The existing carbon emission trading and carbon tax complement each other to achieve synergy, which is helpful to improve the efficiency of carbon emission reduction in power system. A collaborative optimization scheduling method of multi-agent integrated energy system under composite carbon pricing mechanism is proposed. Firstly, the mechanism of carbon tax is studied, and the waveform similarity is introduced as the basis for the allocation ratio of the load side response to the contribution of new energy, and the carbon reward subsidy is obtained based on this. Secondly, the composite carbon pricing mechanism of carbon tax and carbon trading is studied, and a low-carbon economic dispatch model with complementary carbon tax rate and carbon trading in the park is constructed. Finally, aiming at the differentiated park resources and trading strategies, a two-layer master-slave game optimization scheduling model with the goal of maximizing the profit of multi-agent integrated energy system is constructed. Each subject realizes resource allocation and optimal carbon emission reduction strategy through equilibrium game.

Under the"dual-carbon"targets,to promote the green and low-carbon transformation of integrated energy systems,an optimal scheduling method is proposed that integrates green certificate,carbon markets and multi-level hydrogen utilization,addressing both low-carbon technologies and market mechanisms.First,from the market mechanism perspective,a green certificate and carbon trading mechanism is introduced,considering temporal varia-tions in market supply and demand to stimulate trading activity.Second,from the technological perspective,a model is developed for carbon-capture power plants coupled with multi-level flexible hydrogen utilization.Through hydro-gen production and utilization stages,CO2 and hydrogen are managed in capture,sequestration,and utilization.Fi-nally,a scheduling model is established with the objective of minimizing coal costs,carbon sequestration costs,en-ergy purchase costs,green certificate trading costs,and carbon trading costs.Simulation results show that the pro-posed method effectively improves both the system's economic performance and environmental benefits.

With the deep participation of integrated energy systems in the carbon trading market, the impact of carbon trading risks on the scheduling of integrated energy systems is increasing. Considering the impact of market-oriented changes in carbon emission rights prices on the scheduling of integrated energy systems, this paper proposes a low-carbon scheduling method for integrated energy systems based on carbon market transaction risk valuation. Firstly, the generalized autoregressive conditional heteroskedasticity model is introduced to preliminarily predict the carbon price on the dispatching day, and the Monte Carlo method is used to quantify the uncertainty of the carbon price on the dispatching day, so as to deal with the white noise interference in the process of predicting the carbon price and improve the prediction accuracy of the carbon price. Secondly, considering the non-measurable problem of predicted carbon price under the probability distribution and the risk decision-making problem of day-ahead carbon trading, the system return risk caused by the uncertainty of carbon price prediction is measured based on the conditional value-at-risk theory. Finally, an integrated energy system optimal scheduling model based on carbon market transaction risk valuation is established and solved. Through simulation analysis, the validity and practicability of the constructed model are verified.

With the development of renewable energy technology and the strengthening of carbon emission control, microgrid, as an important part of distributed energy system, its operation optimization and resource sharing have become a research hotspot. In this paper, an optimal dispatch strategy for multi-microgrid resource sharing operation considering dynamic carbon trading mechanism is proposed, which aims to achieve carbon reduction and emission reduction while reducing system operating costs. Firstly, a dynamic carbon credit trading mechanism is established to encourage effective energy interaction and resource sharing among microgrids. Secondly, based on the asymmetric Nash negotiation theory, a multi-microgrid resource sharing and cooperative operation model considering the dynamic carbon trading mechanism is established. The alternating direction multiplier method is used for distributed solution to protect the privacy of each subject. Finally, the effectiveness of the proposed strategy is verified by an example simulation.

To further enhance the supply-demand flexibility of integrated energy systems （IES） and achieve low-carbon economic operation， this paper proposes an optimal IES scheduling strategy that accounts for demand response uncertainty and supply-demand responsiveness. First， a refined two-stage power-to-gas （P2G） operational model is established based on the traditional P2G operation process. This model is coupled with carbon capture， combined heat and power （CHP）， and gas boiler systems to develop a supply-side flexible response model， enabling diversified and flexible utilization of hydrogen energy. Second， to address the uncertainty of demand-side participation in response， a comprehensive flexible response model incorporating fuzzy chance constraints is developed. This model is converted into a deterministic using an equivalent clear method. Finally， a tiered carbon trading mechanism is introduced， and an optimal scheduling model is constructed with the objective of minimizing the total operational cost of the IES. Simulation analyses validate the effectiveness of the proposed strategy.

Based on the background of the current electricity market and carbon trading market, aiming at the problems of insufficient consumption capacity and difficult peak shaving of regional power grids with new energy access, an optimal scheduling strategy of regional power grid electricity-carbon joint multilateral trading based on Nash negotiation is proposed. Firstly, considering improving the flexibility of multilateral trading in regional power grids, combined with carbon trading and power generation rights trading mechanisms, an electricity-carbon joint trading model is constructed. Secondly, considering the volatility of wind power, the demand method of wind power flexibility adjustment is adopted. According to the wind power curtailment and time period of day-ahead dispatching, a multi-agent peer-to-peer trading model of electricity-carbon joint is constructed. And then the model is decoupled into two sub-problems: maximizing the benefits of regional power grid alliance and reasonably allocating the benefits of electricity-carbon joint multilateral trading. The alternating direction method of multipliers is used for interactive decoupling to ensure the privacy security of each subject. In addition, in the fair distribution of benefits, the asymmetric bargaining method is selected to quantify the contribution of each subject in peer-to-peer transactions as a bargaining factor to achieve the purpose of fair distribution of benefits within the alliance. Finally, several different scenarios are set up to verify the low carbon and economy of the proposed method and model. The results show that the proposed method can promote the consumption of new energy and the improvement of flexible resource collaborative scheduling ability, and achieves the goal of energy saving and emission reduction.

To enhance the economy and low-carbon of virtual power plant scheduling considering exergy efficiency, a cascade optimization low-carbon scheduling strategy of virtual power plant considering organic Rankine cycle is put forward. Firstly, with the goal of increasing exergy efficiency of heat energy, the mechanism of organic Rankine cycle power generation is studied and a mathematical model of utilization of low quality waste heat output is established. Secondly, the coupling mechanism of organic Rankine cycle and power-to-gas device in virtual power plant is studied, and a carbon reduction guidance model considering cascade carbon trading is established to realize the coupling of cascade utilization of thermal energy and low-carbon scheduling in virtual power plant. Then, the carbon emission right model including cogeneration unit, gas boiler and gas load is established to clarify the carbon emission responsibility. Finally, the multi-load comprehensive demand response model including electricity, heat and gas is established on the load side, and the low-carbon scheduling model of cascade optimization of virtual power plant energy is established with the minimum total cost of virtual power plant as the optimization goal. The simulation results show that the proposed scheduling strategy can improve the energy efficiency and low carbon efficiency of the virtual power plant.

This paper proposes a novel fault current limiter(NFCL) topology tailored for DC distribution systems, aiming to improve fault response speed, system protection coordination, and recovery capability. The NFCL integrates a fast-acting solid-state switching path and an energy-absorbing branch, forming a coordinated fault control mechanism that enables both transient ride-through and permanent fault isolation. A hybrid protection strategy, combining voltage-differential detection and directional overcurrent identification, ensures selective response and enhances protection flexibility. Compared with conventional limiter topologies, the proposed NFCL achieves over 85 % reduction in peak fault current within 0.6 ms, significantly alleviating stress on DC circuit breakers. Experimental tests based on a 10 kV prototype confirm the NFCL's fast dynamic performance and practical applicability. These results demonstrate that the proposed NFCL offers a technically innovative and cost-effective approach to enhancing the reliability and resilience of future DC distribution networks.

With the gradual increase in the proportion of renewable energy generation such as wind power, it is necessary to implement low-carbon transformation of thermal power units and fully exploit the capacity of industrial load demand response, so as to achieve carbon peak and carbon neutralization of energy and power. On this basis, a multi-time scale scheduling method considering typical industrial load production processes is proposed in this paper. Firstly, with regard to the source, a comprehensive flexible operation mode of carbon capture power plants is introduced to coordinate with wind power. In the aspect of the load, the production characteristics of two typical industrial users are specifically considered, and the production models that meet their specific constraints are established. Secondly, the day-ahead and intra-day two-stage economic dispatch model of source–load coordination is constructed so as to optimize the production plan of typical industrial load and the output of units, while improving the problem of load loss and wind curtailment. Finally, an example analysis is carried out. The results show that the scheduling method proposed in this paper can make use of the scheduling advantages of source–load adjustable resources to realize the economic dispatch of power system.

Distributed generation sources provide self-governing power during outages, making microgrids and islanded distribution networks vital for service endurance, superior power quality, reliability, and operative efficiency. However, microgrids structure are difficult to control, particularly in islanded mode where no main power source exists if the main grid fails. Fast responses from discrete generation sources using power electronics can undermine the grid during faults or normal operations without proper regulations. The double-fed induction generator (DFIG) has become the preferred wind turbine generator owing to its low cost and flexibility to varying wind speeds. This paper presents a probabilistic scheduling for day-ahead microgrid programming that includes EV parking lots and dispersed generation resources. The microgrid works in both normal and islanded modes depending on main grid conditions. The uncertainty in EV parking lot usage is modeled hourly using the Z-number method, while wind and solar generation, market prices, and loads are modeled using the Monte Carlo method. Scenario-based incidents in the upstream grid that lead to microgrid islanding are considered, focusing on the time and duration of impact. The optimization model accounts for uncertainty, EV charging/discharging, and operational costs under normal and fault conditions. The fault ride-through (FRT) method for maintaining DFIG stability in islanded microgrids are proposed. In this technique stabilizes terminal voltage during faults by employing a resistor in series with the DFIG stator, enhancing voltage stability and FRT capability. Without these methods, the DFIG may lose stability after clearing transient errors, risking generator loss and threatening microgrid stability, particularly in islanded mode. The effectiveness of these control and protection strategies is validated through comprehensive simulations in MATLAB.

The issue of DC fault ride-through can now be resolved in a novel way thanks to flexible current limiting technology, but its practical worth cannot be realized due to flaws in the technology. This study examines the placement of a controlled current source on the secondary side of a current limiting inductor and the enhancement of clamping voltage stability on the primary side of a current limiting inductor from the standpoint of controlling flexible current limiting technology. It offers a theoretical foundation for the specified secondary-side controlled source through an analysis of the iron core's inductance properties. Two-degree-of-freedom algorithm and model predictive control are combined in a control strategy that is appropriate for flexible current limiting devices to address the issue of inadequate response time and control accuracy under standard control. In MATLAB/Simulink simulation software, the presented theory's soundness is lastly confirmed.

Aiming at the problems of high peak value fault current, fast rising speed, and being unable to ensure the reliability of the power supply in the non-fault zone in a multi-terminal DC system, a new cascade flexible current limiter and mechanical DC circuit breaker for medium- and high-voltage distribution networks are proposed. Firstly, the flexible current limiter is triggered by differential under-voltage protection to achieve the effect of interpole voltage clamping, suppressing the fault current and improving the dynamic recovery characteristics of the DC system after fault clearing. Secondly, according to the breaking speed of the DC circuit breaker, the action time of the current limiter can be set flexibly. The directional pilot protection signal of the circuit breaker is used to ensure the continuous action of the current limiter at the converter station side in the fault zone, until the circuit breaker acts to isolate the fault. The protection strategy can also avoid the blocking of the converter station and reduce the requirements for the breaking speed and breaking capacity of the circuit breaker. Finally, a four-terminal medium voltage distribution network model is built in MATLAB/SIMULINK, and the effect of the current limiter and the feasibility of the proposed protection strategy are verified by simulation.

In order to adapt to the diversification of trading modes in the carbon emission permit market,the trading model of wind-hydrogen-fire coupled energy system based on the rules of existing carbon emission permit trading market is proposed. Based on the price difference between quota and certified emission reduction,the spot trading model of carbon emission permit for the multi-energy coupled system is established in spot market. As for the accumulability of quota and certified emission reduction,combined with the carbon option market trading rules of European Energy Exchange,the carbon emission option trading model is proposed in forward market. The B-S European option pricing model is improved based on the generalized autoregressive conditional heteroskedasticity model to model the call option of Chinese certified emission reduction. The example rationally combines the carbon financial market with power dispatching,and three conditions of the existing mode,spot and forward carbon emission permit trading markets are evaluated and analyzed,which verifies the effectiveness of the model and provides the guideline for the construction of carbon financial market.

In order to fully investigate the low-carbon and economic benefits of virtual power plant (VPP) with concentrated solar power (CSP) plant, this paper proposes an optimal scheduling method of VPP with CSP plant that takes carbon trading and demand response into account. First, a VPP framework with a CSP plant is constructed, and the feasibility of a CSP plant participating in power and heat supply is investigated. In addition, the stepped carbon trading model of VPP has been established in order to effectively control carbon emissions of VPP. Meanwhile, an incentive demand response (DR) model for electrical and heat load is being developed to improve VPP's operational flexibility. Second, with the goal of minimizing total cost, a VPP low-carbon economic optimization scheduling model is built, which optimizes the equipment output and load incentive demand response scheme. Finally, an example is provided to demonstrate the effectiveness of the proposed method in improving the low carbon and economic viability of VPP.

With the rapid development of flexible DC distribution networks, fault detection and identification have also attracted people’s attention. High-resistance grounding fault poses a great challenge to the distribution network. The fault current is very small and random, which makes its detection and identification difficult. The traditional overcurrent protection device cannot identify and act on the fault current. Therefore, this paper proposes a fault detection method based on variational mode decomposition (VMD) combined with the convolutional neural network (CNN) of the inception module. This method first uses VMD to decompose the positive transient voltage. Second, it inputs the decomposed signal into CNN for training to obtain the optimal parameters of the model. Finally, the model performance is tested based on the PSCAD/EMTDC simulation platform. Experiments show that the detection method is accurate and effective. It can realize the accurate identification of seven different fault types.

在含高比例风电的新型电力系统中,针对由于缺乏灵活性调节激励机制造成的灵活性资源调节积极性不高、风电波动性平衡困难的问题,该文提出一种考虑灵活性补偿的高比例风电与多元灵活性资源博弈优化调度方法.首先,计及风电波动性与负荷波动性的时空耦合特性,定义了风电波动性评价指标,并以此为基础提出了风电灵活性调节需求量化方法;然后,考虑各灵活性资源主体决策的差异性和趋利性,提出了基于主从博弈的灵活性供需均衡分析方法,以各主体利益最大化为目标建立灵活性资源供需博弈优化模型;最后,通过仿真案例验证了所提方法可以有效提高源-荷-储多方灵活性资源参与调节的积极性,并促进高比例风电的上网消纳.

Under the background of the dual-carbon target, this paper proposes an optimal scheduling method for the micro-energy grid considering the synergistic effect of direct air capture（DAC） and power to gas（P2G） to solve the problem of optimal scheduling of the micro-energy grid of the low-carbon smart communities. Firstly, a new idea of community air carbon capture application scenario is proposed, and a mathematical model of the coupling relationship between carbon capture and carbon capture energy consumption of DAC is established based on the principle of CO 2 chemisorption.Furthermore, based on the study of P2G-DAC characteristics, the collaborative operation model of P2G-DAC is established. Then, considering the system operation constraints and the adsorption cycle characteristics of carbon capture device, a collaborative scheduling model is constructed to minimize the overall operating cost of the system. Finally, the Yalmip is used to call the Gurobi solver to solve the model, and the simulation verification work is carried out based on a villa community in southwest China. The simulation results show that the proposed P2G-DAC coordinated scheduling strategy of micro-energy grid in smart community can reduce the comprehensive operation cost of the system by 6.77 % and the carbon emission of the system by 75 %. While improving the economy of micro-energy grid operation, it can reduce carbon emissions and has significant environmental and social benefits.

The random output of renewable energy and the disorderly grid connection of electric vehicles (EV) will pose challenges to the safe and stable operation of the power system. In order to ensure the reliability and symmetry of the microgrid operation, this paper proposes a microgrid optimization scheduling strategy considering the access of EVs. Firstly, in order to reduce the impact of random access to EVs on power system operation, a schedulable model of an EV cluster is constructed based on the Minkowski sum. Then, based on the wavelet neural network (WNN), the renewable energy output is predicted to reduce the influence of its output fluctuation on the operation of the power system. Considering the operation constraints of each unit in the microgrid, the network active power loss and node voltage deviation are taken as the optimization objectives, and the established microgrid model is equivalently transformed via second-order cone relaxation to improve its solution efficiency. Based on network reconfiguration and flexible load participation in demand response, the economy and reliability of system operation are improved. Finally, the feasibility and effectiveness of the proposed method are verified based on the simulation examples.

As a complex dynamically strongly coupled system, DC distribution system often suffers from voltage collapse due to system resonance. In order to suppress distribution network resonance and bus voltage fluctuation, this paper proposes a hybrid control algorithm to suppress DC distribution system resonance to further enhance DC system stability. In this paper, the output voltage of the line regulation converter (LRC) is the target of the study. A current prediction model is introduced in the inner loop of the converter control, which can enhance the dynamic responsiveness of the system and eliminate the PWM modulator and parameter tuning, achieve the unitization of the inner loop of the current. By constructing the inverse model of the controlled object, the outer voltage loop is unitized under the control of two-degree-of-freedom. The hybrid control enables the bus voltage to follow the reference voltage exactly, which suppresses resonance peaks in the voltage transfer function and reduces bus voltage fluctuations. Finally, the proposed hybrid control algorithm is simulated and verified in MATLAB/Simulink platform. The results show that the control strategy can effectively suppress the resonance and bus voltage fluctuation of the DC distribution system and enhance the dynamic characteristics and anti-interference capability of the distribution network.