Abstract:The frequent occurrence of extreme events puts forward higher requirements for the resilience of urban infrastructure system. As the two cores of maintaining urban operation, research on resilience of power and transportation have attracted extensive attention. With the development of electrified transportation facilities, the power and transportation systems are increasingly closely linked, laying the foundation for the coordinated planning and operation of the two systems under extreme events. First, the interaction and coordination mechanism of power-transportation system under extreme events are analyzed, and the important value and research logic of urban power-transportation system resilience are clarified. Next, the concept of system resilience is introduced, and the evaluation indexes and methods of power system resilience, transportation system resilience and the comprehensive resilience of the two systems are sorted out. Then, the research status of power-transportation system resilience is summarized from four aspects of planning and construction, prevention and control, post disaster emergency response and fault restoration. Finally, the future research directions are discussed from three aspects of joint modeling and interaction analysis of the power-transportation system, comprehensive evaluation of resilience, and collaborative improvement of resilience.

Abstract:The evolution pathways of carbon emission reduction and carbon sink increment are two critical components of the overall goals of “carbon emission peak and carbon neutrality” revolution. To integrate the holistic view of complex systems with the mechanistic view of multi-level subsystems, the whole-reductionism thinking is used to map the evolution pathways of carbon emission reduction and carbon sink increment in the electric power industry, as well as the overall revolution pathway of carbon emission peak and carbon neutrality, onto a decision-making plane composed of carbon sink increment and carbon emission reduction quantities. On this two-dimensional pathway plane, it is feasible to cluster all reasonable evolution pathways into single-digit number of candidate pathways. Based on the trajectory dynamics thought, the dynamic evolution relationship is modeled. During the process of tracking a designated candidate pathway, the high-dimensional complex system is linearized in short intervals according to the trajectory values. This allows for the high-dimensional linear algorithm to be applied to optimize the minimum opportunity cost required to track each pathway segment within the constraints of various relevant fields. After accumulating the opportunity costs corresponding to the candidate pathways, the optimal pathway can be selected from the candidate pathways. The effectiveness of the proposed whole-reductionism method for the revolution pathway of carbon emission peak and carbon neutrality is demonstrated through a sand-table simulation for the evolution pathways of carbon emission reduction and carbon sink increment in electric power industries of China under the revolution of carbon emission peak and carbon neutrality.

Abstract:With the proposal of the goal of “carbon emission peak and carbon neutrality”, the low-carbon transformation of energy has become a future development trend. Based on the carbon emission flow theory, a joint electricity-carbon optimization operation method is proposed for multiple virtual power plants (VPPs) with dynamic carbon emission factors, and the multiple VPPs bi-layer optimization model is established. Firstly, based on the carbon emission flow theory, an upper-level model for economic dispatch of the upper power grid with the objective of optimal efficiency is established, and the electricity price and dynamic carbon emission factor of each VPP is calculated and sent down to the lower-level. Secondly, considering the coupling of electricity-carbon trading, a lower-level model of multiple VPPs joint low-carbon optimization based on the Nash negotiation theory is established. Then, the accelerated-adaptive alternating direction method of multipliers is used to solve the problem, and the power demand of each VPP is obtained and feedback to the upper-layer model. This method can effectively improve the solving speed on the basis of protecting the privacy of members. Finally, based on the asymmetric bargaining method, a method of electricity-carbon benefit distribution based on the contribution degree of interactive products is proposed to achieve a reasonable benefit distribution of each VPP. The example analysis shows that the proposed method can effectively reduce the operating cost and carbon emission of the VPP, and realize the fair and reasonable distribution of VPP income while taking into account the economic and environmental benefits of VPPs.

Abstract:Park-level integrated energy system (PIES) faces multiple impacts from the long-term uncertainty of carbon trading price, energy price and the short-term uncertainty of renewable energy when participating in carbon trading. How to optimize the configuration of device capacity for PIES under the limited total cost budget to improve the robustness of the system, that is, the adaptability to deal with uncertainty fluctuations needs to be further studied. Therefore, an optimal configuration method based on information gap decision theory (IGDT)-utility entropy is proposed. First, a coupling model of multi-energy flow and carbon trading volume based on energy hub is established to describe the coupling relationship between carbon trading volume and multi-energy flow. Secondly, a deterministic optimal configuration model for PIES is established to obtain the baseline value of the total optimal configuration cost, and the ability of PIES decision makers to accept additional investment is further considered to determine the total cost budget limit. For the two types of uncertainty, IGDT is used to deal with the long-term uncertainty of carbon trading price and energy price, and utility entropy is used to simulate the short-term uncertainty of wind and photovoltaic power output. Then, the robustness coefficient is introduced to describe the adaptability of PIES to uncertainty fluctuations. On the premise that the total cost does not exceed the budget limit, an optimal configuration model aiming at maximizing the robustness coefficient is established to improve the adaptability of PIES to uncertainty fluctuations, and the optimal configuration scheme under the total cost budget is obtained. Finally, a PIES in northern China is taken as a case to verify the effectiveness of the proposed optimal configuration method for PIES.

Abstract:Aiming at the regulation demand of power system with high proportion of renewable energy on grid-side energy storage, an optimal configuration method of siting and sizing for grid-forming battery energy storage station based on multi-timescale regulation is proposed. Firstly, in order to describe the volatility and uncertainty of the net load, an improved iterative self-organized clustering and a multi-timescale scenario set generation method combining with Gaussian mixture model are proposed. On this basis, a multi-scenario and multi-timescale joint optimal operation model is established to deal with the volatility and uncertainty of net load, and optimize the configuration of energy storage capacity. Then, the relationships among inertia constant, cycle life of energy storage battery and its reserve capacity are analyzed, and a multi-objective optimization method of siting and sizing for grid-forming battery energy storage station is proposed, which improves the inertia of the power system and cycle life of energy storage battery. Finally, a case is analyzed based on the actual system from a region in central China. The results show that the method can accurately configure the optimal capacity of energy storage that meets multi-timescale regulation demand in at least 90% of the scenarios. At the same time, after the multi-objective model is used to correct the energy storage siting and sizing, the system inertia distribution index and the cycle life of the energy storage battery are increased by 28.9% and 27.2%, respectively.

Abstract:With the continuous integration of large-scale renewable energy, the traditional scheduling method for reserve allocation from the system level may cause the reserve of different zones to not be shared due to the transmission congestion. It is necessary to optimize the reserve from the regional level. However, the existing partitioning reserve optimization models usually use deterministic methods, and it is difficult to effectively reflect the reserve requirements of different zones due to the influence of multiple uncertain factors such as renewable energy fluctuations, generation unit failures, and line breakage faults. Therefore, an optimization model for partitioning reserves considering the partitioning reliability constraints is constructed to realize the refined reserve management. Firstly, the massive uncertain scenarios are built through the Monte Carlo simulation method to evaluate the line congestion risk in each scenario. Secondly, taking the generation shift distribution factor weighted with congestion risks as the partitioning basis, the nodes are clustered by low-rank doubly stochastic matrix decomposition to realize the dynamic partitioning of power grids. On this basis, the partitioning reliability constraints considering the multiple uncertain factors are constructed, and the optimization model for the partitioning reserve embedded with the partitioning reliability constraints is proposed. The simulation results using the IEEE 118-bus test system and a provincial-level power grid of China demonstrate that the proposed model can effectively alleviate the transmission congestion risk of reserve, and ensure the availability and sufficiency of the reserve allocation, improving the reliability of zonal power supply.

Abstract:Studying the coupling mechanism of electricity prices between the electric power energy and ancillary service markets, helps the source, load and storage participate in the electric power energy and ancillary service markets, and form a more transparent price mechanism of electric power energy and ancillary service market to promote the healthy development of electric power energy and ancillary service markets. Therefore, a joint clearing model for the electric power energy and ancillary service markets and an analysis method for the electricity price coupling mechanism are proposed. Firstly, the energy and power boundary characteristics of load- and storage-type market users are analyzed, and the coupling constraint model for various types of users participating in the electric power energy and ancillary service markets is established. Secondly, the joint clearing model for the electric power energy and four ancillary services markets including the participation of source, load, and storage is constructed. Thirdly, based on the duality theorem and KKT (Karush-Kuhn-Tucker) condition, the concept and the acquisition method of the coupling factor between the electric power energy and ancillary service markets are proposed, and the coupling relationship between the multiple markets is revealed. Finally, the effectiveness of the proposed method is verified by case analysis.

Abstract:In high penetration-distributed generator microgrid (HP-DGMG), the uncertainty of distributed generator (DG) can have an impact on bidding revenues and even increase the operational risk of HP-DGMG and distribution networks. Considering the uncertainty of distributed photovoltaic in HP-DGMG, an active and reactive power bidding and transaction strategy of microgrid based on distributionally robust chance constraint (DRCC) is proposed in this paper. Firstly, considering the power sale and power purchase transaction characteristics of HP-DGMG, the bidding and transaction framework of HP-DGMG in the distribution market environment is constructed, and the bi-layer bidding model of HP-DGMG active and reactive power transactions in the distribution market is further established. Secondly, DRCC is introduced to deal with the uncertainty of distributed photovoltaic power generation in microgrid, and the optimization model of HP-DGMG active-reactive power bidding and transaction based on the moment information is constructed. By using conditional value-at-risk theory and duality theory, the distributionally robust model of HP-DGMG bidding and transaction is transformed into second-order cone programming form. Then, a single-layer mathematical programs with equilibrium constraint (MPCE) model of HP-DGMG bidding in distribution market environment considering photovoltaic uncertainty is proposed by using primal-dual counterpart method, and it is converted into mixed integer second-order cone programming problem for solving. Finally, the HP-DGMG located in 7-bus distribution network and 33-bus distribution network is analyzed and verified, and the effectiveness of the proposed HP-DGMG bidding and transaction strategy is verified.

Abstract:Aiming at the problem of low utilization rate of the energy storage system in the renewable energy accommodation, considering using its idle power and capacity to provide active and reactive power auxiliary services for the distribution network, a multi-objective optimal scheduling strategy for energy storage system based on deep reinforcement learning using improved multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed. First, the collaborative operation mode of energy storage participating in multiple application scenarios is designed. Based on meeting the requirements of renewable energy accommodation, energy storage is used to participate in auxiliary frequency regulation and voltage regulation services, and a multi-objective optimal scheduling model of energy storage considering total system power supply cost, node voltage offset and frequency regulation power deviation is established. Then, the energy storage scheduling problem is transformed into a Markov game process, and the MADDPG algorithm is improved by constructing a dual-critic network structure and a delayed actor network update operation to reduce the impact of the action value overestimation problem on training in reinforcement learning. Finally, the simulation analysis is carried out on a modified IEEE 33-bus test system, and the results verify the effectiveness and superiority of the proposed strategy.

Abstract:Considering the insufficient support capability of traditional distribution communication networks for new types of service flows, the cross-domain integration networking model, which combines the public networks of operators, has been widely applied. Among them, resource reservation is an important technology to enhance the certainty of data transmission. However, the performance of public network channels has unpredictable fluctuations, making it difficult to quantify the impact mechanism of the utility of resource reservation based on interactive modes. Therefore, an interaction-free cooperative reservation algorithm of communication-cache resources for massively distributed new energy service flow is proposed. Firstly, the passive-sensitive arithmetic integration mode based on service flow characteristics is used to construct an interaction-free cooperative reservation system model for heterogeneous resources. Secondly, a corresponding interaction-free cooperative reservation utility function is designed, and the corresponding optimization problem is proposed with the objective of maximizing utility. Finally, the original problem is decoupled using the block coordinate descent method, and the optimal interaction-free resource reservation scheme is quickly obtained through convex optimization theory. Simulation results demonstrate that the proposed method for assessing service flow characteristics can reasonably quantify the fluctuations of channel performance and effectively optimize the cooperative reservation process of heterogeneous resources in an interaction-free mode, thus significantly enhancing the perception certainty of massively distributed photovoltaic data.

Abstract:In order to improve the applicability and accuracy of traditional line loss calculation methods, and solve the interpretability problem of neural network based line loss calculation methods, the theoretical calculation model of line loss in distribution station areas based on core correlation indicators and virtual equivalent resistance is proposed. Firstly, a screening method for core correlation indicators of line loss is proposed. The grey relation algorithm is used to calculate the correlation between line loss correlation indicators and line loss, and to screen out core correlation indicators, thereby improving the interpretability of the model. Secondly, a physics-data driven theoretical calculation model of line loss is proposed. The model considers core correlation indicators and replaces traditional equivalent resistance with virtual equivalent resistance to achieve line loss calculation, thus improving the applicability and accuracy of the model. Then, a parameter estimation method based on the second-order cone algorithm is proposed, and an objective function is constructed to minimize the residual equation between the improvement value of theoretical line loss and the actual value. The critical values of various related indicators are used as constraints to solve the model parameters. Finally, the effectiveness and applicability of the proposed method are verified by applying actual monitoring data and simulation model data.

Abstract:In order to illustrate the influence mechanism of additional virtual inertia control on the damping of DC distribution system and realize reasonable configuration, firstly, utilizing the physical concept of analog capacitance, based on the two-port equivalent of DC network, this paper explains the synchronous enhancement effect of parallel capacitors of load-side on the system inertia and damping. Besides, the difference in system damping between parallel capacitors of load-side and source-side is analyzed. Secondly, for the voltage source converter (VSC) with constant power control and constant AC voltage control for the load oriented power supply, the corresponding compensators to realize the additional virtual inertia is derived, and the virtual capacitance and resistance components that can enhance the system damping are given by modeling and splitting the DC-side impedance. Furthermore, considering the interactive stability, response speed of controller and control error caused by high-frequency harmonics, a virtual inertia control parameter design method based on virtual additional admittance approximation in different frequency bands and parameter feasible region estimation is proposed for load VSC. Finally, taking the two- and three-terminal DC distribution systems as examples, it can be verified that the damping enhancement characteristics of the virtual inertial control of the load VSC and the effectiveness of the proposed virtual inertia control parameter design method by the simulation analysis and the RT-Lab hardware-in-the-loop experiment.

Abstract:In the context of power cyber-physical systems, the communication links in distribution networks are more vulnerable to cyber-attacks compared to transmission networks. Among the existing state estimation methods considering cyber-attacks, the detection-correction method overly relies on attack detection, while the robust estimation method roughly regards cyber-attacks as quantitative outliers. The performance of state estimation under cyber-attacks needs further improvement. Therefore, a forecasting-aided state estimation method for distribution networks based on event-triggering encryption is proposed to enhance the active defense capability of distribution network state estimation against cyber-attacks and ensure the performance of state estimation. Firstly, an event-triggering encryption transmission framework is constructed to enhance the active defense capability of distribution network state estimation against cyber-attacks. Secondly, addressing the uncertainty in the measurement error distribution introduced by the event-triggering encryption transmission framework, an enhanced Cauchy-kernel-based maximum correntropy cubature Kalman filter algorithm is designed to utilize the state prediction values to assist robust filtering for accurate state estimation under unknown measurement noise distribution. Finally, simulation analysis is conducted in modified IEEE 33-bus and IEEE 118-bus distribution network systems to validate the effectiveness of the proposed algorithm.

Abstract:The multi-timescale interaction of “mechanic-electricity-magnetism-control” in the new power system will trigger wideband oscillations from several hertz to several kilohertz, which is becoming a key stability issue and major technical challenge that restricts the power system development. The paper first reviews the power system stabilizer technology for low-frequency oscillations dominated by synchronous machines, and summarizes the existing methods and challenges faced in suppressing wideband oscillations. Then, the basic concept of a new power system stabilizer (NPSS) for wideband oscillation suppression is proposed, and a control structure of “instant measurement-instant identification-instant control” is designed. The key technical challenges in NPSS are analyzed. The future research directions and basic approach to addressing these issues are further given. Finally, a prototype of NPSS designed for the scenario of doubly-fed wind turbines connected to the series compensated AC system and its hardware-in-the-loop test results are presented, indicating the feasibility of the proposed concept, theory, and method of the NPSS.

Abstract:When compressed sensing (CS) is applied to advanced metering infrastructure (AMI) systems, it is necessary to solve the problem of low reconstruction accuracy. Therefore, an optimal construction method for AMI based on multidimensional CS (AMI-MDCS) is deeply explored in this paper, and the data correlation among multiple smart meters is used to improve the reconstruction accuracy. Firstly, the basic principle of AMI-MDCS is analyzed according to the structural characteristics of AMI. Then, in order to improve the adaptability of the model to AMI, based on Kronecker CS (KCS) and multiple measurement vectors (MMV), a high-dimensional AMI-KCS model and a two-dimensional AMI-MMV model are constructed, and the specific processes of the two models are given. Finally, a joint training for sparse-basis and measurement-matrix based on singular value decomposition (JTSM-SVD) algorithm is proposed for the design of key elements in the model. Compared with one-dimensional model, AMI-KCS model and AMI-MMV model can significantly improve the reconstruction signal-to-noise ratio, and the former model has better improvement effect. Compared with the traditional training algorithm, JTSM-SVD algorithm can further optimize the reconstruction effect.

Abstract:Distinguishing whether faults in medium-voltage distribution lines are caused by lines contacting trees is of great significance for clarifying the causes of forest fires and preventing line faults from causing forest fires. The zero-sequence currents of various high-impedance ground faults are obtained through prototype experiments in the paper, and the long-term variation features of the zero-sequence current waveforms of high-impedance ground faults are analyzed. Analysis shows that there are significant differences in the fluctuation, monotonicity, and sharpness of the waveforms of the root-mean-square value of the zero-sequence currents for line tree-contacting ground faults compared to other high-impedance ground faults. A multi-feature fusion parameter set including standard deviation, discrete coefficient, kurtosis, skewness of the zero-sequence current root-mean-square value curve is designed, and an identification method for tree-contacting ground fault of medium-voltage line based on support vector machine is constructed. The results show that the proposed method achieves a fault recognition accuracy of 98%.

Abstract:With a three-level half-bridge structure, the three-level series resonant converter (TL-SRC) has the advantage of significantly reducing the number of power modules and thus reducing the cost, so it is widely used in DC transformers. However, when the TL-SRC operates in the power reverse transmission mode, the inconsistent charging current of the junction capacitors of the inner and outer switches may cause a voltage imbalance of the switching devices, which brings the risk of overvoltage damage to the devices. To solve this problem, this paper analyzes the mechanism of voltage imbalance of the three-level inner and outer switches in the power reverse transmission mode. Based on the mechanism, a passive voltage balancing method with parallel auxiliary capacitors at both ends of the inner switch is proposed, and the parameter design method of auxiliary capacitors is given. Further, an active voltage balancing method for controlling the timing of the inner and outer switches is proposed, and the basis for designing the timing of the inner and outer switches is derived. Finally, the correctness of the theoretical analysis and the feasibility of the proposed method are verified on a 75 kW experimental platform.

Abstract:Load probability forecasting can provide guidance for power grid planning, and the conditional generation model can effectively improve the forecasting performance by mining historical similar day information. However, previous studies did not pay attention to the curve shape information and the noise analysis function of unconditional models, which increased the uncertainty of the generation curve. Therefore, a short-term load probability forecasting method based on conditional enhanced diffusion model is proposed. Firstly, an improved iTransformer daily load forecasting model is constructed to forecast the adjacent daily load data. Secondly, a diffusion model combining multi-head self-attention mechanism and U-net is constructed using a loss function that combines unconditional noise estimation and conditional noise estimation. Then, the daily load forecasting results and characteristics such as temperature are used as conditional inputs. Through the reverse diffusion process of conditional enhanced guidance, multiple sets of random noise are denoised to generate multiple load curves for probability density analysis. Finally, based on a publicly available dataset from a region in China and comparative tests with various models, the case study analysis demonstrates that the proposed method has higher forecasting accuracy.