Table of Content

25 March 2024, Volume 46 Issue 3

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Intelligent & Clean Heat Supply Optimal Operation and Control Low-carbon Technical Economy

Research progress on preparation of liquid fuels by catalytic pyrolysis of pretreated biomass

SU Panpan, WANG Xuetao, XING Lili, LI Haojie, LIU Mengjie

2024, 46(3):  1-11.  doi:10.3969/j.issn.2097-0706.2024.03.001

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With the increasingly growing demand on energy and prominent environmental crisis，catalytic pyrolysis of pretreated biomass has become a decent technical rout for the preparation of liquid fuels， a sustainable resource. However， the bio-oil generated from the direct pyrolysis of biomass has a complex composition， low calorific value， high oxygen content and strong acidity， which restricts its application. Thus， the main factors affecting the quality of bio-oil are analyzed from the pyrolysis mechanism， biomass pretreatment， catalyst selection and the coupling of pretreatment and catalytic pyrolysis. Pretreatment process will prolong the preparation time， and catalyst will lower the production of bio-oil. The coupling technology is a promising solution to the difficulties mentioned. To prepare high-quality liquid fuels by biomass pyrolysis rapidly， the following researches should target on how to make directional preparation on hydrocarbon-rich bio-oil by biomass pyrolysis， select and optimize the catalysts， and couple the pretreatment process with catalytic pyrolysis.

Optimal scheduling of HVAC systems based on predicted loads

SUN Jian, ZHANG Yunfan, CAI Xiaolong, LIU Dingqun

2024, 46(3):  12-19.  doi:10.3969/j.issn.2097-0706.2024.03.002

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With the proposal of "dual-carbon" goal in China，the decarbonization of energy consumption in public buildings has become a key research area，in which optimizing the scheduling strategy for energy supply systems based on the prediction results of hot and cold loads is an effective technological means to achieve the "on-demand energy supply". A hot and cold load prediction model for public buildings is constructed based on the thermal resistance method. According to the load prediction results，control optimization is performed on the parameters of an energy supply system by improved particle swarm algorithm（PSO）， with the objectives of minimizing operating costs，reducing environmental costs and prolonging the service life of units. After iterative optimization on the parameters including the load of the combined cold-heat-power supply system， temperatures and flow rates of supply and return water， openings of valves and number of operating pump units， an optimal operation strategy under all operation conditions is proposed. A public building taking the proposed optimal operation strategy to update its heating system can reduce the power consumption of pump units by 10.66% and cut the operating cost by 21.52%， while meeting the heating demand of the building， balancing the hydraulic conditions and extending the operating life of the units. The consistency of the test results and the theoretical data proves the feasibility and effectiveness of the method proposed，providing an effective reference for on-demand heat supply and energy-saving operation of public buildings.

Intelligent & Clean Heat Supply

Plant-level intelligent operation optimization for cogeneration units equipped with absorption heat pumps

WANG Yongxu, ZHOU Tianyu, DENG Genggeng, XU Gang, WANG Zhuo

2024, 46(3):  20-28.  doi:10.3969/j.issn.2097-0706.2024.03.003

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To improve the overall operational economy of the power plant equipped with cogeneration units and absorption heat pumps， a coal consumption prediction mathematical model for cogeneration units is constructed based on the power plant's historical data which are then pre-processed by sliding window method to define steady-state operating conditions. There is a fitness function implemented to get the minimum total coal consumption of the cogeneration units， and particle swarm optimization algorithm is used to obtain the optimal back pressure of the air cooling unit equipped with an absorption heat pump and the optimal extraction steam flow of each cogeneration unit. Then， an intelligent optimization software for this heating system is designed based on the data above， to realize online monitoring and optimization regulation on the heating system. The optimization regulation can provide guidance for individual heating units to achieve their best economic performances and lowest coal consumptions. Simulation results indicate that the intelligent optimization can save 1.81 t standard coal per hour for the heating system of this case， showing a significant energy-saving effect.

Study on preparation of shape-stable phase-change materials based on cellular concrete and their performances

MENG Qiang, TIAN Xi, XIONG Yaxuan

2024, 46(3):  29-34.  doi:10.3969/j.issn.2097-0706.2024.03.004

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To accommodate massive municipal solid wastes，and avoid environmental pollution resulting from serious waste concrete accumulation， a shape-stable phase-change material（SSPCM） taking waste cellular concrete（WCC） after pre-sintering as its skeleton material and using Na₂CO₃ as its phase-change material is proposed. The test results show that the sintered WCC loading with 45% Na₂CO₃ is of a melting latent heat of 126.7 J/g measured by a differential scanning calorimeter（DSC）. And according to the results of X-Ray diffraction（XRD） and Fourier transform infrared（FT-IR）， the chemical compatibility between the skeleton material and phase change material of the SSPCM is good. The maximum thermal conductivity of the SSPCM is 0.24 W/（m·K） measured by laser flash analysis（LFA）.

Optimal Operation and Control

Control strategy of virtual synchronous generators based on adaptive control parameter setting

DING Leyan, KE Song, YANG Jun, SHI Xingye

2024, 46(3):  35-44.  doi:10.3969/j.issn.2097-0706.2024.03.005

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The new power system with new energy as the main body presents the characteristics of "low inertia and low damping". Since the virtual synchronous generator （VSG） control strategy can simulate the mechanical motion equation and electromagnetic characteristics of the synchronous generator， the strategy can enhance the stability of the power system by making the distributed inverters possess the inertia and damping characteristics of the synchronous generator. However， the strategy will compromise their dynamic regulation performances. Thus， a VSG control strategy based on adaptive control parameter setting is proposed. Firstly，the effects of inertia moment and damping coefficient on system frequency and output in transient process are analyzed by establishing a small signal model of VSGs. Then，the correlation between the adaptive moment of inertia， angular velocity variation ratio and angular velocity offset，and the correlation between the adaptive damping coefficient and the angular velocity offset are analyzed by the power angle-frequency oscillation curve of a synchronous generator. The selection principle for the inertia moment and damping coefficient in different intervals is obtained，and their calculation formula and the trigger thresholds are designed. Furthermore，by adding adaptive droop coefficient considering the upper and lower limits of frequency modulation power，a control strategy of VSGs based on adaptive control parameter setting is proposed. Finally， the simulation analysis on the off-grid VSG model is built by Matlab/Simulink，and the simulation results verify that the proposed control strategy can reduce the frequency offset， and improve the frequency stability and dynamic regulation ability of the system.

Multi-load day-ahead and intra-day forecasting for integrated energy systems based on feature screening

XU Cong, HU Yongfeng, ZHANG Aiping, YOU Changfu

2024, 46(3):  45-53.  doi:10.3969/j.issn.2097-0706.2024.03.006

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Load forecasting is a prerequisite for guiding the scheduling and operation of integrated energy systems（IES）. In order to carry out day-ahead scheduling and intra-day operation optimization on IES more economically and efficiently， a multi-load day-ahead and intra-day forecasting method based on feature screening is proposed. Firstly， by combining three types of feature screening methods in feature engineering，input features of forecasting models are selected. The combining method simplifies the models while preserving the important features， and the input feature sets for day-ahead and intra-day forecasting models are selected respectively. Then， taking hard parameter sharing in multi-task learning， the forecasting models are established based on long short-term memory neural network， achieving information sharing among different subtasks. And the forecasting accuracies of the models are optimized through random search method. Finally， taking an industrial park in Beijing as a study case，its energy system's electricity and heat loads are analyzed，and the comprehensive accuracies of the day-ahead and intra-day forecasting reach 91.3% and 95.2%，respectively. The method provides a sound support for IES day-ahead scheduling and intra-day operation optimization. Compared with the results of forecasting without feature screening and the forecasting on a single load， the method proposed has a higher forecasting accuracy.

Power line fault diagnosis based on GRU and GWO-KELM

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

2024, 46(3):  54-62.  doi:10.3969/j.issn.2097-0706.2024.03.007

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To improve the detection precision and classification accuracy of power line faults， a power line fault diagnosis system based on machine learning is designed and implemented， whose core modules are Gated Recurrent Unit （GRU）neural network—A classical algorithm of machine learning， and Kernel Extreme Learning Machine（KELM）. First，GRU is used to separate fault data from normal data accurately in electrical fault diagnosis. Then，the kernel parameters and penalty factors of KELM are optimized by Grey Wolf Optimization（GWO）. The KELM with optimal parameters can successfully distinguish different types of faults. Proven by experimental data， GRU's accuracy in dataset classification is as high as 98%，and the KELM with the optimal parameters has an accuracy of 99%. Comparing the accuracies of the algorithms obtained by Simulated Annealing（SA），the superiority of GWO can be confirmed. The voltage and current data are visualized，presenting the dataset concisely and intuitively. This article provides a practical and effective method for diagnosing power line faults.

Low-carbon Technical Economy

Business operation modes and risk analysis of large-scale energy storage in western Inner Mongolia

YUAN Shuguang, ZHANG Yuting, WANG Feng, YUAN Guangzhen

2024, 46(3):  63-71.  doi:10.3969/j.issn.2097-0706.2024.03.008

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Energy storage is the key to achieving high-proportion wind and solar energy consumption in new power systems.As an important national energy and strategic resource base，Inner Mongolia has taken the lead in building new power systems with renewable energy as their cores.It is important to find an energy storage business mode suitable for Inner Mongolia. In order to comprehensively compare the potential of existing energy storage business modes，the technical routes，application scenarios and configuration principles of large-scale energy storage projects in western Inner Mongolia are studied.Taking the characteristics of power generation，transmission and consumption in this area into consideration，the feasibility and risks of typical energy storage business modes in China are analyzed.The research results show that chemical energy storage technologies are less affected by natural conditions，among which the LiFePO₄ battery is more suitable for large-scale energy storage of current power system.In the short term，user-side energy storage can only obtain profit through participating in virtual power plant aggregation and demand-side response.Energy performance contracting mode and shared energy storage modes are more suitable for large-scale energy storage projects in western Inner Mongolia.This study can assist investors of energy storage projects in western Inner Mongolia in making decisions，and can also provide reference for decision-making on energy storage projects in other regions.

Calculation and prediction of carbon emission factors for the national power grid from 2005 to 2035

WEI Xikai, TAN Xiaoshi, LIN Ming, CHENG Junjie, XIANG Keqi, DING Shuxin

2024, 46(3):  72-78.  doi:10.3969/j.issn.2097-0706.2024.03.009

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In the view of delayed data updates and inaccurate calculations on the carbon emission factor of the national power grid， a calculation method based on IPCC's carbon emission accounting method is proposed for the factor. IPCC's carbon emission accounting method can be employed on 25 kinds of fuels for power generation. First， carbon emission factors of the national power grid from 2005 to 2022 are obtained by the proposed methods. Then， the calculation results are compared with the official published data with an average deviation of 1.45%， which prove the accuracy of the algorithm. Finally， the emission factors from 2023 to 2035 are predicted under three scenarios， basic scenario， low-carbon scenario， and intensive carbon reduction scenario. In 2035， the emission factor decreased to 0.506 4， 0.480 7，and 0.443 8 kg/（kW·h） under the three scenarios，keeping the carbon emissions from power industry constantly low. As the proposed method has a high accuracy， it can dynamically reflect the current situation and development trend of China's power structure， and provide support for accurate evaluating on carbon emissions from electricity consumers.

Risk analysis and response strategies of grid enterprises' electricity purchasing agent service under electricity reform

LI Yangao, LIN Jian, MA Yutong

2024, 46(3):  79-86.  doi:10.3969/j.issn.2097-0706.2024.03.010

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In view of current power shortage and the deepening of power price marketization， power grid enterprises are advancing electric power user side reform by acting as electricity purchasing agents. Based on agent power purchasing policies， the key information and uncertain factors of this service are analysed. Then， the whole-process risk identification is conducted for the electricity purchasing agent service， and a power purchasing agent whole-process risk index system and a comprehensive risk assessment model for power grid enterprises are constructed， which can comprehensively percept and assess risks during the process. Finally， strategies to reduce the risk in electricity purchasing agent service are proposed for power grid enterprises， providing theoretical guidance for power grid enterprises.