs between the three objectives of minimizing cost, carbon emissions and inconvenience, the Pareto front of the multicriteria linear program is approximated with a Sandwiching algorithm. Then, it is explored using Pareto navigation. This allows to see all (approximate) Pareto points and also to visually observe the behavior of the decision variables by navigating across the front by pulling sliders in a specific software. In a case study, the navigation is demonstrated and ways to overcome modeling limitations that arise from using a fully linear programming approach are shown. [Display omitted] • Introduce novel workflow for optimal co-design for building energy supply. • First-time application of Sandwiching and Pareto navigation to energy system design. • Focus on finding trade-offs between costs, carbon emissions and inconvenience. • Demonstrate approach in case study for an office building at an early planning stage. [ABSTRACT FROM AUTHOR]

Global warming, mainly caused by rising carbon dioxide (CO 2) emissions in the atmosphere, has led to severe effects worldwide. Hence, reducing carbon emissions has become an urgent need to mitigate the adverse effects. To do so, accurately forecasting CO 2 emissions is crucial for policymakers in formulating climate and energy policies. Existing CO 2 emission forecast models rely on various longitudinal economy, energy or environment data collected at the same periods, e.g., yearly, as indicators. However, these data usually come from diverse sources with different sampling frequencies, e.g., monthly vs. daily. Therefore, in this paper, we introduce for the first time a Mixed-frequency data Sampling Grey system Model (MSGM) that can exploit the potential information in high-frequency sampling (HF) data of economic and social indicators to effectively forecast annual CO 2 emissions. In MSGM, (1) we directly model low-frequency CO 2 emissions and HF economic-energy influence indicators without undertaking same-frequency conversion, thereby avoiding potential information loss and enhancing prediction accuracy; (2) we introduce polynomial weight functions to address the frequency mismatch, and its parameters are obtained through search optimization algorithms; (3) unlike existing grey system models, the whitening equation of MSGM adopts a reduced-order form to address the exponential explosion issue caused by HF data accumulation; (4) the time and cumulative terms are also introduced separately to explore the effects of temporal development and historical data on future CO 2 emissions. Extensive Monte-Carlo simulations show that MSGM exhibits high accuracy on both training and testing datasets and outperforms many other state-of-the-art rival techniques like MIDAS, AR-MIDAS, AMTGM, MLRM, BPNN, SVR, ARIMA and TVGBM. We further demonstrate the potential of MSGM to forecast China's CO 2 emissions under three distinct scenarios. Out-of-sample forecasting results indicate that only under a tight-contraction scenario does the growth rate of CO 2 emissions decrease. To the best of our knowledge, the MSGM is the first technique that can exploit different frequency data to effectively forecast CO 2 emissions. [Display omitted] • A novel grey system forecast model based on mixed-sampling frequency is proposed. • Introducing more higher frequency data from economic and social indicators. • Selecting popular local search algorithms to find optimal unstructured parameters. • The CO 2 emission forecast for 2022–2026 is carried out under three distinct scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a privacy-preserving non-iterative decentralized optimal energy flow for integrated hydrogen-electricity-heat system based on projection method is proposed. A centralized operation model of the integrated hydrogen-electricity-heat system is first established, which is further decomposed into hydrogen, electricity, and heat subproblems, respectively. The proposed projection method transforms the operation space of each subproblem into an equivalent projection space containing whole information of the original operation space. After the operation space transformation, the private information of each system is concealed, thus preserving the privacy of each system. The equivalent projection space of each subsystem constitutes the reformulated convex hull of the integrated energy system. By solving the optimization problem in the reformulated convex hull, the optimal solutions of coupling variables of the integrated energy system are obtained. The optimal solution of coupling variables is used to decompose the integrated energy system and reformulate the subproblem of each subsystem. Optimal energy flow of each subsystem is obtained by solving the reformulated subproblem. Iterations are avoided throughout the solution process of the proposed method, thus addressing various disadvantages of conventional decentralized methods, like poor computational performance and potential non-convergence. The economic performance of the proposed operation model and the computational performance of the proposed projection method were tested on a 6‐6-8 nodes system and a 40‐118-13 nodes system. The low cost and carbon emission of the proposed model and the accuracy, privacy preservation, simplification of model, and less computational time of the proposed method are verified. • A centralized operation model of integrated hydrogen-electricity-heat system is proposed. • Decentralized optimal energy flow is proposed for integrated hydrogen-electricity-heat system. • Privacy-preserving and non-iterative of the proposed projection method are introduced. • Two test systems are provided to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Carbon emissions have become particularly urgent with the acceleration of industrialization. Currently, AI-based carbon emission modeling provides essential support for solving the problem of predicting, managing, and optimizing carbon emissions. However, there are still challenges in accurately predicting carbon dioxide emissions and establishing an appropriate carbon emission regulatory model to manage carbon dioxide emissions effectively. Firstly, this paper introduces a framework for learning seasonal and trend representations called DeTF (Decoupling representation for Time and Frequency domain of time series). The framework is designed for time series data of CO 2 emissions used for power generation from 1973 to 2023. Secondly, we use various data enhancement techniques to improve the robustness of the learned representations. Finally, the prediction results establish a tripartite evolutionary game relationship between the state, regulatory agencies, and enterprises. We use MSE and MAE as evaluation metrics, and our model overall outperforms current state-of-the-art end-to-end algorithms. It outperforms the best performing end-to-end prediction method by 27.08% (MSE) in the prediction results. Besides, the time series prediction results are innovatively combined with the tripartite game model to analyze the main factors determining the optimal strategy. • Time domain augmentation boosts robustness with strong and weak methods. • The New DeTF framework splits time and frequency in time series analysis. • The overall loss consists of two main components: the time domain and the frequency domain. • Time series prediction merges with a tripartite game and explores strategies and equilibria. [ABSTRACT FROM AUTHOR]

With the continuous development of the global economy, the demand for natural gas is increasing year by year. At the same time, the widespread adoption of emission reduction measures around the world prompts many countries to switch to cleaner energy sources, which is driving the growth in demand for natural gas. In order to regulate the balance of natural gas supply and demand, maintain the stable operation of natural gas systems and reduce environmental pollution, this paper proposes an incentive-based demand respond (DR) model for natural gas, which takes carbon emissions and load volatility into account, and in the end, it concludes an effective response strategy. In order to get the multi-objective incentive-based DR, first of all, the reward function is constructed by adopting the TOPSIS method, and then it is solved by using the DQN algorithm, in the end, a multi-objective reinforcement learning method based on DQN is proposed. The DR model is evaluated by adopting a real load data from Xi'an city and DR strategies are proposed for different gas users in two periods: the heating and non-heating periods. The DR strategy proposed in this paper can effectively increase gas users motivation, reduce peak loads, maximize the revenue of gas suppliers and gas users, mitigate load volatility and reduce carbon emissions. • An incentive-based DR model for natural gas is proposed. • A multi-objective RL method based on the DQN algorithm is proposed. • Comfort loss cost parameters are improved to enhance DR model adaptability. • An evaluation of the DR strategy is carried out using data from the city of Xi'an. [ABSTRACT FROM AUTHOR]

[Display omitted] • Estimated relationship between nighttime stable light data and carbon emissions. • Reference to shared social economy and representative concentration to set scenarios. • Classification of cities based on industry output and GDP. • The carbon emission pathway of the city from 2021 to 2060 was dynamically simulated. • Service cities are more likely to have autonomous carbon peaks. As cities are critical actors in mitigating climate change and achieving the "3060″ target, multi-scenario studies on urban carbon emissions can provide a scientific basis for formulating urban carbon peaking action plans. To remedy the problems of missing regional statistics, inconsistent caliber, and lack of city-scale studies in carbon emission research, this paper uses the sparrow optimization neural network algorithm to fit carbon emission data with nighttime stable light for training. Carbon emission data were obtained for 281 cities in China during 2000–2020. The rates of change of influencing factors are set based on shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) for different periods and different scenarios. The carbon emission and carbon peaking evolution paths of service, industrial and comprehensive cities from 2021 to 2060 are dynamically simulated. The results show that (1) service cities are significantly higher than industrial and comprehensive cities in population, GDP, secondary industry output, and energy consumption. (2) The economic development effect, as the primary driver of carbon emission growth, increases and then decreases in all five categories of cities, with 2010 as the inflection point. Industrial structure improvement has an increasingly strong offsetting effect on carbon emissions and is one of the critical directions for future carbon emission reduction. (3) Service cities such as Beijing and Shanghai are already at the completion stage of urban transformation and are more likely to reach the carbon peak on their own than other types of cities. In the low carbon following scenario, comprehensive cities such as Kaifeng, Rizhao, and Jilin can achieve their carbon peaking targets efficiently. The findings of this paper can provide valid theoretical support for carbon peaking action programs in China and other countries. [ABSTRACT FROM AUTHOR]

[Display omitted] • Robust low-carbon planning method is proposed for the SMGUS in steelmaking plant. • Decarbonizing steel plant through renewable energy, hydrogen, energy storage & CCUS. • Planning under different market conditions and CO 2 reduction routes are discussed. • Considering uncertainties promotes the deployment of flexibly adjustable equipment. • CCUS is the key for decarbonization of steel plant before massive use of renewables. Decarbonization of the steelmaking industry is imperative in achieving the carbon neutrality target. This paper proposes a two-stage low carbon robust planning method for the steel mill gas utilization system (SMGUS), which is the major CO 2 emission source of the conventional blast furnace route based-steelmaking plant. The planning aims to find the optimal installed capacities and relevant deployment time of multiple low-carbon technologies, such as renewable energy, energy storage, hydrogen energy and carbon capture & utilization, so that the demand of steel production can be met in an economically competitive and environmentally friendly way. Case studies are carried out on the low-carbon planning of a steelmaking plant with annual production of 1.5 million tons of crude steel, in which different carbon market conditions and CO 2 reduction routes are considered. The results show that the low carbon planning can reduce the CO 2 emissions to 1.22 tons per ton of steel by 2045–2050 through the proper deployment of the low carbon technologies. The CO 2 emissions can be further reduced to 0.31 tons per ton of steel if the BF-BOF process is replaced by H2-DRI and EAF technologies. This paper presents an effective planning method for the decarbonization of steelmaking plant. [ABSTRACT FROM AUTHOR]

• An improved multi-timescale coordinated control strategy is proposed to be used in an integrated energy system. • A hybrid energy storage system is introduced into the system. • Operating costs and carbon emissions are considered as the optimization objectives. • General rules for sizing hybrid energy storage system are proposed in this paper. In view of the complex energy coupling and fluctuation of renewable energy sources in the integrated energy system, this paper proposes an improved multi-timescale coordinated control strategy for an integrated energy system (IES) with a hybrid energy storage system (HESS). The power response characteristics of various devices in IES at different time scales are analyzed which shows that the multi-timescale coordinated control method can be well coupled with the IES including HESS. The advantages of HESS over single energy storage system in stabilizing power fluctuation and extending energy storage life are compared and analyzed while the control method of supercapacitor under multi-time scale coordinated control strategy is proposed. The primary energy consumption, operating costs, carbon dioxide emissions and load shedding costs are considered the optimization objectives in the day-ahead rolling optimization stage according to the day-ahead forecast renewable energy (RE) data. In the intraday rolling adjusting stage, the proposed method can reduce the effect of the RE day-ahead forecasting errors to achieve the intraday energy scheduling balance and ensure the safe operation of the devices of IES. Considering the background of IES with a high proportion of renewable energy, this paper makes innovative use of the advantages of HESS to improve the power response characteristic of the system. The high-frequency part of RE is balanced by HESS in the real-time coordinated control stage. A typical structure of IES is applied and the simulation results are analyzed to show the performance of the proposed method in this paper. Moreover, general rules for sizing of HESS are also proposed. The influence of the hybrid energy storage capacity allocation ratio on battery capacity loss and carbon emission reduction is discussed. The Pareto front including the configuration and cost of supercapacitor has been obtained. As shown in the case, the application of HESS in the IES can improve the power response characteristic, extend the lithium-ion battery (LiB) life and has more benefits in reducing carbon emissions. [ABSTRACT FROM AUTHOR]

The escalating CO 2 emissions in recent years underlined the need for advanced Carbon Capture and Utilization (CCU) technologies. This context has spurred the exploration of novel materials for promoting CCU efficiency, among which electrospun nanofibers have emerged as a promising candidate. Electrospinning coupled with various post-treatment (such as heat-treating, in-situ growth, selective removal, etc.) offers a versatile approach to fabricate diverse nanofibers, including polymeric, carbonaceous, metallic, and composite fibers, with tunable surface morphologies and chemical properties. This paper provides a comprehensive review of electrospun nanofibers in CCU processes, including adsorption, membrane separation and absorption, electrochemical and photo-electrochemical reduction, thermal catalytic hydrogenation, and more. Each CCU technology is examined in depth to cover the recent research achievements with a specific focus on the contributions of electrospun nanofibers. Lastly, a critical discussion and a list of potential future research directions for this evolving field are provided. • Latest electrospun nanofiber-based CCU technologies are thoroughly reviewed. • Electrospinning enables creating various nanofibers, aiding multiple CCU technologies. • Provide critical discussion and future directions for this evolving research field. [ABSTRACT FROM AUTHOR]

Projecting future urban CO 2 emissions is of paramount importance in comprehending and addressing climate change, as well as evaluating the impact of various policies on future greenhouse gas emissions. While previous studies on projecting CO 2 emissions have primarily focused on large administrative scales, fine-resolution mapping of CO 2 emissions is also vital for identifying CO 2 emission hotspots and delineating emission reduction responsibilities. In this paper, we projected the future CO 2 distribution at a fine-resolution (1 km) under the shared socioeconomic pathways (SSPs) scenario framework, based on which we assessed the peak time, peak volume, spatial distribution, and CO 2 emission intensity (emissions per unit building volume) for cities in the Pearl River Delta (PRD) region. We first simulated the future three-dimensional (3D) spatial patterns of cities by using the FLUS-3D model, and then projected fine-resolution CO 2 emissions by combining the 3D information and socioeconomic factors. Results show that the total CO 2 emissions in the PRD region will reach 180–345 million tons (Mt) by 2030, and 67% of cities in the PRD region will peak CO 2 emissions before 2030 under all five SSP scenarios. Three cities (Jiangmen, Zhongshan and Foshan) will witness a continual rise in CO 2 emissions after 2030 under the SSP5 scenario. Additionally, we found that the time (2010) to reach maximum CO 2 emission intensity of different urban functional types precedes the peak time (2010−2030) of city-level CO 2 emissions. This suggests that the escalation in CO 2 emissions since 2010 will be primarily driven by urban expansion and population growth, rather than an increase in the CO 2 emission intensity of buildings. These findings provide extensive knowledge for future spatial distribution of CO 2 emissions and contribute to the construction of low-carbon cities. • Future gridded CO 2 emissions (1 km resolution) are projected under the shared socioeconomic pathways (SSPs). • The future evolution of three-dimensional urban structures is considered in CO 2 emission projection. • Most cities in the PRD will peak CO 2 emissions before 2030 under all five SSPs. • The CO 2 emission intensity of urban lands declines since 2010 for most of cities. [ABSTRACT FROM AUTHOR]

The sustainable energies take increasing proportion in the power systems due to the "net-zero emission" goal, and the future trend is to make the new type power systems operate safely and stably while maintaining low carbon and economic efficiency. This paper proposes a novel smart generation system (SGS) architecture and a smart generation system consensus (SGSC) algorithm from the perspective of automatic generation control. The SGS is based on stratified sequencing method and multi-agent theory, which describes the dynamic and optimal generation dispatch of the units as a hierarchical decentralized multiple objectives programming problem. Further, the SGSC is proposed based on distributed Newton algorithm and leaderless consensus algorithm to solve the security performance degradation caused by decentralization. All the units are configured in parallel with the designed communication topology to achieve dispatch decentralization, thus ensuring low-carbon, economical and safe operation of the new type power system. The effectiveness of the proposed algorithm is verified on a multi-layer, multi-zone smart generation system model with a high proportion of sustainable energies from macro and micro aspects. Different control methods are also used for performance comparison on the two-area load frequency control model with lower regulation costs and lower carbon emissions achievement. • A decentralized control Architecture of autonomous SGS is developed. • A novel SGSC algorithm is proposed for dynamic generation command optimization of AGC. • The proposed algorithm not only minimizes regulation costs and carbon emissions but also ensures the stability of system operations, even during communication failures. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic energy storage systems (DPVES) offer a proactive means of harnessing green energy to drive the decarbonization efforts of China's manufacturing sector. Capacity planning for these systems in manufacturing enterprises requires additional consideration such as carbon price and load management. This paper proposed a triple-layer optimization model for DPVES capacity configuration in the manufacturing sector using a chemical fibre manufacturing enterprise for demonstration. Refined photovoltaic generation and energy storage lifetime models were used. Beyond the considerations of electricity prices and meteorological conditions, we further studied the influence of carbon price and user load management on system capacity configuration and associated economic feasibility. Firstly, without considering carbon, minimizing user costs requires maximizing PV capacity up to the area limit while adjusting the ES to its optimal capacity and power. The optimal DPVES annually reduces the grid electricity consumption and carbon emissions, resulting in a 12.73% annual cost reduction. When considering the costs of carbon emissions, the carbon reduction contributed by DPVES can reduce the annual costs, making the whole system more economically feasible. However, the presence of substantial carbon emissions costs diminishes the economic feasibility of the ES, leading to a reduction of 24.51% in the optimal capacity configuration. Finally, user load management can further reduce system costs because it replaces some of the functions of energy storage. This results in a decrease of over 39% in the optimal energy storage capacity and a further reduction in related costs. Additionally, we found that load management by enterprises is more effective during the low carbon price than the high carbon price, implying that companies should implement load management as soon as possible. • Established a triple-layer optimization model for capacity configuration of distributed photovoltaic energy storage systems • The annual cost can be reduced by about 12.73% through capacity and power configuration optimziation • High carbon prices may reduce the economic viability of the energy storage system, causing reduction in its optimal capacity • Load management can further reduce the energy storage capacity demand by 39%, bringing economic benefits especially in low carbon prices [ABSTRACT FROM AUTHOR]

The urge to reduce the dependence on natural gas for heating at the residential level has led to the deployment of different fossil fuel-free alternatives. In the Netherlands, two technologies are leading the transition: heat pumps, due to their high COP, and photovoltaic–thermal systems, due to their dual electric-thermal output. However, both represent a challenge for users and grid operators, aside from their stochastic behavior. Heat pumps alone can surpass a typical Dutch house's total energy and power consumption. Photovoltaic–thermal systems, as their only electric homologs, usually have a mismatch between generation and demand, causing energy injections to the grid. From the electric perspective, storage systems are a proven solution to reduce the energy exchange with the distribution network. This paper proposes four multi-carrier energy system configurations for a Dutch household, comprising different combinations of a photovoltaic–thermal system, a battery energy storage, a heat pump, and an underground water tank thermal energy system, providing analytical models for every component (including the thermal losses from the thermal storage to the ground), and the space heating and electrical demands. We determined the components' compatibility and evaluated the combinations considering their thermal performance, electrical performance, and equivalent CO 2 emissions. The results suggest that using a heat pump combined with a photovoltaic system and a battery provides the best trade-off. The photovoltaic–thermal system alone could not supply the thermal demand required for comfortable space heating nor reach temperatures high enough to charge the thermal storage. Combining the thermal storage with the heat pump allows a certain degree of flexibility for the heat pump activation at the cost of COPs between 0.8 and 1.38 when used to charge the thermal storage, thus increasing energy consumption and equivalent emissions considerably. [Display omitted] • Heat pumps and gas boilers have similar CO 2,eq emissions in Dutch houses. • A PV system can compensate for the CO 2,eq emissions of a heat pump in Dutch houses. • PVT cannot reach temperatures to charge a thermal storage system for space heating. • Thermal storage provides flexibility for heat pump activation but can reduce its COP. [ABSTRACT FROM AUTHOR]

Deploying negative emission technologies has become crucial for limiting the global temperature rise to approximately 1.5 °C above preindustrial levels. DAC technologies are being explored as one of the prospective options. These technologies have been thoroughly investigated as a potential project to capture CO 2 emissions and provide purified air, natural gas, or fuel oil. An applied approach, on the other hand, was not taken into consideration while evaluating the influence that this technology has on emissions. For this reason, British Columbia provides a substantial chance to examine emissions that were produced after the DAC actions were put into place in 2015. In this study, the difference-in-differences methodology is employed for the very first time to compare the emissions that are produced by the transport sectors in British Columbia with those emitted by other provinces in Canada. The role that GDP and population play in the release of emissions is also taken into consideration in this paper. Based on the research results, it can be observed that the implementation of DAC initiatives has yielded notable effects. Evidence shows that the DAC effort has led to an average reduction of 0.08 in logarithmic CO 2 emissions in the transport sector. By accounting for GDP and population, the empirical results indicate that DAC technology reduced CO 2 emissions in British Columbia compared to provinces without DAC facilities. DAC initiatives are expected to become increasingly prevalent between the mid-2030s and 2040s. Overall policy implications suggest that there is a need for DAC technologies to collaborate with alternative mitigation technologies, or alternative technologies should collaborate with DAC technologies that are more efficient to achieve the targeted goals in a short time. • The study offers empirical evidence of DAC technology's impact, providing insights beyond theoretical conjectures. • Based on the research results, it can be seen that the implementation of DAC facilities has yielded notable effects. • DAC technology in British Columbia has led to a significant and negative impact on CO 2 emissions compared to other provinces without DAC facilities. • The study highlights the differential effectiveness of DAC across transport sectors, pointing toward a need for tailored implementation strategies. [ABSTRACT FROM AUTHOR]

This paper delves into an econometric analysis to uncover the differences between Nordic European Countries - NEC (Norway, Denmark, Finland, Sweden) and South-East European Countries - SEEC (Greece, Bulgaria, Romania, Hungary). The focus is on exploring the interplay among electricity generation from Renewable Energy Sources (RES), CO 2 emissions, urban population and GDP per capita in these two European regions. The study employs cross-sectional dependence, first and second-generation panel unit root tests, Pedroni panel and Westerlund panel cointegration tests, ARDL, Dumitrescu-Hurlin causality test to establish the relation among variables. The findings reveal that in both regions, CO 2 emissions are significantly influenced by GDP per capita, urbanization and RES generation. Furthermore, urbanization plays an important role. When examining causality in SEEC, three bidirectional relationships are observed: urbanization and CO 2 , urbanization and GDP, urbanization and generation from RES, and two unidirectional causalities: CO 2 and GDP, generation from RES and CO 2. In contrast, for the NEC, there are two bidirectional causalities: urbanization and CO 2 , urbanization and generation from RES, and two unidirectional causalities: GDP and CO 2 , generation from RES and CO 2. These results highlight the nuanced and varied nature of environmental and economic dynamics across various European regions. • Relationship between CO 2 emissions, GDP per capita and electricity production from RES. • Investigating two groups of EU countries located in the North and SE Europe. • Estimations with Fully Modified and Dynamic Ordinary Least Squares. • Causality and cointegration between variables. [ABSTRACT FROM AUTHOR]

Sensitivity analysis has been widely used in building energy analysis to identify key variables influencing building energy and carbon emissions. However, most previous studies only focus on the single time scale energy use of buildings when conducting sensitivity analysis. The multiple time-scale sensitivity analysis would lead to increased complexity and challenges in building energy analysis. This paper proposes a new systematical procedure for global variance-based sensitivity analysis of multiple time-scale energy use of buildings. A case study of an office building is used to demonstrate the application of this approach using the Bayesian adaptive spline surface (BASS) models at three different time scales (annual, monthly, and daily). The results indicate that the procedure proposed here can provide fast and reliable sensitivity results for the multiple time-scale building energy assessment. The BASS learning models have the advantages of high predictive performance and the fast computation of sensitivity indicators in a closed form without Monte Carlo integrals. The application of principal component analysis can deal with the highly correlated multi-output energy use at a smaller time scale, which can further reduce computational costs. Moreover, the sensitivity analysis at the different time scales can provide new insights into energy characteristics due to the intrinsic variations of weather conditions. This systematical procedure can provide useful guidance for the multiple time-scale model calibration and multi-step ahead predictions of buildings. Moreover, the method proposed here can be extended to the sensitivity analysis of energy use in different time scales (sub-hourly, hourly, or daily) for other energy systems (such as PV, solar thermal, and wind). • Bayesian adaptive spline surfaces (BASS) in sensitivity analysis of building energy. • Multiple time-scale sensitivity analysis of buildings using BASS models. • Building energy assessment using principal component analysis and BASS. • Reduce computational time for sensitivity analysis of building energy using BASS. [ABSTRACT FROM AUTHOR]

Cooling for domestic applications using low/medium-grade heat sources is essential to reduce CO 2 emissions and promote environmental friendliness. Thermoacoustic technology is quite promising to meet such demand. This paper proposes a compact and highly efficient single-stage thermoacoustic refrigerator for utilizing low/medium-grade heat source. It is different from conventional design by containing only one thermoacoustic core unit and enhancing cooling efficiency by improved acoustic matching incorporating a cavity structure with no less cooling performance than a multi-unit thermoacoustic refrigerators. For this, a matrix transfer method based on thermoacoustic theory is applied to investigate the onset characterizes of the thermoacoustic cooling system. The present results reveal that the lowest onset temperature difference of 62 K can be achieved when using nitrogen as the working gases. Steady-state characteristics of the system are then investigated, including the exergy loss analysis, the axial distributions of key parameters, cooling performance under different working conditions and mixed working gases. The analysis based on the introduced cavity is subsequently done in terms of the position, volume, and quantity of cavity. Further comparisons are made between the single-cavity and multi-cavity structures, demonstrating a superior performance of the single-cavity structure. Finally, the proposed thermoacoustic refrigerator is compared with existing heat-driven cooling technologies. The results demonstrate that the thermoacoustic refrigerator can compensate the deficiencies of the existing heat-driven cooling technologies at low cooling temperatures. • A thermoacoustic refrigerator using low/medium-grade heat is proposed for domestic cooling. • The increased mean pressure plays a favorable role in reducing the onset temperature difference. • The refrigerator achieves a cooling efficiency 0.24 to 0.61 at cooling temperatures of −23 °C to 7 °C. • Single-cavity structure outperforms multi-cavity structures in terms of cooling performance. • The refrigerator compensates deficiencies of current heat-driven cooling at low temperatures. [ABSTRACT FROM AUTHOR]

In recent years, the potential of Solid oxide fuel cell (SOFC) technologies as energy conversion devices with high efficiency and zero‑carbon emissions has been underestimated, where high fuel utilization plays a crucial role. Although the SOFC system with anode off-gas single recirculation has received much attention as a means to increase fuel utilization, this problem is not thoroughly addressed. To overcome this limitation, this paper proposes a novel SOFC system concept featuring anode off-gas "dual" recirculation, designed to maximize fuel utilization up to the theoretical limit of 100%. This innovative approach inherently eliminates carbon dioxide emissions through an integrated carbon capture process, thereby maintaining systematic carbon balance. A comparative analysis among the novel dual recirculation system, traditional single-pass, and other anode off-gas recirculation systems, reveals that the proposed system outperforms existing configurations. It achieves electrical and overall combined heat and power efficiencies of 49.6% and 85.7%, respectively, even after accounting for the energy required for carbon capture. This represents a 21.5% increase in electric efficiency over the single recirculation system, with a six-fold increase in tail-gas CO 2 concentration, reaching 26.3%. The study categorizes the four analyzed systems into "open" or "closed" based on whether the anode off-gas is directly discharged into the environment. Systematic investigations into carbon deposition potential, fuel utilization, flow rate, operating voltage, carbon capture ratio, and condensation temperature disclose distinct patterns between open and closed systems. The closed configurations exhibit superior performance, suggesting they are more suitable for SOFC applications. [Display omitted] • Merit of fuel utilization to SOFC systems is reevaluated. • Novel SOFC system with anode off-gas dual recirculation is proposed. • The system achieves systematic fuel utilization of 100%. • The system achieves superior energy and carbon capture performance. • Introduced "open" and "closed" SOFC system categories. [ABSTRACT FROM AUTHOR]

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

• H-RE-CCHP system is formed by integrating hydrogen device, renewable energy and CCHP. • System's capacity planning and operation are studied under source-load uncertainties. • Distributionally robust optimization model with cost and carbon targets is proposed. • The proposed model can resist multiple uncertainties and reduce conservatism. • The formed system can achieve notable environmental benefit by increasing less costs. Hydrogen is one type of clean renewable energy. By introducing hydrogen production, storage and conversion devices into the combined cooling, heating and power (CCHP) system to replace its fossil fuel components, the hydrogen-involved total renewable energy CCHP (H-RE-CCHP) system is formed, thus bringing an effective way to promote energy transition towards low carbon. However, the conflict between economy and environment, and interference of source-load uncertainties hinder the low-cost construction as well as economic, stable, and low-carbon operation of H-RE-CCHP. In view of this, this paper proposes a multi-objective distributionally robust optimization (DRO) model for H-RE-CCHP planning. Firstly, a moment-based ambiguity set is constructed to define the possible range for the joint distribution of solar irradiation and multiple loads. Secondly, considering the "worst-case" distribution within the ambiguity set, the economic and environmental objective functions are presented for minimizing system construction, maintenance and operation costs while reducing carbon emission. Furthermore, the sizing- and operation-related constraints are formulated according to such principles as maintaining multi-energy balance when the forecasting errors of uncertainties occur. Finally, by applying strong duality and augmented ε-constraint for model solving, Pareto optimal solution set is obtained, and from which final planning and operation scheme is selected according to decision preference and interactions between objectives. Simulations mainly verify: 1) due to considering multiple objectives, H-RE-CCHP planning and operation reach the reasonable trade-off between economy and environment, that is, carbon emission/economic cost can be reduced by 99.81 %/66.70 % comparing with single economic/environmental objective; 2) with applying DRO in resisting uncertainties, 100 % renewable energy accommodation is achieved while meeting stable H-RE-CCHP operation; etc. Accordingly, this paper provides an effective approach for renewable energy system planning, thus contributing to the low carbon development of economic society. [ABSTRACT FROM AUTHOR]

• A BIM-supported energy performance analysis for green building design is proposed. • This systematic analytical framework integrates explainable machine learning and multi-objective optimization. • It can serve as a data-driven decision tool to guide green building development even under uncertainty. • The top important features that greatly affect the building energy performance can be quantitatively identified. • Building energy performance can be improved by 13.43% using the obtained optimal strategy. Supported by the combination of the advanced BIM technique with intelligent algorithms, this paper develops a systematic framework using explainable machine learning and multi-objective optimization to realize the automatic prediction and optimization of building energy performance towards the sustainable development goal. There are three critical parts incorporated, including the DesignBuilder simulation, BO-LGBM (Bayesian optimization-LightGBM) and an explainable method SHAP (SHapley Additive explanation)-based prediction and explanation of building energy performance, and AGE-MOEA algorithm-based multi-objective optimization (MOO) under sources of uncertainty. It has been verified in a case study for green building design. Results show that: (1) The predictive BO-LGBM model can make a highly precise prediction in agreement with the simulation data, reaching up the R2 larger than 93.4% and MAPE smaller than 2.13%. From the SHAP calculation, features related to the HAVC (Heating Ventilation and Air Conditioning) system tend to contribute more to affecting the prediction results. (2) The AGE-MOEA-based optimization can identify a set of Pareto optimal solutions in simultaneously minimizing the building energy consumption, CO 2 emission, and indoor thermal discomfort degree, arriving at the highest optimization rate of 13.43% under proper adjustment of certain features. (3) In the MOO task, the consideration of model and data uncertainty by prediction intervals and Monte-Carlo simulation can further increase the optimization rate by around 4.0% than the deterministic scenario, resulting in more desired strategies in optimizing the green building performance. In short, this paper enriches the area of green building development. For one thing, it raises the transparency and interpretability of machine learning to make the prediction more convincing. For another, it efficiently determines the passive and active design solutions along with the detailed profile of influential factors for green building design. [ABSTRACT FROM AUTHOR]

The demand response, as an effective means of carbon reduction in the demand side, generally uses carbon emission factor (CEF) to evaluate the carbon emissions. However, in the existing research, the output of demand-side renewable energy units is neglected in CEF, which will lead to an inaccurate assessment of carbon emissions and is not helpful for the carbon reduction by the demand response. In this paper, a bi-level incentive-based integrated demand response (IBIDR) approach is proposed to reduce the carbon emission cost with multi-energy time-varying CEFs. In particular, the multi-energy time-varying CEFs model is proposed considering the demand-side proportion of solar output in the total energy consumption. To explore the role of CEF in IBIDR, the coupling effect of the multi-energy time-varying CEFs on the response willingness model is constructed to promote the interaction among different energy resources to reduce the carbon emission. Finally, the proposed bi-level IBIDR optimization model is transformed into a single-level nonlinear problem based on the backward induction method, which can obtain the optimal incentive strategy of IBIDR. Simulation results show the effectiveness of the proposed approach in reducing the incentive cost and carbon emission cost of multi-energy aggregators. • A low carbon bi-level IBIDR model is proposed to reduce the carbon emission. • Multi-energy time-varying CEF model is established to calculate the carbon emission. • Coupling impact of the multi-energy CEFs on the response willingness model is constructed to promote the interaction among different energy resources. • The proposed bi-level IBIDR model is transformed into a single-level model through the backward induction method. [ABSTRACT FROM AUTHOR]

This paper explores the viability of utilizing agricultural biomass-based energy potential, employing mathematical, engineering, and economic modeling techniques. Moreover, the potential of a biogas-based co-digestion (CD) system, integrating its techno-economic performance and environmental sustainability in terms of electricity generation, has also been studied. In this investigation, the categorization of 25 different plant species into two groups: arable field crops (AFCs) and horticultural plants (HPs), was performed. Data was collected during the 2021‐–2022 cropping season in Bangladesh from various sources, including literature reviews, governmental, and non-governmental organizations. The findings revealed that the available agricultural biomass residues, totaling 1,02,585.75 KT, have the capacity to generate 1,33,815 million m3/year of biogas. This energy potential corresponds to 291,125.85 TJ/year or 9231.60 MW of electricity, which can fulfill 88% of the national total energy demand. In terms of levelized cost, the proposed approach is more competitive and shows a greater promise compared to other technologies. Furthermore, it demonstrates environmental friendliness by reducing CO 2 emissions by 156 tons at a cost of $7/ton while earning $1092 annually from the potential carbon-credit market. This approach presents a potential solution to address Bangladesh's energy crisis. The payback period of the system ranged from 2.93 to 3.75 years, with and without the inclusion of a slurry, respectively. The recommended methods hold significant promise for meeting national energy demands. A case study was provided as a proof-of-concept (PoC) to validate the approach. This study is the first of its kind, providing valuable insights into the renewable energy potential in Bangladesh. The results will assist policymakers in formulating sustainable energy policies. • Mathematics, engineering, economics and environmental modeling explored renewable energy. • Techno-economics and environmental sustainability validated the proposed methodology. • Bioenergy-based electricity could supply 88% of the national energy demand of Bangladesh. • A case study was provided as a proof-of-concept (PoC) to validate the approach. [ABSTRACT FROM AUTHOR]

Equilibrium capacity expansion is an effective tool to help regulators predict the generation evolution of power systems by analyzing the decision-making of generation companies (Gencos). With the development of carbon markets (CMs) and electricity markets (EMs), Gencos' decisions in these two markets (encompassing investment and bidding decisions related to carbon emission allowances and electricity) are deeply intertwined through market clearing results. However, existing studies mainly focus on equilibrium capacity expansion with fixed carbon prices and usually ignore the realistic clearing process of CMs, which cannot simulate Gencos' coupled decision-making and accurately predict generation evolution under the CMs and EMs. This paper proposes a two-level equilibrium capacity expansion model for power systems which considers the coupled decisions of Gencos between CMs and EMs. At the upper level, multiple Gencos' coupled decision models are proposed based on their interaction with market clearing results. At the lower levels, CMs and EMs are respectively cleared to determine the varying carbon and electricity prices. Furthermore, the developed equilibrium capacity expansion model is reformulated as an equilibrium problem with equilibrium constraints (EPEC), which is solved by a diagonalization-based solution procedure. Finally, a provincial power system of southeast China is used to validate the performance of the proposed framework. The results illustrate that under CMs and EMs, non-fossil energy would account for 81.3% of total capacity and 73.88% of total generation in 2050. Compared to the existing model using fixed carbon prices, the consideration of realistic carbon market clearing will promote the installation of renewable energies and improve social welfare. The proposed model can provide beneficial references for regulators to perceive the evolution of power systems in CMs and EMs. • A two-level equilibrium capacity expansion model for power systems is proposed. • A novel coupled decision model for Gencos in CMs and EMs is developed. • The proposed model is solved by a diagonalization based solution procedure. • The results can guide the transition of power systems under the CMs and EMs. [ABSTRACT FROM AUTHOR]

The proportion of spot trading and short-term contracts has gradually increased in the rapidly growing LNG market, leading to more uncertainties in LNG demand and prices that significantly challenge LNG shipping decisions. In this paper, a mathematical model is developed to minimize transportation costs from multiple exporting countries to multiple importing countries under demand and price uncertainty, both of which are results of LNG market liberalization. A Gaussian process metamodel based simulation optimization method is proposed to solve the fleet planning problem, accounting for various uncertainties. A case study is given to illustrate the effects of uncertainties on the optimal solutions. The results demonstrate that shipping companies may purchase fewer ships and charter more ships to hedge against the risk of uncertainty. LNG market liberalization can significantly reduce its transportation costs and carbon emissions. The results suggest the need for further LNG market liberalization and measures to mitigate uncertainties for shipping companies, such as removing destination clauses. • More price and demand uncertainties stemming from LNG market liberalization challenge fleet planning decisions. • Consider a fleet size and composition optimization problem with a mathematical model. • A Gaussian process based simulation optimization method solves the fleet planning problem. • Shipping companies may purchase fewer ships and charter more ships to hedge against the risk of uncertainty. • LNG market liberalization can significantly reduce transportation costs and carbon emissions. [ABSTRACT FROM AUTHOR]

Substituting renewable energy for coal power under the carbon neutrality goal is an inevitable path for power system transformation, while the high share of renewable energy leads to a rapid rise in system flexibility demand. Currently, coal-fired power is still one of the most economical and reliable large-scale flexible resources in China. Therefore, the issue of how to mitigate the conflict between cleanliness and flexibility for coal-fired power is urgent to be solved. Accordingly, this paper established an optimization framework for the retirement and technology adoption of coal-fired power units with high spatial-temporal resolution, and the regional carbon emission budget is estimated from a top-down perspective. Secondly, a dynamic carbon quota allocation method considering technological innovation and adoption is proposed. The carbon trading cost of the units is evaluated from a bottom-up perspective, to realize the interaction between the obligatory policy constraints of the top-level target and the flexible incentives of the market mechanism. Finally, the empirical analysis was conducted in the Beijing-Tianjin-Hebei region of China. The optimal solution achieved by proposed method results in a substantial 27% reduction in total costs while keeping the increase in carbon emissions below 3%. By analyzing the dynamic evolution path of coal and the scale of diffusion of multi-type technologies, the differences in the roles coal power assuming at various stages of decarbonization are discussed. The robustness of the model and the validity of the proposed method were verified by comparing the dynamic/static carbon quota allocation. Overall, this study provides an effective optimization model for investigating the low-carbon technology adoption timing for coal power in Beijing-Tianjin-Hebei region, and offers valuable insights for other coal-dominated emerging economies seeking for power system transformation roadmap in the forthcoming net-zero carbon age. • A decision-making optimization model of CCPs low-carbon technology is proposed. • A dynamic CET system with upper-lower layer interaction is introduced. • The proposed dynamic CET system results in 27% reduction in total costs. • The high-resolution data of coal power in Beijing-Tianjin-Hebei region are used. • The strategy to solve the conflict between cleanliness and flexibility is given. [ABSTRACT FROM AUTHOR]

With the increasing penetration of distributed renewable energy, prosumers, a kind of customers capable of producing power, are able to share the information of the power consumption and the power price in a community, which evolves into an energy trading between producers and consumers over time. In this paper, considering the benefit conflict whether paying for the power from green power market, or suffering the cost from electricity market together with carbon market due to the carbon cap, a game-theoretic approach is proposed for the prosumers, who have to face the carbon emission constraints, in order to optimize their own utility with an energy trading in a community. It is considered that producers provide the optimal power price for the consumers based on the non-cooperative game, then the consumers decide which producer to purchase power from according to the evolutionary game, afterwards the interaction between the producers and the consumers are analysed in terms of the Stackelberg game. The low-carbon energy trading process in a community is solved with a mixed integer programming method based on the equilibrium of the games. Simulations show that the proposed approach is able to significantly improve the utility of the prosumers in the community. • This is a new exploration to focus on an energy trading for prosumers faced with carbon emission limits in a community. • Whether purchasing power from green power market or suffering the cost from electricity and carbon markets is considered. • A trading method with three gaming processes is applied to explore the benefit conflict among the prosumers in the community. [ABSTRACT FROM AUTHOR]

Hydrogen is among the energy carriers, which are most often considered for bringing about a sustainable energy system. Yet, so far, hydrogen has not delivered as an energy carrier. The core objective of this paper is to provide a comprehensive analysis of the state-of-the-art and the future prospects of green hydrogen in the European energy system from economic, energetic and CO 2 emissions point-of-view. The analysis shows that there are some increasing opportunities for hydrogen use in industry and in the transport sector, when electrification is not possible or is too expensive, as well as a storage in the European electricity system. However, a hydrogen-based energy system will remain a vision at least over the next decades. The major reason for this is the unfavorable economics mostly due to high investment costs in the whole supply chain. In addition, the overall efficiencies in the hydrogen chains are moderate in general. The full environmental benignity of hydrogen as an energy carrier is only provided when renewable energy sources are used for hydrogen production. However, in Europe, the potentials for green hydrogen are very limited due to the insufficient expansion of renewable electricity generation. For this reason, many European countries are considering options for green hydrogen import. The future of hydrogen is highly dependent on the supporting policy framework. To reduce the risk in the investment in hydrogen infrastructure, as well as to justify the promotion of green hydrogen, it is very important that Europe works out a very clear and realistic long-term implementation strategy. • Hydrogen might have potential in the European energy system, yet a hydrogen-based energy system stays visionary. • Green hydrogen from renewable electricity via electrolyzers is key for environmental benefits and decarbonization. • Hydrogen should be prioritized for uses where electrification is not feasible. [ABSTRACT FROM AUTHOR]

In order to facilitate the carbon emission reduction to mitigate climate change, this paper proposes a coordinated multi-stage planning strategy for the transmission-level integrated electricity-gas-heating system. The strategy coordinates installations of gas-fired generation plants, renewable energy plants, gas wells, power-to-gas plants, transmission lines and pipelines, considering retirements of coal-fired generation plants. It can help facilitate carbon emission reduction of the electricity system as well as the integrated energy system. The objective is to minimize the total present value of the investment and the operation economic cost, with consideration of constraints from each sub-system and carbon emission restriction. In the case studies, the proposed planning strategy is verified to be able to obtain the installation plan of the related facilities. With which the system can reduce the proportion of fossil fuel energy utilization and increase renewable energy utilization. Hence, carbon emission reduction is achieved. In addition, it has demonstrated that integrating the multi-energy systems can help reduce the economic cost in the planning result. • Multi-stage planning strategy of the integrated energy system is proposed. • Energy conversions among different sub-systems are considered. • An aggregated model of district heating system is adopted. • Capability of carbon emission reduction is verified. • Contributions from flexibility of gas and heating systems are demonstrated. [ABSTRACT FROM AUTHOR]

Committed to achieving carbon neutrality by mid-century, South America faces unique challenges in carbon mitigation due to its distinct socio-economic, technological, and institutional backgrounds compared to developed and other developing economies. Previous research on South America's carbon emissions has limited country and sector coverage and typically adopts a top-down approach that focuses on the overall emissions of an economy, neglecting individual energy sources and emission sectors. Therefore, this paper compiles a comprehensive inventory of CO 2 emissions for South American countries based on a meticulous bottom-up approach and multi-source data integration, followed by a thorough analysis of their temporal evolution patterns and the driving forces behind them. Between 2010 and 2020, South America's overall emissions peaked in 2014 at 1092.3 Mt., led by emissions from oil consumption and from the transport and electricity sectors, and dropped to 865.6 Mt. in 2020, due to the economic fluctuations and COVID-19. Specifically, Brazil was the largest emitter, Chile experienced the most significant increase in emissions, and Paraguay recorded the highest emission growth rate. Generally, expanding population was constant driver of emission growth. High energy intensity led to significant emission increases in Brazil and Ecuador, while elevated carbon intensity was the main driver in Argentina. Energy structure changes played a positive role in most countries to different extents (e.g., much more significant in Brazil than in Chile) but aggrevated emissions in Paraguay and Guyana. Therefore, carbon mitigation strategies should be tailored to each country's unique national circumstances. • During 2010–2020, South America's CO 2 emissions from fossil fuels peaked in 2014. • Transport and electricity sectors comprised over 60% of total emissions. • Brazil was the top emitter, while Chile saw the largest increase in emissions. • Emission patterns and driving factors varied significantly across countries. • Carbon reduction strategies should be tailored to local circumstances. [ABSTRACT FROM AUTHOR]

This study assesses alternative energy technologies (i.e., PV and battery systems, electric heat pumps, hybrid gas heating with solar thermal energy) in terms of profitability and CO 2 emissions, for the case of two simulated typical households living in detached houses in Germany. Under the status-quo regulatory framework, the energy transition in the heating sector is fostered through grants for replacing old heating systems, whereas PV generation is fostered by feed-in tariffs and indirect subsidies for self-consumption. This study considers an alternative regulatory scenario with a more market-oriented approach, finding that a CO 2 -oriented reform of energy surcharges and taxes, as well as a reform of network charges, can support a more cost-efficient energy transition in the residential sector. The paper concludes with a discussion of the consistency, cost efficiency and effectiveness of past and current policies underpinning the energy transition in the residential sector. • Assessment of home energy systems across 2 households and 2 regulatory scenarios. • Consideration of PV, batteries, heat pumps and hybrid gas-solar heating systems. • Comparison of subsidies and flat energy tariffs vs. cost-reflective, dynamic tariffs. • Market-oriented approach incentivizes far more cost-efficient CO 2 emission reduction. • The German residential energy sector needs a consistent regulatory framework. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy with stochastic characteristics and continuous refinement of carbon emission policies put forward higher requirements for the construction of new power systems. This paper proposes a risk-averse stochastic capacity planning and peer-to-peer (P2P) trading collaborative optimization method for multi-energy microgrids (MEMGs) considering carbon emission limitations. First, a cooperative operation model for MEMGs considering the capacity planning of distributed generation units, uncertainty from renewable energy generations, carbon emission limitations and P2P electricity trading among MEMGs is formulated. Second, a risk-averse stochastic programming method is applied to avoid the potential risk losses caused by randomness and intermittence of renewable energy. Third, an asymmetric Nash bargaining approach is adopted to ensure the fair allocation of benefits and maintain the willingness of individual MEMGs to participate in cooperation. Then, to protect the privacy of individual MEMGs belonging to different stakeholders, the alternating direction method of multipliers is used to solve the two subproblems in a distributed manner. Meanwhile, to further alleviate the computation burdens, a diagonal quadratic approximation method is applied to linearize the quadratic penalty term in the augmented Lagrangian function and realize the parallel solution of all optimization subproblems. Moreover, the influence of different carbon emission targets on the optimal resource combination strategy of the system is investigated by introducing carbon emission factors. Simulations on different models, strategies, and distributed algorithms are conducted to verify the effectiveness and superiority of the proposed method. • Developed a risk-averse stochastic collaborative optimization model for MEMGs. • Proposed an ANB method to ensure the fairness of benefits allocation. • Investigated the relationship between CRR and optimization strategy for MEMGs. • Designed a parallel distributed algorithm to protect privacy and save computation time. [ABSTRACT FROM AUTHOR]

This research paper presents an in-depth analysis of diesel–electric power systems in offshore Platform Supply Vessels (PSVs). The main contribution is the use of real data obtained from a PSV to produce and validate computational models subsequently used to accurately calculate fuel consumption and emissions, with representative load demands, considering different configurations of non-hybrid and hybrid power systems with the addition of lithium-ion batteries to provide a reliable analysis. Power dispatch optimization had a significant impact, achieving fuel and CO 2 reductions of around 7%–12% in non-hybrid systems, depending on the generator loading limits. Hybridization and battery integration further enhanced reductions, with up to 10% improvements in fuel consumption and CO 2 emissions. Particulate matter (PM) emissions and running hours were substantially reduced, but NO x varied. Validity checks accounted for biases, and the study provided reliable assessments of trends. The differences between monthly averages and robust approaches were minimal. Overall, the strategies presented achieved reductions of up to 15% in fuel consumption and CO 2 emissions, 20% in NO x emissions, and 68% in PM emissions for the complete mission. • Analyzing Platform Supply Vessel's power system with real data and computation models. • Power dispatch optimization yields significant fuel consumption and CO 2 reductions. • Battery integration further improves fuel efficiency and CO 2 reductions. • Particulate matter emissions and running hours reduce considerably, but NO x varies. • Validity checks account for biases providing a reliable assessment of trends. [ABSTRACT FROM AUTHOR]

Ammonia-diesel dual-fuel engines have been demonstrated as a promising technology to reduce the greenhouse gas (GHG) emissions. However, understandings of the operational boundaries and combustion mechanism in the ammonia-diesel dual-fuel engines are far from adequate, and the published works are mostly done with relatively low ammonia substitution ratios. To bridge the gap between the technology status and the potential in engineering applications, a series of engine bench tests of the pilot diesel-ignited ammonia combustion with detailed analysis of the combustion performance and exhaust gas emissions are conducted in this study to explore the GHG reduction potential. As the first report, the effects of the diesel injection timing and ammonia energetic ratio on the performance and exhaust gas emissions of the dual-fuel engine at various loads and a fixed speed are evaluated in this paper. As the ammonia energetic ratio increases, the diesel injection timing for the stable engine operation becomes narrower. Increasing the ammonia energetic ratio decreases the indicated thermal efficiency. The ammonia-diesel dual-fuel engine can maintain the stable of COV IMEP below 3% at a wide ammonia energetic ratio range, the indicated thermal efficiency with the tested upmost 90% ammonia energetic ratio can reach about 34% if an optimized diesel injection timing is implemented. The unburned ammonia increases linearly with the ammonia energetic ratio while the unburned ammonia ratios of total input ammonia are similar at the different ammonia energetic ratios. In comparison to the conventional pure-diesel mode, while the CO 2 emission decreases by 50% and 72%, the equivalent CO 2 emission (i.e., CO 2 + 265 N 2 O, labelled as CO 2e) decreases by 24% and 55% at the 60% and 80% ammonia energetic ratios in the ammonia-diesel mode, respectively. In the ammonia-diesel dual-fuel mode, the specific CO 2e , N 2 O and unburned ammonia decrease with the engine load increasing from 50% to 100%, while the highest indicated thermal efficiency is reached at the 75% load. • The effects of different diesel injection timings and ammonia energetic ratios (AERs) at various loads are investigated. • The dual-fuel engine for 90% AER can maintain the stable of COV IMEP below 3%. • The indicated thermal efficiency for 90% AER can be about 34% if the proper diesel injection timing is chosen. • The equivalent CO 2 emission (CO 2e , CO 2 + 265 N 2 O) decreases by 24% and 55% for 60% and 80% AERs, respectively. • The better effect of CO 2e reduction can be achieved at the higher load in the dual-fuel mode. [ABSTRACT FROM AUTHOR]

As an efficient technique to manage massive distributed energy resources (DER), the virtual power plant (VPP) can leverage the aggregated flexibility to enhance the economy and security of the system. However, few efforts have been reported on the carbon emission reduction benefit of the VPP. In this paper, a novel incentive mechanism for low-carbon demand response is designed, which is based on dynamic pricing and carbon intensity. Considering the uncertainty of renewable energy output characterized by the chance-constrained approach, a coordinated optimization model in the joint day-ahead energy, reserve and carbon market for the VPP operator and flexible consumers is proposed, including the pre-dispatch model, the energy storage systems (ESSs) incorporated carbon emission flow calculation model, and the bi-level final-dispatch model. Numerical simulations of both small-scale and large-scale cases demonstrate that the proposed low-carbon dispatch strategy can boost the payoff of the VPP operator, reduce carbon emissions in the joint energy-reserve-carbon market, and improve the robustness of the power system against uncertainty. • Low-carbon demand response (DR) to fully utilize the potential of the demand side. • Carbon emission flow model integrating energy storage systems. • Chance constrained approach to compensate for the uncertainties based on the versatile distribution. • The VPP engages in the joint energy-reserve-carbon market considering renewable energy uncertainties. [ABSTRACT FROM AUTHOR]

A novel zero carbon emission combined power and hydrogen system is developed in this paper, which integrates power generation, hydrogen production, and energy storage. The novel combined system is composed of solar power tower subsystem, supercritical carbon dioxide recompression Brayton cycle subsystem, hydrogen gas turbine subsystem, hydrogen production subsystem and thermoelectric generator subsystem. Considering the solar instability, three operating modes are set for the combined system: pure hydrogen mode, solar‑hydrogen hybrid mode and pure solar energy mode. Comprehensive energy, exergy and economic analyses are performed for the combined system. The effects of five key parameters on the system performance are investigated. Taking the maximum exergy efficiency and the minimum levelized cost of energy as the objective function, the key parameters are optimized by the genetic algorithm. Results show that in pure hydrogen mode, the thermal and exergy efficiency of the combined system are 44.79% and 45.49%, respectively. Compared with pure hydrogen mode, the levelized cost of energy is reduced by 44.76% and 75.16% in solar‑hydrogen hybrid and pure solar energy mode. There is an optimal compressor pressure ratio to make the combined system have better thermodynamic and economic performance. Parameter optimization results show that for pure solar mode, the levelized cost of energy of the combined system is 75.4% and 56.3% lower than that of pure hydrogen mode, solar‑hydrogen hybrid mode respectively in the levelized cost of energy optimal design. The derived significant contributions of this work highlight strong potentials for combined system based on solar energy‑hydrogen complementary utilization. [Display omitted] • Development of a novel system for production power, hydrogen and energy storage. • Complementary power generation technology of solar energy and hydrogen is proposed. • Three operating modes have been proposed to reduce the impact of solar fluctuation. • Compared with pure hydrogen mode, the LCOE is reduced by 75.16% in pure solar mode. [ABSTRACT FROM AUTHOR]

Due to the proliferation of distributed renewable energy, bidirectional electricity and carbon interaction appears in the coordinated transmission and distribution network and increases complexity in entities' decision-making. To address the issues, this paper develops a hierarchical market framework integrated with electricity and carbon trading. Specifically, an electricity market which combines locational marginal price (LMP) and peer-to-peer (P2P) trading is established considering transmission and distribution coordination. Meanwhile, based on carbon emission flow and P2P trading, a hierarchical coordinated carbon market is also designed to precisely measure and price carbon responsibility and contribution from prosumers. In addition, a hierarchical distributed algorithm is developed to clear the proposed market with privacy protection. Numerical results authenticate the necessity for transmission and distribution coordination, benefits of distribution electricity P2P trading as well as superiority in carbon measurement and pricing method. • An LMP-based electricity P2P market is designed for transmission and distribution network. • A systematic method is proposed to precisely price prosumers' carbon responsibility and contribution. • A hierarchical distributed algorithm is proposed to clear the P2P market under TSO-DSO-prosumer coordination. [ABSTRACT FROM AUTHOR]

This paper aims at evaluating the best allocation of potential biomethane generation for the decarbonization of the transport system, presenting a case study in Italy. The country has some peculiar features, such as several operating biogas plants, additional potential feedstock for biogas/biomethane generation, a well-developed natural gas network and established relevant natural gas uses in different final sectors, including transport. Based on current estimates for sustainable biomethane potential by 2030, ranging from 2.3 to 7.6 billion cubic meters depending on the scenario, the analysis compares technologies for the generation, distribution and final use of biomethane. The results of the analysis confirm the potential interesting contribution of biomethane in decarbonizing the Italian transport system: a billion cubic meters of biomethane can lead to 2.33–4.37 Mt CO 2 e savings, depending on the feedstock mix and the application. On a national basis, annual climate emission savings in 2030 range from 10.0 to 26.7 Mt CO 2 e , depending on the scenario. Additional 3.1–8.1 Mt CO 2 of emissions can be avoided if the CO 2 captured during the biomethane upgrading can be stored or reused. The proposed methodology could be used to extend the analysis to other countries, and to the European context. • We analyze the climate emissions of biomethane for different supply chains. • We evaluate the effects of feedstock, distribution strategy and electricity mix. • The results confirm the important role of biomethane for transport decarbonization. • We discuss the best allocation in transport segments based on emissions savings. • We compare alternative biomethane scenarios for Italy in 2030. [ABSTRACT FROM AUTHOR]