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With the goal of "carbon peak and carbon neutrality," the need for environmentally friendly gases to replace SF6, a high greenhouse effect gas, is urgent. C4F7N, as an environmentally friendly gas with the greatest potential to replace SF6 as an arc extinguishing medium in circuit breakers, can be mixed with CO2 to greatly improve the shortcomings of its high liquefaction temperature, and the calculation of the physical parameters of the mixed gas plasma is a prerequisite for the computational simulation of the arc process in the opening of circuit breakers. Because solving the physical parameters is expensive, based on the system of differential equations, this paper adopts several machine learning algorithms by mining the relationship between the data using the known physical parameter data to predict the results of the physical parameters to be solved under certain conditions, which greatly reduces the cost of computation. The machine learning algorithms used in this paper are K-nearest-neighbor regression, decision tree, random forest, support vector machine, and gradient boosting regression, of which for the support vector machine, hyperparameters find it difficult to determine the problem of optimization using the gray wolf algorithm. The prediction results of several algorithms show that they are more accurate and that the problem can be solved better by using the method of machine learning. Finally, the comparison results show that the support vector machine exhibits better performance in most cases and that the gray wolf algorithm can make the results of the support vector machine more accurate. [ABSTRACT FROM AUTHOR]

In the context of the global low-carbon energy development strategy, the massive influx of renewable energy leads to the instability of the power system, so it is necessary to carry out effective scheduling strategy and management mechanism for energy. On this basis, the Integrated Energy System including Electricity/Heat/Storage (EHSIES) is discussed in this paper. First, an ordered clustering method using a combination of simulated annealing and improved profile coefficients for load evolution number approximation is proposed. Second, a hybrid load clustering algorithm based on hierarchical clustering algorithm and K-means++ is presented and four types of load scenarios are obtained. Third, an Improved Harris Hawk Optimization (IHHO) algorithm based on the hybrid strategy is proposed and solved to the model of EHSIES. Finally, under the premise of the dynamic carbon trading mechanism considering historical compliance and load aggregator management, the scheduling strategies of flexible electric loads and thermal loads participating in EHSIES are discussed and solved by CPLEX. The results show that the solution time of IHHO is 50% and 33.3% lower than that of particle swarm optimization (PSO) and Harris hawk optimization (HHO), and the number of iterations is 76.6% and 56.25% lower than that of PSO and HHO, respectively. In addition, considering a flexible load aggregator and a compliance-based dynamic carbon trading mechanism in EHSIES scheduling can effectively reduce the actual operating cost by 27.37%, the source-side energy spill rate by 68.75%, and the carbon emission by 51.92% and improve the operating efficiency and economic benefits of EHSIES. [ABSTRACT FROM AUTHOR]

With the development of electric vehicles (EVs), a large number of electric vehicle charging stations (CSs) have been rapidly rolled out to meet the charging demand of EVs. However, high construction costs and long payback periods motivate investigations to improve the profits of CSs. Considering the profit improvement of CSs and carbon emission reductions, this paper first proposes a carbon revenue model for CSs to participate in the carbon trading market. A charging price strategy is proposed to share the carbon revenue with EV users to reduce the charging cost of users, increase the charging income of CSs, and reduce carbon emissions. By describing the EV users' response to the charging price based on the fuzzy theory, this paper establishes the charging behavior model of EV users and solves the profit optimization of the dynamic charging price model by particle swarm optimization algorithm. Finally, the results of the simulation case demonstrate the effectiveness of the proposed strategy. A sensitivity analysis of various grid power purchase prices illustrates the difference between the fixed and dynamic charging price methods. The dynamic charging price method is superior, and under the power purchase price of 0.5 yuan/kWh, it can lower EV user charging costs by 16.25%, improve CS profits by 30.09%, and reduce carbon emissions by 74.68%. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic power generation will not only help to achieve the strategic targets of peaking carbon emissions and carbon neutrality but also cause a series of impacts on the power grid at the same time. Forecasting the long-term development of regionally distributed photovoltaics can provide a reference for power grid planning and stable operation. In this paper, considering the effect of factors such as subsidies and countywide promotion policy of photovoltaics, a forecasting model for the development tendency of regionally distributed photovoltaics based on system dynamics is established. Then, taking Beijing as an example, an empirical analysis is carried out, and the effect of the proportion of self-consumption and the time when the subsidy is adjusted on distributed photovoltaic penetration is explored through sensitivity analysis. The simulation results show that the installed capacity achieved by the countywide promotion policy will become the main source of the installed capacity growth of distributed photovoltaics in Beijing after 2024. To continuously boost distributed photovoltaic penetration, relevant policymakers should consider the appropriate time when the subsidy is adjusted according to the installation cost of photovoltaic systems. [ABSTRACT FROM AUTHOR]

The uncertainty of renewable energy output threatens the operation safety of multi-agent integrated energy system (MAIES), which makes it difficult to balance the low-carbon economic operation demands of various stakeholders. However, the existing research solely focuses on the operational strategy of multi-agent game involving integrated energy suppliers and users in deterministic scenarios, overlooking the complementary supporting role and game interaction of shared energy storage and wind farm as independent entities of interest under the instability of renewable energy power generation. Hence, this paper first establishes the optimal operation models for integrated energy system operator (IESO), user aggregator (UA), shared energy storage operator (SESO), and wind farm operator (WFO) considering the stepped carbon trading. Second, in the face of the actual situation of uncertainty of photovoltaic and wind power output, fuzzy chance-constrained programming is adopted for processing. Then, a bi-layer game equilibrium model with IESO as a leader and UA, SESO, and WFO as followers is proposed, and the existence and uniqueness of Stackelberg equilibrium solution are proved. Finally, simulation calculation is carried out based on the YALMIP toolbox in the Matlab R2023a software, and the improved particle swarm optimization algorithm and CPLEX solver are used to solve the model. The results demonstrate that the participation of SESO and WFO as independent stakeholders in the game interaction can improve the economic and environmental benefits of MAIES. The iterative optimization of demand response subsidy prices can effectively motivate users to participate in demand response, improve the ability of MAIES to cope with the uncertain risks of renewable energy generation and load, and reduce the power grid dispatch pressure. [ABSTRACT FROM AUTHOR]

The high-quality development of new energy enterprises is of great significance to promote carbon peak and carbon neutrality and cope with the global warming crisis. However, with the increasing intensity of market competition and the appropriate weakening of the expected future subsidies, how to improve their performance through the fulfillment of the social responsibility of stakeholders has become a key scientific problem to be solved. Given the features of the new energy industry, including substantial initial investment, formidable technical barriers, and a pronounced reliance on policy support, this paper takes 182 new energy concept enterprises listed in China's A-shares in 2011–2020 as the research object. Employing qualitative comparative analysis, we extract four key rules for achieving high performance in new energy enterprises from the perspective of value co-creation of core stakeholders, including capital stakeholders (shareholders and creditors), technical stakeholders (employees), policy stakeholders (government and society), and upstream and downstream stakeholders (suppliers and customers). Then, we explore the performance improvement rules of typical cases. Our findings reveal that within the realm of new energy enterprises, capital-intensive enterprises with cost leadership and tax incentives, energy-manufacturing enterprises with suppliers dependence and saving environmental input, technology-innovation enterprises with cost leadership and talents dependence, and comprehensive-mature enterprises with suppliers dependence and tax incentives are more likely to achieve high performance. The findings can better guide management practice and promote the high-quality development of new energy enterprises. [ABSTRACT FROM AUTHOR]

Micro energy grids (MEGs) play a vital role in realizing carbon neutrality and efficient utilization of renewable energy resources. This research focuses on optimizing the synergy of MEG interconnections. Given the diverse development paths of different operating entities within the system, information barriers emerge among MEGs, creating great difficulties for the collaborative system management. In response, this paper proposes a decentralized coordinated dispatch model targeting multiple stakeholders within the system. This model accounts for energy interactions between MEGs and the inherent uncertainty associated with renewable energy sources. Specifically, stochastic optimization approach was applied to characterize the uncertainty of renewable energy output by generating stochastic scenarios. Furthermore, it incorporates the analytical target cascading (ATC) method to decouple objective functions and constraints, creating autonomous scheduling sub-models for individual MEGs. This decentralized approach ensures independent modeling and coordinated problem-solving. Simulations verify that (1) the ATC-based inter-MEG energy interaction strategy effectively achieves decentralized coordinated scheduling of multiple MEGs and (2) the decentralized coordinated scheduling solution closely approximates the global optimum while considering the interest of various system entities. [ABSTRACT FROM AUTHOR]

Against the backdrop of "carbon peak and carbon neutrality," the hydrogen and fuel cell vehicle industry are rapidly developing. Within the on-board hydrogen supply system, the hydrogen circulation pump serves as an essential component of the hydrogen fuel cell system. The spiral disk, as a core part of the hydrogen fuel cell system's vortex hydrogen circulation pump (VHCP), plays a crucial role in determining the performance of the hydrogen circulation system in hydrogen fuel cell vehicles. To meet the requirements for high-performance and high-reliability development of the hydrogen circulation pump, the VHCP scheme is adopted as the choice for the hydrogen pump solution. Through the magnetic suspension and no connection shaft structural design, the feasibility of applying the high speed and high flow hydrogen turbine was initially validated. Utilizing Fluent analysis software and high precision performance test bench, a comprehensive three-dimensional numerical simulation of the turbine design under various operating conditions was conducted and performance test verification, demonstrating that the performance meets the required specifications. By conducting research in both strength optimization design and performance requirement, two major technical challenges in turbine pump application were overcome. Combined with the experimental results of the turbine medium, it is concluded that the vortex pump can meet the flow and pressure rise under the premise of low power consumption in the hydrogen circulation system so as to perfectly increase the hydrogen return amount. Based on these findings, recommendations are proposed for the future development direction of hydrogen supply systems in hydrogen fuel cell vehicles. [ABSTRACT FROM AUTHOR]

Understanding the interactions between hydrogen flame and turbulent vortices is important for developing the next-generation carbon neutral combustion systems. In the present work, we perform several direct numerical simulation cases to study the dynamics of a hydrogen diffusion flame embedded in the Taylor–Green Vortex (TGV). The evolution of flame and vortex is investigated for a range of initial Reynolds numbers up to 3200 with different mass diffusion models. We show that the vortices dissipate rapidly in cases at low Reynolds numbers, while the consistent stretching, splitting, and twisting of vortex tubes are observed in cases with evident turbulence transition at high Reynolds numbers. Regarding the interactions between the flame and vortex, it is demonstrated that the heat release generated by the flame has suppression effects on the turbulence intensity and its development of the TGV. Meanwhile, the intense turbulence provides abundant kinetic energy, accelerating the mixing of the diffusion flame with a contribution to a higher strain rate and larger curvatures of the flame. Considering the effects of the non-unity-Lewis number, it is revealed that the flame strength is more intense in the cases with the mixture-averaged model. However, this effect is relatively suppressed under the impacts of the intense turbulence. [ABSTRACT FROM AUTHOR]

Against the background of seeking to achieve carbon neutrality, relationships among renewable-energy companies around the world have become multiple and complex. In this work, the Pearson, Kendall, tail, and partial correlation coefficients were applied to 51 global companies—including solar and wind firms, independent power plants, and utilities—to explore the linear, nonlinear, extreme-risk, and direct relations between them. Sample data from 7 August 2015 to 6 August 2021 were considered, and three sub-periods were extracted from these sample data by analysis of the evolution of multiple correlations combined with event analysis. A four-layer correlation network model was then constructed. The main results are as follows. (1) The multiple relations among the selected firms underwent dramatic changes during two external shocks (the China–US trade war and the COVID-19 pandemic). (2) The extreme-risk network layer verified that the trade war mainly affected the relationships among companies in the solar industries of China and the US. (3) During the COVID-19 pandemic period, the linear and direct relationships among wind firms from Canada, Spain, and Germany were significantly increased. In this sub-period, edge-weight distributions of the four different layers were heterogeneous and varied from power-law features to Gaussian distributions. (4) During all the sub-periods, most companies had similar numbers of neighbors, while the numbers of neighbors of a few companies varied greatly in the four different layers. These findings provide a useful reference for stakeholders and may help them understand the connectedness and evolution of global renewable-energy markets. [ABSTRACT FROM AUTHOR]

Scientific prediction of net carbon emissions in the Beijing–Tianjin–Hebei (BTH) region is of significance to examine carbon emission reduction in the context of the "double carbon" target. In this study, the carbon peak and carbon neutrality states in the BTH region are determined through a regional double carbon target analysis framework, and a logistic chaotic sparrow search algorithm backpropagation neural network hybrid model (LCSSA-BP) optimized by a logistic chaotic sparrow search algorithm (LCSSA) is used to forecast the net carbon emissions. The findings reveal that the net carbon emissions in the BTH region generally increased during the study period; Beijing, Tianjin, and Hebei are in different stages toward the realization of the double carbon target; population size, affluence, and urbanization rate are positively correlated, whereas the proportion of foreign direct investment and energy intensity is negatively associated with net carbon emissions; the prediction accuracy of the LCSSA-BP is superior to that of the SSA-BP and BP neural network, and it can be used to forecast the net carbon emissions in the BTH region. [ABSTRACT FROM AUTHOR]

In the context of China's dual carbon targets, reducing carbon emissions has become even more urgent and important. In order to reduce carbon emissions during the operation of a virtual power plant (VPP), a carbon market containing carbon emission allowances and the Chinese certified voluntary emission reduction project was introduced, and a VPP day-ahead and real-time coordinated scheduling optimization model was developed. This model was optimized to maximize benefits and minimize carbon emissions, further increasing the emphasis on carbon reduction from the VPP. The deep Q network (DQN) in the deep reinforcement learning algorithm was introduced to solve the complexity and non-linearity of the VPP model. Finally, to verify the validity and feasibility of the model and the solution algorithm, a VPP was chosen for the analysis of arithmetic examples. The results showed that: (1) in the calculation example, the VPP obtained more than 30 000 yuan of carbon revenue per day by participating in carbon trading, which effectively mobilizes interest in carbon emission reduction. (2) Compared to the scheduling result with profit maximization as the goal, the scheduling result considering dual goals reduced carbon emissions by 32.7% at the cost of a 2.6% reduction in revenue, obtaining a better compromise. (3) The two-stage coordinated scheduling optimization model obtained 3.6% higher gains than the day-ahead scheduling optimization model that took into account deviation penalties. (4) By comparing the scheduling model solution results from the Yalmip toolbox and DQN algorithm, the effectiveness and feasibility of the DQN algorithm were verified. [ABSTRACT FROM AUTHOR]

In this research, a distributed multi-generation system combining photovoltaic-thermal collectors with biomass generating units is established in a numerical simulation environment. This system aims to fulfilling heating demand of single-storey cottage located in Xi'an, Northwest China. The feasibility of this system and stepped utilization of different levels of energy are validated and illustrated. In order to achieve reachable maximum efficiency, key parameters of the system, such as collector area, thermal storage capacity, and set heating temperature among other parameters, are combined in a multitude of different ways, and a set of optimal parameters are obtained. After running the model, throughout the heating season, the results show that solar energy contributes 1698 kWh accounting for nearly 60% of the total energy input, while biomass accounts for 40% with 852.95 kWh, which directly determines the stability of the system energy supply. Once-daily maximum electricity buffer generated by the biomass generator holds at 21.67 kWh, the system is able to meet most of the heating load scenarios in Xi'an, China, without auxiliary energy input, as well as achieves the carbon neutrality goal. [ABSTRACT FROM AUTHOR]