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Table of Content

    28 September 2024, Volume 58 Issue 9 Previous Issue    Next Issue
    New Type Power System and the Integrated Energy
    Multi-Energy Flow Modeling and Optimization of Electric-Gas-Thermal Integrated Energy System
    LI Bingjie, YUAN Xiaoyun, SHI Jing, XU Huachi, LUO Zixuan
    2024, 58 (9):  1297-1308.  doi: 10.16183/j.cnki.jsjtu.2022.494
    Abstract ( 3222 )   HTML ( 24 )   PDF (8902KB) ( 361 )   Save

    In view of the fact that the conversion of various energy forms such as electricity, gas, and heat in the regional integrated energy system (RIES) seriously affects the economy of the system operation, a mathematical model and an optimization model of RIES energy flow are established to improve the economy of the system and the absorption of renewable energy. First, the mathematical models of all kinds of energy conversion equipment in the system are established to determine the constraints of three kinds of energy transmission networks, namely electricity, natural gas, and heat. Then, taking economic operation as the primary objective, and taking into account the objective function of low carbon emissions and increasing the uptake rate of renewable energy, the RIES multi-energy flow optimization model is constructed. Finally, based on the large-scale integrated energy system, the load side demand response is introduced and the simulation model is established. The simulation results show that the introduction of demand response improves the flexibility of system scheduling, reduces the dependence of the system on energy storage equipment, and effectively reduces the energy consumption cost of users.

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    Shared Energy Storage Multi-Objective Allocation Strategy Considering Integrated Energy Microgrid Access to Active Distribution Network
    MI Yang, CHEN Yuyang, CHEN Boyang, HAN Yunhao, YUAN Minghan
    2024, 58 (9):  1309-1322.  doi: 10.16183/j.cnki.jsjtu.2023.021
    Abstract ( 1730 )   HTML ( 8 )   PDF (4556KB) ( 126 )   Save

    In order to give full play to the advantages of shared energy storage in improving economy and energy utilization, while considering the role of multi-energy complementation and coupling of integrated energy microgrids in active distribution networks, a multi-objective optimal allocation strategy of shared energy storage is proposed for the active distribution network connected with integrated energy microgrid. First, the optimization objectives of the economy and voltage stability of the distribution network and the configuration capacity of the shared energy storage are analyzed, the coordinated operation of the source-net-load side multi-flexible resources of the active distribution network is considered, and the active distribution network and the integrated energy microgrid are modeled. Then, the model is solved based on the Pareto optimal multi-objective particle swarm algorithm. Finally, the optimization algorithm of shared energy storage configuration is established in conjunction with the IEEE 33-node distribution system to verify the effectiveness of the proposed configuration strategy.

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    Robust Optimal Scheduling of Micro Energy Grid Considering Multi-Interval Uncertainty Set of Source-Load and Integrated Demand Response
    MI Yang, FU Qixin, ZHAO Haihui, MA Siyuan, WANG Yufei
    2024, 58 (9):  1323-1333.  doi: 10.16183/j.cnki.jsjtu.2023.022
    Abstract ( 1890 )   HTML ( 4 )   PDF (1898KB) ( 136 )   Save

    Aiming at the uncertainty of the source and load in micro energy grid, a robust optimal scheduling model considering multi-interval uncertainty set of source-load and integrated demand response is proposed. First, considering the uncertainty of wind power, photovoltaic output and electric, and thermal and cooling loads in the micro energy grid, a multi-interval uncertainty set of source-load is established. Then, in order to fully tap the potential of load side dispatching, an integrated demand response model is established, which includes reducible electric load, transferable electric load, flexible cooling, heating load, and replaceable load, based on which, the uncertainty of integrated demand response is considered. Afterwards, with the lowest dispatching cost of micro energy grid as the objective function, a two-stage robust optimal scheduling model of micro energy network is constructed, which considers the multi-interval uncertainty set of source load and the integrated demand response. The model is solved by the column and constraint generation algorithm, the strong duality theory, and the large M method. Finally, the rationality and effectiveness of the proposed model are verified through the analysis of numerical examples.

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    Pattern Recognition and Ultra-Short-Term Probabilistic Forecasting of Power Fluctuating in Aggregated Distributed Photovoltaics Clusters
    WANG Yubo, HAO Ling, XU Fei, CHEN Wenbin, ZHENG Libin, CHEN Lei, MIN Yong
    2024, 58 (9):  1334-1343.  doi: 10.16183/j.cnki.jsjtu.2023.048
    Abstract ( 1699 )   HTML ( 4 )   PDF (4167KB) ( 361 )   Save

    The quantitative evaluation of the uncertainty in distributed photovoltaic power is significant for the safe and stable operation of distribution network. Considering the significant differences in power characteristics of different output fluctuation patterns, in order to obtain a prediction model suitable for different fluctuation patterns and to perform a refined assessment of power uncertainty, this paper proposes a method for pattern recognition and ultra-short-term probabilistic forecasting of power fluctuating in aggregated distributed photovoltaic clusters. First, the satellite cloud images and photovoltaic power data are integrated, and the pattern recognition model of fluctuation is constructed via the feature extraction of power fluctuation, realizing the mining of fluctuation rules. On this basis, the difference in predictability of different fluctuation patterns and the correlation between fluctuation patterns and prediction errors are considered via classification modeling, so that the width of prediction interval can better adapt to the characteristics of prediction error distribution. Thus, refined consideration of power uncertainty of different fluctuation patterns is realized to improve the precision of probabilistic prediction, provide more references for power grid dispatching, and weaken the influence of the strong volatility in distributed photovoltaic power on the power system.

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    Optimal Allocation of Electric-Thermal Hybrid Energy Storage for Seaport Integrated Energy System Considering Carbon Trading Mechanism
    LIN Sen, WEN Shuli, ZHU Miao, DAI Qun, YAN Lun, ZHAO Yao, YE Huili
    2024, 58 (9):  1344-1356.  doi: 10.16183/j.cnki.jsjtu.2022.428
    Abstract ( 2708 )   HTML ( 3 )   PDF (5125KB) ( 345 )   Save

    With the continuous increase of electrification in seaports, the single energy supply mode of seaport microgrid is evolving towards multi-energy integration. Aimed to achieve the goals of peak carbon and carbon neutrality, an optimal carbon trading mechanism-based allocation scheme of hybrid electric and thermal storage system is proposed to further maximize the economic and environmental benefits. First, the integrated energy system model of a seaport is established, incorporating a scheme within the carbon trading market. Then, a bi-level optimization framework is proposed, in which the upper layer is utilized to optimize the allocation of the hybrid energy storage system and the lower layer is employed to optimize the operation. Afterwards, a combination algorithm of the mesh adaptive direct search and the adaptive chaotic particle swarm optimization is developed to solve the proposed problem. Finally, the real-world data of Tianjing port is utilized to verify the method. The numerical results demonstrate that with the help of the proposed method, both the cost and carbon emissions are dramatically reduced.

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    Two-Stage Day-Ahead and Intra-Day Rolling Optimization Scheduling of Container Integrated Port Energy System
    ZHOU Siyi, YANG Huanhong, HUANG Wentao, ZHOU Ze, JIAO Wei, YANG Zhenyu
    2024, 58 (9):  1357-1369.  doi: 10.16183/j.cnki.jsjtu.2023.016
    Abstract ( 2312 )   HTML ( 4 )   PDF (7518KB) ( 234 )   Save

    In view of the fact that the current integrated port energy system (IPES) considers neither the time scale difference of refrigerated containers in port scheduling nor the impact of renewable energy and load uncertainty, this paper proposes a day-ahead and intra-day two-stage rolling optimization scheduling method for a container IPES. In day-ahead scheduling, based on the temperature rise process of refrigerated containers, a port cold chain energy demand model is established, which is combined with the logistics process after the arrival of refrigerated containers. Then, the day-ahead output values of each unit in the system are obtained with the goal of the lowest operating cost. In intra-day scheduling, a two-layer rolling model is proposed to obtain the adjusted output of the port energy equipment, which considers the prediction error of shore power load and renewable energy as well as the different response speeds of cooling, heating and power. The calculation results show that the collaborative optimization scheduling of refrigerated containers and the container IPES can effectively reduce the port operation cost and carbon emissions. The two-stage day-ahead and intra-day rolling optimization scheduling can improve the economy and stability of the system.

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    Joint Economic Optimization of AGV Logistics Scheduling and Orderly Charging in a Low-Carbon Automated Terminal
    WANG Xuan, WANG Bao, CHEN Yanping, LIU Hong, MA Xiaohui
    2024, 58 (9):  1370-1380.  doi: 10.16183/j.cnki.jsjtu.2023.027
    Abstract ( 2007 )   HTML ( 5 )   PDF (3702KB) ( 510 )   Save

    To improve the current automated guided vehicle (AGV) charging strategy at automated terminals, which is not fully coordinated with the distributed power supply, a joint optimization method of AGV logistics scheduling and orderly charging is proposed. First, the synergetic relationship between AGV logistics scheduling and charging scheduling is analyzed, and a joint optimization framework is built. Then, a method to calculate the distance traveled by AGVs while considering the segregation requirements of trucks inside and outside the terminal is proposed. Afterwards, for the AGV charging module, the judgment conditions of AGV charging status and the pile selection method are defined. Furthermore, to minimize the cost of purchasing electricity at the terminal, a joint optimization model of logistics scheduling and orderly charging is constructed by considering time-of-use tariff, distributed power feed-in tariff, power balance constraint, state of charge constraint at the termination moment, upper and lower bound constraints of decision variables, and logistics scheduling constraint. Finally, a fast solution method based on improved particle swarm optimization algorithm is proposed, of which the effectiveness and economic efficiency are verified by an actual case of a terminal.

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    Switching Modeling and Application in Fault Diagnosis Algorithm Testing of Distribution Network
    XUE Guiting, LIU Zhe, HAN Zhaoru, SHI Fang, WANG Ti, WANG Xiao
    2024, 58 (9):  1381-1389.  doi: 10.16183/j.cnki.jsjtu.2023.129
    Abstract ( 2276 )   HTML ( 3 )   PDF (4150KB) ( 67 )   Save

    Fault diagnosis in power distribution networks is crucial for fault location, enhancement of fault processing efficiency, and reduction of power outage losses. Currently, the impact of switch operations and other interferences is seldomly considered in fault diagnosis algorithm designing and testing, which may lead to frequent mal-function and poor performance in practical applications. In this paper, a detailed analysis and modeling of the transient process of switch operation in distribution networks is proposed with the combination of the Mayr and the Helmer models. The transient waveform of the on-site operation process is compared and analyzed with the simulation waveforms generated in PSCAD. Based on the accuracy verification of the model, typical fault scenarios in distribution networks, including switch operation processes, are constructed for fault diagnosis algorithm tests. Compared to the traditional model, the model proposed can simulate and generate disturbance data close to the on-site switch operation process for reliability testing of fault diagnosis algorithms. Finally, several suggestions for optimizing the fault diagnosis algorithm and testing process are proposed through result analysis.

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    Online Monitoring Method for Inertial Support Capacity of Point-to-Grid in New Power Systems
    DENG Xiaoyu, LIU Muyang, CHANG Xiqiang, NAN Dongliang, MO Ruo, CHEN Junru
    2024, 58 (9):  1390-1399.  doi: 10.16183/j.cnki.jsjtu.2023.029
    Abstract ( 1845 )   HTML ( 4 )   PDF (2472KB) ( 20 )   Save

    An accurate and timely monitoring for the inertia support capability of the point of interconnection of aggregated sources to the grid in a low-inertia new power system is crucial for the safety, stability, and economic operation of the system. In order to explain the basic idea of the online point-to-grid inertia monitoring method, the definition of inertia of power system based on the swing equation and existing online monitoring methods are analyzed. Then, in order to improve the accuracy of the existing online inertia monitoring method, an equivalent inertia constant identification method based on the regression method is developed. Combining the proposed inertia constant identification method with the online inertia monitoring method, a systematic method for online monitoring of the inertia support capacity of point-to-grid in new power system is developed based on synchronous phasor measurement units. Finally, the simulation analysis of a modified New England 10-machine 39-bus system proves the accuracy and the feasibility of the developed real-time inertia monitoring method for the new power system.

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    Two-Stage Robust Expansion Planning of Transmission Network Considering Uncertainty of Offshore Wind Power
    TIAN Shuxin, HAN Xue, FU Yang, SU Xiangjing, LI Zhenkun
    2024, 58 (9):  1400-1409.  doi: 10.16183/j.cnki.jsjtu.2023.028
    Abstract ( 1232 )   HTML ( 4 )   PDF (1718KB) ( 11 )   Save

    The complex and multiple uncertainties of offshore wind power pose great challenges to the safety and robustness of transmission grid structures. In order to improve the adaptability of grid structure to offshore wind power, a robust expansion planning method based on Vague soft set is proposed. First, Monte Carlo simulation is employed to construct the offshore wind Vague scenarios, which transform multiple comprehensive uncertainties of offshore wind power into uncertain parameter sets from true membership function, pseudo-membership function, and unknown information measure based on the Vague soft set theory. Then, a two-stage robust expansion planning model based on Vague scenario set is established for transmission network with offshore wind power penetration. The minimum total investment cost of offshore and onshore line and network loss is taken as the objective function in the first stage, while the minimum objectives of wind abandonment and cutting load for offshore wind power are proposed with the alternating current power flow constraint based on second-order cone relaxation in the second stage. Based on the expected values of wind abandonment and cutting load returned by the second stage model, the operation variables of the first stage model are modified to ultimately obtain the iterative transmission network robust planning scheme. Finally, the Gurobi mathematical optimization engine is used to analyze the Garver 6-node system and IEEE 39-node system to verify the effectiveness and feasibility of the proposed robust expansion planning method.

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    Dispatching Method of Combined Wind-Storage System for Multi-Time Scale Scenarios Application in Electricity Markets
    YIN Gaowen, SHEN Feifan, HUANG Sheng, WEI Juan, QU Yinpeng, WANG Pengda
    2024, 58 (9):  1410-1419.  doi: 10.16183/j.cnki.jsjtu.2022.493
    Abstract ( 2222 )   HTML ( 10 )   PDF (2736KB) ( 255 )   Save

    Aimed at the coupling problem of the combined wind-storage system participating in different call time scale scenarios in electricity markets, an optimal dispatching method of the combined wind-storage system oriented to the application of multi-time scale scenarios in electricity markets is proposed to guide the combined wind-storage system to suppress short-term wind power fluctuation, and participate in the electric energy market and the reserve ancillary service market, so as to realize the collaborative optimization among different call time scale scenarios application and maximize the economic benefits of the combined wind-storage system. First, considering the profit mechanism of different scenarios, the objective function is established with the objective of maximizing the economic benefits of multiple scenarios of the combined wind-storage system. Then, the constraints of the combined wind-storage system participating in various application scenarios and multi call time scale coupling constraints are established. Finally, the numerical simulation verifies that the proposed method can improve the comprehensive operation profit of the combined wind-storage system in the day-ahead electric energy market and the reserve ancillary service market while ensuring that the wind power fluctuation does not exceed the limit.

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    Improved Transformer-PSO Short-Term Electricity Price Prediction Method Considering Multidimensional Influencing Factors
    SUN Xin, WANG Simin, XIE Jingdong, JIANG Hailin, WANG Sen
    2024, 58 (9):  1420-1431.  doi: 10.16183/j.cnki.jsjtu.2023.065
    Abstract ( 2227 )   HTML ( 4 )   PDF (3027KB) ( 306 )   Save

    With the construction of a diversified electricity market, the factors affecting electricity prices are gradually increasing, and the market environment has undergone more drastic changes. In order to improve the accuracy of short-term electricity price prediction, an improved Transformer-particle swarm optimization (PSO) short-term electricity price prediction method considering multiple factors affecting electricity prices is proposed. First, based on the consideration of historical electricity prices and historical loads, the relevant factors of electricity price formation are further analyzed. The autocorrelation function is used to analyze the multi-cycle characteristics of electricity price and adjust input sequence, which overcomes the problem of limited prediction accuracy caused by using historical data only and adjusting the input sequence by experience. Then, by combining long short-term memory (LSTM), self-attention mechanism, multi-layer attention mechanism, and adopting a multi-input structure, an improved Transformer model is established to further enhance the ability of the LSTM model to capture long short-term dependencies between different time step information, to overcome the information utilization bottleneck of LSTM, and to adapt to complex multiple sequence inputs including historical electricity prices and various electricity price causes. In addition, the PSO intelligent algorithm is utilized to search for the optimal learning rate of the model at different learning stages, overcoming the limitations of manually adjusting the learning rate. Finally, the PJM market electricity price is used for example analysis. The results show that the proposed short-term electricity price prediction model can be applied to the market environment where electricity prices are affected by various factors and drastic changes, and effectively improve the accuracy of short-term electricity price prediction.

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    Robust Optimal Scheduling of Agricultural Microgrid Combined with Irrigation System Under Uncertainty Conditions
    YANG Sen, GUO Ning, ZHANG Shouming
    2024, 58 (9):  1432-1442.  doi: 10.16183/j.cnki.jsjtu.2023.035
    Abstract ( 1837 )   HTML ( 4 )   PDF (2680KB) ( 68 )   Save

    Agricultural microgrids offer a promising solution for energy supply in remote rural areas in a low-cost manner. In this paper, under uncertain conditions of renewable energy output and electricity load demand, a robust optimal scheduling model combined with the isolated agricultural microgrid and irrigation system containing a pumped hydro storage (PHS) power station is proposed, considering the factors that the wind-landscape pumped storage integrated agricultural microgrid can satisfy the uncertain fluctuations of power load demand and water load demand. By utilizing the abundant water resources in rural areas and the advantages of landscape drainage and storage compensation, the total cost of the system is minimized while the absorption of renewable energy is increased. Considering distributed generation, power load demand and water load demand, turbine flow, and irrigation flow, the proposed model is characterized by diversity, multi-constraint, and discontinuity. A gravitational whale optimization algorithm (GWOA) is proposed to solve the model. The simulation results of an agricultural microgrid show that the GWOA can obtain a more competitive solution than the CPLEX solver and other newly developed algorithms do. In addition, the impact of the change of water load demand caused by precipitation uncertainty on the operating cost of the irrigation system and the necessity of using PHS power station are explored.

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    Coordinate Scheduling Model of Electric Vehicle-Unmanned Aerial Vehicle Joint Rescue System
    BAI Wenchao, BAN Mingfei, SONG Meng, XIA Shiwei, LI Zhiyi, SONG Wenlong
    2024, 58 (9):  1443-1453.  doi: 10.16183/j.cnki.jsjtu.2023.052
    Abstract ( 1841 )   HTML ( 5 )   PDF (3151KB) ( 351 )   Save

    The rapid development of electric vehicles (EVs) and unmanned aerial vehicles (UAVs) provides new ways for personnel search and material distribution during emergency periods. This paper proposes an EV-UAV joint rescue system, in which the UAVs use the EVs as charging and maintenance base stations to provide various services for the objects to be rescued, and the EVs can use distributed generations to obtain diversified electricity supply, which improves the adaptability and endurance level of the system in emergencies. The coordinated scheduling model of the EV-UAV system is established in the mixed-integer linear programming (MILP) formulation, which considers factors including electricity consumption, electricity replenishment, loading capacity, distribution route, and distribution time window of the EVs and the UAVs. Case studies verify the validity of the model proposed, compare the EV-UAV and ground vehicle (GV)-UAV rescue systems, and illustrate the technical characteristics and application potential of the EV-UAV system in emergency assistance.

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    SOH Estimation Method Based on RBF-BLS for Low-Carbon and Safe Travel of Electric Vehicle
    LI Chunxi, QIAO Hanzhe, YAO Gang, JIANG Haoyu, CUI Xiangke, GE Quanbo
    2024, 58 (9):  1454-1464.  doi: 10.16183/j.cnki.jsjtu.2023.051
    Abstract ( 1265 )   HTML ( 3 )   PDF (2492KB) ( 363 )   Save

    The charging safety of electric vehicle (EV) is closely related to the state of health (SOH) in power battery pack. Therefore, the high-performance and real-time estimation of SOH is an important basis for safety detection in the charging process. Power battery is deeply effected by factors such as complex structure, types of battery cell, driving habits, temperature, and charging behavior. Compared to SOH estimation methods based on experimental data from one or few battery cells, research on real-time SOH estimation of the power battery meets with insufficient problems in battery model, data getting, real-time, accuracy, and so on. Aimed at these drawbacks, a high performance SOH estimation method in power battery pack is proposed by introducing the broad learning system(BLS) optimized by radial basis function (RBF) into the empirical battery degradation model based on the idea of multi-method integration and fusion. First, the empirical degradation model and offline historical charging data are used to obtain the initial SOH value. Then, a radial basis function neural network is applied to get the initial weight matrix of the BLS to optimize the BLS method, and establish the RBF-BLS neural network. The estimation error can be trained by the RBF-BLS neural network and real-time charging data, and compensate for the initial SOH to gain a higher precise SOH value. Finally, a computer simulation example based on actual charging data from a charging operation enterprises is used to verify the effectiveness and superiority of the proposed method.

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    Array Optimization of Wave Energy Converters via Improved Honey Badger Algorithm
    YANG Bo, LIU Bingqiang, CHEN Yijun, WU Shaocong, SHU Hongchun, HAN Yiming
    2024, 58 (9):  1465-1478.  doi: 10.16183/j.cnki.jsjtu.2023.030
    Abstract ( 183 )   HTML ( 7 )   PDF (3437KB) ( 219 )   Save

    In order to enhance the generation efficiency of wave energy converter (WEC) arrays, an optimization method for three-tether WEC array based on an improved honey badger algorithm is proposed. First, to overcome the shortcomings of the primal honey badger algorithm (HBA), such as slow convergence speed and low convergence accuracy, three improvement strategies are introduced, i.e., good point set initialization, chaos mechanism, and honey badger population mutation. Then, three wave farms including 2-buoy, 10-buoy, and 20-buoy are tested to verify the advancement and effectiveness of the improved honey badger algorithm (IHBA). The simulation results of the 2-buoy array demonstrate that there are multiple groups of optimal solutions in WEC array optimization. Furthermore, IHBA, HBA, genetic algorithm, and particle swarm optimization can find these optimal solutions at different speeds. Nevertheless, with increasing size of the WEC array, three comparative algorithms fall into local optima solutions. On the contrary, IHBA still exhibits a strong optimization ability and can seek global optima solutions. Finally, the q-factor values obtained by IHBA in 10-buoy and 20-buoy arrays reach 1.059 and 0.968, respectively, which are dramatically larger than those of other algorithms.

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