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28 May 2024, Volume 58 Issue 5 Previous Issue   
New Type Power System and the Integrated Energy
Key Technologies and Applications of Shared Energy Storage
SONG Meng, LIN Gujing, MENG Jing, GAO Ciwei, CHEN Tao, XIA Shiwei, BAN Mingfei
2024, 58 (5):  585-599.  doi: 10.16183/j.cnki.jsjtu.2022.360
Abstract ( 226 )   HTML ( 8 )   PDF (4173KB) ( 289 )  

Under the goal of “carbon peaking and carbon neutrality”, the penetration rate of renewable energy continues to rise, whose volatility, intermittency, and uncertainty pose significant challenges to the safe and stable operation of the power system. As a typical application of the sharing economy in the field of energy storage, shared energy storage (SES) can maximize the utilization of resources by separating the “ownership” and “usage” of energy storage resources, which provides a new solution to the problem of imbalance between supply and demand caused by the large-scale integration of renewable energy into the grid, and has broad development prospects. The business model of SES is explored based on value positioning, cost modeling, and profitability strategies, and a detailed summary of SES trading varieties, operational structure, and engineering applications is discussed. Finally, the future trend of shared energy storage is discussed and envisioned.

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Dynamic Optimization of Carbon Reduction Pathways in Coastal Metropolises Considering Hidden Influence of Decarbonization on Energy Demand
XIAO Yinjing, ZHANG Di, WEI Juan, GE Rui, CHEN Dawei, YANG Guixing, YE Zhiliang
2024, 58 (5):  600-609.  doi: 10.16183/j.cnki.jsjtu.2022.437
Abstract ( 87 )   HTML ( 3 )   PDF (1733KB) ( 128 )  

Setting a reasonable carbon reduction plan in coastal metropolises is the key part to reach the global carbon target. Carbon reduction will change urban climate and influence energy demand, both of which affect the optimization results of carbon reduction pathways. Current generation expansion optimization models consider direct abatement contribution and solve most problems of planning for long-term carbon emission reduction in energy systems. However, the construction of new type power systems also indirectly impacts carbon emissions by changing microclimate factors such as heat island intensity. By combining generation expansion with carbon emission prediction model, the proposed approach in this paper considers the hidden mechanism of carbon and heat emission change on air-conditioning loads and dynamically optimizes the carbon reduction pathways in coastal metropolises. Taking Pudong Area in Shanghai as an example, the estimated cost of carbon reduction is reduced by the proposed approach. Some suggestions for the carbon reduction in coastal metropolises are made according to the simulation results.

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Bi-Level Optimization Operation Method of Multi-H2-IES Considering Dynamic Carbon Emission Factors
FU Wenxi, DOU Zhenlan, ZHANG Chunyan, WANG Lingling, JIANG Chuanwen, XIONG Zhan
2024, 58 (5):  610-623.  doi: 10.16183/j.cnki.jsjtu.2022.225
Abstract ( 27 )   HTML ( 4 )   PDF (2901KB) ( 7 )  

In the context of achieving “carbon peaking and carbon neutrality”, the low-carbon transformation of the energy system is the development direction in the future. Hydrogen, known for its high calorific value and low pollution, has received extensive attention in recent years. Based on the carbon emission flow theory, a bi-level optimization operation model of multi-integrated energy system with hydrogen (H2-IES) is proposed considering dynamic carbon emission factors. At the upper level, an economic dispatch model is established by the main energy grid based on the principle of optimal benefit, and the energy prices and carbon emission factors of each park are determined and distributed to the lower level. At the lower level, a multi-park low-carbon cooperative operation model is established based on the Nash negotiation theory, and the adaptive alternating direction method of multipliers (A-ADMM) is used for distributed solution to determine the energy demand of each park and provide feedback to the upper level. The coordinated operation of both levels is realized in multiple iterative interactions. To equitably distribute the benefits of cooperation, a revenue distribution method based on comprehensive bargaining power is proposed. The analysis of a case study shows that the bi-level optimization method proposed in this paper can realize the coordinated operation between the upper and lower levels, and take into account the low-carbon and economical properties of multi-parks operation. Because the income is reasonably distributed, the enthusiasm of parks to participate in cooperation can be guaranteed.

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Low Carbon Economic Operation of Hydrogen-Enriched Compressed Natural Gas Integrated Energy System Considering Step Carbon Trading Mechanism
FAN Hong, YANG Zhongquan, XIA Shiwei
2024, 58 (5):  624-635.  doi: 10.16183/j.cnki.jsjtu.2022.377
Abstract ( 168 )   HTML ( 3 )   PDF (3488KB) ( 229 )  

Hydrogen energy plays a crucial role in meeting the “carbon peaking and carbon neutrality” goals, and the carbon capture technology is a vital technique for emission reduction in the energy industry. Blending hydrogen with natural gas to produce hydrogen-enriched compressed natural gas (HCNG) facilitates the transportation and utilization of hydrogen energy. At the same time, applying the carbon capture technology to retrofit thermal power units can effectively promote the large-scale consumption of renewable energy and reduce carbon emissions. For this purpose, a detailed model of hydrogen production equipment and fuel cells is established. Then, aimed at the problem of system carbon emissions, a carbon emission and output model of carbon capture thermal power units and a mathematical model of hydrogen doped cogeneration are established, and a stepped carbon trading mechanism is introduced to control carbon emissions. Based on this, an optimal scheduling model for hydrogen-enriched compressed natural gas integrated energy system is established with the goal of minimizing the sum of energy purchase cost, carbon emission cost, wind abandonment cost, and carbon sequestration cost, and taking into account the constraints such as hydrogen blending ratio and carbon capture in the pipeline network, which is solved by using the particle swarm optimization algorithm in conjunction with CPLEX. The analysis of the models built in different scenarios verifies the advantages of the proposed scheduling model in low-carbon economy.

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Multi-Region Optimal Scheduling Strategy for Electric Vehicles Considering Compensation Incentives
SUN Yi, GE Mingyang, WANG Xianchun, BAO Huiyu, YANG Hongyue, YAO Tao
2024, 58 (5):  636-646.  doi: 10.16183/j.cnki.jsjtu.2022.348
Abstract ( 105 )   HTML ( 4 )   PDF (2979KB) ( 107 )  

Aimed at the problem of uneven supply and distributed renewable energy (DRE) in charging regions and electric vehicle (EV) loads, a multi-region optimal scheduling strategy for EVs considering compensation incentives is proposed to guide EVs to choose different charging regions, so as to promote local consumption of distributed energy. First, an EV charging response model is established based on the price elasticity of demand and users’ time anxiety. Then, based on the principle of remaining power availability and idle time redundancy, EVs are divided into responsive cluster and non-responsive cluster. A charging area decision model based on regret theory is used to further divide the responsive cluster by region. Finally, a multi-region optimal scheduling model for EVs is established, and the charging price is optimized in terms of maximizing the economic benefits of charging service providers and the consumption of DRE. Simulation cases show that the proposed optimization strategy can fully consider the impact of time anxiety and price elasticity on EV users, fully tap the users’ response potential, and has obvious effects in reducing the deviation of distributed new energy consumption and improving the economic benefits of charging service providers.

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Low-Carbon Optimal Operation Strategy of Integrated Energy System Considering Generalized Energy Storage and LCA Carbon Emission
SUN Yi, GU Jiaxun, ZHENG Shunlin, LI Xiong, LU Chunguang, LIU Wei
2024, 58 (5):  647-658.  doi: 10.16183/j.cnki.jsjtu.2022.350
Abstract ( 133 )   HTML ( 3 )   PDF (3035KB) ( 110 )  

Integrated energy system (IES) is the key to achieve the “dual carbon goals” in face of the current energy industry transformation and low-carbon development. In order to improve the carbon emission reduction capacity of the IES, it is necessary to make full use of the load resources on the demand side and the generalized energy storage resources such as traditional energy storage equipment to participate in the optimization of the IES. First, an IES optimization operation model considering renewable energy, energy conversion equipment, generalized energy storage equipment, and energy market transaction is established. Then, the life cycle assessment (LCA) method is used to calculate the carbon emission of the whole process of energy cycle and equipment cycle in the IES, and the carbon emission cost is included in the total cost of the system. The results of simulation experiments show that the proposed model is not only conducive to reducing the total scheduling cost of the IES, but also able to reduce the carbon emissions of the system and effectively promote the low-carbon development of the IES.

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Urban Energy System Expansion Planning Considering Short-Term Operational Flexibility
WEI Zhinong, YANG Li, CHEN Sheng, MA Junchao, PENG Yan, FEI Youdie
2024, 58 (5):  659-668.  doi: 10.16183/j.cnki.jsjtu.2022.259
Abstract ( 92 )   HTML ( 2 )   PDF (1939KB) ( 69 )  

Urban cities are the main force of energy consumption and carbon emission. In the context of “dual carbon”, promoting low-carbon transformation of urban energy systems has become the top priority of urban planning. However, while the share of renewable energy output increases, the requirement for system flexibility also increases. To this end, an urban energy system expansion planning model that accounts for both long-term and short-term uncertainties is proposed. Multiple forms of energy, including electricity, gas, and heat are encompassed in this model. At the planning level, uncertainty and operational flexibility during the real-time operation stage are estimated, and a stochastic optimization approach is employed for solving. The capacity expansion of renewable energy generators and energy hubs (EHs) is considered by the model, with the imposition of carbon emission quota constraints to ensure the attainment of carbon emission reduction targets. The results show that the model can effectively improve the economy of urban energy system and the rate of consumption of renewable energy, and can meet different carbon-emission reduction requirements.

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Low Carbon Economy Optimization of Integrated Energy System Considering Electric Vehicle Charging Mode and Multi-Energy Coupling
ZHANG Cheng, KUANG Yu, CHEN Wenxing, ZHENG Yang
2024, 58 (5):  669-681.  doi: 10.16183/j.cnki.jsjtu.2022.364
Abstract ( 158 )   HTML ( 5 )   PDF (4514KB) ( 136 )  

In order to enable a multi-energy coupling integrated energy system (IES) to meet the needs of load diversity in low-carbon economic operation, a bi-level optimal configuration method for low-carbon economic operation of multi-energy coupling IES in different charging modes of electric vehicles (EVs) is proposed. First, an IES including cold-thermal-electric-gas coupling is established. Then, in the day-to-day operation stage, factors such as hierarchical carbon trading mechanism and different charging modes of EVs are considered to achieve the lowest daily scheduling cost. In the configuration planning stage, based on the daily operation cost, the equipment capacity is configured with the lowest equipment investment cost and annual operation cost. Finally, Cplex is used to solve the above two-stage objective functions and obtain the optimal configuration scheme and scheduling results through mutual iteration. The results show that the charging method considering the remaining charge of EVs and carbon trading mechanism can significantly reduce carbon emissions and operating costs of the system. The proposed configuration approach can well realize low-carbon economic operation of the multi-energy coupling IES.

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Coordinated Active Power-Frequency Control Based on Safe Deep Reinforcement Learning
ZHOU Yi, ZHOU Liangcai, SHI Di, ZHAO Xiaoying, SHAN Xin
2024, 58 (5):  682-692.  doi: 10.16183/j.cnki.jsjtu.2022.358
Abstract ( 167 )   HTML ( 2 )   PDF (2823KB) ( 223 )  

The continuous increase in renewables penetration poses a severe challenge to the frequency control of interconnected power grid. Since the conventional automatic generation control (AGC) strategy does not consider the power flow constraints of the network, the traditional approach is to make tentative generator power adjustments based on expert knowledge and experience, which is time consuming. The optimal power flow-based AGC optimization model has a long solution time and convergence issues due to its non-convexity and large size. Deep reinforcement learning has the advantage of “offline training and online end-to-end strategy formation”, which yet cannot ensure the security of artificial intelligence (AI) in power grid applications. A coordinated optimal control method is proposed for active power and frequency control based on safe deep reinforcement learning. First, the method models the frequency control problem as a constrained Markov decision process, and an agent is designed by considering various safety constraints. Then, the agent is trained using the example of East China Power Grid through continuous interactions with the grid. Finally, the effect of the agent and the conventional AGC strategy is compared. The results show that the proposed approach can quickly generate control strategies under various operating conditions, and can assist dispatchers to make decisions online.

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Fast Fault Location Technology for Distribution Network Based on Quantum Ant Colony Algorithm
BI Zhongqin, YU Xiaowan, WANG Baonan, HUANG Wentao, ZHANG Dan, DONG Zhen
2024, 58 (5):  693-708.  doi: 10.16183/j.cnki.jsjtu.2023.004
Abstract ( 125 )   HTML ( 3 )   PDF (2880KB) ( 132 )  

Integration of distributed generations into distribution networks has become one of the important features of new power systems. The integration of distributed generation and the uncertainty of power generation make the power flow in distribution networks complex and variable, which poses higher technical requirements for rapid fault location in distribution networks. However, existing intelligent optimization algorithms may encounter problems such as slow convergence speed and susceptibility to local optimization when solving the problem of fault section location in distribution networks. To address these challenges and problems, a rapid fault section location technology based on quantum ant colony algorithm (QACA) is proposed. First, a location mathematical model is constructed based on the state approximation idea and the minimum fault set theory. Then, an information self-correction method is proposed for the missing information uploaded by feeder terminal unit, and a hierarchical location model is proposed to shorten the location time. Afterwards, three improvement techniques are proposed to improve the QACA. The update mechanism of the quantum rotary gate is improved, the rotation angle is dynamically adjusted in the form of function control, and the elite strategy is introduced to accelerate the convergence speed of the algorithm. Finally, after the key parameters are determined, the effectiveness of the improved technique, the information self-correction method, and the hierarchical positioning model is verified. A comparison with 7 different algorithms indicates that the improved QACA can effectively locate the fault section, and has a fast convergence speed, great accuracy, and fault tolerance.

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Operation Optimization for Integrated System of Wind-PV-Thermal-Storage with CC-P2G
CHENG Renli, LI Jiangnan, ZHOU Baorong, ZHAO Wenmeng, LIU Ya
2024, 58 (5):  709-718.  doi: 10.16183/j.cnki.jsjtu.2022.270
Abstract ( 160 )   HTML ( 2 )   PDF (3049KB) ( 154 )  

The carbon capture (CC) and power to gas (P2G) devices can utilize the abundant new energy of the system to capture the carbon emissions generated by thermal power combustion and generate usable gas, forming a carbon resource recycling chain. In order to reduce the carbon emission of the power system, promote new energy absorption, and improve the operation flexibility of the power system, an integrated system architecture including CC and P2G is proposed and its optimization operation model is designed. The operational characteristics of power flow and carbon flow in this architecture are mainly discussed. Considering the benefits of carbon emission trading under the quota system, an optimized operation model for the integrated system of wind-PV-thermal-storage with CC-P2G is proposed, aimed at maximizing the comprehensive benefits of the integrated system and taking the operation characteristics of various equipment as constraint conditions. Furthermore, the effectiveness of the CC-P2G system in improving new energy consumption capacity and system operation efficiency is verified. The results show that the participation of the CC-P2G system needs to be effectively coordinated with market mechanisms such as carbon emissions quota trading, which can reduce the overall carbon emissions of the system and improve its operation efficiency.

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Optimization of Lithium Battery Lifetime Based on Dual-Stage Active Topology
ZHANG Cheng, JU Changjiang, XIONG Can, YANG Genke
2024, 58 (5):  719-729.  doi: 10.16183/j.cnki.jsjtu.2022.285
Abstract ( 121 )   HTML ( 3 )   PDF (1687KB) ( 222 )  

With the increasing demand for energy storage charging stations, many energy storage systems utilize lithium batteries as the major carriers. However, due to frequent charging and discharging at high power levels, the cycle life of lithium batteries is greatly reduced, which increases the energy storage costs. Given the longevity of supercapacitors, a supercapacitor-lithium hybrid energy storage system has been developed to effectively extend the lifespan of lithium batteries and reduce both investment and operational costs of energy storage charging stations. Based on the dual-stage active topology, a hybrid energy storage system combining supercapacitor-lithium is proposed. Under mild load conditions, two supercapacitor modules are alternatively charged by the lithium battery. Then, the supercapacitor modules are discharges when high power demands are encountered. Accordingly, based on working conditions of the charging pile, a multi-stage strategy, integrating state-of-power estimation and programming, is proposed to optimize the power distribution, smooth the power fluctuation of the lithium battery, and protect the lithium battery. The simulation results show that compared with the lithium batteries only energy storage and the traditional full active topology energy storage, the dual-stage active topology energy storage significantly improves the cycle life of lithium batteries.

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Identification of Inrush Current and Fault Current Based on Long Short-Term Memory Neural Network
ZHANG Guodong, LIU Kai, PU Haitao, YAO Fuqiang, ZHANG Shuaishuai
2024, 58 (5):  730-738.  doi: 10.16183/j.cnki.jsjtu.2022.352
Abstract ( 98 )   HTML ( 6 )   PDF (2182KB) ( 127 )  

The problem of differential protection maloperation caused by inrush current during no-load closing of transformer has not been completely solved so far. To solve this problem, a method using long short-term memory (LSTM) neural network to identify inrush current and fault current is proposed. First, the simulation model of no-load closing and internal fault of transformer is built on the PSCAD software platform, and a large amount of three-phase current instantaneous sampling data is generated through simulation as the sample set to train the neural network. Then, the LSTM neural network model is built and trained by using the Keras platform. Finally, the new simulation data and fault recorder data is used to test the trained LSTM neural network. The results show that the LSTM neural network can quickly and accurately distinguish the inrush current and fault current under various conditions, which proves the effectiveness of the proposed method.

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Distribution Network Fault Diagnosis Technology Based on Multi-Source Data Fusion
ZHANG Chunmei, XU Xingque, LIU Silin
2024, 58 (5):  739-746.  doi: 10.16183/j.cnki.jsjtu.2022.317
Abstract ( 492 )   HTML ( 5 )   PDF (1475KB) ( 158 )  

How to make full use of existing information to improve the accuracy of fault diagnosis in distribution networks, and provide accurate research and judgement for emergency repair of distribution networks, is an urgent problem to be solved. To address the problem of the single source of fault diagnosis information in existing distribution networks, a fault diagnosis model of distribution network is proposed which integrates the medium and low voltage information of the distribution networks and the outgoing current information of the substation. The model first applies the existing overcurrent diagnosis method to the problem of large-scale distribution network, and adopts hierarchical reduction of the size of the distribution networks to improve the location speed of fault section. Then, in view of the accuracy of overcurrent alarm information, an auxiliary fault judgment method for distribution networks based on switch relay protection sequence of events (SOE) data and substation outgoing load sag data is proposed. Finally, the steps for fault diagnosis in distribution networks of multi-directional information and data fusion in practical engineering are summarized, which provides reference for fault diagnosis of dispatchers. Engineering practice proves that the method proposed in this paper can effectively diagnose faults and is very adaptable to large-scale distribution networks. The auxiliary diagnosis model combining switch operation SOE and telemetering voltage information can compensate for the accuracy requirements of the overcurrent diagnosis model for remote communication information, which is complementary to each other and has a good engineering value.

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A Highly Robust State of Health Estimation Method for Lithium-Ion Batteries Based on ECM and SGPR
CUI Xian, CHEN Ziqiang
2024, 58 (5):  747-759.  doi: 10.16183/j.cnki.jsjtu.2022.221
Abstract ( 122 )   HTML ( 5 )   PDF (6197KB) ( 99 )  

Accurately estimating the state of health (SOH) of lithium-ion batteries is of great significance in ensuring the safe operation of the battery system. Addressing the issue where traditional SOH estimation methods fail under variable working conditions, an online SOH estimation method for lithium-ion batteries based on equivalent circuit model (ECM) and sparse Gaussian process regression (SGPR) is proposed. During the constant current charging process, the parameters of the ECM of lithium-ion battery are dynamically identified by two online filters, based on which, a condition-insensitive health indicator is constructed. In combination with the SGPR, the indirect SOH estimation is achieved. This method uses the unified signal processing method and feature mapping model under various working conditions, and features strong robustness with low redundancy. The experimental results show that the average absolute error of the method proposed under various working conditions does not exceed 0.94%, and the root mean square error stays below 1.12%. When benchmarked against existing methods, this method has significant advantages in comprehensive performance.

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Day-Ahead Scheduling of Traction Power Supply System with Photovoltaic and Energy Storage Access
GAO Fengyang, SONG Zhixiang, GAO Jianning, GAO Xuanyu, YANG Kaiwen
2024, 58 (5):  760-775.  doi: 10.16183/j.cnki.jsjtu.2022.253
Abstract ( 106 )   HTML ( 4 )   PDF (8888KB) ( 121 )  

In recent years, in order to achieve the goal of “carbon peaking and carbon neutrality” of the electrified railway, a number of railroad energy optimization initiatives have been implemented, but with little success. To further reduce the carbon emissions of the electrified railway, its energy supply structure is changed by connecting photovoltaic and energy storage devices to the traction power supply system. First, the composite traction power supply system is constructed, and its working conditions are classified according to the composition of system energy supply. Then, the priorities are set based on the priorities of system operation constraints, and the optimal state of the system under different operating conditions is realized through hierarchical optimization. Finally, the converter capacity is reasonably optimized to achieve the minimum carbon emission under the win-win situation of the system operation performance and economic benefits. The simulation results show that the composite traction power supply system ensures the stable operation of the system while greatly reducing the carbon emission of the system and achieving the optimal performance.

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Road Recognition Method of Photovoltaic Plant Based on Improved DeepLabv3+
LI Cuiming, WANG Hua, XU Longer, WANG Long
2024, 58 (5):  776-782.  doi: 10.16183/j.cnki.jsjtu.2022.224
Abstract ( 26 )   HTML ( 5 )   PDF (8455KB) ( 11 )  

Aiming at the problem that mobile cleaning robot needs to identify road accurately and quickly when it operates in photovoltaic plants, a target recognition model of improved DeepLabv3+ to identify the roads within photovoltaic plants is proposed. First, the backbone network of the original DeepLabv3+ model is replaced with an optimized MobileNetv2 network to reduce complexity. Then, the strategy that combines diverse receptive field fusion with depth separable convolution is employed, which enhances the atrous spatial pyramid pooling (ASPP) structure and improves the information utilization of ASPP and the training efficiency of model. Finally, the attention mechanism is introduced to improve the segmentation accuracy of the model. The results show that the average pixel accuracy of the improved model is 98.06%, and the average intersection over union is 95.92%, which are 1.79 percentage points and 2.44 percentage points higher than those of the DeepLabv3+ basic model, and SegNet and UNet models. Furthermore, the improved model has fewer parameters and a good real-time performance, which can better realize the road recognition of mobile cleaning robot of photovoltaic plants.

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