Proceedings of the 8th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control (PMF2023)

With the continuous increase of the proportion of wind power access, the energy coordination capacity in the power system is weakened and the power quality is reduced. Based on this, this paper proposes a method to solve the problem of flattening energy fluctuations in the synergistic power system of electro-hydrogen hybrid energy storage, and uses the hybrid energy storage capacity optimization method composed of supercapacitor and PEM electrolyzer to solve the problem of optimal allocation of wind power fluctuations in grid-connected. Firstly, the structure model of hybrid energy storage system with supercapacitor is proposed, and on this basis, the original signal of wind power is decomposed by empirical mode method to obtain the low-frequency component directly connected to the grid and the high-frequency component that needs to be flattened by hybrid energy storage. Then, the high-frequency fluctuation of wind power in grid-connected is solved by optimizing the hybrid energy storage capacity with supercapacitors. Finally, based on the actual wind power data in Northeast China in 2022 as an example, combined with the example analysis in the MATLAB2018B + Gurobi solution environment, the effectiveness of the flattening strategy is verified by the example analysis, which provides an effective scheme for the flattening of wind power fluctuations, which can effectively improve the system economy.

Consider the availability of remaining reservoir resources to pumped-storage reserve ancillary services, and establish a day-ahead market clearing model for the wind-thermal and pumped-storage complementary power generation system. Based on the optimality conditions, the energy service price and reserve ancillary service price are decomposed into shadow price components and water value components. The scarcity of reservoir and installed resources is reflected through the shadow price. The influence of pumped-storage constraints on the water value was analyzed from the perspective of power supply and load. The cross-time relationship between water value and shadow price is derived, and the impact of reservoir capacity on water value and remaining reservoir resource value is analyzed. Taking the modified IEEE-39 system as an example, different cases are designed to analyze the changing characteristics of various resource values in different periods, as well as the impact of scarcity of remaining resources on reserve auxiliary service price. Analyze the impact of pumped-storage installed capacity, reservoir capacity, and wind power penetration rate on clearing electricity price and its main components.

Driven by the goal of “carbon peak and carbon neutrality”, a comprehensive energy system that integrates multiple energy structures has emerged. How to realize multi-energy complementarity and collaborative optimization among different sources, effectively improve energy utilization efficiency and promote the consumption of renewable energy has become a research hotspot. In this paper, the architecture of the user-side multi-energy complementary integrated energy system is studied, and the coupling equipment and energy supply network are analyzed. A multi-time scale coupling model, including a static coupling model and dynamic coupling model, is established for the multi-energy conversion equipment, Furthermore, the multi-energy coupling integration model is described. Then, a multi-energy coupling collaborative optimization method was proposed, and the objective function and constraint conditions of the system optimization operation were established. Finally, based on the coupling model and optimization method proposed in this paper, a multi-energy complementary comprehensive energy management and control system is developed. The system has been piloted and applied in several typical comprehensive energy scenarios across the country. Through data analysis and practical application verification, energy efficiency and the economy can be effectively improved.

In recent years, the technology in smart substation has been rapidly developed in China, and core technical advantages such as digitalization and standardization of information sharing in smart substation have been further demonstrated. However, the advantages of smart stations such as logical isolation and process layer data sharing are not fully utilized in the maintenance of protection device in smart substation. At present, isolator link mechanism is still widely used in the maintenance of protection device in smart substation, and plugging and unplugging of equipment fibers are often required during the testing process, which brings potential risks to the safe operation of the protection equipment. In order to solve these problems, this paper proposes a technical solution of remote online relay protection testing system architecture by deeply exploring the digital advantages of smart substations. Firstly, the virtual circuit logical isolation technology for relay protection devices based on IEC61850 ED2 is discussed in detail. Secondly, it focuses on the design and functional application of the smart substation remote online testing APP; Based on the current technical situation, the feasibility of realizing network remote secure access from the dispatching center to the smart substation side and the entire test data flow are emphatically analyzed. Combined with the pilot project, the remote online relay protection testing system is preliminarily verified. The results show that remote online testing of relay protection can effectively improve the efficiency of operation and maintenance of relay protection in smart substations.

The robust control method is effective in suppressing the phenomenon of low frequency oscillations (LFOs) in power systems. Aiming at the problem of LFOs in AC/DC transmission system, a coupling robust damping controller design method based on multiple input multiple output (MIMO) system model is proposed to enhance the damping of specific oscillation modes by utilizing the interaction between different control loops instead of decoupling the control loops. Firstly, the global least squares—rotation invariant (TLS-ESPRIT) technique is used to identify the reduced order model of MIMO system and the oscillation modes of the system. Then a kind of hybrid H2/H∞ method is used to design coupling robust damping controller based on different control loops. The balanced truncation method is used to reduce the order of the controller, which has both robust performance and practical engineering application. Finally, a four-machine two-area AC/DC test system is built in PSCAD/EMTDC. The time domain simulation results show that the coupling robust controller can effectively suppress the LFOs under various disturbances and faults, and the system can quickly recover stable operation. At the same time, output feedback control is utilized, which is convenient for engineering practice.

Wildfire disasters can cause transmission line failures characterized by low re-closing rate and high breaker rejecting rate, which dramatically increase the risk of cascading failures in power grids. Firstly, the characteristics of power grid cascading failures under multiple wildfire disasters are analyzed from the perspectives of multi fire high impedance faults, evolution pathways of power grid cascading reactions, and emergency disposal requirements for power grid cascading failures under multi-fire disasters. Secondly, establish quantitative decision-making indicators for emergency disposal on cascading failures risk, such as, the probability of transmission line cascading failures, the risk of power grid cascading failures, and the weight of transmission line cascading failures risk. Thirdly, an optimal decision-making method for emergency disposal of cascading failures risk was proposed, which considered the deployment of fire extinguishing equipment and the adjustment of power grid operation mode. Finally, the effectiveness of the proposed method is verified by a practical case of power grid.

Aiming at the problem of unstable DC bus connection in building energy routers, this paper proposes a grid connection control strategy for building energy routers based on pre-synchronous virtual synchronous generator (VSG) control technology. Firstly, an energy router topology is designed, and secondly, the pre-synchronous VSG is analyzed. Pre-synchronous VSG control is adopted for the DC bus connection in the energy router to maintain the stability of the system and reduce the voltage and current impact of the common coupling point. In this paper, the simulation model of pre-synchronous VSG control energy router connected to the grid is established by MATLAB/simulink. In MATLAB, simulation results show that the pre-synchronous virtual synchronous machine (VSG) control has the characteristics of small overshoot. The proposed control strategy proposes a new solution for the grid-connected operation of energy routers.

Extreme weather, such as cold waves and frosts, can significantly impact the output of wind farms. To address this issue, this paper proposes a short-term power prediction and correction method suitable for wind farms during extreme weather, based on the K-nearest neighbor algorithm and multiple linear regression model. By verifying that wind power output is affected by wind speed, temperature, humidity, wind power kurtosis, and other factors, the K-nearest neighbor algorithm is used to obtain the sample set of similar days on the day to be measured. The regression model of influencing factors and output errors is established to improve the power prediction accuracy. A wind farm in northwest China is used as a test example to verify the effectiveness of the output correction method.

At present, China’s offshore wind power generation technology has a lot of room for development. As the offshore distance of offshore wind farms grows, the traditional AC integration approach has gradually been replaced by the flexible DC transmission integration approach. The difference from the traditional AC integration approach is that there are a multitude of power electronic devices in the VSC-HVDC system, which makes the fault characteristics of the system greatly changed. This paper mainly studies the fault properties of doubly-fed induction generator (DFIG) with flexible DC integration. Firstly, the topology and control strategy of DFIG Wind Farm with flexible DC integration are analyzed, and a mathematical model of the system control part is established. Secondly, the DFIG short-circuit current is calculated, and the factors affecting the size of the short-circuit current are found through analysis. Finally, the fault characteristics are analyzed by building a DFIG Wind Farm model with flexible DC integration on the PSCAD/EMTDC simulation platform.

For the steady under voltage problem of the system, currently, measures such as switching on/off capacitors and reactors, adjusting transformer taps, etc. are generally taken to make the voltage rise firstly. When the control measures are insufficient, it is necessary to cut off some of the load. Considering that when the local steady under voltage level of the system is above 0.8 p.u, the control time requirement is not high, it is proposed to use dynamic zoning technology to coordinate with load shedding control and improve the refinement level of load shedding control. The impact evaluation indicators for dynamic zoning and the comprehensive cost indicators for load shedding are defined, and the direct search method of rotating variables is adopted to reduce the complexity of optimization search. Firstly, a zoning scheme is determined, and based on the control sensitivity index of the load, available load shedding points are selected. Furthermore, based on the evaluation results of load shedding priority, load shedding points are selected sequentially, and the minimum comprehensive control cost under this zoning scheme is calculated. Finally, by rotating the zoning points sequentially and iterating continuously, the optimal comprehensive control strategy is obtained. The effectiveness of this method in reducing load shedding and comprehensive control costs has been verified based on actual power grid examples.

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements and real-time measurements are represented as a graph according to the topology of the distribution network, which are further processed by a graph attention network to capture the mapping relationship between the graphical measurements and the state variables based on the error modeling of pseudo-measurements. The robustness against anomalous measurements is achieved through the embedding of structural information of DN. The modeling of pseudo-measurements further enhance its robustness by guiding the formulation of edge weights of the graph neural network. Comparative tests are carried out on a IEEE 119-node system to demonstrate the effectiveness and robustness of the proposed method.

Life prediction of energy storage battery is very important for new energy station. With the increase of using times, energy storage lithium-ion battery will gradually age. Aging of energy storage lithium-ion battery is a long-term nonlinear process. In order to improve the prediction of SOH of energy storage lithium-ion battery, a prediction model combining chameleon optimization and bidirectional Long Short-Term Memory neural network (CSA-BiLSTM) was proposed in this paper. The maximum discharge capacity of the battery was used to define the battery SOH. The chameleon optimization algorithm was introduced into the architecture of the bidirectional short-short memory network to optimize the network. The percentages of MAE and RMSE were 2.501 and 2.511% before optimization, and 1.292 and 1.420% after optimization, respectively. The optimized model has high prediction accuracy.

The long-term evolution of coal power installed capacity in the physical dimension is affected by social factors such as coal-related policy mechanisms (carbon pricing and capacity electricity prices) and the decommissioning decision of existing coal power. Under the guidance of the Cyber-Physical-Social system in Energy (CPSSE), a hybrid simulation model covering coal power policy, existing coal power decommissioning decision, and long-term evolution of coal power installed capacity is established. The evolution process of the long-term financial condition and decommissioning time of each coal power plant with different carbon emission costs and capacity price revenue are obtained by the simulation model. And impact of coal power plant decommissioning on the long-term power generation balance of the whole system are analyzed. The results show that high carbon emission costs may have a fatal impact to the financial condition of coal power plants, and the resulting large-scale decommissioning in advance of coal power plants can cause long-term power generation balance risks of the whole system, and a reasonable capacity price can help to address the above risks.

Against the backdrop of the “dual carbon” goal and the green of energy transformation, the volatility and randomness issues of the new power system dominated by wind and solar power are becoming increasingly prominent, affecting the safe and stable operation of the power system. Therefore, the role of auxiliary services in promoting the use of alternative energy sources and ensuring power quality is becoming increasingly. In this context, this article summarizes and organizes the current development and operations status of the auxiliary services market, carbon market, green certificate market, and green electricity market in mature foreign markets, as well as the experience that can be used for reference. It also sorts out the relationship between the interaction and influence of the auxiliary services market and various other markets. Finally, based on the practical needs for market development to facilitate China’s green energy transformation, development recommendations are provided for the design of the auxiliary services market and various market coupling mechanisms, to fully leverage the synergistic effect of the auxiliary services market with the carbon market, green certificate market, and green electricity market, support China's green energy transformation.

At present, due to the characteristics of many kinds of port loads, large scale and large differences in load timing, it makes the ship shore power with large uncertainty. To address such problems, this paper researches the prediction method of port load, and proposes the prediction method of ship shore power load based on Monte Carlo simulation by analyzing the law of ship port call; and for the problem of port system energy scheduling and optimization, it considers the optimal scheduling of each unit within the port, and proposes the optimization model based on demand response and internal and external cooperation with the goal of minimizing system operation cost. The optimization model of port energy management based on demand response and internal and external cooperation is proposed. The proposed method is validated with the example of Taihu Port Terminal and Tongli Lake Port Terminal in Suzhou City, Jiangsu Province, which shows that the proposed method reduces the operating cost of the port words.

Various stability problems have arisen in the current renewable energy generation systems based on Self-synchronous Voltage-source Inverters (SVI). Therefore, there is an urgent requirement to analyze the impedance characteristics of SVI multi-unit parallel systems. Firstly, the impedance model of a single unit is derived based on the control strategy of SVI using the harmonic linearization method. Then, an SVI multi-unit parallel equivalent impedance modeling method is proposed based on the condition that the parameters are consistent. The multi-unit system is equated to the parallel connection of SVIs with the same parameters, so as to derive the SVI multi-unit parallel equivalent impedance model. Meanwhile, the impedance is verified by MATLAB sweep-frequency simulation. Finally, according to the established impedance model, the effects of LC filter parameters, parallel line impedance, and the number of parallel units on the equivalent impedance characteristics are analyzed, which provides a reference for the stability analysis and optimization of renewable energy generation systems.

Wind and solar power generation, as well as the entire power system, are accompanied by randomness and fluctuations. With the continuous improvement of renewable energy forecasting accuracy, in the process of grid operation, online real-time optimization with further trend consideration can provide relatively reasonable real-time instructions based on the trend of renewable energy changes. This paper gives the adjustable space of CSP between adjacent time points according to ultra-short-term wind and solar forecasting and the active power regulation rate of CSP. By discretizing the adjustable space, different combinations of active power of power plants are obtained in each time point. From the set of power plant combinations that simultaneously satisfy all constraints of real-time power generation control optimization decisions, the power plant combination with the weighted optimal objective function value alone is selected as the coordinated optimization decision result for real-time power generation control, and instructions are issued to solve the problem of prediction correction and rolling optimization caused by the sharp increase in random factors in grid operation, and improve the decision accuracy of real-time power generation control.

In response to the issue of insufficient frequency and voltage regulation capability in high-penetration renewable energy system, a control strategy combining grid-forming voltage source converter (GFM-VSC) with model predictive control (MPC) is proposed. Based on the main circuit topology of the converter, a power prediction model is established. Then a cost function concerning both inductor current and power output is constructed and a control strategy based on the which is introduced. A fast dynamic response to frequency and voltage variations has been achieved. Simulations have confirmed the effectiveness of the proposed control strategy.

In order to optimize the storage capacity configuration to improve the utilization rate of renewable energy and improve the efficiency and reliability of system operation. This paper looks for effective ways to maximize the use of renewable energy resources. Combined with the requirements of power grid balance and stability, the sum of the cost of wind and solar energy resource waste and energy storage investment is taken as the objective function. Mathematical modeling and sparrow search algorithm optimization recurrent neural network are used to determine a reasonable energy storage capacity allocation scheme. At the same time, the energy storage scheduling strategy is designed, which can not only minimize the battery loss and deterioration, but also ensure that the system can meet the energy demand. The research results can provide guidance for the allocation of renewable energy storage capacity and promote the sustainable development of renewable energy power generation.

Energy storage is an effective tool in microgrids to absorb new energy output and smooth its fluctuations. Multiple users within a microgrid have their own distributed energy storage (DES). In this paper, we propose an energy storage sharing (ESS) model aggregated by a common platform within a microgrid to improve user benefits and energy storage utilization. The electricity cost of users and the benefits from sharing the owned energy storage are fully considered in the model, which effectively promotes the consumption of new energy in the microgrid and maximizes the benefit of users. The proposed operational strategy is divided into two phases: energy dispatch and transaction payment. The new energy consumption rate is effectively improved based on the energy dispatch in the shared mode, and the problem of profit distribution among multiple users is fairly addressed by a transaction payment method based on Asymmetric Nash Bargaining. The model is solved by the alternating direction multiplier method, which can effectively protect the privacy of each subject. The results shows that the benefits of the three users increased by 8.37, 0.1076 and 0.3375 times respectively through the proposed method. Users who share more power and energy storage have greater benefits, which proves that the fairness of the distribution is improved by the proposed distribution method. It is conducive to increasing the enthusiasm of users to participate in sharing.

With the energy crisis and environmental problems highlighted, power users access distributed generation in the distribution network to achieve the goal of energy conservation and emission reduction, forming a user side active distribution network. However, the current active distribution network on the user side has caused source and load mismatch due to the dual fluctuation of source and load, resulting in issues such as voltage exceeding limits and power reverse transmission. Therefore, it is necessary to evaluate the source and load matching degree. However, existing evaluation methods lack the evaluation of source and load power quantity matching from the user side perspective. This article proposes a method for evaluating the source and load matching of user side active distribution networks. Firstly, an evaluation index system for source and load matching in user side active distribution networks was established from the perspectives of electricity matching and power matching; Secondly, the evaluation steps and methods for source and load matching in user side active distribution networks were proposed; Finally, the effectiveness of the proposed method in this paper is verified through numerical analysis of actual user side data.

As the key equipment connecting regional power stations, substations and load, overhead transmission lines can be easily affected by ice disaster, which cause huge loss to the power system. Therefore, this paper establishes an icing growth model to predict the development trend of ice. First of all, the original data is preprocessed, and the feature variable with the most important rank is selected as the input. Secondly, nine machine learning algorithms are used to construct the prediction model, including three linear regression models (ridge regression, lasso regression, and elastic net regression), three single algorithm models (decision tree, K-nearest neighbors, and support vector regression), and three ensemble learning algorithms (gradient boosting regression, random forest, and adaptive boosting). Finally, the analysis is concluded that the decision tree, gradient lifting regression, random forest and adaptive lifting algorithm have demonstrated excellent performance on the test set. Furthermore, the predictive capability of these four optimal models is further validated by predicting new datasets.

A deployment strategy for a ring sparse array camera applied to digital twin real-time scene fusion is proposed to address the challenges of virtual and physical data synchronization and consistency in power grid equipment. Faced with the current situation of numerous components, diverse shapes, and complex occlusion relationships in power grid equipment, the optimization goal is to use the minimum number of cameras to cover the target area and synthesize any freely viewed synthetic image. The strategy described in this paper can improve the accuracy, fidelity and real-time of image acquisition while reducing the number of cameras to control the cost of image acquisition, thus breaking through the key difficulties of real-time replication and fusion of the main transmission and transformation equipment scene, promoting the mutual supplement of spatial data and live data, and strengthening the real-time perception and response capability of the physical state, Breaking the current low-level visualization application status of the digital twin “3D modeling, data hooking, and 3D display” in the power grid.

The scale of power grid equipment, the number of power customers and the complexity of grid structure in China are among the highest in the world. In addition, the rapid change of power grid and the frequent transformation of equipment make the automatic generation of single line diagram, especially the single line diagram of distribution network GIS, increasingly difficult. The traditional manual drawing method has the disadvantages of heavy workload, low efficiency and uncontrolled accuracy. At the same time, due to the influence of human drawing habits, the readability of the finished product is low, which cannot meet the requirements of the increasingly updated power grid for its own maintenance and management. In this paper, a specific algorithm is used to determine the flatness of the original drawing, realize orthogonal mapping, and flatten the complex electrical wiring information. Finally, the experimental results show that the algorithm can realize the decoupling of complex wiring diagram, and significantly improve the mapping efficiency and accuracy.

The flexible low-frequency AC transmission technology based on the voltage source AC-to-AC converter makes large-scale networking easy, which can enhance the line transmission capability with its flexible regulation and control functions like power flow regulation, so it is a new transmission technology worthy of in-depth research. The fault characteristics of the flexible low-frequency transmission system (LFTS) are affected by topological structure and control strategies, possibly resulting in a problem of adaptability of conventional protection methods, i.e. relay protection faces a serious challenge. This paper presents an analysis of the basic structure and fault characteristics of the flexible LFTS based on the modular multilevel matrix converter (M3C). On this basis, the adaptability of the relay protection technology to the flexible LFTS is summarized. To solve the problem of adaptability inadequacy of some protection technologies, the research prospect of key protection technologies adapting to the LFTS is made, such as fast amplitude calculation method, sampling value differential protection and time-domain distance protection.

The proposed work presents a coordinated robust supplementary control scheme for the static synchronous compensator (STATCOM) and wind farm (WNF) to stabilize the multi-machine power network by simultaneously modulating the reactive power of STATCOM and WNF via d-axis control loops. The control scheme is solved based on a linear matrix inequality (LMI) framework with multi-objective mixed- $$H_{2} /H_{\infty }$$ H 2 / H ∞ output feedback structure, followed by ensuring the predefined damping level of low-frequency modes (LFMs). Furthermore, the design strategy also takes into account multiple input and output channel communication delays. Eigenvalue analysis confirms the significant enhancement in the damping ratio of the dominant mode using the proposed strategy. Finally, time-domain simulations, subject to typical disturbances and uncertainties caused by wind speed and time delays, are used to validate the effectiveness of the strategy.

The phasor measurement units (PMUs), which are widely distributed at key nodes in the power network, provide a large amount of measurement information for dynamic state estimation. An accurate model is the foundation for ensuring dynamic state estimation. However, due to uncertain factors such as cyber-attacks, aging of device components and differences in operating environments, unknown inputs may exist in the model, seriously affecting the estimation accuracy. To upgrade the estimation performance of cubature Kalman filter (CKF) under unknown inputs, an adaptive CKF method is proposed. By utilizing adaptive factors, the error variance matrix of state variables can be adaptively updated to suppress the impact of unknown inputs on state estimation. Finally, simulations are conducted on the IEEE 39-bus test power system. Compared with traditional unscented Kalman filter (UKF) and CKF, the proposed method has better performance in estimation accuracy and algorithm robustness.

The transmission expansion planning (TEP) problem is one of the perilous issues, which allows electricity transmission planners to design a cost-effective and reliable strategic model for the implementation of optimal transmission reinforcements in existing power grid networks. In this paper, a novel TEP model is proposed considering long- and short- term uncertain factors. The three-stage adaptive robust optimization (ARO) method deals with long-term uncertainties while prudently representing short-term uncertain parameters via scenarios. The formulated strategic scheme is elucidated through a modified decomposition algorithm that applies primal cutting planes and focuses on the subproblem feasible solution. The efficacy of the presented model is demonstrated through realistic case studies based on a 6-bus test system.

Accurate wind power forecasting is crucial for the stability of modern power systems and fostering wind power utilization. However, very short-term forecasting faces challenges due to its limited input duration, and the utilization of long sequences is rarely employed in this context. The reason behind this limitation lies in the fact that traditional forecasting models often encounter the issues of gradient disappearance or gradient explosion when handling long sequences. Therefore, this paper presents a novel very short-term wind power forecasting model based on Transformer (TF), aiming to explore the feasibility of utilizing long sequences for very short-term forecasting. The proposed model is evaluated through case using real-world engineering data. The obtained numerical results demonstrate that TF is capable of effectively processing long sequences, providing valuable insights for the advancement of future forecasting models.

Electric vehicles are being used on a large scale, and virtual power plants are redefining electric vehicles. A profit maximization model of EVs charging/discharging is constructed in this paper.The model is aimed at the maximum profits, while being constrained by power/energy storage batteries charging/discharging capacities and the travel needs of EVs.The model also express the charging/discharging decision of EVs very well. An analysis and calculation of the economic benefit and charging distribution of EV charging/discharging have been made by simulating user travel needs with Monte Carlo method. The data of the user travel rule come from NHTS (National Household Travel Survey) in 2021. The results indicate the rational charging/discharging model which can be significantly improved by responding to the TOU(Time Of Use) and RT (Real-Time) electricity price. Meanwhile, due to the cheaper off-peak electricity price at night, the expensive on-peak electricity price during the day, electric vehicles tend to charge at low load time and discharge at peak load time inversely so as to achieve peak load shifting. The power/energy storage batteries storage function of EVs is worth further developing.

For the problem of siting and capacity of PV and energy storage connected to distributed PV distribution network with high penetration rate, a PV energy storage siting and capacity strategy based on dynamic network reconfiguration and cluster division is proposed. The method first proposes a cluster division model considering dynamic reconfiguration for cluster division method, on this basis, a PV energy storage siting and capacity setting model based on dynamic network reconfiguration and cluster division is established, the upper-level planning model takes the equal annual value installation cost, annual operation and maintenance cost, cluster power purchase cost active network loss minimization as the objective function, each cluster DG capacity, ESS capacity and power as the decision variables for The upper-level planning, the lower-level planning model takes network loss minimization as the objective function, and the DG and ESS access capacity and access location of each cluster internal node as the decision variables for the lower-level planning. Finally, the feasibility and effectiveness of the method are verified with the improved IEEE33 node system.

Due to the global warming caused by the excessive use of fossil energy, the uncertainty and volatility of new energy have put pressure on the regulation of the power system, resulting in problems such as abandoning wind and light. The customer directrix load has been proposed to define the ideal load curve shape to smooth the fluctuation, and achieve the balance of adjustable resources and non-adjustable resources. However, this method cannot personalize and guide different types of users, and fails to fully tap the user's adjustment potential. Therefore, a customer directrix load decomposition method based on power customers load profile clustering is proposed. Firstly, the research on user clustering and load customer directrix load is analyzed, especially for the problems faced by personalized clustering of users’ electricity consumption. Secondly, the decomposition scheme of the customer directrix load, the model of the sub-customer directrix load and the measurement of the effect are proposed, and a set of decomposition mechanism of the sub-customer directrix load is designed based on the clustering algorithm of user characteristics. The results of the example analysis show that compared with the existing mechanism, the sub-customer directrix load decomposition mechanism can fully tap the user's adjustment ability and guide the user closer to the customer directrix load. This mechanism is suitable for different kinds of clusters of spontaneous clustering according to the user power consumption characteristics. Finally, this mechanism can effectively reduce the problem of wind and light abandonment and improve the absorption capacity of new energy.

This paper describes the corresponding control strategies for the normal and fault operation states of the islanded microgrid system. The islanded microgrid system consists of distributed energy resources (DER) such as photovoltaic (PV), hybrid energy storage (HES) and microturbine (MT) and loads such as pumping unit load (PUL) and equivalent load. In the normal condition, it adopts a voltage and frequency (V-f) control strategy for the inverter of MT to maintain constant voltage and frequency of the whole system and a constant power control strategy for the rest of the power inverters. In the fault condition, the front and rear stages of the PV system adopt constant power control and reactive power compensation priority control respectively, thus realizing low voltage ride-through (LVRT). The proposed control strategies can ensure the stability of voltage and frequency of the microgrid during normal conditions and the ability of the PV system to achieve LVRT during fault conditions. A simulation model of the islanded microgrid system based on PSCAD/EMTDC is constructed and the effectiveness of the control strategies under normal and fault conditions is verified.

Photovoltaic (PV) power generation has attracted widespread attention due to its environmental friendliness and cost-effectiveness. However, the intermittency and unpredictability of PV power production pose challenges to the reliable operation of the electric power system (EPS), especially in complex weather conditions where the output power of PV becomes even more difficult to predict. Therefore, the development of an accurate ultra-short-term PV power forecasting system is crucial in assisting power system operators in maintaining grid stability and enabling effective energy trading among market participants. This study proposes a novel ultra-short-term power forecasting model that combines the GRU neural network and K-means clustering method. The model integrates the time-series data of various meteorological parameters over a certain period, considering the periodicity and continuity of weather changes. Additionally, it incorporates power time series data to extract information about the operational characteristics of the PV plant. Furthermore, the model defines several variables that measure the level of weather fluctuations, enabling effective classification of weather types on a short-time scale and providing references for the forecasting process. The experimental results demonstrate that the proposed method can improve the accuracy of ultra-short-term photovoltaic power prediction, and its advantages are more prominent in complex weather conditions.

The rapid development of distributed photovoltaic (PV) is conducive to energy conservation and emission reduction, but its large-scale access also have influence on the low-voltage distribution network line loss. In this paper, a calculation method of low voltage line loss is proposed based on the power flow calculation method of backward-forward substitution. The analytical model of line loss calculation under uniform power network is established, and the relationship between the PV output and the change of line loss is given. An example model of a 15-node low-voltage distribution network with PV is built, and the correctness of the proposed method is verified with the comparison of the analytical model and the simulation model. The influence of three-phase unbalance, PV access capacity and access position on system line loss is analyzed, which provides theoretical basis and decision support for distributed PV access, low-voltage distribution network loss reduction and energy saving.

Due to the large-scale access of new energy, its volatility and intermittent have brought great challenges to the power grid dispatching operation, increasing the workload and work difficulty of the power grid frequency regulation, and the increase in the installed proportion of new energy has also led to the further expansion of the peak-valley power difference. Electrochemical energy storage has bidirectional adjustment ability, which can quickly and accurately respond to scheduling instructions, but the adjustment ability of a single energy storage power station is limited, and most of the current studies based on the energy storage to participate in a certain type of auxiliary services, which cannot be fully utilized within the range of its life cycle. To solve this problem, a two-stage power optimization allocation strategy is proposed, in which electrochemical energy storage participates in peak regulation and frequency regulation. In the first stage, the adjustment cost, adjustment capacity and health status of each energy storage station in the region are considered, and the output of each energy storage station is determined with the goal of pursuing dispatching economy and reliability. In the second stage, the output of each energy storage power station is sent to each energy storage unit under the power station as the total power, and the goal is to quickly balance the SOC of each energy storage unit, which is conducive to the overall scheduling of the energy storage power station.

Large-scale new energy access to the power grid provides clean power for the power system, but the uncertainty of new energy output leads to security and stability problems and new energy abandonment in the power system. Pumped storage and battery storage technologies are important means to transfer power and provide power regulation for the system. In this paper, a multi-timescale optimal scheduling model for pumped storage hydropower plants and battery storage systems is developed for large-scale new energy consumption enhancement. The model takes reducing the peak-to-valley difference of the net load curve of the grid and reducing the peaking cost of conventional units as the optimization objectives, and considers the cumulative frequency regulation demand constraints for multiple time periods during the day. By converting the established model into a mixed-integer linear programming (MILP) problem, the optimal scheduling scheme considering the system peak and frequency regulation demands can be solved. A simulation example is carried out with the Beijing-Tianjin-Tangshan power grid to verify the effectiveness of the proposed method.