Abstract:The last section of zero-sequence current protection of AC line adopts 300 A, which has the risk of disordered tripping.Therefore, a new principle of high-resistance grounding distance relay based on zero-sequence reactance line and non-fault phase polarization is proposed. The relay adopts the technical route of phase selection before measurement. The phase selection element combines zero-sequence reactance line and non-fault phase polarization method to form a variety of combined criteria to complete the phase selection. Due to the phase difference between the zero-sequence current at the protection installation and the zero-sequence current at the fault point, the zero-sequence reactance lines of the single-phase grounding fault phase and the advance phase of the inter-phase grounding fault have aliasing region when the fault point is near the setting point. The large variation of the operation voltage of the non-fault phase is not conducive to distinguishing the two types of faults in the aliasing region, and thus the phase selection element is divided into low-resistance module and high-resistance module. The low-resistance module adopts the zero-sequence reactance line with the downward bias, which is used to identify the near end and low-resistance short circuit. With the assistance of the low-resistance module, the high-resistance module only needs to deal with the faults near the set point, which reduces the difficulty of distinguishing the two types of faults . After phase selection, the operation voltage before fault is obtained by non-fault phase polarization method, so as to determine the operation characteristics of the relay. The ability of high-resistance distance relay to withstand the transition resistance is far beyond the requirements of the regulations, which improves the selectivity of ground backup protection to high-resistance faults.

Abstract:With the proposal of China's "carbon emission peak and carbon neutrality" strategy, power generation companies not only need to participate in the electricity energy and coal market trading, but also need to participate in the carbon market trading according to the actual carbon emission level. However, due to the characteristics of different trading rules, asynchronous trading timing and multiple trading random elements the among multiple markets of electricity, carbon and coal, the traditional trading strategies of power generation companies facing a single market are difficult to apply, and a multi-market collaborative trading strategy that takes into account multiple randomness is urgently needed. Considering the uncertainties of multi-market price risks and carbon market verification, the paper proposes a long-term rolling trading decision model of electricity-carbon-coal for power generation electricity-carbon-coal. First, the model determines the annual contract electricity volume and coal purchase volume based on pre-forecasted prices for the upcoming year. Then, according to the latest monthly price forecast, the monthly contract electricity volume, monthly coal purchase volume, and carbon quota trading volume in the remaining months of the year are optimized on a rolling basis. Finally, the coal power generation companies as the research object for case analysis, are divided into two basic scenarios of carbon buyer and carbon seller for comparative study. The results show that the proposed model can provide strategic guidance for power generation companies to trade in the electricity-carbon-coal multi-market. In the scenario of power generation companies as carbon buyers and carbon sellers, the total profits of enterprises can be increased by about 7.6% and 6.4%, respectively.

Abstract:The accurate and effective transient stability assessment for power systems is of great significance for the safe and stable operation of new power systems. At present, transient stability evaluation methods based on deep learning are faced with problems such as difficulty in time-series feature space representation and serious imbalance of sample categories, which affect the reliability of evaluation results. In order to make up for the shortcomings of existing studies, a transient stability assessment method for power systems based on the imbalanced sample enhancement of denoising diffusion probabilistic model (DDPM) is proposed. First, an improved HSV color model is constructed to process the high-dimensional data in two-dimensional image, so as to visually represent the high-dimensional data and facilitate subsequent training. Then, based on DDPM algorithm, the imbalanced unstable sample space is characterized and learned, and the enhanced samples with the same probability distribution are generated on a large scale to solve the category imbalance problem. Finally, a gradient-weighted class activation mapping convolutional neural network is proposed to construct a transient stability assessment model to improve the reliability and interpretability of the model. The simulation results of IEEE 39-bus system test show that compared with other methods, the proposed model has higher stability discrimination accuracy, and the recognition rate of unstable samples is significantly improved, which verifies the effectiveness of the proposed method.

Abstract:When compressed sensing (CS) is applied to advanced metering system (AMI), 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) 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 reconstructed 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:Aiming at the problems of insufficient use of sky image information and large errors in ramp power forecasting, which lead to the limited predictive performance improvement, an ultra-short-term photovoltaic power forecasting method based on multi-level sky image features and broad learning is proposed. Firstly, the multi-level features of the ground-based sky image are extracted as the image features of the power forecasting model. At the same time, the cloud coverage and cloud change rate are introduced as image features of the ramp recognition model. Secondly, combined with the historical power data, the photovoltaic power forecasting model and the ramp recognition model based on the broad learning are developed. Finally, if the ramp recognition result is a non-ramp event, the forecasting results are obtained according to the power forecasting model, but if the ramp recognition result is a ramp event, the power forecasting model is incrementally updated using the historical data related to the ramp event, and the forecasting results are obtained based on the updated power forecasting model. The experimental results show that the proposed method can effectively improve the forecasting accuracy of ultra-short-term photovoltaic power.

Abstract:The integration of a high proportion of distributed photovoltaic into distribution networks exacerbates the system's uncertainty. Moreover, it is challenging to accurately acquire data such as the network topology and line parameters of distribution networks, rendering traditional control methods for distribution networks based on precise physical modeling ineffective. With the widespread application of measurement devices in distribution networks, it becomes increasingly easier to obtain operation data of distribution networks. In this paper, a model-free voltage control method for active distribution networks based on measurement data of distribution networks is proposed. Firstly, a Hankel matrix is constructed based on the historical data of the distribution network to establish the relationship between the node voltages of the network and the output power of energy storage. Secondly, using local measurement data and considering uncertain disturbance factors and the attenuation model of the energy storage lifespan, an optimization framework for distribution network voltage under data-enabled predictive control is constructed to achieve the rolling optimization of distribution network voltage within the control cycle. Finally, the effectiveness and superiority of the proposed method are verified through simulations using the IEEE 34-bus standard example and the modified IEEE 123-bus example.

Abstract:In the “Three North” regions of China, wind resources are abundant but system flexibility resources are scarce. During the heating period, the proportion of electric output of thermoelectric unit is high, occupying the space for wind power integration and posing severe challenges to the safe and economic operation of the system. To improve the economy of wind power accommodation, a collaborative robust planning method for electric and thermal flexibility resources considering reserve optimization is proposed. First, the peak shaving operation mechanism of promoting thermoelectric decoupling and its collaborative planning mechanism through various resources has been studied. On this basis, a min-max-min three-layer two-stage light robust planning model is established. The main problem aims to minimize the sum of the planned annual incremental investment cost, operation cost, and risk cost of insufficient reserve, optimizes all kinds of resource investment schemes and day-ahead deterministic optimal scheduling. Taking into account the uncertainty of wind power based on day-ahead scheduling results,the sub-problem minimizes the risk of insufficient reserve in the worst scenario, reschedule the equipment within days, searches for the worst scenario, and assesses the risk of insufficient reserve. The main problem and sub-problems are solved iteratively based on the column-and-constraint generation algorithm and the strong duality theory. Finally, the validity of the model is verified by a numerical example, and the robustness and risk of the model are analyzed.

Abstract:Shore power system reduces the emission of port area by shutting down the power generation equipment of docked ships, and is the central equipment of port energy-transportation integration. Its operation and planning play an important role in realizing the goal of "carbon neutrality" in the future port integrated energy system. In order to clarify the current research status and point out the bottleneck problems restricting the development of shore power system, the port shore power system is reviewed from three aspects: environmental benefit assessment, technical standards and deployment scheme of shore power system, policy planning and operation strategy of shore power system. The current situation analysis shows that the shore power system plays a key role in port green development, but it is also pointed out that the installation rate and utilization rate of shore power system present a great contradiction. In view of the future development of shore power system, the following suggestions are put forward. The whole life cycle benefit assessment of shore power system is the premise and foundation of economic planning, and the low level of energy-transportation integration is an important reason for the low utilization rate of shore power system. Also, the multi-agent operation management collaboration is the key way to improve the operation efficiency of shore power system.

Abstract:With the increasing penetration of distributed photovoltaics (DPV), the proportion of traditional energy units continues to decline, resulting in a reduction of inertia and primary frequency regulation capabilities in power systems. Therefore, it is crucial to explore the frequency support capabilities of DPV in low-inertia systems. On this basis, firstly, this paper proposes an improved frequency active support method for voltage-type DPV-virtual synchronous generator (DPV-VSG) with variable reserve rate to enhance the system inertia, reduce the response delay, and improve the ability of DPV for active participation in system frequency support. Then, an optimal selection model for setting the power and frequency parameters based on the transient search optimization (TSO) algorithm is proposed to solve the optimal initial reserve power and best frequency regulation parameters of DPV, which improves the limitation of fixed photovoltaic initial reserve power. Finally, the proposed model is applied a single-generator model and an actual distribution station area model with high penetration of DPV to verify its effectiveness.

Abstract:Current research on 5G base stations as flexible resources to participate in power grid interaction mostly focuses on the utilization of energy storage resources of 5G base station, ignoring the regulatory potential of 5G base station communication load. Therefore, a day-ahead interactive operation method for distribution network operator (DNO) and mobile network operator (MNO) based on interstation migration of communication load is proposed. Firstly, a DNO-MNO dual-layer interactive operation optimization model is constructed. In this model, DNO is an incentive strategy that comprehensively considers the system network loss cost and the incentive cost of the 5G base station, and takes the system N-1 safe operation criterion as a constraint and aims at the the lowest total system operating cost. MNO comprehensively considers the power consumption cost of the 5G base station and the grid demand response benefits, and develops the 5G base station power operation plan with the goal of the lowest net operating cost of the 5G base station. Secondly, to solve the problem that the lower MNO 5G base station scheduling model has non-convex and nonlinear constraints which are difficult to solve analytically, an algorithm based on feasible domain iteration is proposed to solve the model accurately. Thirdly, the algorithm of DNO embedded MNO dual-layer interactive model based on elite genetic algorithm is proposed. Finally, a numerical example is given to verify the effectiveness and practicability of the proposed method.This work is supportedFoundation by the State Key Laboratory of Power System Operation and Control (No. SKLD23KM15) and Basic and Applied Basic Research Foundation of Guangdong Province (No. 2022A1515110896).