State Key Laboratory of Power Grid Safety and Energy Conservation,China Electric Power Research Institute,Beijing,China

The power system is experiencing a higher penetration of renewable energy generations(REGs).The short circuit ratio(SCR)and the grid impedance ratio(GIR)are two indices to quantify the system strength of the power system with REGs.In this paper,the critical short circuit ratio(CSCR)is defined as the corresponding SCR when the system voltage is in the critical stable state.Through static voltage stability analysis,the mathematical expression of the CSCR considering the impact of GIR is derived.The maximum value of CSCR is adopted as the critical value to distinguish the weak power system.Based on the static equivalent circuit analysis,it is proved that the CSCR is still effective to evaluate critical system strength considering the interactive impact among REGs.Finally,we find that the GIR can be neglected and the SCR can be used individually to evaluate the system strength when SCR>2 or GIR>5.The correctness and rationality of the CSCR and its critical value are validated on ADPSS.

Design and Optimization of a Hybrid Energy System for Decentralized Heating

The performances of a hybrid energy system for decentralized heating are investigated.The proposed energy system consists of a solar collector,an air-source heat pump,a gas-fired boiler and a hot water tank.A mathematical model is developed to predict the operating characteristics of the system.The simulation results are compared with experimental data.Such a comparison indicates that the model accuracy is sufficient.The influence of the flat plate solar collector area on the economic and energy efficiency of such system is also evaluated through numerical simulations.Finally,this system is optimized using the method of orthogonal design.The results clearly demonstrate that the solar-heat pump-gas combined system is more convenient and efficient than the simple gas system and the heat pump-gas combined system,whereas it is less convenient but more efficient than the solarassisted gas system.

Automatic Voltage Control of Differential Power Grids Based on Transfer Learning and Deep Reinforcement Learning

In terms of model-free voltage control methods,when the device or topology of the system changes,the model’s accuracy often decreases,so an adaptive model is needed to coordinate the changes of input.To overcome the defects of a model-free control method,this paper proposes an automatic voltage control(AVC)method for differential power grids based on transfer learning and deep reinforcement learning.First,when constructing the Markov game of AVC,both the magnitude and number of voltage deviations are taken into account in the reward.Then,an AVC method based on constrained multiagent deep reinforcement learning(DRL)is developed.To further improve learning efficiency,domain knowledge is used to reduce action space.Next,distribution adaptation transfer learning is introduced for the AVC transfer circumstance of systems with the same structure but distinct topological relations/parameters,which can perform well without any further training even if the structure changes.Moreover,for the AVC transfer circumstance of various power grids,parameter-based transfer learning is created,which enhances the target system’s training speed and effect.Finally,the method’s efficacy is tested using two IEEE systems and two real-world power grids.

Analysis of Low Frequency Oscillation and Source Location in Power Systems

The major problem in current online diagnosis and analysis for power system oscillation is mainly concerned with finding the oscillation source in a fast and correct way using the data collected by the Wide Area Measurement System(WAMS).This paper for the first time proposes a scheme of cut set energy based on WAMS.Independent of accurate parameters,the scheme can make full use of WAMS data based on cut set energy construction and fast calculation to locate the source during oscillation.Afterwards,a scheme of torque decomposition is proposed,based on which the controller’s torque can be divided into damping torque and synchronous torque by calculation through WAMS data,and this paper puts forward the abnormal response and simulation models calibration of influential controllers.Analysis of an oscillation case shows how the cut set energy scheme and the torque decomposition scheme are applied in a real-world power system,and the schemes are proven to be reliable and practical in identifying and locating oscillation sources.

Analysis and Design of Vector Control for VSC-HVDC Connected to Weak Grids

Voltage source converter based high voltage direct current transmission(VSC-HVDC)is considered one of the most suitable technologies to integrate renewable energies.However,connecting VSC to a weak grid is challenging since traditional vector control tends to become unstable under high power demand conditions.In this paper,an improved vector control method is proposed wherein a feed forward branch based on steady state and small signal analysis of the VSC system is added under weak grid situations.The feed forward branch promotes faster reactive power response,thus enhancing the stability of the VSC system.Since the improved vector control uses the same inner loop as traditional vector control,the proposed method allows for the ability to retain fault current suppression capabilities.Furthermore,the control parameters of the outer loop of the improved vector control need not vary according to the variation of the operating points,which makes it easy to implement.The feed forward branch is implemented by solving a nonlinear equation or through use of a look-up table.The influence of the estimation errors of short circuit ratio(SCR)on the control performance is also studied.The effectiveness of the improved vector control is demonstrated through small signal model analysis and time domain simulations.

Hierarchical Task Planning for Power Line Flow Regulation

The complexity and uncertainty in power systems cause great challenges to controlling power grids.As a popular data-driven technique,deep reinforcement learning(DRL)attracts attention in the control of power grids.However,DRL has some inherent drawbacks in terms of data efficiency and explainability.This paper presents a novel hierarchical task planning(HTP)approach,bridging planning and DRL,to the task of power line flow regulation.First,we introduce a threelevel task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes(TP-MDPs).Second,we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task units.In addition,we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist HTP.Experimental results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization,a state-of-the-art deep reinforcement learning(DRL)approach,improving efficiency by 26.16%and 6.86%on both systems.

Framework for Artificial Intelligence Analysis in Large-scale Power Grids Based on Digital Simulation

The planning,design,operation,control and scientific research of power systems all require a variety of simulation analysis.Thus power grid simulation analysis is a fundamental supporting technology of large-scale power grids.In power grid simulation analysis,in addition to simulation calculations,there are many links for analysis and decision-making,relying on specialists.The introduction of advanced artificial intelligence technology provides a new method to improve the efficiency and accuracy of power grid simulation analysis.Nevertheless,the research of the related artificial intelligence technologies face a great deal of new challenges due to the complexity of the largescale power grid simulation data,including massive volumes,high dimensionality,strong coupling and complex correlations.Also a great deal of knowledge and experience need to be integrated in the process of analysis.In order to deal with these challenges,based on the existing works,this paper focuses on the core scientific problem of artificial intelligence analysis and decision making related to the massive simulation results of large-scale power grids,and proposes an artificial intelligence analysis method framework for large-scale power grids based on digital simulation,which includes the power grid simulation analysis knowledge model with application method,the power grid simulation knowledge mining method and the artificial intelligence models with transfer learning ability of diversified grids as well as analyzing and calculation adjusting for largescale power grid simulation results,etc.This work is expected to open up a new technical approach for large-scale power grid simulation analysis and provide strong technical support for the safe and stable operation of large-scale power grids.

Reducing power grid cascading failure propagation by minimizing algebraic connectivity in edge addition

Analyzing network robustness under various circumstances is generally regarded as a challenging problem.Robustness against failure is one of the essential properties of large-scale dynamic network systems such as power grids,transportation systems,communication systems,and computer networks.Due to the network diversity and complexity,many topological features have been proposed to capture specific system properties.For power grids,a popular process for improving a network’s structural robustness is via the topology design.However,most of existing methods focus on localized network metrics,such as node connectivity and edge connectivity,which do not encompass a global perspective of cascading propagation in a power grid.In this paper,we use an informative global metric algebraic connectivity because it is sensitive to the connectedness in a broader spectrum of graphs.Our process involves decreasing the average propagation in a power grid by minimizing the increase in its algebraic connectivity.We propose a topology-based greedy strategy to optimize the robustness of the power grid.To evaluate the network robustness,we calculate the average propagation using MATCASC to simulate cascading line outages in power grids.Experimental results illustrate that our proposed method outperforms existing techniques.

Mode-based Damping Torque Analysis in Power System Low-frequency Oscillations

The mode-based damping torque analysis(M-DTA)method for studying the effect of an external controller on power system low-frequency oscillations is proposed in this paper.First,based on the interconnection model between the system and the controller in the frequency domain,the oscillation loop corresponding to the electromechanical oscillation mode is built,and then the mode-based damping torque of the controller can be calculated.Then,the application of the M-DTA method in the power system is illustrated.The derivation shows that in the single-machine infinite-bus power system,the M-DTA method is completely equivalent to the classical damping torque analysis(C-DTA)method.In the multi-machine power system,the mode-based damping torque directily reflects the effect of the controller on the oscillation mode,overcoming the shortcomings of the C-DTA method in which there is no direct correspondence between the damping torque and the oscillation mode.By deriving the relationship with the residue index,the M-DTA method shows higher accuracy than the residue method in applications,such as controller parameter adjustment.Finally,two example power systems are presented to demonstrate the application of the proposed M-DTA method.Index Terms-Electromechanical oscillation mode,FACTS,interconnection model in the frequency domain,mode-based damping torque analysis(M-DTA),power system low-frequency oscillation,PSS,residue method.

Dynamic State Estimation for Integrated Electricity-gas Systems Based on Kalman Filter

In recent years, integrated electricity-gas systems(IEGSs) have attracted widespread attention. The unifiedscheduling and control of the IEGS depends on high-precisionoperating data. To this end, it is necessary to establish anappropriate state estimation (SE) model for IEGS to filter theraw measured data. Considering that power systems and naturalgas systems have different time scales and sampling periods, thispaper proposes a dynamic state estimation (DSE) method basedon a Kalman filter that can consider the dynamic characteristicsof natural gas pipelines. First, the standardized state transitionequations for the gas system are developed by applying the finitedifference method to the partial differential equations (PDEs) ofthe gas system;then the DSE model for IEGS is formulatedbased on a Kalman filter;also, the measurements from theelectricity system and the gas system with different samplingperiods are fused to ensure the observability of DSE by using theinterpolation method. The IEEE 39-bus electricity system and the18-nodes Belgium gas system are integrated as the test systems.Simulation results verify the proposed method’s accuracy andcalculation efficiency.