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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]

With the high proportion of renewable energy connected to the grid, the problem of insufficient flexibility in the power system has emerged. Renewable energy and controllable distributed resources can be aggregated and managed through virtual power plants, reducing the need for flexibility to a certain extent. Although building new energy storage systems can compensate for the lack of flexibility, it requires high initial investment costs. To address this, this paper proposes a lease mechanism for coal-fired units based on the combination of carbon credits and prices, providing the right to use coal-fired units for virtual power plants. Subsequently, different demand response strategies are utilized to control the controllable loads of various users, thereby providing certain controllable resources for the virtual power plant. Additionally, to ensure the optimal decision-making of the virtual power plant operator, a cost model accurately describing the capacity degradation state of the energy storage system is adopted. Moreover, a multi-time scale scheduling strategy of virtual power plant is implemented to fully utilize controllable resources of different time scales, effectively dealing with the power imbalance caused by multiple uncertainties. The results show that utilizing the leasing mechanism of coal-fired units and employing demand response strategies from different users can provide flexibility to the virtual power plant. The accuracy of the capacity degradation model used by the operator significantly impacts the optimality of the scheduling plan. • The scheduling model of VPP consider capacity degradation of ESSs, carbon trading, and DR strategies. • A lease mechanism for coal-fired units based on the combination of carbon credits and prices is proposed. • This paper proposes ISBDR strategies for reduceable loads. • The ESSs capacity degradation model adopted takes into account DOD and SOC. • A multi-time scale scheduling strategy of VPP is adopted to deal with multiple uncertainties. [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]

In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operation of reliable distribution networks. However, these applications require not only probabilistic demand forecasts, which involve quantification of the forecast uncertainty, but also determining which consumers to include in the aggregation to meet electricity supply at the forecast lead time. While research papers have been proposed on the supply side, no similar research has been conducted on the demand side. This paper presents a method for creating a portfolio that optimally aggregates demand for a given energy demand, minimizing forecast inaccuracy of overall low-level aggregation. Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. An evaluation of probabilistic load forecasts demonstrates that all three approaches enhance the accuracy of forecasts produced by random portfolios, with the Seasonal Residual approach for Korea and Ireland outperforming the others in terms of accuracy and computational efficiency. • We proposed new low-voltage aggregation techniques based on portfolio theory to improve probabilistic demand forecasting. • The increased volatility at the low voltage level requires probabilistic forecasting to measure uncertainties. • The proposed methods were found to outperform random aggregation with the real-world data from Korea and Ireland. • A simple forecasting approach minimizing the standard deviation of deseasonalized demands was accurate and efficient. • The new method can contribute to achieving carbon neutrality, enable P2P energy sharing, and benefit prosumers. [ABSTRACT FROM AUTHOR]

China's energy transformation has been accompanied by potential energy security issues. However, the influence degree and driving mechanism behind this phenomenon remain unclear. In this paper, we developed a complex network-based technical resilience assessment framework and quantified both the historical trajectory and the prospective pathway of energy technical resilience in China. We proposed to reveal the relationship between cleaner energy supply and technical resilience by defining three energy technology resilience indicators: energy robustness, energy recovery rate, and energy performance loss. The results showed a consistent decline in energy technical resilience within China, predominantly characterized by an average diminution of 6.83% in energy robustness from 2010 to 2018. Taking carbon neutrality as a target, China's energy system transformation, characterized by increasing clean energy supply, leads to a significant improvement of 7.73% in energy robustness from 2020 to 2060. Conversely, the nationally determined contribution scenario exhibited negligible changes (±0.57%) in energy robustness, while a decline of 2.21% was noted under the business as usual scenario. Furthermore, energy robustness exhibited increments of 12.07%, 2.3%, and 4.17% for every 10% rise in the share of clean energy supply during 2020–2060, demonstrating greater resilience for cleaner energy systems against disruptions, including energy import interruptions, malicious attacks, and random failures. It has been concluded that enhancing the supply of clean energy can significantly contribute to bolstering China's energy technical resilience, while simultaneously addressing climate change concerns. • Network analysis is used to assess China's energy technical resilience. • Energy technical resilience is evaluated regarding robustness, recovery rate, and performance loss. • Energy system robustness significantly increases under carbon neutrality scenario. • Increasing clean energy supply strengthens resilience against shocks. [ABSTRACT FROM AUTHOR]

Wind and solar power are expected to play important roles in many countries to achieve carbon neutrality; however, their inherent instabilities pose significant threats to existing power grids. Because hydropower has been recognized as a viable compensatory resource for solar and wind energy uncertainties, many studies have sought to determine optimal scheduling strategies for hydro-PV, hydro-wind, and hydro-PV-wind systems. However, few studies have simultaneously considered the sizing and scheduling of large-scale hydro-PV-wind hybrid systems connected to long-distance transmission lines. Therefore, this paper develops a mathematical metric to measure the wind and solar output complementarity and incorporates it into a multi-objective sizing and scheduling model for a hybrid hydro-PV-wind system, in which resource complementarity, profit generation, system reliability, and power curtailment trade-offs are concurrently considered. The ɛ -constraint method is employed and decision makers' attitude parameters are introduced to transform the proposed model into its equivalent single objective form. A case study in China reveals that the maximum wind and solar power output complementarity rate can be at least 0.19 for the studied hybrid hydro-PV-wind system. The incorporation of wind enables the hybrid hydro-PV-wind system to provide 4.11% more load than a hydro-PV system. In addition, if 2% of power curtailment is permitted, 1.53% more load can be served and 14.9% higher profits can be earned. To better explore the application of hybrid systems, management recommendations are provided for system operators, the power industry, and management administrations. • Multi-objective sizing and scheduling method for the hybrid renewable system is proposed. • Mathematical metrics to measure wind and solar power complementarity is provided. • A complementarity rate of 0.19 can be reached with 100% reliable service and no curtailment. • Integrating wind power into the hydro-PV system can reliably serve 4.11% more load. • An optimal rather than minimal level of the renewable power curtailment is more efficient. [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]

Distribution networks (DNs) are under severe requirements of security and ecology, such as maintaining continuous power supply for critical loads under extreme disasters and contributing to the carbon neutrality by 2060. However, the investment of infrastructures in DNs, such as electric vehicle charging stations (EVCSs), are hardly balanced as a result of the coupled relationship between the resilience, economics and ecology with different magnitudes and dimensions. This paper proposes to avoid an overinvestment of the equilibrium configuration strategy of EVCSs in low-carbon resilient urban DNs. Firstly, a bi-level optimization model is established to configurate EVCSs. Multiple objectives, such as the security, economics and ecology of DNs, are enhanced simultaneously in the upper level. Indexes of these objectives are formulated by generating typical scenarios in the lower level, such as economic operation, fault recovery and extreme disaster restoration. Then, the generalized Nash equilibrium (GNE) model is utilized to deal with the equilibrium and coupled relationship between multiple objectives in the upper level, which is solved by the incremental penalty function algorithm. Finally, simulation tests of a 3-feeder 62-node 10 kV DN verify the superiority of the proposed GNE-based equilibrium configuration model of EVCSs compared with other approaches, and the resilience and carbon emission reduction can be improved with an equilibrium configuration scheme of EVCSs under a limitation of investment. • Resilience, economics and ecology indexes in DNs are described accurately considering the V2G-based charging / discharging behaviors of EVs. • Multiple typical operation scenarios are simulated to improve the evaluation of charging / discharging capabilities of V2G-based EVCSs in the equilibrium configuration. • The configuration strategy of V2G-based EV charging stations is proposed balancing the resilience, economics and ecology via generalized Nash equilibrium, in which the shared decision variables and the coupling relationship between multiple objectives and constraints are considered. • The optimal the amount and locations of EVCSs are obtained from a resilience and decarburization oriented perspective with limited investment. [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]

As global concern shifts from developed countries—many of which have made significant strides towards carbon neutrality—to developing nations, an urgent need arises: how can the latter effectively borrow and adapt strategies that have proven successful in the former? This review thoroughly explores the successes and challenges encountered by developed countries in terms of emission accounting, mitigation strategies, and transitioning to renewable energy sources. We then explore how these strategies can be feasibly integrated into the economic, social, and political contexts of developing countries. Furthermore, this paper highlights the importance of customizing these strategies to the unique challenges and opportunities of developing nations, rather than simply replicating efforts. Through a comparative analysis, we present a roadmap to carbon neutrality that acknowledges the distinct contexts of these developing countries while drawing inspiration from global successes. This approach underscores the necessity of a collaborative, global effort in addressing climate change, recognizing the interconnectedness of all nations in this endeavor. It also sets the stage for future research and policy development that are both locally informed and globally conscious. [Display omitted] • Comprehensive analysis of emission accounting successes in developed countries. • Exploration of the unique challenges and opportunities in developing nations. • Emphasis on tailoring, not duplicating, carbon neutrality strategies. • Proposing a cohesive, global approach grounded in local context and solutions. [ABSTRACT FROM AUTHOR]

Industrial parks allow industries to share infrastructure and thus saving money, finally redounding in improving the economy of many countries worldwide. Given the objectives of carbon neutrality imposed by different entities, it results mandatory promoting energy efficiency in industrial parks. Aligning with such objective, encouraging industries to provide energy flexibility becomes essential. In the electricity sector, such flexibility can be provided through optimally managing local assets such as energy storage and flexible loads. However, flexibility provision should be promoted by implanting proper pricing mechanisms. This paper focuses on this issue by developing a local market clearing mechanism for industrial parks, whose main novelty redounds in the inclusion of a fair pricing mechanism through which industries are paid by flexibility provision. Different types of flexible loads are considered and modelled (i.e. curtailable, interruptible and deferrable), so that the new proposal is suitable for leveraging fully capabilities of industrial flexible loads. The whole pricing mechanism is raised as a bi-level game-based model, by which local energy and flexibility prices are revealed in a coordinated way. Challenges brought by the inclusion of binary variables (needed for modelling some types of flexible loads) are solved by proposing a solution algorithm based on the well-known Column & Constraint Generation Algorithm. The resulting optimization framework is Mixed Integer Linear Programming, being therefore solvable by off-the-shelf solvers. A case study is presented to validate the new proposal as well as highlight some important aspects related to local markets in industrial parks and its practical implantation. • A new market model for electricity provision in industrial parks is proposed. • Different flexible loads are modelled including binary variables. • Binary variables are accommodated in a developed Benders-based algorithm. • Local storage has a direct impact on shaving peak load prices. • Renewable generation may discourage to partake in flexibility provision. [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]

Direct air carbon capture (DAC) is a promising negative carbon technology to balance unavoidable carbon emissions from distributed sources. Deploying DAC into the integrated renewable energy system provides an effective way to achieve regional carbon neutrality. Developing a reasonable configuration approach for the DAC-based integrated energy system (IES) is the premise to ensure the stable, flexible, economic and net-zero carbon operation. However, DAC system captures CO 2 from the air through adsorption and desorption with a much lower respond speed than other energy generation, conversion and storage equipment in the IES. Meanwhile, the CO 2 adsorption/desorption rate varies significantly with time, making the energy consumption of DAC does not fully correspond to the amount of CO 2 being captured. Conventional steady-state IES configuration methods fail to reflect such characteristics of the DAC, which may lead to non-optimal or even infeasible configurations. To this end, this paper develops a configuration-oriented dynamic model for the DAC process to correctly reflect the energy consumption characteristics under given internal operating conditions and the variations of the CO 2 adsorption/desorption rate over time. The DAC model is embedded into the configuration framework of the DAC-based IES, formulating a novel configuration approach that optimizes the investment, operation and maintenance, carbon reduction and renewables utilization performance. The optimal capacity of DAC and the corresponding time periods for adsorption, desorption and shutdown operation in typical scenario are determined, revealing the coordinated manner of DAC system with other devices within the IES. Case studies demonstrate the effectiveness of the proposed configuration approach, which can meet the carbon neutral target with the lowest economic cost. Further comparisons also verify the advantages of flexible operation of the DAC system to achieve better coordination with renewable generations and more efficient CO 2 capture. [Display omitted] • Configuration-oriented dynamic model is developed for the S-TVSC based DAC process. • Configuration approach is proposed for DAC-IES considering dynamic behavior of DAC. • Differences in characteristics during CO 2 adsorption and desorption are considered. • The proposed approach achieves carbon neutral target with the lowest economic cost. • Flexible operation of DAC promotes renewable integration and CO 2 capture efficiency. [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]

Due to the imperative nature of addressing climate change, the energy transition is currently underway, prompting the recognition of its urgency. Energy system optimisation models have emerged as crucial tools to assist policymakers in formulating laws and regulations that facilitate the transition towards carbon neutrality. While numerous models have been developed to explore various scenarios and define long-term objectives, only a few models focus on optimising the specific pathway to achieve these objectives. Many existing models lack the necessary time resolution to capture the integration of intermittent renewable energies; or are not open-source, creating a challenge in terms of transparency and reproducibility. This paper introduces EnergyScope Pathway, an open-source and documented model that addresses these limitations. It specifically optimises investment strategies for the whole-energy system over a 30-year period, or more, and optimising its hourly operation. This approach allows for a comprehensive evaluation of the effective integration of intermittent renewable energy sources. The model has a concise and efficient formulation, enabling its execution on personal laptops within approximately 15 min. By applying the model to the case study of Belgium, which presents challenges due to limited potential for renewable energy, we illustrate the importance of four pillars: energy efficiency, renewable energies, sector coupling, electrification and imports. The result pave the way to a new and incremental tool to support decision makers. In comparison to non open-source models, we verified the model's results with similar studies and found consistency in terms of technico-economic estimations. • Linear formulation of an optimisation model to represent a whole energy system transition pathway with an hourly resolution. • Short computational time, 15 min on a personal laptop • Quantify the pillars of the transition: efficiency, renewable energies and sector coupling. • Offers a new and incremental tool to support decision-makers in the energy transition. • Comparison of different approach to represent the transition. [ABSTRACT FROM AUTHOR]

Carbon market is the most effective market tool for carbon emission reduction. China, the largest carbon emitter in the world, established the national carbon market in 2021, covering over 2000 key units in the power sector. Therefore, the forecasting of carbon price has profound implications for environmental and energy policies. In this paper, two traditional econometric model and three kinds of machine learning (ML) algorithms are used to predict carbon returns in the Chinese carbon trading market based on carefully selected predictors. Among all forecasting models, the Random Forest (RF) has the best forecasting performance, followed by some GARCH models using various factors. Compared with the traditional benchmark of ARMA model, the BP neural network (BP) and the GA improved BP method (GA-BP) are less competitive in predicting carbon returns in China because of their large forecasting errors. According to the optimal model, social attention to the carbon market, the international crude oil returns and the overall performance of the stock market are the most important predictors. The findings are robust to the change in the sample set. Overall, the ML approach shows an advantage in forecasting carbon returns, but the selection of predictors is also important. • Machine learning and econometric models are used to forecast carbon returns in China. • The selection of predictors is based on economic analysis. • The Random Forest (RF) outperforms the other ML and econometric methods. • The energy market is extremely important for predicting carbon returns. • Social concerns and stock market performance improve forecasting accuracy. [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]

Solar-driven CO 2 /H 2 O splitting via a two-step solar thermochemical cycle is a promising approach for fuel production and carbon neutrality to address the intermittent instability and low energy density of solar energy while taking advantage of its clean and nonpolluting nature. However, current experimental efficiencies are still below theoretical levels due to various limitations such as material properties and energy loss occurring in the reactor. The reactor, as an important component for commercialization, still faces various challenges including high reaction temperature, limited reactor material longevity, high temperature sealing of moving parts, and slow adsorption and desorption rate of porous structure lining. This paper reviews researches on solar thermochemical oxygen carriers and related reactors, with particular focus on analyzing reactor design criteria, modeling methods, energy efficiency and energy loss analysis. In addition, obtaining low partial pressure oxygen for reduction reactions, heat recovery methods, and synergies between the reactor and solar concentrating system play a significant role in further improving energy efficiency. This work demonstrates the feasibility of two-step solar thermochemical cycles in renewable energy technologies and provides guidance for future innovations in this field. [Display omitted] • A review of two-step solar thermochemical cycle systems including materials, reactors and solar concentrating systems. • The ideas of The idea of mutual selection of solar reactor and oxidied reduced material. • Energy loss and consumption analysis at the reactor level and reactor design considerations. [ABSTRACT FROM AUTHOR]

• BRICS countries exhibit expansionary strategies in solar and wind power. • DE countries exhibit conservative strategies in solar and wind power. • The solar and wind power markets in BRICS countries are capital-oriented. • BRICS countries should prevent the new energy technology dilemma. Carbon neutrality is proposed by many countries for responding to climate change and energy transformation. Developing new energy is crucial to achieving carbon neutrality goals. In the context of carbon neutrality, this paper analyzes the factor structure and market orientation of the new energy market in BRICS countries (including the Federative Republic of Brazil, Russian Federation, the Republic of India, People's Republic of China, and the Republic of South Africa) and developed European (DE) countries in recent years. The primary conclusions are as follows: (1) In wind and solar energy markets, the BRICS countries exhibit an elastic substitution effect in production, indicating they have chosen the expansionary strategy with unbalanced factors. Through the contribution analysis, we find the development of wind and solar energy in BRICS countries is capital-oriented. However, the DE countries have opted for a conservative strategy with balanced factors, in which the wind and solar energy markets need promotional measures for capital investment and technological progress. (2) In the biomass energy market, both the BRICS countries and DE countries adopt the expansionary strategy. Besides, the development of the biomass energy market is technology-oriented except for India. (3) For the nuclear energy market, the BRICS countries and DE countries choose the conservative strategy and expansionary strategy, respectively. However, due to safety concerns, the expansion of nuclear energy in all countries has slowed. (4) Although a capital-oriented strategy can quickly promote the development of the new energy market, this practice compromises the local welfare and the benefits of the new energy technology progress to varying extents. The core insight of this paper is that BRICS countries should avoid the new energy capital trap and prevent the new energy technology dilemma. For the BRICS countries, policymakers should further evaluate the balance between capital-oriented interest and technology-oriented development. Also, achieving carbon neutrality is more difficult for the BRICS countries than DE countries. Hence, the BRICS countries should further refine their energy strategies for the long term. [ABSTRACT FROM AUTHOR]

• More reliable modeling assumptions and methods are proposed. • Price regulation may increase industrial output in China. • The reform can lead to a cleaner power mix and better economic structure. • The reform can be of great help to alleviate the fiscal deficit. • Impact mechanisms through cost linkages in different situations are put forward. China promised to achieve carbon neutrality in 2060. However, electricity prices are still regulated, which may affect the effect of carbon neutrality, especially in China, because 70% of China's electricity comes from thermal power generation. In this paper, a dynamic recursive computable general equilibrium model (CEEEA/CGE model) is applied to simulate the electricity market-oriented reform under the target of carbon neutrality from 2020 to 2060. The results show that the target may increase the demand for labor and capital to cope with increasing energy costs, and the reform may reduce GDP. However, power reform can significantly optimize the energy and economic structures and alleviate the fiscal deficit. Moreover, this paper finds and concludes the mechanism causing these effects (such as factor substitution, price advantage, government revenue, and structure), which provides policymakers with a new perspective of power reform. [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 addresses the escalating need for low-carbon solutions and discusses the integrated control system for the electric-hydrogen DC microgrids. A multi-time scale control mechanism for the DC microgrid is proposed, which aims to enhance its adaptability and scalability. The proposed multi-time control scheme comprises two control layers. The system-level control layer utilizes optimization to address economic issues and carbon trading, aiming to minimize operational costs and carbon emissions in the microgrid. The local-level control layer incorporates an advanced nonlinear control method based on the exponential reaching law-powered terminal sliding mode control (ERL-TSMC) for accurate tracking capabilities at the local-level and to handle dynamic control challenges in real-time. Furthermore, an event-triggered control mechanism is introduced to reduce considerable control efforts while guaranteeing system robustness under various disturbances. Both simulations and hardware experiments demonstrate that the proposed control method guarantees efficient low-carbon operation, achieving a significantly lower average carbon intensity of 0.19 kg CO 2 /kWh. Additionally, it exhibits robust performance at the local-level, with a steady-state error of only 0.03%. • A novel multi-time scale control strategy for electric-hydrogen DC microgrid is proposed. • System-level energy management that optimizes costs and carbon emissions is discussed. • Local-level event-triggered exponential reaching law based terminal sliding mode control is proposed. • The proposed control structure promises rigid DC bus voltage and low-carbon operation. • Real-time processor-in-the-loop (PIL) tests are performed to verify the simulation results. [ABSTRACT FROM AUTHOR]

A prevailing hurdle to the rollout of renewable energy is resistance on local levels. Aiming to address this issue, the German government introduced a classification for regional green electricity in 2019. A recent survey indicates that consumers appreciate this classification but also raises concerns of lacking transparency and credibility of its current communication. This study analyzes whether visual labels for regional green electricity can address this issue. Drawing on Signaling Theory, this paper studies how the label affects consumer attitudes and behavior and how familiarizing experiment participants with the label moderates its effects. We test the model by means of a multi-method lab experiment comprising eye-tracking and survey data. We find positive effects of the label with both objective (visual attention, time to decision) and subjective (stated trust) measures. Further, giving participants a briefing on the labels prior to the experiment moderates the labels' effects on visual attention and trust. These findings suggest that a regional green electricity label could provide a potent tool for policymakers to accelerate the transition towards a carbon neutral energy sector. [Display omitted] • German legislation introduced a classification for regional green electricity but consumer communication is being criticized. • This multi-method study evaluates how a visual label could address this issue, building on eye-tracking and survey data. • Findings suggest that labels are potent as they effectively alter consumer attitudes and behavior. • Building the label's brand is important as familiarity with the label is a critical success factor for such an initiative. [ABSTRACT FROM AUTHOR]

In the context of global warming, photovoltaic (PV) industry is growing rapidly to advance carbon neutrality. A new generation PV technology based on perovskite solar cells has attracted high attention from the industry due to its advantage of low cost and high photoelectric conversion efficiency, and has gradually carried out relevant technology research and development (R&D), as well as small and medium-sized product trials. However, there are still some generic technical issues with perovskite solar cells, such as the stability of large area preparation, efficiency over large areas, and long service life under high efficiency. Collaborative R&D around generic technologies among module suppliers across PV supply chains will help create complementary synergies and accelerate breakthroughs in generic technologies. To explore R&D strategies and subsidy policies for the generic technology in competing PV supply chains, twelve game-theoretical decision models considering cooperative/non-cooperative R&D with/without government subsidy under four scenarios are developed, analyzed and compared respectively. The research results indicate that: (i) in all four scenarios, the adoption of a centralized operational strategy is more effective in terms of government subsidies and the operational performance of the PV supply chains than a hybrid or decentralized operational strategy. (ii) the government should adopt an appropriate subsidy intensity for the dual-competing PV supply chains and set a "threshold" subsidy policy. (iii) the positive externality of R&D effort has a positive spillover effect on profits, social welfare and other indicators. The positive externality of R&D effort for the generic technology can be better exploited by the two PV supply chains jointly deciding on the level of R&D expenditure and by the government providing financial subsidy support. This study is a pioneering study of dual competing PV supply chains in terms of cooperative R&D of generic technology, which is very limited in the PV supply chain management and subsidy policy research literature. The findings and some unnoticed issues in this paper provide a basis for future PV supply chain research. • Generic technology R&D decision models in competing PV supply chains are developed. • R&D strategies for the generic technology in competing PV supply chains are investigated. • Subsidy policies for the generic technology R&D in competing PV supply chains are explored. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are widely recognized as promising solutions to reduce carbon emissions from the transportation sector. The rapid proliferation of EVs intensifies the interdependency between the power distribution system (PDS) and transportation system (TS). This paper proposes a distributed low-carbon economic dispatch scheme for the integrated power and transportation system (IPTS). Carbon emissions from the TS are quantified by a carbon dioxide calculation model, and the emissions from the PDS are determined by the electricity generation of fossil-fueled generators and the carbon reduction of carbon capture and storage (CCS) devices. An integrated model reformulation method is employed to transform the original non-convex model into a tractable one. Then a distributed solution method based on the spectral penalty parameter based adaptive alternating direction method of multipliers (SPPA-ADMM) is developed to protect the privacy of the PDS and TS. The reformulated model is solved iteratively, and the penalty parameter of the SPPA-ADMM is adjusted adaptively to improve the convergence performance of the method. Numerical results on three test systems demonstrate the effectiveness of the proposed scheme in achieving low-carbon economic dispatch of IPTS. Moreover, case studies validate the superiority of the SPPA-ADMM based distributed solution method in computation performance. • A low-carbon economic dispatch scheme for integrated power and transportation system is proposed. • An integrated model reformulation method is designed to transform the non-convex model into a tractable one. • The SPPA-ADMM based distributed solution method is used to protect the privacy and improve computation efficiency. • Effects of the proliferation of EVs on costs and carbon emissions are discussed in sensitivity analysis. [ABSTRACT FROM AUTHOR]

In addition to its important economic value, carbon trading is also an important tool in international politics and diplomacy. Carbon price forecast accuracy has far-reaching implications for economic development and the environment. Few existing studies have been more accurate in predicting carbon prices over longer periods. In this paper, the incremental Gaussian nonlinear trend fuzzy granulation method and the Gaussian nonlinear trend fuzzy granulation method are innovatively proposed to predict carbon prices. This study first converts carbon prices into incremental granulation time series. A time-varying core line is added to produce nonlinear trend granulation on the foundation of linear trend granulation. The nonlinear trend and residual information are then predicted using the DeeAR network, respectively, and the final prediction result is obtained by adding the predicted values for each. Moreover, a new evaluation index, the comprehensive evaluation of RMSE, MAE, and MAPE as three indicators, is proposed to consider the accuracy of the evaluation index more comprehensively and reliably. The results show that the prediction method has the smallest error in long-term prediction compared with other models. The daily closing price datasets of carbon exchanges published in Shenzhen and Beijing are used to validate the efficacy of this methodology. • A modified Gaussian nonlinear trend that can better fit the actual data is proposed. • Incremental Gaussian nonlinear trend fuzzy granulation method with F test is proposed. • A new evaluation index is proposed to make the prediction result more interpretable. [ABSTRACT FROM AUTHOR]

As the world's largest producer and consumer of coal, coal phaseout is critical for China to achieve carbon neutrality and contribute to global decarbonization. Here, utilizing the Global Change Assessment Model, this paper simulates China's coal phaseout pathways at the sectoral level between 2015 and 2060, considering the deployment of carbon capture and storage (CCS). The results show that China's coal consumption peaks at 96.4 Exajoules (EJ) in approximately 2020 and needs to decrease to 10.9 EJ by 2060 to achieve carbon neutrality. Specifically, the power sector bears the most significant proportion of responsibility, as its coal consumption must fall to 1.4 EJ by 2060, which is approximately 97.2% lower than the peak in 2020. The industrial sector is next and needs to decrease consumption to 9.2 EJ of coal in 2060, which is a reduction of 32.3 EJ below the 2020 level. Electricity will replace coal as the dominant energy source in the industrial sector after 2040. However, a portion of feed coal will remain in the industry for a long time. Among the industrial subsectors, the steel sector needs to put significant pressure on efforts to phase out coal, followed by the cement and nitrogen fertilizer sectors. In 2060, these three sectors will consume 5.7 EJ of coal, accounting for 64.1% of the total coal consumption in the industrial sector. The deployment of coal-CCS is one of the alternatives to achieve carbon neutrality and ensure smooth coal phaseout. Under the carbon neutrality scenario, the annual deployment of coal-CCS needs to exceed 0.13 EJ from 2025 to 2045, with a peak at 3.3 EJ in 2045. • China's coal phaseout pathway is assessed in the carbon neutrality scenario (CNS). • Coal consumption needs to drop to 10.9 EJ in 2060 to achieve carbon neutrality. • The power sector's coal will decrease to 97.2% below the peak by 2060 in the CNS. • The industry will pass the power sector as the top coal consumer by 2045 in the CNS. • The deployment of coal-CCS will peak at 3.3 EJ after 2045 in the CNS. [ABSTRACT FROM AUTHOR]

The carbon allowance trading mechanism provides an economic basis to facilitate emission reductions for market participants. As a compliance mechanism, allowance-based emission control demands emitter to surrender the same amount of carbon allowances as it emits during the accounting period, the duration of which is often set to one year by the administrators. This requires emitters to determine the upper limit of annual emissions and allocate it to each day based on the current carbon price to minimize their overall operating costs over the longer carbon accounting period. This paper proposes a low-carbon economic dispatch for IES with CCUS considering multi-time-scale allocation of carbon allowance. In the upper level, a scenario-based stochastic optimization is adopted to decide the annual allowance and complete the allocation from year to month and then from month to day. The lower level is a Wasserstein ambiguity set-based two-stage distributionally robust optimization (TSDRO) model to find the optimal dispatch strategy of each day. The uncertainty from the long-term available renewable energy resources and user demands and from the short-term forecasting errors is both considered. Simulation results show that the proposed model can realize reasonable carbon allocation and decrease the annual operation costs. • A two-level low-carbon economic dispatch model for IES with CCUS is designed. • Multi-time-scale allocation of carbon allowance for the single emitter is considered. • The uncertainty of multi-variables at different time scales is considered. • A numerical simulation of one-year duration is conducted. [ABSTRACT FROM AUTHOR]

[Display omitted] • A multi-energy operation simulation model with biomass energy flow balance is established. • An optimal planning strategy is developed to determine the portfolio of solar, biomass, etc. • A thermodynamics-based model is formulated to characterize the heat transfer in biogas production. • An ordinary least squares approach is applied to linearize the biogas production model. Recent years have witnessed an increasing proliferation of biomass technologies since biomass is recognized as a natural carbon-neutral fuel. Existing studies have paid extensive attention to the concept and modeling of zero-carbon energy systems or buildings. However, few studies have explored the multi-energy coupling method and techno-economic evaluation of biomass to achieve near-zero carbon systems. Therefore, we propose an optimal sizing framework for integrating waste-to-biogas in regional energy systems to achieve near-zero carbon emissions. To establish energy balance between supply and demand and regional self-sufficiency, biomass is added to the existing natural gas energy framework. Biomass produces biogas through anaerobic digestion reactions, and the biogas output can be constructed as an analytical function related to the anaerobic reaction environment temperature and reactant volume. Furthermore, this paper constructs a planning model with the goal of minimizing the investment and operating costs of biomass, solar and storage, respecting carbon emission constraints induced by local electricity and gas energy flows. The impact of carbon emission regulations on the investment of each component of the energy system is determined using a sensitivity analysis of carbon emissions. To solve the nonlinearity issues of the biogas production function, we propose a convexification method based on least squares fitting. Case studies based on realistic datasets demonstrate that, under the constraints of carbon emissions, the integration of biogas can significantly improve the supply adequacy of the energy system, reduce investment costs by up to 77.07%, and carbon emission reduction intensity is 5.11 kg CO 2 kg-1 Biogas, all while lowering the Levelized Cost of Energy (LCOE). In comparison to prior research, the model provided in this paper more accurately portrays the biogas production process, lowers the system investment cost and LCOE, and emphasizes the benefits of biomass. [ABSTRACT FROM AUTHOR]

Accurate and timely carbon emission accounting (CEA) is vital to industrial corporations, especially those who participate in the carbon market. With the rapid development of artificial intelligence and power systems, the power data-based method provides a new way for real-time CEA. However, the extensive installation of distributed photovoltaics (PV) significantly increases the accounting difficulty of corporate carbon emissions. This paper proposes a non-intrusive method of real-time CEA for industrial corporations from the perspective of modern power systems. First, a device operation state (DOS) estimation model based on a modified Informer algorithm is proposed to calculate corporate direct carbon emissions. Wherein, an equivalent distributed PV output estimation model is used to decrease the impact of invisible PVs on direct emission accounting. Second, an improved carbon emission flow model is proposed to calculate corporate indirect carbon emissions, which considers "prosumers" arising from the installation of distributed PVs. Finally, the total corporate carbon emissions, including direct and indirect parts, are obtained by using the CEA model. Case studies based on four typical high‑carbon-emission factories in Zhejiang province, China demonstrate that the proposed method can make accurate CEA for industrial corporations by effectively lessening the impact of distributed PVs. • A non-intrusive carbon emission accounting method for industrial corporations. • A novel perspective of modern power systems for carbon emission accounting. • Calculating carbon emissions considering distributed photovoltaics' impacts. • The method becomes superior as the photovoltaic output proportion increases. [ABSTRACT FROM AUTHOR]

In order to achieve the target of carbon peak and carbon neutrality, electric vehicles (EVs) have increasingly received a prominent interest to electrify the transportation sector due to their advantages of mobility and flexibility on handling complicated transportation and power networks. However, it is still challenging to realize the significant potential of EVs towards an emerging low-carbon transition. Previous works have focused on vehicle-to-grid (V2G) technology that allows for an increased utilization of EVs to make arbitrage by the temporal differentials of electricity prices. Nevertheless, the economic potential of EVs flexibility may not be fully exploited lacking an appropriate business model. This paper addresses this challenge by developing a coupled power-transportation network for cooperative EVs to optimize the provision of multiple inter-dependent services, including charging service, demand management service, carbon intensity service, and balancing service. In order to unlock this value, the EVs operation problem has already been tackled using model-based optimization approaches, which may raise privacy issues since the requirement for global information and also can be time consuming due to the high variability of transportation and power networks. In this paper, we propose a model-free hierarchical and hybrid multi-agent reinforcement learning method to learn the routing and scheduling decisions of EVs in a coupled power-transportation network with the objective of optimizing multi-service provisions. To this end, EVs do not reply on any knowledge of the simulated environment and are capable of handling system uncertainties via the learning process. Extensive case studies based on a 15-bus radial power distribution network and a 9-node 12-edge transportation network are developed to show that the proposed method outperforms the conventional learning algorithms in terms of policy quality and convergence speed. Finally, the generalizability and scalability are also investigated for different environment circumstances and EV numbers. • EVs coordination is formulated as a Decentralized Partially Observable Markov Game. • Charging, demand management, carbon intensity and up/down balancing services are considered. • A hierarchical and hybrid multi-agent reinforcement learning approach is proposed. • Transportation and power networks are captured in the simulation environment. • EVs benefit from multi-service provision and reduce renewable curtailment. [ABSTRACT FROM AUTHOR]

• An overview of the state-of-the-art of conservative power theory is provided. • Comparative analysis of main non-sinusoidal power theories is performed. • Evolution of conservative power theory is comprehensively revealed. • Current applications of conservative power theory in smart grid are presented. • Future possible research opportunities of conservative power theory are predicted. As the worldwide trend of "carbon neutrality" action spreads, renewable energy substitution for fossil fuels in the electricity sector is inevitable. The traditional power system is updating to a new paradigm of smart grid to promote the consumption of renewable energy and phasing out of fossil fuels. However, this transition would bring about new tremendous challenges on power quality and operation & control of the power system. As the traditional power theories may not be able to handle the non-sinusoidal voltages, unbalanced and/or non-linear loads that are prevalent in evolving smart grid, a novel time-domain Conservative Power Theory (CPT) was proposed in the early 2000 s. So far, more than 190 papers have been published focusing on this attractive theory. This paper gives a summative and comparative overview of CPT and its innovative applications in modern smart grids. Horizontal comparisons among CPT and other common non-sinusoidal power theories are briefly performed and then the mile-stones in CPT's development are highlighted. Basic power and current definitions under the CPT framework for both single-phase and three-phase systems are described in detail, and four main applications of CPT are discussed, which are load characteristic identification, accountability and revenue, filter and compensation, and power converters. Based on the review, the motivations, definitions, developments, and applications of CPT are comprehensively presented, some possible research aspects of CPT are itemized, and a future vision of CPT-based smart grid scenario is then conclusively predicted. [ABSTRACT FROM AUTHOR]

• An energy-consumption permit trading scheme (ECPTS) was implemented in China in 2016. • This policy's contribution to reducing energy intensity is proved by a PSM-DID model. • The pilot ECPTS generated an average energy saving of about 43 Mtce in 2016–2019. • Different mediating effects of industrial structure and energy structure are proved. • A national ECPTS is suggested to promote linkage with the emissions trading scheme. The Chinese government implemented the energy-consumption permit trading scheme (ECPTS) pilot program in 2016, in order to achieve energy saving and carbon reduction. Based on the PSM-DID model, we attempt to test the effects of the ECPTS on energy consumption and energy intensity. With a counterfactual simulation, this paper also evaluates the potential gains and different mechanisms of this policy. The results show that, under the constraints of the established resources and policy efficiency, there is a positive relationship between the ECPTS and low-carbon economic transformation; the implementation of the above policy in the pilot provinces brought about an average energy saving of 43 Mtce during the period of 2016–2019. However, the potential gains of this policy need to be further increased to meet China's energy control targets. Additionally, a complete mediating effect of the energy structure indicates that the impact of the ECPTS on energy consumption and energy intensity depends significantly on energy structure adjustments. Finally, this paper highlights the key policy implications associated with the empirical results. [ABSTRACT FROM AUTHOR]

• Distributed energy system integrating concentrating PV/T and air source heat pump for domestic heating supply. • Operation strategy aiming to enhance the system performance under different working conditions. • Operation parameters of the concentrating PV/T can be regulated according to the real-time DNI and heating load. • Operation mode of the air source heat pump can be shifted according to the real-time heating load and ambient temperature. • The proposed system is subject to thermodynamic and economic analyses. The distributed energy system incorporating solar energy plays an important role in saving fossil fuels to realize the goal of carbon neutrality. In this paper, a distributed energy system integrating concentrating photovoltaic/thermal with air source heat pump is proposed for domestic heating. The thermodynamic characteristics of the concentrating photovoltaic/thermal and air source heat pump under different working conditions are analyzed, based on which an operation strategy of the two subsystems is developed aiming at operation performance enhancement. Besides, the thermodynamic and economic performances of the proposed system are evaluated through a case study for supplying a building load on four typical days during the heating period. The results show that the highest exergy efficiency of the proposed system can reach up to 40.23 %, with the corresponding average COP of 5.06 for the air source heat pump. The annual cost is 24.22 USD/m2, with the 7572.20 USD saved thanks to the concentrating PV/T produced electricity and thermal energy. [ABSTRACT FROM AUTHOR]

The public transport market in most global cities is dominated by diesel buses, which can have significant adverse impacts on the environment and human health. To reach carbon neutrality, many public transport operators are attempting to replace their existing bus fleets with electric buses (EBs). This paper develops a generic bus replacement model to help public transit (PT) operators progressively renew their existing bus fleets so as to reach particular electrification goals within a given planning horizon. Unlike existing studies, our model more comprehensively considers the external costs, which include external climate costs, external health costs and used battery recycling costs/profits. Furthermore, by incorporating different incentives, variants on the model can be adopted to investigate how different government incentives can affect the replacement behaviors of PT operators. Finally, our proposed approach is applied to real-world data from a bus operator in Singapore which is aiming to achieve a 100% cleaner energy bus fleet within 20 years. The experiments indicate that an EB subsidy can effectively accelerate the electrification process, and a 25% EB subsidy could achieve full electrification eight years earlier than if no EB subsidy were applied. Moreover, we explore how the diesel tax and diesel bus (DB) tax affect EB replacement. Our experiments suggest a 10% diesel tax rate can achieve good results but imposing a DB tax will not encourage PT operators to purchase EBs earlier. • A novel model for the bus fleet replacement problem is proposed. • Various external costs that borne by the society and environment are considered. • Our approach evaluates different policy incentives. • Our real case study provides managerial insights for PT operators and policymakers. [ABSTRACT FROM AUTHOR]

• Integrated optimization of multi-process parameters for energy-saving is proposed. • Simulation-assisted comprehensive manufacturing cost optimization model is built. • Dynamic solution mode and algorithms with parallel computing are proposed. • Two representative case studies are performed to verify the proposed approach. • The proposed approach can bring a 10%–20% comprehensive cost reduction. Aluminum casting is one of the most important, yet energy-intensive, lightweight-enabling technologies. To achieve carbon peak and carbon neutrality goals, it is critical to reduce energy consumption without sacrificing productivity. Production optimization of aluminum casting for green and intelligent—i.e., greentelligent—operation is an effective way to accomplish this objective, but this is not sufficient for separately optimizing different processes. Instead, simultaneously optimizing multiple processes is a more advantageous approach, which, however, has not yet been performed. This paper proposes a simulation-assisted approach for the integrated optimization of three primary processes in aluminum casting (i.e., melting, transferring, and holding) to fill this gap. A comprehensive manufacturing cost optimization model that considers the energy, material loss, manpower, and pauses in production for the three processes was first built. Simulation was the key to the formation of the optimization model. Then, a dynamic solution mode with parallel computing and four algorithms (pattern search, genetic algorithm, particle swarm optimization, and simulated annealing) were selected to solve the optimization model. The proposed approach was applied to two die-casting factories to verify its optimization performance. The integrated optimized parameters reduced the comprehensive cost by approximately 8.4% and 16.4% for cases 1 and 2, respectively, for which energy reduction was the primary contributor to the comprehensive cost reduction. The proposed approach was found to be promising for energy conservation and related carbon emissions reduction in the aluminum casting industry. [ABSTRACT FROM AUTHOR]

• A general charging scheduling problem for an electric bus fleet, which can be used a building block of more complex electric bus system planning or operations problems. • Time-based charging windows, battery state-of-charge bounds, time-of-use charging tariffs, and station-specific electricity load capacities considered. • A mixed linear integer programming model with charging location and time decisions specified by location-specific charging time slots. • A Lagrangian relaxation framework for decomposing the fleet charging scheduling problem by individual buses. • A bi-criterion dynamic programming algorithm for each single-bus charging scheduling subproblem. Electrification poses a promising low-carbon or even zero-carbon transportation solution, serving as a strategic approach to reducing carbon emissions and promoting carbon neutrality in the transportation sector. Along the transportation electrification pathway, the goal of carbon neutrality can be further accelerated with an increasing amount of electricity being generated from renewable energies. The past decade observed the rapid development of battery technologies and deployment of electricity infrastructure worldwide, fostering transportation electrification to expand from railways to light and then heavy vehicles on roadways. In China, a massive number of electric buses have been employed and operated in dozens of metropolises. An important daily operations issue with these urban electric buses is how to coordinate their charging activities in a cost-effective manner, considering various physical, financial, institutional, and managerial constraints. This paper addresses a general charging scheduling problem for an electric bus fleet operated across multiple bus lines and charging depots and terminals, aiming at finding an optimal set of charging location and time decisions given the available charging windows. The charging windows for each bus are predetermined in terms of its layovers at depots and terminals and each of them is discretized into a number of charging slots with the same time duration. A mixed linear integer programming model with binary charging slot choice and continuous state-of-charge (SOC) variables is constructed for minimizing the total charging cost of the bus fleet subject to individual electricity consumption rates, electricity charging rates, time-based charging windows, battery SOC bounds, time-of-use (TOU) charging tariffs, and station-specific electricity load capacities. A Lagrangian relaxation framework is employed to decouple the joint charging schedule of a bus fleet into a number of independent single-bus charging schedules, which can be efficiently addressed by a bi-criterion dynamic programming algorithm. A real-world regional electric bus fleet of 122 buses in Shanghai, China is selected for validating the effectiveness and practicability of the proposed charging scheduling model and algorithm. The optimization results numerically reveal the impacts of TOU tariffs, station load capacities, charging infrastructure configurations, and battery capacities on the bus system performance as well as individual recharging behaviors, and justify the superior solution efficiency of our algorithm against a state-of-the-art commercial solver. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions and energy data are used to show the rationale for global hydrogen trade. • We identify countries likely to import or export hydrogen, highlighting cost differences. • Our international trade model highlights renewable energy, carbon policies, and market distances. • Numerical simulations show a market shift from fossil-based to renewable energy-based hydrogen. Existing research offers insights into production, operation, and cost efficiencies for the expanding variety of hydrogen (H 2) technologies, suggesting that not a single one, but a portfolio of technologies, may be competitive in emerging markets. In turn, policy studies point out that economic and market conditions may be altered to promote certain options, for instance, renewable energy-based "green" hydrogen over fossil fuel-based alternatives. Such regionally oriented analyses, however useful, do not provide a perspective on a global market or international trade development, which is critical for infrastructure development, demand expansion, and supply commitments. This paper aims to fill this gap by addressing two interrelated questions: 1) what are the prerequisites for hydrogen trade in the context of a global transition to carbon neutrality and 2) how will regional energy system characteristics and regulations affect the growth and the "color" of the hydrogen market? Focused on green H 2 , we start our analysis with the evaluation of renewable energy generation potential of individual countries to identify geographic and economic factors and constraints for regional H 2 development. Revealing resource-rich countries with green H 2 export potential and resource-poor regions, likely green H 2 importers, we turn to energy price data showing why green H 2 is likely to face competition from other H 2 colors. We capture those insights with an international hydrogen trade model, focusing on H 2 supply diversity and factors determining local demand, including prices for renewable energy versus fossil fuels, emission constraints, and cost associated with carbon utilization and or disposal. Incorporating supply and demand drivers within a common framework enables us to embrace the complexity and to understand the discrepancies in existing H 2 trade projections. We use the model to explore the role of carbon regulations, rate of renewable energy adoption, transportation costs, and input energy prices in the future H 2 market evolution. To gain further insights into the future trade dynamics, we perform simulations mimicking the emerging German market. Hence, our work provides a unique comprehensive energy system perspective at the global scale, supported by analytical and empirical data analyses. [ABSTRACT FROM AUTHOR]

• Distributed ground source heat pump heat storage system is considered. • Energy storage is applied to balance the energy network of integrated energy systems. • The carbon capture and storage technology are applied to store carbon. • A balanced broad learning prediction model is established for load forecasting. • The optimal dispatch model is verified to achieve carbon neutrality under four cases. With the development of social industry, numerous carbon emissions have led to global warming and caused a huge negative impact on the environment. Carbon neutrality has been proposed to solve this problem; one of the primary issues in achieving carbon neutrality in integrated energy systems is to reduce carbon emissions. This paper utilizes distributed ground source heat pump heat storage systems in integrated energy systems to simultaneously improve the utilization efficiency of wind energy and solar energy for the first time. Moreover, the carbon capture and storage technology in integrated energy systems is considered to store excess CO 2 in geological layer. Energy storage in integrated energy systems is applied to adjust the energy network balance. Finally, a balanced broad learning prediction model considering various heterogeneous data is established for load forecasting with 96.12% prediction accuracy. In four cases of the lower or higher wind and solar energy generation curves, carbon emissions are reduced to 82.02% of the original at least. [ABSTRACT FROM AUTHOR]

In this paper, we study the interactions between energy hubs under a hybrid topology with an incomplete network for local electricity trading and a complete network for local carbon trading. Based on the hybrid topology, we develop a two-stage communication-efficient coalition graph game-based framework to achieve communication cost reduction and fair profit distribution for energy hubs. An electricity trading optimization problem and a carbon trading optimization problem are considered in the first and second stages, respectively. A communication-censored alternating direction method of multipliers (COCA) with a censoring strategy is applied to solve the optimization problems in a decentralized manner. The profits obtained through the hybrid topology are fairly distributed by the profit distribution schemes based on the Myerson value and Shapley value, which are designed for incomplete and complete networks, respectively. Simulation results demonstrate that the profit distribution schemes can fairly distribute the profits obtained through the local trading. Moreover, compared to the noncooperative benchmark, the proposed framework can reduce the total payment and carbon emissions of energy hubs by 22.64% and 33.45%, respectively. Compared to the alternating direction method of multipliers (ADMM), the COCA can reduce total communication cost of energy hubs by 18.99%. • A two-stage coalition graph game framework for energy hubs is proposed. • A communication-efficient decentralized algorithm is proposed. • Profit distribution schemes based on the Myerson and Shapley values are proposed. • Communication costs and energy and carbon costs are reduced for energy hubs. [ABSTRACT FROM AUTHOR]

• Hydrogen industry chain is integrated into complex integrated energy system. • Wasserstein distance is introduced in the optimal copula selection. • CCS technology consider time lag between carbon capture and natural gas synthesis. • CO 2 emission rate and clean energy consuming rate are improved by the proposed system. • The margin of P2H under different seasons are explored by the simulations. The application of hydrogen is promising for achieving carbon neutrality. To promote hydrogen utilization and carbon emission reduction, this paper attempts to integrate the hydrogen industry chain, carbon capture and storage (CCS) into a regional integrated energy system (forming a complex regional integrated energy system (CRIES)) and proposes an energy scheduling model for optimizing CRIES operation considering wind and photovoltaic co-generation (WT-PV) uncertainties. Firstly, to measure the uncertainties of WT-PV, the Wasserstein distance with better geometric properties is introduced to select the best Copula to model the correlation between wind and photovoltaic generation and to generate a typical scenario of WT-PV for each season. Secondly, based on MILP, the system is modularly modeled considering the pressure difference of various gases, the time interval of carbon capture and natural gas synthesis, and the energy coupling relationship. Finally, based on the above WT-PV prediction and modeling, the energy dispatch status in each season is investigated, and the economics of the proposed system is verified in the minimum hydrogen demand variation of hydrogen metallurgy plant, hydrogen blending ratio, and uncertainties of electric vehicles and hydrogen vehicles loads. Results show that 1) hydrogen-rich natural gas improves system's energy use efficiency, CCS weakens system's dependence on the upstream natural gas network, and the multi-energy coupling improves system's arbitrage ability under different prices; 2) hydrogen is richest in the summer, causing system's revenue to be 8.54%–32.17% and 29.29%–244.21% higher than in the transition season and winter. 3) zero-carbon energy interaction with the outside world and fully accommodating WT-PV are achieved by integrating the hydrogen industry chain; 4) the proposed CRIES operates reliably and economically under multiple uncertainties. [ABSTRACT FROM AUTHOR]

For making earlier realization on peak carbon dioxide emissions and carbon neutrality, hydropower development in countries all over the world can effectively reduce the Greenhouse Gas (GHG) emissions and solve the problem of global climate change. This paper proposes a novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index (OSGSVI) model to accurately predict the hydropower generation in some countries. Firstly, for enhancing the fitting accuracy, the initial condition is optimized based on the weighted average methods and the data grouping with OSVI is utilized by seasonal divisions. Secondly, an OSGSVI model is established coupled optimization on both optimized initial conditions and seasonal divisions. Thirdly, the Whale Optimized Algorithm (WOA) is employed to determine estimated parameters to further enhance the fitting accuracy for the hydropower generation. Finally, the experimental results of the prediction study show that three error measure values are all the smallest in all the fitting results and the MAPE values are converged before 30 iterations by utilizing our proposed model when compared with a set of baseline prediction models. It demonstrates the superiority of our proposed model over the others on the fitting accuracy with fast-convergence for the hydropower generation in these selected countries. • An OSGSVI model is proposed to accurately predict the hydropower generation. • The model uses the seasonal data grouping and OGSVI to enhance the predictive accuracy. • The WOA is proposed to further improve the predictive accuracy. [ABSTRACT FROM AUTHOR]

• A method for scenario analysis that takes into account the uncertainty of renewable energy was developed. • Two demand side response approaches were utilized to evaluate the response impacts of two different types of loads. • Carbon trading market was considered in the suggested microgrid model, and carbon emissions could be decreased by a maximum of 10.13 percent compared to case 1 when the market was not considered. • A multi-stage optimization framework for microgrid energy management was proposed. The benefits of more comprehensive energy use and promotion of renewable energy (RE) consumption enable large-scale microgrid deployment. However, the uncertainty of renewable energy sources and the diversity of load types pose a threat to the microgrid's stability. Recently, energy scheduling optimization for microgrids (MGs) has been primarily based on ideal models, but incorporating as many real-world characteristics as feasible can improve the reliability of the optimization outcome. This paper provides a multi-stage methodology for solving the energy management optimization (EMO) problem of MG under uncertainty considering carbon trading market and demand side response (DSR). To begin, scenario analysis method was used to address the uncertainty associated with RE in MG, and four typical scenarios of renewable energy were generated. Then, the flexible configuration and operational constraints of each power source in MG are dealt with under the premise of considering the carbon trading market. The third stage involved merging the characteristics of various load types and analyzing the response impacts of different percentage residential and industrial loads, respectively, using the price-based and load-transfer-based DSR approaches. Finally, quantum particle swarm optimization (QPSO) algorithm was used to obtain the optimal solution. The acquired results demonstrate the efficacy of the proposed multi-stage energy optimization framework, and support the following two conclusions: 1) The carbon trading market policy contributes to the reduction of carbon emissions and fossil fuel consumption. 2) A high load participation rate in DSR can increase MG operation economics by up to 27.48% compared to not considering DSR. [ABSTRACT FROM AUTHOR]

• Developing a framework to estimate solar PV potential of urban fleet-wide electric buses. • Publicly street-view panoramas, GPS trajectory data and DEM data are used as input. • A case study of Qingdao, China with 547 bus routes, 28,661 street-view panoramas is studied. • Benefits under different driving conditions and operation schedules are evaluated. Transforming the road public transport to run on renewable energy is vital solution to achieve carbon neutral and net zero goals. This paper evaluates the potential of using solar radiation-generated electricity as an auxiliary power supplementary for the battery of electric buses, based on a developed framework that using publicly street-view panoramas, GPS trajectory data and DEM data as input parameters of solar radiation model. A case study of Qingdao, China with 547 bus routes, 28,661 street-view panoramas shows that the solar-radiation electricity generated at noon during the operation accounts for about one-fifth, one-eighth of the total electricity consumption of a bus traveling one kilometer in a sunny day and a cloudy day, respectively. Spatial variability shows significant solar-radiation power generation advantages in newly-launched areas and expressway. The solar power generated in a sunny day can make a bus half of passengers and with air conditioner off at least one extra trip in 2:1 replacement schedule, and nearly close to one extra trip in 4:3 replacement schedule. A correlated relation between the solar-radiation power generation benefit and the operation schedule of electric buses is observed, implying that the high cost of 2:1 replacement schedule for long-distance routes during summer or winter can be reduced. The proposed framework can help us evaluate and understand the feasibility of solar radiation-generated electricity energy of electric bus fleets covering the large-scale urban areas at different times, locations, and weather conditions, so as to support effective decisions at better planning of PV-integrated electric buses. [ABSTRACT FROM AUTHOR]

• We provide the most up-to-date CO 2 emission inventory for China's cement industry. • We construct the G-LEAP model to project the CO 2 emissions of China's cement industry. • The CO 2 emissions could be reduced maximum by 70 percent in 2060, while the remainder will need to come from technological innovation and new growth horizons. This paper aims to assess the emission reduction potential of combined mitigation technologies in China's cement industry, thus transitioning towards carbon neutrality through a suitable technology portfolio and exploring a low-carbon pathway. The G-LEAP model is constructed for future carbon emission projection, incorporating the cement demand projection and technology application. The pathways based on different technology portfolios are developed to estimate the CO 2 emission trajectory of China's cement industry. We maximize the utilization of currently available technologies and assume a high level of innovative technology diffusion rate in the integrated mitigation pathway to explore the maximum abatement potential of the cement industry. The result shows that short-term mitigation mainly relies on improving energy efficiency and alternative low-carbon fuels, which would contribute 9–12% and 17–22% of the cumulative emissions reduction in the integrated mitigation pathway compared to a frozen scenario. Alternative clinkers can significantly reduce process-related emissions, but the potential is determined by the availability of raw materials, which would contribute 30–39% of the cumulative emission reduction. Post- and oxygen-combustion capture is expected to be deployed by 2030 and contribute about 28–44% to cumulative emission reduction. The technology portfolio in the integrated mitigation pathway would reduce China's cement CO 2 emissions by 63–73% compared to the frozen scenario, and the remainder of CO 2 emission would be 300–400 Mt in 2060, which will need the technological innovation and new growth horizons, such as carbon sink approaches or carbon trading. [ABSTRACT FROM AUTHOR]

• The influencing factors of carbon prices in China's carbon market are analysed. • The influencing factors and historical data are combined to predict the carbon price. • The EEMD-BP-ELM model and the CHINAGEM-E model are constructed and combined. Carbon price is one of the core indicators of the carbon market. Making accurate predictions of carbon prices based on insight into the volatility characteristics of the carbon market will not only provide a scientific basis for investors and regulators to make decisions and greatly promote emission reduction, but also effectively promote the healthy development of the carbon financial market. Based on the historical carbon price data of the national carbon market, this paper constructs the EEMD-BP-ELM model to predict the high and low frequency components of China's future carbon price. On this basis, according to China's goal of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060, the CHINAGEM-E model is used to predict the trend component of the carbon price under the baseline and the 2060 carbon neutral scenarios. Finally, the trend component is combined with the high and low frequency components to obtain the price range from 2022 to 2060. We find that under the baseline scenario, China's carbon price range in 2060 is [343, 785] CNY/tCO2, while under the 2060 carbon neutral scenario, this price range is [1543, 3531] CNY/tCO2. [ABSTRACT FROM AUTHOR]

• Constructing a new welfare function considering economy, equity and ecology. • Optimizing carbon emission pathways from multiple perspectives. • Focusing on the emission pathways in China achieving the ambitious targets. With the increasing climate warming, reducing carbon emissions has gradually become a consensus around the world. However, the process of carbon emission reduction will inevitably lead to the transformation of economic structure, industrial structure and energy structure, which will bring huge changes in economic output, employment and other aspects, thus affecting the overall social welfare and equity. Therefore, it is particularly important for each country to determine a scientific and effective carbon emission pathway which take into account both social development and climate targets. As the largest carbon emitter, China has put forward the targets of carbon peak and carbon neutrality. In this regard, the corresponding carbon emission reduction scenarios are designed according to the new situation of China's emission reduction in this paper, firstly. Furthermore, under the premise of maximizing social welfare, an optimization model which integrating economic growth-equity-environment is developed to determine China's emission pathways under different scenarios from 2020 to 2050. Besides quantitatively showing the future total amount and peak value of carbon emissions under different scenarios, the results can also identify its evolution pathway over time. Therein, the peak under most scenarios will concentrate in 2025–2030, which is about 10.7 to 11.8 billion tons. With the increasingly stringent constraints on carbon emission reduction, the cost of emission reduction will increase correspondingly, but the overall social welfare will increase slightly. Finally, our findings provide practical guidance on achieving carbon emission reduction and enlightenments on policymaking. [ABSTRACT FROM AUTHOR]

The promotion of carbon neutrality targets will significantly influence the energy management of multi micro energy grid (MMEG) systems that can supply flexible local loads using various distributed energy resources. So far, few works incorporate the carbon emissions management with the fully-distributed coordination of MMEG systems to protect the environment and individual privacy and improve the energy efficiency. To fill this gap, in this paper, a hierarchical optimization framework is proposed for the MMEG system to solve an environmental economic dispatch problem, such that the energy coordination strategy could be obtained in a fully-distributed way. At the upper level, under a local and time-varying communication network, we develop an aggregate model based on a real-time price policy for the MMEG system. On this basis, a fully distributed consensus-based gradient projection method is proposed to minimize the total system generation cost with the coupled emission constraint satisfied, wherein a piecewise linearization technique is applied to handle the non-convexity and non-smoothness of the cost function caused by valve-point loading effects (VPLE) of thermal units. At the lower level, we apply an accelerated distributed augmented Lagrangian (ADAL) method to deal with a cost minimization optimization problem for users, such that each user could implement its own optimal strategy simultaneously based on the obtained aggregated strategy from the upper level. The effectiveness of the proposed approach is demonstrated by numerical simulations on an MMEG test system. • Coordinated optimization of multi micro energy grids improves the energy efficiency. • Coupled quadratic carbon emission constraints foster utilization of renewable energy. • Hierarchical framework achieves the system aggregation and individuals' autonomy. • Consensus-based gradient projection performs microgrids' fully-distributed operation. • Accelerated distributed Lagrangian method enables the local decision-making of users. [ABSTRACT FROM AUTHOR]

• An IESs co-trading market with carbon trading mechanisms is proposed. • The parameters and solving process are redesigned with the improved MADDPG algorithm. • The improved algorithm achieves fair trade and entity privacy protection among multiple entities. • The optimization strategy guides IESs to carry out "energy-carbon" joint management. With the development trends of carbon neutrality, carbon trading is graduating embedded into the energy management of integrated energy systems (IESs). The dual benefices of carbon emission reduction and economics can be achieved by coordinatively optimizing the complementarity and flexibility between multiple IESs. However, this leads to the increased complexity of the market transactions, which poses significant challenges in terms of the benefits distribution among multiple entities, the convergence of trading processes, and the privacy-preserving issues. The multi-agent reinforcement learning (MARL) is capable of solving complex sequential-decision problems and acquiring the optimal strategies for each entity through the interactions between the multiple agents and the market. The MARL deployed on the local agent can provide online trading decisions for individual market entities considering their own interests, which offers new potential to solve the abovementioned difficulties. In this paper, we proposed an IESs co-trading market including electricity, natural gas and carbon trading. The multi-agent energy management coordinative optimization problem is solved by an improved Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm to achieve fair trade and entity privacy protection. The case study results verify that the proposed optimal energy management strategy based on the improved MADDPG algorithm can efficiently guide the IESs in the energy and carbon co-trading market. [ABSTRACT FROM AUTHOR]

• Data-driven piecewise probabilistic harmonic model is proposed for harmonic sources. • Probabilistic and time-varying properties are firstly considered in probabilistic harmonic power flow. • High accuracy and efficiency piecewise probabilistic harmonic power flow method is used to evaluate, forecast, and mitigate harmonics. Large-scale electrification in the end-user and renewable energy fields is one of the key pathways to achieving carbon neutrality by 2050. Meanwhile, a large number of power electronic devices are used in residential, commercial, and office loads and photovoltaic (PV), which leads to the power quality harmonics in the power distribution system (PDS) becoming more prominent than ever before. Due to the nature of the random behavior of users and random changes in external factors, e.g., illumination and temperature, the harmonics in the PDS are strongly random and time-varying, which makes it hard to evaluate and mitigate the harmonics using the individual deterministic harmonic power flow (HPF) approach. This paper proposes a data-driven piecewise probabilistic HPF method for the PDS with PVs. First, a data-driven piecewise probabilistic harmonic cross coupling model is proposed for analyzing the harmonics generated by different harmonic sources, and the probabilistic and time-varying features can be manifested via this model. Moreover, this proposed harmonic model has a certain predictive capability. Then, the decoupled method based on graph theory and injection current is developed for computing the HPF. Finally, a field theory-based piecewise probabilistic HPF is applied for assessing the probabilistic harmonics of the PDS with PVs. Actual measurements for various harmonic sources and simulations in three different sizes of IEEE systems validate the precision, effectiveness, and efficiency of the proposed models and methods. [ABSTRACT FROM AUTHOR]