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• This paper proposes a bi-level Stackelberg low-carbon economic dispatch model with reward and penalty stepped carbon trading mechanism, horizontal and vertical integrated demand response, and dynamic supply and demand multi-energy pricing. • This paper also provides a multi-angle sensitivity analysis of the impact of different parameters of the stepped carbon trading mechanism on the operation of the CIES. • An innovative analysis of the impact of single and two-factor carbon interval length, carbon growth rate, and carbon base price on the CIES is presented. • The sensitivity analysis of horizontal integrated demand response is also carried out. • Through horizontal and vertical integrated demand response, the dynamic supply and demand multi-energy pricing of CIES is further improved. To solve the problem of coupled and coordinated dispatch of multi-energy between campus integrated energy system operator and users. A bi-level Stackelberg low-carbon economic dispatch model with horizontal and vertical integrated demand response and dynamic supply and demand multi-energy pricing (DSDMEP) is proposed. Firstly, the DSDMEP is established to effectively coordinate the interests of the upper CIESO and lower users. Secondly, the combined heat and power containing the power to gas and carbon capture system and the reward and penalty stepped carbon trading mechanism (SCTM) are introduced on the energy supply side to minimize carbon emissions. Then, the HVIDR is introduced to the users to increase the energy coupling and adjustable price characteristics of supply and demand. Finally, the results show that the proposed model compared to other scenarios has its carbon emissions reduced by 21.72%, the benefits of CIESO are increased by 27.86%, and the cost of the users is reduced by 6.9%. This paper also provides a multi-angle sensitivity analysis of the impact of different parameters of the SCTM on the operation of the campus integrated energy system (CIES), as well as a sensitivity analysis of the HVIDR and peak and valley electricity price difference. [ABSTRACT FROM AUTHOR]

• Carbon emission flows of distribution networks are traced considering power loss and reactive power. • Individuals and the overall customers are harmonized by the proposed low-carbon allocation method at the demond side. • The decarbonization potential of shared PVs and ESSs is exploited based on CEF calculation and carbon quota mechanism. • Optimal amounts and locations of shared PVs and ESSs are obtained via the proposed method with the limited investment. With the targets of carbon peaking and carbon neutrality, carbon emission reduction measures have become significant issues in new-type power systems. Distribution networks (DNs), which multiple power sources and flexible loads access to, play an essential role in exploiting the potential of reducing carbon emissions on the demand side. To achieve a global carbon emission reduction considering the carbon quota of each customer, shared photovoltaics (PVs) and energy storage systems (ESSs) are allocated with a centralized calculation and optimization conducted by DNs. To solve two key points in demand-side planning of shared PVs and ESSs in distribution networks, i.e., the accuracy of carbon emission flow (CEF) calculation and carbon quota-oriented optimization planning, this paper proposes a low-carbon oriented planning method for shared PVs and ESSs via CEF tracing. Firstly, a novel CEF calculation method is proposed based on the forward current sweep and backward voltage sweep algorithm, which considers the tracing of reactive power and power losses. Next, carbon emission index and economic index of allocating shared PVs and ESSs are defined. Subsequently, a bi-level optimization model is presented, considering the excess carbon emissions of each load based on carbon accounting and carbon quotas. Finally, the superiority of the proposed method is verified through a modified IEEE 33-node distribution network, and the effects of various investment constraints and carbon quotas are discussed. [ABSTRACT FROM AUTHOR]

• Integration of blockchain characteristics into the collaborative carbon emission reduction game model of both the supply and demand sides of electricity is proposed to encompass source control and terminal suppression. • Blockchain is utilized to trace electricity types within the electricity market and to quantify the carbon emissions generated by the demand side of electricity. • By incorporating blockchain, users can actively engage in individual-level carbon trading to control and reduce carbon emissions. • Blockchain facilitates enhanced carbon emission reduction efforts by the power supply side. The task of carbon emission reduction is severe in the power industry in China under the national goal of "carbon peaking and carbon neutrality". The current carbon reduction is mainly based on supply side, but the effect is limited. To solve this problem, this paper proposed a coordinated supply–demand carbon emission reduction strategy based on blockchain technology. Based on the carbon emission reduction of complementary thermal power and renewable energy under the carbon trading mechanism of power supply side, users are made to participate in the individual level carbon trading mechanism with the help of blockchain technology, and the carbon emission reduction in power demand side is guided through the market mechanism, thus forming a supply–demand collaborative carbon emission reduction strategy of source side control and terminal inhibition. By analyzing the decision changes of both the supply and demand sides of electricity before and after the introduction of blockchain, quantifying the influence of blockchain on electricity quantity, electricity price and users' utility in turn, and establishing the personal carbon trading mechanism supported by blockchain, a game model of two-side interaction between supply and demand was constructed. The simulation results show that the collaborative carbon emission reduction strategy based on blockchain gives full play to the potential of carbon reduction in power demand side, and the personal carbon trading mechanism can better inhibit terminal carbon emissions, which is conducive to the deep carbon emission reduction of the power industry. [ABSTRACT FROM AUTHOR]

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

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

• Proposed cooperative game model of hierarchical trans -regional electricity trading. • Analyzed the impact of carbon quota systems on trans -regional electricity trading. • Proposed a reasonable profit distribution scheme based on the Owen graph value. • Demonstrated that the formation of priori union can improve the profits of trans-regional electricity trading. • Under loose low-carbon demand policy, carbon quota sharing responsibility are more effective than producer responsibility. Trans-regional electricity trading is a meaningful way to optimize resource allocation and achieve carbon peak and carbon neutrality goals. In this paper, we construct the trans -regional electricity trading cooperative game models considering the impact of local microgrid integration. Based on the cooperative game theory with coalition and graph structures, the optimal mode of trans -regional electricity cooperation and the impact of different carbon quota systems on cooperation profits are studied. The profits obtained through the trans -regional electricity trading are reasonably distributed by the profit distribution scheme based on the Owen graph value. The theoretical and numerical simulation results demonstrate that the local microgrid and the bulk power grid form a priori union, and then participate in the trans -regional electricity trading can obtain more profits. In addition, when the low-carbon policy on the demand side of electricity becomes more relaxed, the implementation of a carbon quota shared responsibility between producers and consumers will be more effective than producer responsibility. Compared with the Myerson value, the profit distribution scheme based on the Owen graph value is more suitable for distributing the profits obtained through trans -regional electricity trading. [ABSTRACT FROM AUTHOR]

Planning and managing energy production, transmission, and distribution face new challenges as renewable energy sources become more widely used. These sources enhance existing issues of grid operation due to their unpredictability and intermittency. As a result, sophisticated energy system control techniques and technologies that offer novel flexibility solutions to the inelastic demand side are necessary to ensure the power system's stability. This paper reviews the feasibility of demand response as a tool for effective grid management and a measure for properly sized renewable energy systems (RES) capacities, both of which could accelerate the phase-out of fossil fuels. Research also focuses on tapping into the building stock's demand response capacity in an effort to decrease operational CO 2 emissions. In this line of approach, current status and goals of the respective European policies are analyzed. The role of market based decarbonization is studied and presented along with the status of demand response market implementation in several countries. [ABSTRACT FROM AUTHOR]

Dynamic tariffs play an important role in demand response, contributing to smoothing power consumption and reducing generation capacity requirement and carbon emission. However, in the existing works, tariffs are usually designed without comprehensive consideration, such as potential user responses to tariffs. Thus, assuming an electricity trading market contains a utility company and multiple residential users, a dynamic tariff design method is proposed in this paper, considering user responses to tariff changes. Leveraging the non-intrusive load monitoring technique, rated power and user preference features for each appliance are acquired by the utility company to quantify user comfort (discomfort) based on derived user appliance usage habits. Then, a bi-level Stackelberg game model is built on the supply side for designing optimal dynamic tariffs and imitating the influence of tariff changes on DR plans for users. The upper level represents the utility company, trying to maximize utility profit, social welfare and carbon emission reduction. While the lower level represents users, aiming to minimize electricity bills and user discomfort. By solving such an optimization problem with multiple objectives, a novel hybrid probabilistic multi-objective evolutionary algorithm balancing evolutionary efficiency and stability is applied where random forest is adopted to boost performance. The proposed model is benchmarked with two state-of-the-art pricing methods and validated on a publicly accessible REFIT dataset, where low-rate power measurements are collected from real houses in the UK. The experimental results show the proposed model generally outperforms benchmarks on dynamic tariff design in achieving peak-shaving and low carbon emission while preserving user satisfaction. Furthermore, a case study is implemented, which verifies the necessity of various objectives employed in the proposed method. • A bi-level Stackelberg game model for designing tariff based on user demand response. • Electricity-consuming feature extraction on non-intrusive load monitoring results. • A hybrid probabilistic multi-objective algorithm balancing efficiency and stability. • A random forest classifier for reducing computational complexity in late iterations. • Extensive case studies on evaluating objectives and visualizing optimization process. [ABSTRACT FROM AUTHOR]

The Yangtze River Delta region is one of the key areas of energy transition for China's carbon neutrality. The pathways to carbon neutrality are diverse in the regional level due to the different conditions on the energy supply and demand side. How to construct comprehensive assessment index system and evaluate regional pathways to carbon neutrality is an urgent problem to be solved. To fill this gap, this paper evaluates the carbon neutrality pathways of supply-side and demand-side coordination in the Yangtze River Delta region using the Low Emissions Analysis Platform with five scenarios (64 sub-scenarios): Business as usual, energy intensity decrease, energy structure reform, power supply technology innovation and policies mix. The simulation results indicate that: (1) 22 of the 64 sub-scenarios can achieve carbon neutrality by 2060. (2) In energy intensity decrease scenario, an annual 6% reduction in energy intensity could achieve carbon neutrality before 2060. (3) The policies mix scenario can lead to 21 sub-scenarios with pathways to achieve carbon neutrality when the average annual energy intensity decreases by 4% or more. Additionally, the end-use sectors electrification level should reach 64.19% at least by 2060. (4) There can be 14 sub-scenarios to achieve zero carbon emissions in the power plant sector, but annual energy intensity should be reduced by at least 4%. Applying Analytic Hierarchy Process analysis, the article evaluated the 22 sub-scenarios with the index of regional pathways to carbon neutrality. The result shows that the mixed policies of energy intensity reduction, energy structure reformation and power technology innovation can promote the coordination of supply-side and demand-side. This study can provide reference for policymakers to formulate effective policies to realize carbon neutrality. • The coordination of energy supply and energy demand is assessed. • A model with five scenarios (64 sub-scenarios) is developed for carbon neutrality. • Energy intensity drops by 4% or more can lead to multiple carbon neutral pathways. • A comprehensive assessment index of regional carbon neutral pathways is developed. [ABSTRACT FROM AUTHOR]

Household consumption has great potential to reduce CO 2 emissions from the demand side which is an important way to achieve carbon peaking and carbon neutrality goals. However, the shrinking household size will have a significant impact on household carbon emissions. In this paper, we have investigated the impacts of the decrease in Chinese household size on the direct and indirect household carbon emissions by using the input-output model and the bidirectional fixed-effect model, based on China's Urban Household Income and Expenditure Survey dataset. Results have shown that household size has a significant negative effect on household carbon emissions per capita, and such a negative effect on indirect carbon emissions is much greater than that on direct carbon emissions in China. Reducing the average household size by one has led to an increase of 0.128 t of the direct household carbon emissions per capita and 1.344 t of indirect household carbon emissions per capita, respectively. We also projected the potential change of household carbon emissions by 2030 under scenarios of increasing total population and shrinking household size and found that there may generate a 15.3%–18.8% growth in household carbon emissions compared to that in 2012, with other demographic characteristics unchanged. [ABSTRACT FROM AUTHOR]

With the popularization of electric vehicles and hydrogen fuel vehicles, establishing an eco-friendly and economic-affordable fuel supply system turns out to be critical for the decarbonization of transportation sector. To achieve this, this paper proposes a systematic methodological framework to facilitate optimal configuration planning of an electric-hydrogen micro-energy system with vehicle sharing stations (EHVS). The proposed approach is based on the notion of integrated resource planning, which combines supply side with demand side simultaneously. The problem is formulated as a two-stage hybrid stochastic-interval programming, where the first-stage is aimed at minimizing the investment cost by determining the optimal configuration planning, while the second-stage is aimed at maximizing the expected benefits through optimal operation simulation of the system. The potential flexibility of vehicular demands in response to the rental price-setting has been explicitly considered in this work and represented using an evolutionary game model. Through this, the substitutability between different vehicle demands and its impact on the long-term planning of electric-hydrogen integrated energy system can be properly captured. A hybrid interval-stochastic method is presented to handle different types of uncertainties involved in the discussed problem, wherein the scenario analysis, the interval order relation and the fuzzy preference methods are jointly utilized. The resultant problem is solved with a genetic-algorithm based framework, and implemented in the MATLAB environment. The simulation results show that, compared with conventional independent planning, there could be an improvement in the carbon emission reduction by 24.74% and an additional cost saving of 11.97% achievable using the proposed approach. • Decarbonization via electric-hydrogen system with vehicle sharing stations is studied. • Flexibility of vehicular energy demands based on rental pricing is considered. • Long-term evolution of users' behavioral pattern is modeled via evolutionary game. • Hybrid interval-stochastic approach is used to handle different uncertainties. • A carbon emission reduction of 24.74% and cost saving of 11.97% are achieved. [ABSTRACT FROM AUTHOR]

Two trends have shaped the European car market over the past decade: electrification and SUVisation. Recently, they have merged in a new product: the electric sport utility vehicle. This paper contrasts the view that regards this innovation as a technological solution to mitigate road transport emissions with a broader view that takes into account the battery requirement and profitability of such type of electric car. This broad view is formalised in a system dynamics model that captures the potential effects of supply side constraints. Four scenarios are simulated. The results show that the levels of CO 2 emissions of new cars are substantially reduced only in the one that assumes lower motorisation. Based on these simulations, the conclusion that the diffusion of electric sport utility vehicles does not necessarily contribute to mitigating car emissions in Europe is drawn. Less reliance on technology fixes, downsizing and reduced motorisation are identified as solutions worth investigating to meet ambitious climate goals. Further research on how vehicle manufacturers distribute batteries is needed. [Display omitted] • A novel system dynamics model of the new electric SUV market was developed. • Battery supply constraints considered as natural boundaries of road transport. • Four scenarios explored to simulate CO 2 emissions from cars in Europe. • Electric SUVs do not necessarily contribute to emissions mitigation. • Downsizing and lower motorisation are worth pursuing. [ABSTRACT FROM AUTHOR]

As the world's largest developing country, China has set the target of "reaching carbon peak by 2030 and carbon neutrality by 2060". China's power sector faces many challenges in reducing carbon emissions. This article forecasts electricity consumption from 2021 to 2060 by selecting five influential elements on power demand. Furthermore, the system dynamics model simulates the carbon emission system on the supply and demand side of the power industry. This paper innovatively incorporates the technology factor of carbon capture, utilization, and storage (CCUS) into the supply-side model. In this study, the process of carbon neutrality is separated into three stages, including carbon peak stage (2020–2030), deep low-carbon stage (2030–2050) and zero-carbon stage (2050–2060). The factors of energy storage technology and CCUS technology make significant contributions in the latter two stages. Based on the different development levels of the variables, three scenarios are set as business-as-usual scenario (BAU), low carbon scenario(LC) and ultra-low carbon scenario (ULC). Then scenario analysis is employed to simulate the carbon emission in the future. The scheme under the ULC scenario is the optimal solution to achieve carbon neutrality. Improving flexible transition of thermal power units and promoting the growth of renewable energy are China's primary strategies of achieving carbon peak and carbon neutrality. • System dynamics model is applied to simulate scenarios on carbon emission. • The carbon emission models of the power supply and demand sides are constructed. • Three scenarios for achieving the dual-carbon target are proposed. • The electric energy substitution and CCUS are investigated in the model. [ABSTRACT FROM AUTHOR]

As the world's largest developing country, China has set the target of "reaching carbon peak by 2030 and carbon neutrality by 2060". China's power sector faces many challenges in reducing carbon emissions. This article forecasts electricity consumption from 2021 to 2060 by selecting five influential elements on power demand. Furthermore, the system dynamics model simulates the carbon emission system on the supply and demand side of the power industry. This paper innovatively incorporates the technology factor of carbon capture, utilization, and storage (CCUS) into the supply-side model. In this study, the process of carbon neutrality is separated into three stages, including carbon peak stage (2020–2030), deep low-carbon stage (2030–2050) and zero-carbon stage (2050–2060). The factors of energy storage technology and CCUS technology make significant contributions in the latter two stages. Based on the different development levels of the variables, three scenarios are set as business-as-usual scenario (BAU), low carbon scenario(LC) and ultra-low carbon scenario (ULC). Then scenario analysis is employed to simulate the carbon emission in the future. The scheme under the ULC scenario is the optimal solution to achieve carbon neutrality. Improving flexible transition of thermal power units and promoting the growth of renewable energy are China's primary strategies of achieving carbon peak and carbon neutrality. • System dynamics model is applied to simulate scenarios on carbon emission. • The carbon emission models of the power supply and demand sides are constructed. • Three scenarios for achieving the dual-carbon target are proposed. • The electric energy substitution and CCUS are investigated in the model. [ABSTRACT FROM AUTHOR]