To improve renewable energy consumption under the background of dual carbon, and solve problem of multi-source coordinated scheduling with DC transmission, a multi-source coordinated scheduling of receiving power system considering voltage stability based on information gap decision theory (IGDT) is proposed. Firstly, the voltage stability indicator is constructed, which indicates distance between the system voltage state and the voltage stability limit state. Then, aim at minimizing the system operation cost and wind curtailment penalty, a multi-source power receiving ability promotion model considering thermal/ cascade hydro/wind power generation, energy storage and DC transmission coordination is proposed. The models of multi-sources in the system are established, and the AC power flow and hydropower conversion constraints are linearized by Taylor series expansion and triangle approximation. On this basis, voltage stability constraints are generated through voltage stability margin threshold, and voltage stability constraints are added to multi-source coordinated optimization problem for the receiving power system through each iteration to achieve the improvement of voltage stability indicator. To consider uncertainty of wind power output and load demand, a multi-source coordinated scheduling of receiving power system considering voltage stability based on IGDT is proposed. Finally, the effectiveness of the proposed model is verified by case studies.

Abstract High shares of renewables and power electronics (“double-high”) have become the main development trends for future power systems. In this context, power systems will face the practical problem of insufficient inertia and frequency regulation capability. This paper investigates frequency stability enhancement of the sending-end power grid by incorporating regulation capabilities of multiple resources. Based on power flow and transient calculation data, an equivalent model of frequency response for a practical power grid is established and validated. The frequency response under different scenarios considering various disturbance and renewable output scenarios is conducted, taking into account the simultaneous participation of resources such as direct current frequency limit control, energy storage, and renewables of different scales in frequency regulation to improve frequency stability and mitigate the risk of renewables trippings. Simulation results indicate that incorporating the potential frequency regulation capabilities of multiple resources into “double-high” frequency control strategy plays an important role in improving transient frequency response, reducing the risk of renewable tripping, and enhancing frequency stability margins.

In order to reduce carbon emissions from the power grid, photovoltaic (PV) generation units and controllable nonlinear loads based on power electronic devices are gradually becoming more prevalent in the power system. In a PV grid-connected system featuring controllable nonlinear loads, the interplay among PV grid-connected inverters, the loads, and the grid can potentially lead to voltage oscillations. To tackle this challenge, this paper introduces an optimization-based method for suppressing oscillations, which carefully balances system stability with response performance. Firstly, an impedance model of the system is established by applying the harmonic linearization method, and system stability is analyzed using the “logarithmic frequency stability criterion”. Subsequently, impedance relative sensitivity is used to identify key parameters that affect system stability, and the interaction between key parameters is considered to analyze the stability range for these parameters. On this basis, a parameter optimization method based on the particle swarm optimization algorithm is proposed to balance system stability and response performance. The effectiveness and robustness of this method are verified through a simulation analysis.

In photovoltaic grid-connected systems, the interaction between grid-connected inverters and the grid may cause harmonic oscillation, which severely affects the normal operation of the system. To improve the quality of the output electrical energy, photovoltaic grid-connected systems often use LCL filters as output filters to filter out high-frequency harmonics. Taking the three-phase LCL-type photovoltaic grid-connected inverter system as an example, this paper addresses the issue of harmonic resonance. Firstly, based on the harmonic linearization method and considering the impact of the coupling compensation term on the grid-side voltage, a modular positive and negative sequence impedance modeling method is proposed, which simplifies the secondary modeling process of the converter under feedback control. Then, the stability analysis is conducted using the Nyquist criterion, revealing the mechanism of high-frequency resonance in photovoltaic grid-connected systems. Furthermore, this paper delves into the impact of changes in system parameters on impedance characteristics and system stability. The results indicate that the proportional coefficient of the internal loop current controller has a significant influence on system impedance characteristics. Additionally, this paper proposes an active damping design method that combines lead correction and capacitor current feedback to impedance-reconstruct the easily oscillating frequency band. Finally, the effectiveness of this method is verified in the simulation platform. Simulation results confirm the effectiveness of this method in suppressing harmonic resonance while maintaining rapid dynamic response.

The increasing penetration of renewable energy poses intractable uncertainties in cascade hydropower systems, such that excessively conservative operations and unnecessary curtailment of clean energies can be incurred. To address these challenges, a quantum neural network (QNN)-based coordinated predictive control approach is proposed. It manipulates coordinated dispatch of multiple clean energy sources, including hydro, wind, and solar power, leverages QNN to conquer intricate multi-uncertainty and learn intraday predictive control patterns, by taking renewable power, load, demand response (DR), and optimal unit commitment as observations. This enables us to exploit the stability and exponential memory capacity of QNN to extrapolate diversified dispatch policies in a reliable manner, which can be hard to reach for traditional learning algorithms. A closed-loop warm start framework is finally presented to enhance the dispatch quality, where the decisions by QNN are fed to initialize the optimizer, and the optimizer returns optimal solutions to quickly evolve the QNN. A real-world case in the ZD sub-grid of the Sichuan power grid in China demonstrates that the proposed method hits a favorable balance between operational cost, accuracy, and efficiency. It realizes second-level elapsed time for intraday predictive control.

Large-scale energy storage can be used to solve the transient voltage problem caused by high penetration of renewable energy resources. Therefore, it is necessary to study the optimization method of the configuration scheme of energy storage devices. This paper investigates the transient voltage stability of a HVDC sending-end grid containing large-scale new energy sources. By optimizing the capacity configuration of energy storage, the transient voltage stability of the HVDC sending-end grid is significantly improved. Firstly, the over-voltage phenomenon in the actual grid is introduced and the causes of this phenomenon are analyzed. Then, the capacity and parameter optimization methods of energy storage are proposed, including the calculation of grid reactive-voltage index, and the iterative optimization method of capacity configuration driven by electromagnetic transient (EMT) simulation. Finally, the correctness and feasibility of the proposed method are verified by using by using the EMT model of the actual grid.

The wind power capacity has increased a lot recently and the number of close energy storage systems has also rapidly increased. To enhance the frequency stability support ability of such wind–storage combined systems, this paper proposes a virtual synchronous control strategy for a wind–storage combined system considering the battery state of charge (SOC). The virtual synchronous control is used to make the wind turbine generate more active power when the system is in disturbance. Most importantly, to ensure that the stored energy is used efficiently, the wide-area SOC concept of the energy storage system is considered, which is realized by designing the batteries with additional control that is related to the other batteries’ SOCs. This means that the low-SOC energy storage system’s power shortage can be compensated for by the high-SOC batteries. The above unified control enhances the wind–storage combined system’s frequency supporting ability. According to the simulations, the frequency drop can be suppressed through the help of the virtual synchronous control and the stored energy. Additionally, the instability can also be eliminated when wide-area SOC is considered, such that the proposed efficiency and correctness are finally verified through the simulations.

The study of dispatching methods for large-scale interruptible loads and electric vehicle clusters is of great significance as an optional method to alleviate the problem of overload in interface power flow. In this paper, the distribution model and transfer capacity of large-scale interruptible load and electric vehicle in two dimensions of time and space were firstly introduced. Then, a large-scale interruptible load and electric vehicle dispatching model considering transmission interface power flow balance was established. Finally, a case study was carried out with the city power grid as the research object. Studies show that by dispatching large-scale interruptible load and electric vehicle, the overload rate of interface power flow can be reduced by 12–17%, while the proportion of clean energy generation increased by 4.19%. Large-scale interruptible load and electric vehicles are quite different in terms of the role they play in grid regulation. The regulation cost of electric vehicles is higher than that of large-scale interruptible load, but it also has the advantages of promoting the consumption of clean energy and improving the overall operating economy. Which type of resource should be given priority is based on the actual state of the grid. In addition, the cost of electricity has a significant impact on the load response behavior of electric vehicles. It should be determined according to various factors, such as interface power flow control requirements, regulation costs, and power grid operation costs.

Increasing scale of power grids induces higher computational complexity of static contingency analysis, which is in contradiction with the real-time requirements. Towards fast credible contingency analysis, a power flow calculation method based on graph neural network was proposed. This method can realize fast fitting of nonlinear relationship between new energy output, load data and branch power flow, node voltage in multiple scenarios. To avoid the disappearance of edge graph nodes caused by branch disconnection and to ensure the robustness of the model, a matrix reflecting the topology changes is designed in response to the network structure changes caused by various anticipated accidents. Besides, the load-source data is separated and the input eigenvector considering the change of the load-source data is constructed. The test of IEEE 30-bus and 118-bus system shows that the proposed model can adapt to changes of network topology brought by N-1 faults and the fluctuations of new energy, realize fast fitting of power flow, and provide a new tool for online fast credible contingency analysis.

This paper proposes a deep Bayesian active learning (DBAL)-based scheme for online transient stability assessment in power systems. The Bayesian neural network (BNN) is used to predict the transient stability status while adaptively evaluating the predicting confidence by the posterior probability. The uncertainty-based active learning strategy is used to select the most informative samples to maximize the predicting performance of the BNN-based transient stability predictor while reducing the computational burden of time-domain simulation-based stability assessment for sample labeling. Case study on the IEEE 39-bus system demonstrates that the proposed scheme can achieve both the predicting accuracy and the data efficiency.

The asynchronous interconnection between the Southwest China power grid (SCPG) and Central China power grid (CCPG) reduces the inertia of rotation of SCPG to 1/5, which leads to a prominent risk of frequency stability. Moreover, in order to suppress the ultra-low frequency oscillations, the parameters of hydropower governors were optimized, which further worsen the frequency regulation capability of the system. Therefore, the frequency control (FC) of high voltage direct current (HVDC) line along with automatic generation control (AGC) are designed to improve the frequency restoration after a large power disturbance. However, the characteristic of FC regulation is contradictory to the subsequent AGC regulation. In order to improve the post-contingency frequency response of power system, an improved AGC strategy is developed. The proposed strategy involves the FC regulation power and distribute the control power according to the characteristics of different generator units. Case study on the actual model of SCPG proves the effectiveness of the strategy.

In China, the monitoring regarding power grid dispatch usually adopts section monitoring rather than real-time static safety analysis. The manual calculation process is so complicated that it is necessary to calculate the thermal stability limit of the transmission section through optimization. In this paper, the active power flow of the section after the expected disconnection is expressed as a linear function of the initial active power flow of the section, with the aid of the concept of transfer ratio. The constraints on the initial active power flow of the sections are determined according to the current carrying capacity. The maximization of the minimum ratio between the sectional thermal stability limit and the initial active power flow is taken as the basic principle. The optimization model of the sectional thermal stability limit is proposed and is solved with simplex algorithm from linear programming. The correctness of the model and the effectiveness of the calculation method are verified with the case study of an actual provincial power grid.

Nowadays, emission reduction becomes the main direction of energy system development. Replacing fossil fuel with renewable energy in energy sectors such as the power and transport sector serves as one of the essential measures. However, scientific gaps appear in how this renewable transition can be reached in an energy system, including the power and transport sector under the goal of “carbon neutral” in China towards 2060. Taking Sichuan province in China as an example, this study establishes the renewable transition pathways in an energy system including the power and transport sector and evaluates the feasibility of different renewable technologies in the two sectors. Three renewable scenarios based on various renewable electricity in the power sector and three renewable scenarios based on the electrification degree in the transport sector are formulated in the EnergyPLAN tool. The results show that all the renewable scenarios can reduce the emissions to zero in Sichuan in 2060, with the CO2 emissions in the business as usual (BAU) scenario reaches 135.7 Mt. Moreover, adding wind power to its maximum potential has better capacity to meet the requirement of further electrification in the transport sector than solar power in Sichuan province. From the economic perspective, all the renewable scenarios have similar energy system cost with the BAU scenario, reaching a range of 203 and 244 BCNY/y. All in all, the methodology and results in this study contribute to the policy-makers for constructing a carbon-neutral energy system.

A method of steady-state security analysis in wind power systems is presented. The wind farm velocity and output model are established, and Latin Hypercube Sampling is used to simulate the impact of wind farms output fluctuation which considers correlativity among different wind farms by the Cholesky Decomposition. Safety confidence is fast obtained finally by the linearized power flow function. Tests in IEEE 30 system demonstrated that the proposed approach carries out of the safety confidence accurately and rapidly and provides the reliable theoretical basis for power system steady-state security analysis.

Frequency response analysis under disturbance plays an important role in power system security awareness. However, massive high-order components, as well as discrete variables such as unit commitment (UC), induce immense challenges in analyzing frequency security. To conquer this barrier, a low-order frequency response approximator that adapts to UC is proposed. At the outset, first-order dynamics are applied to model governor responses, such that an average system frequency (ASF) approximator lays the first stone. Then, a module that approximates UC impacts is introduced in the ASF model. Finally, by modelling parameter identification of the ASF as a optimization problem, genetic algorithm is used to enable an precise equivalent low-order ASF for real-world high-order frequency dynamics. The numerical study on a provincial power grid in southwest China verified accuracy and universality of proposed method.

For DC fed into the receiving end grid, avoiding commutation failure and maintaining voltage stability are two important issues. In order to reduce the risk of commutation failure, the actual DC control system is usually equipped with a commutation failure prediction control function module. In the disturbance process, predictive control reduces the firing angle α of the inverter to increase the commutation margin. This control will change the reactive power characteristics of the inverter station and even affect the voltage stability of the receiving power grid. However, re-search on this impact and countermeasures is rarely reported. Based on the actual engineering DC control system, this paper establishes an UHVDC simulation model; reveals the commutation failure predictive control and predictive parameters, which affect the nonlinear reactive power trajectory and voltage stability of the inverter station; on this basis, proposes optimize measures to improve the prediction parameters of the large disturbance reactive power demand characteristics of the inverter station. The time-domain simulation results of large disturbances in the UHVDC receiving end power grid verify the effectiveness predictive control parameter optimization to improve the voltage stability of the receiving end power grid.