Recently, in the face of global climate problems and challenges posed by the scarcity of conventional fossil energy sources, governments have successively proposed strategic goals for energy conservation, emission reduction, and support for renewable energy development. The carbon emissions market and renewable portfolio standards are important market tools for reducing carbon emissions and promoting renewable energy consumption. As the main responsible body of CO2 emission, the low-carbon green transformation of power system is the key link to help the “carbon peak, carbon neutral” target, and the coupling of electricity-carbon-renewable portfolio standard will help to promote CO2 emission reduction and renewable energy consumption to a greater extent. First, it analyzes the interaction mechanism between the electricity and carbon markets and the electricity and renewable portfolio standards. Second, it summarizes the current research status of coupled electricity-carbon-renewable portfolio trading from the perspectives of trading mechanism design, trading optimization, and market trading technology. Furthermore, this study describes the current status and mechanisms of the domestic and international implementation of carbon markets and renewable portfolio standards to reflect the policy environment for emissions reduction in different countries. Finally, the challenges and limitations faced by the construction of China’s electricity-carbon-renewable portfolio-coupling mechanism are sorted out. In addition, the prospect of the synergistic development of the electricity market, carbon market, and renewable portfolio standards is proposed to provide a reference for constructing China’s coupling trading mechanism and help achieve the goal of “carbon peak and carbon neutrality.”
Promoting the carbon market will help achieve carbon peaking and carbon neutrality goals. The power industry will face a multi-entity and multi-market coordinated trading pattern of electricity-carbon market coupling, in which environmental costs are transmitted into the electricity market through the carbon market. However, in the current market-clearing mechanism, the environmental benefits of power generators have not been effectively included, resulting in unsatisfactory results for low-emission units, mainly based on new energy sources, which affects the realization of goals. Therefore, by introducing incentive factors to adjust the bidding strategy of unit power generation, an incentive-clearing mechanism to promote the absorption of new energy was designed under the background of electricity-carbon market coupling. Thus, a two-layer model is established: the upper layer models the bidding strategy of heterogeneous power generators, and the lower layer models the incentive market-clearing mechanism. The model is solved with a deep reinforcement learning algorithm. The analysis indicates that the design model can correctly describe the interaction between the coupling markets and that the designed clearing mechanism increases the proportion of power generation in new energy units, promotes the absorption of new energy, and improves the social and emission reduction benefits of the power industry.
Aiming at the current low-carbon scheduling on the load side of a single low-carbon means, and the source and load sides of the carbon reduction method being relatively independent of the lack of linkage, this study establishes a two-layer low-carbon optimization scheduling model of a power system considering the source-load carbon potential coupling in the market a few days ago. The upper model is based on the ladder carbon trading market, calculates the carbon market transaction cost of the source side, and establishes a low-carbon economic dispatch model of the power system considering the source-load carbon potential constraints in accordance with the load classification, and solves the initial scheduling scheme of the units; the lower model adopts the theory of carbon emission flow, and calculates the carbon potential indexes of each node of the load side and the carbon emission responsibility sharing amount, and establishes a two-layer low-carbon optimization dispatch model of the power system considering the source-load carbon potential constraints. The lower model employs the carbon emission flow theory to calculate the carbon potential index of each node on the load side and the carbon emission responsibility sharing amount based on the scheduling scheme of the upper model, establishes a multi-type demand response model on the load side, and utilizes the user-side regulation ability to optimize the load distribution to further realize low-carbon system benefits. Finally, under the premise of wind power uncertainty modeling, an example analysis is conducted based on the improved IEEE 30-node system, and the results indicate that the scheduling method can effectively promote wind power consumption and reduce carbon emissions.
A park-integrated energy system (PIES) is the key to optimizing and upgrading energy systems and reducing carbon emissions in the power industry. How parks participate in the electricity-carbon-green certificate market by formulating trading strategies, improving overall efficiency, and reducing system carbon emissions is key to the rapid development of parks in the future open market environment. This study is based on the noncooperative game model of three parties in a park, constructing a power market operation framework of green electricity day-ahead market, conventional energy day-ahead market, and real-time market, and introducing a green certificate-carbon emission equivalent offset mechanism to achieve a connection between green certificate trading and carbon trading. A park comprehensive energy system day-ahead optimization scheduling model that considers the coupling of the electricity-carbon-green certificate market was proposed. Finally, the improved particle swarm optimization algorithm and alternating direction multiplier method are utilized to solve the nonlinear model and verified in a typical park in Shanghai. The results indicated that the proposed method could effectively reduce carbon emissions in the park, and the proportion of green electricity in the total electricity consumption of the park increased by 6.4%.
With the introduction of the “double carbon” target, microgrid is considered an effective form that integrates renewable power sources and improves energy utilization efficiency. In line with the increasing number of microgrids, peer-to-peer (P2P) energy trading among multiple microgrids is regarded as an effective solution for energy sharing and integration of distribution generation. This study proposes a P2P energy trading model that accounts for demand response and carbon emission characteristics to achieve supply and demand coordination while determining the optimal operation strategy. First, the microgrid in the model solves an economic optimization problem by participating in the demand response. Second, by considering carbon emission factors, the model offers a great opportunity to reduce operation and carbon emission costs, accounting for energy transactions and energy storage scheduling. To incentivize energy sharing among multiple microgrids, a P2P energy-trading model based on generalized Nash bargaining was developed that enables energy sharing and revenue allocation. In addition, optimal power flow constraints were incorporated into the model to enhance the security of power system operations. Finally, a simulation of the IEEE-33 system demonstrates that P2P energy trading among multiple microgrids effectively reduces operating costs and promotes the integration of renewable energy sources.
The development trend in urban and rural power distribution networks is to gradually access a high proportion of distributed power sources. However, the various types of disturbances caused by uncertain factors, including power sources, loads, and the natural environment, cannot be ignored. Therefore, the distribution network must have the ability to resist various disturbances and recover quickly, which is called the resilience of the distribution network. Meanwhile, all types of flexible resources from the source, network, load, and storage of distribution networks can provide support for the balance between supply and demand, and improvement of toughness of the distribution network. Accurately describing the impact of various disturbance events and effectively determining the synergy of flexible resources on the source, network, load, and storage sides are key factors in studying the resilience of distribution networks. This study aims to provide insights and constructive ideas for the planning, safe and stable operation, and resilience research of new distribution networks. This study first introduces the basic concept of distribution network resilience, and analyzes various types of disturbances faced by the distribution network and the characteristics of distribution network resilience under disturbances. The study then summarizes the research progress in extreme disaster disturbances, wave disturbance modeling methods, and resilience evaluation methods. Next, the key technologies for improving the resilience of distribution networks in the pre-disturbance, mid-disturbance, and post-disturbance stages are also introduced. Finally, the key problems in the future research on distribution network resilience are discussed.
In order to cope with the loss from power outages caused by extreme disasters, it is necessary to construct a reasonable post-disaster recovery model and explore effective post-disaster emergency decision-making methods. In this study, a Markovian decision process model for post-disaster emergency recovery considering critical load failure events is constructed. It also considers the impact of outage duration on the functionality of different types of critical loads when calculating the losses. For post-disaster emergency decision-making, an online decision-making method based on a preview algorithm is proposed to quickly evaluate the action-state value function (i.e., Q function) by means of multi-scenario simulation and generate an online optimization strategy. It is demonstrated that the decision scheme based on the Q function generated by the rollout algorithm is better than that provided by the underlying strategy in the simulation. The proposed method does not rely on precise mathematical models and parameters and can update the policy in real time according to the system state, which is a more practical approach than that of the traditional offline optimization model.
During the system restoration process after a major power outage, the system has a low resistance to external disturbances. The transmission system is not yet complete, particularly during the network reconstruction phase. If a deliberate physical attack occurs against a power system, it can have a severe impact on system restoration, causing secondary faults impacting the system such as protection failure and disoperation. To quantitatively evaluate the impact of deliberate physical attacks and their secondary faults on the network restoration process, a comprehensive skeleton network resilience assessment method is proposed. First, a model for calculating the system load loss under deliberate physical attacks is proposed based on the attack-defense game model. Second, considering the overload protection malfunction caused by power flow transfer after a disturbance, a set of deliberate physical attacks and secondary fault scenarios are constructed. Finally, based on the forced load loss in the fault scenario set before and after the disturbance, a quantitative assessment method for skeleton network resilience is proposed, and an optimization model of the skeleton network considering active resilience improvement is constructed. An example of an IEEE-57 node system is used to verify the effectiveness of the proposed resilience assessment method.
The proposal of the “dual carbon” goal has accelerated the integration of new energy generation represented by wind and solar power into the grid. The grid connection of new energy generation with a high proportion of power electronic interfaces will weaken the inertia support capacity and prominent frequency stability problems in the power system. Distributed synchronous condensers serve as rotating components in wind and solar power stations, providing voltage support and rotational inertia to the system, thereby enhancing the frequency support strength of the power grid. However, an efficient and feasible configuration method for distributed condensers must be developed. This paper proposes a distributed condenser location and sizing method that considers frequency stability constraints. Node inertia is used to evaluate the system inertia distribution, identify weak areas of the system frequency support, and select the condenser configuration location. Considering critical inertia as an inequality constraint, a distributed condenser location and sizing strategy is designed with the optimization goal of minimizing the configuration capacity. Based on the analysis of numerical examples, it is shown that the distributed condenser location and capacity determination strategy proposed in this paper is direct and efficient and can more effectively improve the system frequency support strength while minimizing the total configuration capacity of the condenser.
High-frequency oscillations caused by the interaction between an SVG and direct-drive permanent magnet synchronous generator (PMSG) threaten the safe and stable operation of PMSG-based wind farms. To resolve this problem, a high-frequency impedance model of a PMSG-based wind farm with SVG is first established in this paper, and the formation mechanism of high frequency oscillation is analyzed. The frequency distribution of the negative damping interval of the SVG is derived, and the oscillation stability of the system is analyzed, based on which the stable value range of the system control parameters is obtained. Based on the oscillation mechanism, an additional damping control method based on current feedback is proposed in this study. The high-frequency oscillation of the wind farm is effectively suppressed by SVG high-frequency phase compensation, and the parameters are adjusted. Finally, an electromagnetic transient simulation model of a PMSG-based wind farm is developed in MATLAB/Simulink to verify the effectiveness of the oscillation analysis method and additional damping control strategy.
To deal with the problem of insufficient inertia of new energy, the control technology of virtual synchronous generators (VSG) in grid-forming energy storage has been continuously developed, which provides inertia support for the system while causing power oscillation and reducing the power angle stability of the system. To solve this problem, the mechanism of the influence of the virtual inertia and virtual damping parameters of the virtual synchronous generator on the system stability is explained, and a parameter optimization strategy is proposed for the virtual synchronous generator. This strategy simultaneously considers the frequency stability and small-signal stability of the system to establish an objective function optimization model. The objective function minimizes the maximum frequency deviation and the maximum sum of the damping ratio of the system oscillation mode. The stability interval of each control parameter is considered the constraint condition. The strategy was solved using a fast non-dominated sequencing genetic algorithm (NSGA-II) with an elite strategy. Finally, the effectiveness of this method is verified through a simulation.
This work is supported by National Natural Science Foundation of China(No. 52277096).
The quantitative assessment of the transient stability of a power system constitutes the basis of the online security defense of a large power grid, and the construction of a transient stability prediction index based on the dynamic trajectory information of the power grid is key. The grid voltage phasor trajectory information contains richer stable behavior characteristics. In this study, using a dual-machine system as an example, the evolution law between the transient energy conversion of the generator and the voltage phase trajectory is first analyzed from the perspective of trajectory geometry. The reasoning is based on the EEAC. Thus, the universality of the multi-machine system is verified. To realize the fast prediction of transient stability, a trajectory fitting method based on the alternating direction multiplier method is proposed, which has obvious advantages in fitting accuracy and speed. Based on the arc length distance of the voltage phasor trajectory fitting curve as the data support, a fast prediction index of transient stability is constructed. Finally, the evaluation accuracy of the proposed method is verified using the simulation data of a 10-machine, 39-node system in New England and a provincial power grid as examples.
Commutation failure on the receiving side can be caused by a fault in the line commutated converter-based HVDC (LCC-HVDC) on the sending side, and an over-compensated reactive power increases the risk of commutation failure. In this paper, for a transmission system in which the sending-side LCC-HVDC is in parallel with a synchronous condenser and voltage source converter-based HVDC (VSC-HVDC), the reactive power control mechanisms and response characteristics of synchronous condenser and VSC-HVDC are discussed. We observe that the over-compensated reactive power of VSC-HVDC increases the risk of commutation failure of the LCC-HVDC, and the reactive power regulation capacity of the synchronous condenser is not fully utilized. Based on this, a reactive power-coordinated control scheme between the synchronous condenser and VSC-HVDC is proposed to accelerate the reactive power response of the VSC-HVDC, reduce the excess reactive power compensation to reduce the risk of commutation failure of the LCC-HVDC, and improve utilization of the reactive power regulation capacity of the synchronous condenser. Finally, simulation results of typical examples reveal that the proposed scheme can fully utilize the dynamic reactive power regulation capacity of the synchronous condenser, inhibit transient low voltage at the moment of fault, and reduce the risk of commutation failure after the fault is cleared.
With national targets for carbon peaking and carbon neutrality, electric vehicles (EVs) are gaining popularity owing to their advantages of being green, low-carbon, energy-saving, and environmentally friendly. EVs have both load and energy storage characteristics, and their charge-discharge behavior is random and fluctuates in time and space. Accurate prediction of the spatiotemporal distribution of EV charging and discharging loads is the basis for studying the influence of EV entering the grid, power grid planning and operation, and interaction with the power grid. The main factors influencing the prediction of the spatiotemporal distribution of the EV charging load are analyzed. The modeling of the charging load and prediction method for the spatial and temporal distributions are systematically described. Considering that electric vehicles can be used as mobile energy-storage devices to participate in grid interactions, the discharge potential is evaluated, and the research scenario of V2G technology is reviewed. Finally, the challenges faced by existing research methods are summarized and discussed.
The bidding strategy of an electric vehicle aggregator (EVA) in the energy-regulation market determines the value of electric vehicle flexibility, whereas the bidding decision-making process of an EVA is influenced by many uncertain factors based on the market. Therefore, to address the uncertainty modeling problem of market price and regulation signals, a distributionally robust modeling method that considers the multi-timescale correlation of uncertainty factors is proposed. First, a scenario tree model based on the hierarchical clustering method is proposed to depict the temporal correlation of different market uncertainties. Second, a multi-time-scale bidding decision model of EVA participation in the energy-regulation market based on two-stage optimization is established. A fuzzy set of scenario tree probabilities is constructed based on mixed norm distance to realize the solution of EVA bidding decisions under the framework of distributionally robust optimization. Then, the four-layer robustness problem of max-min-max-min constructed in this study is solved using a column and constraint generation method. Finally, the advantages of the proposed model in solving the two-stage uncertainty optimization problem and improving the economy of the bidding strategy are verified by simulation.
