Table of Content

25 July 2024, Volume 46 Issue 7

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Integrated Energy System Energy Storage Technology

Integrated Energy System

Construction of the hierarchical autonomous power balance model for software-defined new power systems

WANG Zening, LI Wenzhong, LI Donghui, XU Taishan, YU Jun

2024, 46(7):  1-11.  doi:10.3969/j.issn.2097-0706.2024.07.001

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As a typical complex system， a new power system energy is of severe power and energy imbalances resulting from the strong uncertainty of sources and demands. The balances are challenged by the basic alteration of electric energy and power balance logic. To make a new balance between coordinated and interactive sources， networks， loads and storage devices， a hierarchical autonomous power balance model with energy autonomous units is constructed. The hierarchical structure can simplify complex systems， and energy autonomous units have self-dispatching and self-balancing capacities. The units whose forms vary with different electricity market models provide effective paths for multiple resources to participate in the power market. In software paradigm theory， an energy autonomous unit is of an architecture that can integrate multiple elements， an adaptive operation mechanism， and a life-cycle evolution mechanism. Additionally， the construction methodology of software-defined energy autonomous units based on software-hardware decoupling is proposed. Finally， the key technologies applied in the units are discussed and looked forward.

Short-term wind power forecasting based on DenseNet convolutional neural networks

YIN Linfei, MENG Yujie

2024, 46(7):  12-20.  doi:10.3969/j.issn.2097-0706.2024.07.002

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Wind energy， as a clean and renewable energy source， plays a crucial role in the energy transformation. And accurate prediction on wind power output is important for the safe and efficient operation of the power system. However， the volatility and randomness of wind speed challenges the wind power prediction. To improve the accuracy of the prediction， a short-term wind power prediction model based on DenseNet convolutional neural network is proposed. The DenseNet160 network obtained from a simplified DenseNet160 network is of an outstanding densely connected structure，and proper depth and width，capable of solving vanishing gradient in the training process and realizing deep supervision by sending information from a upper layer to a deeper layer. Based on the 378-day wind power dataset collected from Natal in Brazil， the wind power output of the next day was predicted by DenseNet160 network and other 27 algorithms. The prediction results show that the mean absolute error （MAE） ， mean squared error （MSE） and mean absolute percentage error （MAPE） of the DenseNet160 network is 10.89%，4.98% and 8.68% smaller than that of the second best algorithm， respectively. Meanwhile， the MAE of the DenseNet160 network is 25.56% smaller than that of the hybrid economy model using the same dataset. This results indicate that the proposed prediction model can fit the wind power data more accurately and obtain more reliable wind power prediction results.

Modeling and control optimization of photovoltaic-thermal heating system based on MPC

WANG Zhe, CHENG Gang, XING Zuoxia, FU Qitong, FU Changtao

2024, 46(7):  21-28.  doi:10.3969/j.issn.2097-0706.2024.07.003

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To reduce the energy consumption and fight against environmental pollution crises caused by heating in northern China， and to improve the insufficient light utilization efficiency in areas with abundant or relatively abundant solar resources， a distributed energy system combining solar energy and building heating is proposed，taking the advantages of off-peak electricity and sufficient illumination. The model of the proposed photovoltaic-thermal heating system is built based on TRNSYS dynamic modeling and numerical modeling. Then， considering the time-lag of the heat-supply system for a small area and the output of each device in the system， a model predictive control（MPC） strategy is developed based on Matlab， and an MPC-based optimization control strategy which can realize real-time error correction is made. According to the analysis results： the MPC-based optimization control can keep the maximum error of tracking heat load within 4.16%，and decrease the average error by 2.79%； and the optimization control can keep the maximum deviation of indoor temperature within 1.2 ℃，which is 0.2 ℃ lower that without the control； under a solar radiation intensity approaching 800 W/m²，the difference between solar energy utilization rates with and without the optimization control goes up to a maximum of 8.9%.The results indicate that the MPC can track heat load fluctuations in buildings quickly and accurately，suppress indoor temperature fluctuations effectively and increase the utilization rate of clean energy.

Fault detection for transmission line insulators based on Real-ESRGAN and improved YOLOv8n

REN Yiming, DU Dongsheng, DENG Xiangshuai, LIAN He, ZHAO Zhemin

2024, 46(7):  29-39.  doi:10.3969/j.issn.2097-0706.2024.07.004

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To solve the difficulties in insulator fault detection during drone-based transmission line inspections， an innovative method for insulator fault detection which combines Real Enhanced Super Resolution Generative Adversarial Network （Real-ESRGAN） and improved YOLOv8n is proposed. Firstly， Real-ESRGAN is adopted to perform super-resolution reconstruction on the dataset， in order to optimize the quality of the dataset and effectively reduce the interference of complex backgrounds. Then， an efficient visual transformer framework is used to replace the backbone of YOLOv8， enhancing the model's feature extraction ability and accelerating the image process at inference stage. Lightweight processing is carried out on detection heads of YOLOv8 to further accelerate the inference of the model. Experimental results show that the mean average precision of this detection method reaches 86.7%， demonstrating its excellent object detection performance in complex backgrounds. Finally， by analyzing heat maps， the differences between the results made by the proposed method and the traditional YOLOv8 in the focus area are displayed， revealing the internal working mechanism of the mode.

Construction of new power system in Shaanxi Province with the collaboration of source-network-load-storage

HE Fangbo, PEI Ligeng, ZHENG Rui, FAN Kangjian, ZHANG Xiaoman, LI Gengfeng

2024, 46(7):  40-46.  doi:10.3969/j.issn.2097-0706.2024.07.005

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In the context of achieving "dual carbon" target，the domestic power industry is actively conducting researches on energy transformation path ，aiming at building a new power system with new energy as the mainstay. Shaanxi Province is the main battlefield of the "dual carbon" action due to its rich resource endowments and its favorable location as a major sending end for clean energy. Based on the fact of the power industry in Shaanxi Province， the current situation and problems are analyzed from source，network，load and storage end，and a construction concept of the new power system in Shaanxi Province with the collaboration of source-network-load-storage is put forward. The source end should take new energy as the main body and the traditional units as the guarantee；the network should be able to accommodate high-proportion new energy， get multiple loads accessed and take in diversified market entities；the load side should make active responses to resource adjustments and endeavor to build a multi-energy complementary system for new energy consumption；the storage end should establish and improve relevant mechanisms as soon as possible to facilitate energy storage and new energy accommodation. Finally，suggestions which can extend the application scope of the proposed concept to other provinces and cities are given.

Energy Storage Technology

Research on Ca and Fe co-doped PrBaCo₂O_5+δ as a cathode material of solid oxide fuel cells

YANG Lei, WANG Rui, MA Lili, SUN Ning, LI Xuelian, CHEN Ting, WANG Shaorong, SHI Caixia

2024, 46(7):  47-52.  doi:10.3969/j.issn.2097-0706.2024.07.006

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Solid oxide fuel cell （SOFC） is an efficient energy conversion device， which has demonstrated broad application prospects. The oxygen reduction reaction kinetics and stability of the cathode have been considered as two important factors of SOFC. Solid-state method is adopted to synthesize PrBa_0.8Ca_0.2Co_1.5Fe_0.5O_5+δ（PBCCF） with a tetragonal perovskite structure. The co-doping of Ca and Fe ions effectively reduces the thermal expansion coefficient of PrBaCo₂O_5+δ.The polarization resistance of a symmetrical half cell with a structure of PBCCF|GDC|SSZ|GDC|PBCCF at 800 ℃ is 0.082 Ω·cm²，indicating that the PBCCF cathode has high activity in the electrochemical reaction process. A single cell is prepared based on a NiO-YSZ|YSZ|GDC structured half cell with screen-printed PBCCF cathode filaments. And the half cell is composited by NiO-YSZ anode support and YSZ electrolyte through tape casting-lamellating hot pressing method， as well as screen-printed Gd_0.2Ce_0.8O_1.9（GDC）barrier layers. The power density of the single cell can reach 1.0 W/cm² at 800 ℃.In addition， the single cell also exhibits good short-term stability during the 60 h durability test. These results demonstrate that PBCCF is a very promising cathode material for SOFC.

Spatiotemporal distributed parameter modeling of solid oxide electrolysis cells

DOU Zhenlan, LI Jiawen, ZHANG Chunyan, CAI Zhenqi, YUAN Benfeng, JIA Kunqi, XIAO Guoping, WANG Jianqiang

2024, 46(7):  53-62.  doi:10.3969/j.issn.2097-0706.2024.07.007

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There are complex thermoelectric couplings in solid oxide electrolysis cells （SOEC）operating under high temperature environment. Thus， temperature control and voltage control are crucial to the stable and safe operation of SOEC stacks. In view that SOEC stacks are complex， nonlinear and spatiotemporal distributed systems， a spatiotemporal distributed model for the temperature and voltage of an SOEC is constructed based on the spatiotemporal least square support vector machine（LS-SVM）. A kernel function is adopted to represent the spatial correlations at different locations along the flow channel， and dynamic regression equation is used to represent the temporal features of the temperature and voltage of the SOEC stack. A mechanism model of the SOEC stack is established in Simulink to obtain the sample data，which are used for the training and testing of the spatiotemporal distributed model. The simulation results show that the developed model can effectively predict the spatiotemporal distributions of the temperature and voltage in SOEC and has an excellent generalization ability， which can guide the further optimization and control of electrolysis cells.

Comprehensive benefit analysis on the cascade utilization of a power battery system

HUANG Xiaofan, LI Jiarui, LIU Hui, TANG Xiaoping, WANG Ziyao, WANG Tong

2024, 46(7):  63-73.  doi:10.3969/j.issn.2097-0706.2024.07.008

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With the development of clean energy， new energy vehicles gradually entered the market. As an energy storage device and an important component of a new energy vehicle， the power battery will see its performance degradation with the extension of time and changes in working conditions until its decommissioning. The retired power battery can be applied to other fields to improve its full-life cycle value. A life-cycle assessment（LCA） model and a life-cycle cost（LCC） model for the cascade utilization of a power battery system are developed. The environmental impacts of a pack of lithium iron phosphate batteries' five stages from production to recycling on global warming potential （GWP） are calculated by the LCA. The GWP， fine particulate matter formation （FPMF）， terrestrial acidification （TA）， marine eutrophication （MEP）and fossil resource scarcity （FRS） of the battery system under four scenarios are analyzed， and sensitivity analyses on parameters such as energy consumption and charging and discharging efficiency are conducted. LCC assessment analyses the net present value （NPV） and levelized cost of electricity （LCOE） of the battery system， and sensitivity analyses are performed on parameters that affected LCOE， such as energy storage efficiency and discharge depth. The results show that retired batteries processed by wet recycling applied to wind energy storage have favorable social benefits， leading to a smallest GWP of 194. The NPV and LCOE of the system with a 15-year service time are -42.066 million yuan， 2.44 yuan/（kW·h）， respectively. Making quantitative analyses on the social and economic benefits of the cascade utilization of power battery energy storage systems is of great significance for comprehensive utilization of resources and environmental protection in China.

An estimation method for state of health of sodium-ion batteries

SUN Wenjie, YANG Zhile, GUO Yuanjun, YAO Wenjiao, XU Huan, ZHOU Bowen

2024, 46(7):  74-80.  doi:10.3969/j.issn.2097-0706.2024.07.009

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Sodium ion batteries are promising energy storage devices due to their economy and abundant material sources.An accurate assessment on a battery's state of health（SOH） is essential to ensure its efficient and safe operation. Integrating the techniques of Recurrent Neural Networks（RNN） and Extended Kalman Filtering（EKF），a novel framework for SOH estimation is proposed.The RNN， with its capability to process time series data，offers a sound support for the SOH estimation，while the EKF ensures the robustness of state estimation. Through experimental validation on three sodium-ion batteries，the proposed method demonstrates outstanding estimating performances，with an average absolute error of less than 1.79%，a root mean square error of less than 1.38%，and a model fitting up to 96.28%. This research not only provides an efficient approach for the SOH estimation of sodium-ion batteries，but also offers valuable insights for battery management and maintenance in practical applications.

State of charge prediction for lithium-ion batteries based on KF-RCMNN

XU Zhifan, LI Huasen, LI Wenyuan, YU Kai

2024, 46(7):  81-86.  doi:10.3969/j.issn.2097-0706.2024.07.010

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Since energy storage systems（ESSs） are widely used in electric vehicles，distributed power generation and other fields，the reliability of batteries has become an issue of concern for researchers. The state of charge（SOC）is a crucial parameter reflecting the battery endurance. This study proposes a new method for lithium-ion battery SOC estimation to keep the stable working of an ESS. Recurrent cerebellar model neural network（RCMNN）and Kalman filter（KF）are both applied in the estimation method. Integrating recurrent units in associative memory space and weight memory space can improve the RCMNN in dynamic feature capture. The RCMNN and KF with inputs of voltage，current and temperature can simulate the charging and discharging of the ESS. Considering the complexity of discharging process of the battery， the measured and simulated values of the SOC under different charging and discharging conditions with varied SOC initial values are compared. The results show that the proposed method has decent accuracy and robustness under different conditions.