Electric Power

Architecture and Key Technologies of Hybrid-Intelligence-Based Decision-Making of Operation Modes for New Type Power Systems

GUO Qinglai, LAN Jian, ZHOU Yanzhen, WANG Zhengcheng, ZENG Hongtai, SUN Hongbin

2023, 56(9): 1-13. DOI: 10.11930/j.issn.1004-9649.202308102

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With the construction of new type power systems, the number of operating scenarios and computational workload that need to be considered in analyzing operation modes increases greatly, the safety and stability mechanism becomes more complex, the uncertainty of the safe operation boundary increases, and the difficulty of adjusting operating mode increases significantly. The traditional decision-making method based on human experience is facing major challenges. Artificial intelligence provides new solutions but still faces challenges such as insufficient samples, poor interpretability, low exploration efficiency, etc. Focusing on the specific problems of the operation mode decision-making of the new type power systems, this paper proposes a research framework for operation mode decision-making of the new type power systems based on hybrid intelligence. The analysis and discussion are carried out from four aspects: sample generation of operation mode, analysis of the safety boundary and stability influencing factors, intelligent adjusting of operation mode, model interpretability and update, which provide a feasible technology path for applying hybrid intelligence to new type power systems.

A Decomposition and Optimization Method for Government-Authorized Contracts in a Dual-track Spot Electricity Market Based on Game Equilibrium

WU Mingxing, WANG Ning, WANG Haohao, ZHU Tao, CHEN Qing, WANG Xuanding, YANG Saite

2023, 56(9): 15-26. DOI: 10.11930/j.issn.1004-9649.202303130

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In the electricity spot market, the curve decomposition method of government authorization contracts has significant impacts on the funds scale of the market and the interests of participants. Hence it is becoming urgent to solve the decomposition of the government-authorized contracts in a reasonable way. According to the actual requirements of the electricity spot pilot project, this paper first takes into full consideration the correlation between the scale of unbalanced funds, the decomposition results of the government-authorized contracts, the quotation strategy of the power generation entities and the results of market clearing. Based on game equilibrium, a decomposition and optimization method for government-authorized contracts in a dual-track spot electricity market is constructed. Then, the government-authorized contract decomposition model to constraint the unbalanced funds scale, the quotation decision model of power generation companies considering the decomposition results of the government-authorized contracts, and the market clearing model are established respectively. A three-layer particle swarm optimization algorithm is proposed to achieve iterative corrections between different models, such that the contract decomposition results that balance the individual and the overall interests of the market are obtained. Finally, case studies are conducted and analyzed by means of numerical simulations . The effect of the proposed method on reducing the unbalanced funds scale in the market is verified under the interactions of various factors and game equilibrium.

Stochastic Evolutionary Game Analysis on Collusion Pricing Behavior of Electricity Retailers

CHEN Xi, CHEN Wanlu, TIAN Hongli, LIN Jianyi, JIANG Tianyan, BI Maoqiang

2023, 56(9): 27-34,133. DOI: 10.11930/j.issn.1004-9649.202208044

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At the early stage of electricity retail market construction, high concentration of ownership is likely to take place and hence the research on collusive pricing behavior of electricity retailers could provide reference for the establishment of reasonable policies. In this paper, the collusive pricing behavior is considered as the strategy choice problem of a “finite population” of electricity retailers under the context of “finite rationality”. The Moran process is introduced into the stochastic evolutionary game model of collusive pricing. Then the effects of key parameters such as collusion coefficient and supply/demand ratio on the collusive pricing strategy of power sales companies are analyzed by virtue of detailed case studies. The results has shown that the success rate of collusive pricing is higher in oligopolistic markets, especially in highly oligopolistic markets; for different types of markets, the collusion coefficient demonstrates different effects on collusive pricing strategies, and furthermore, the impact of random factors in the internal and external market environment on collusive pricing behavior becomes more significant as the market concentration increases; market management agencies should enhance the analysis and research of retail electricity prices and strengthen the supervision of competition effectiveness as well as the information sharing and coordination with anti-monopoly enforcement departments, credit departments and other units.

Market Clearing Method for Regional Coordinated Transaction Scheduling with Reserve Ancillary Services Sharing

LIU Shuo, ZHANG Menghan, YU Songtai, XIANG Mingxu, YANG Zhifang

2023, 56(9): 35-47. DOI: 10.11930/j.issn.1004-9649.202305084

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To achieve the targets of carbon peak and carbon neutrality with the increasing utilization of renewables, regional coordinated transaction scheduling has emerged as a promising approach that can deliver complementary economic advantages across regions while promoting renewable energy consumption. However, market clearing of reserve ancillary services sharing between different regions remains a challenging problem. This paper proposes a method for building the tie-line feasible region with joint consideration of power energy and reserve ancillary services, and develops a market clearing model for regional coordinated transaction scheduling with reserve ancillary services sharing. The proposed model explicitly incorporates reserve ancillary services sharing constraints and sculpts the energy-reserve transaction feasible region. Based on the feasible regions, the market clearing method provides for synchronous energy-reserve transactions through tie-line transmission. A case study of an interconnected network demonstrates that the proposed method can achieve reserve ancillary services sharing across different regions, promote renewable energy accommodation, and reduce total operating costs.

Clearing Model for Inter-provincial Spot Electricity and Reserve Coupling Considering Reserve Sharing

XU Ling, ZHANG Xipeng, CAO Yiqi, ZHANG Bingjin, DONG Cheng, TAN Zhenfei

2023, 56(9): 48-56. DOI: 10.11930/j.issn.1004-9649.202305131

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The installed capacity of new energy increases year by year, resulting in huge new energy accommodation pressure on the power system. The regional inter-provincial electricity spot market, as an important means for power sharing in regional power grid, still faces such problems as poor market operation efficiency and serious inter-provincial barriers. It is a key tool for constructing the inter-provincial electricity spot market to further tap the reserve sharing capacity of each province and alleviate the new energy curtailment problems in energy-rich provinces and the power shortage problems in energy-poor provinces. An inter-provincial electricity spot clearing model is constructed with the maximum social welfare of the inter-provincial market as the objective, which comprehensively considers the reserve shortage capacity, reserve capacity sharing, inter-provincial transmission cost and network loss. The optimal allocation of inter-provincial power resources is realized by the inter-provincial power spot clearing with consideration of reserve sharing, which effectively reduces the new energy curtailment within a province and alleviates the reverse distribution of power resources in a region. A case study of an IEEE 39-node system and a regional power grid for inter-provincial power transaction clearing has verified the rationality and practicability of the proposed model.

Research on Power Grid Security Check for Provincial Electricity Spot Market

WU Di, WANG Zhengfeng, GAO Weiheng, YING Yiqiang

2023, 56(9): 57-65. DOI: 10.11930/j.issn.1004-9649.202301016

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For the provincial electricity market model in which the inter-provincial AC tie-lines are equivalent to load injections, the clearing results obtained from the models of market optimization clearing and corresponding security check need to be improved. With regards to such modeling strategy, the causes and effects of the deviations in the clearing results are analyzed in this paper. Then the regional power grid model-based optimization clearing model of provincial electricity spot market and the security check method are proposed. By consolidating the models of the entire power grid to generate the topology interface of the future period, the full network sensitivities are calculated to obtain the complete network sensitivity matrix for market optimization clearing and full network security verification iterations, such that the accurate power flow and locational marginal price (LMP) of the power grid can be derived. The case studies based on the actual system has verified the accuracy and effectiveness of the proposed method.

A Balance Method for Power Supply-Demand Adapting to High Uncertainties of Renewable Energy in Northwest Power Grid

REN Jing, GAO Min, CHENG Song, ZHANG Xiaodong, LIU Youbo

2023, 56(9): 66-78. DOI: 10.11930/j.issn.1004-9649.202306099

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Due to the high uncertainties of renewable energy, the power supply-demand balance state shows new features in Northwest power grid. The power grid faces big challenges in both renewable energy accommodation in low load period and reliable power supply in peak load period. This paper firstly evaluates the characteristics of power supply-demand imbalance risks in Northwest China, according to the actual operation data and historical statistical data. Then, the key technologies and supporting market mechanisms for regional power supply-demand balance in China and abroad are reviewed. Based on the development trend of grid-source-load in Northwest China, some measures and suggestions are proposed for power supply-demand balance in Northwest power grid to guarantee the reliable supply of electric power and efficient accommodation of renewable energy.

Income Redistribution Effect of Electricity Price Cross-Subsidies and Improvement of Ladder Electricity Price

LIU Siqiang, DING Na, SUN Yingkai, YE Ze, WU Yongfei, WANG Yali

2023, 56(9): 79-86. DOI: 10.11930/j.issn.1004-9649.202212045

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Whether we can “properly handle the cross-subsidies of electricity price” is becoming the key problem throttling the progress of new round of power market-oriented deepening reform. Based on the big data of electricity consumption of residents in Hebei Province in 2017, the price-gap approach, Dagum Gini coefficient and its dividing standard are applied in this paper to empirically analyze the fairness of cross-subsidies of residential electricity price from the perspective of income redistribution. The results show that grouped by the scale of electricity consumption from low to high, residents benefiting the cross-subsidies of electricity price within the group, exhibit a U-shaped fluctuation trend, i.e., high Gini coefficient values at both ends and low values in the middle part of the curve. Moreover, polarized differences are observed between groups. Particularly with the increasing gap of electricity consumption between these groups, the levels of inequality between groups rise as well, and the corresponding Gini coefficient reaches as high as 0.8971. The empirical study shows that the current step price policy results in extremely unfair redistribution effect of cross-subsidy income due to the coarse-grained settings of step quantity and graded electricity quantity. According to the principle of fairness, the improvement of step quantity and graded electricity division are put forward to make the subsidies more precise.

A Multi-stage Expansion Planning Method for Distribution Networks Based on Explicit Reliability Index

LIU Cencen, XIA Tian, LI Yan, NI Huxuan, HE Xiaohui, GUO Kai

2023, 56(9): 87-95. DOI: 10.11930/j.issn.1004-9649.202212101

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Modern distribution networks are often constructed in grid and operated in a radial manner to improve the transfer capacity under fault conditions. The traditional distribution network planning method generally adopts the two-stage iterative calculation method of planning design and reliability evaluation, which can only obtain an extensive planning scheme; the resulting planning scheme is either over invested or unable to meet specific reliability requirements. Therefore, a multi-stage distribution network expansion planning method considering reliability constraints is proposed. The reliability index calculation process is analyzed and embedded into the planning model, which can accurately consider the fault isolation, load transfer and recovery strategies. Based on the linearized power flow, the planning model is a typical mixed integer linear optimization problem, which can be effectively solved. The performance of the proposed method is verified in the Portugal 54-node system. The simulation result proves the effectiveness and flexibility of this method.

An Evaluation Method for Multi-type Flexible Resource Regulation Capability on the User Side of Distribution Networks

ZHANG Juncheng, LI Min, LIU Zhiwen, TAN Jing, TAO Yigang, LUO Tianlu

2023, 56(9): 96-103,119. DOI: 10.11930/j.issn.1004-9649.202304092

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As the penetration rate of new energy gradually increases, the matching difficulty of grid-source-load in the distribution network also increases. It is therefore necessary to fully tap the adjustment potential of user-side flexible resources. In order to evaluate the adjustment ability of multiple types of flexible resources on the user side of the distribution network, a framework for evaluating the adjustment ability of flexible resource clusters in the distribution network is proposed, which includes two levels: node level and system level. Secondly, based on the conditional value at risk model, a response model of user-side flexible resources inluding temperature controlled load, electric vehicles, and distributed energy storage is established, and the adjustable capacity indexes of node level flexible resources are obtained. And then, taking the minimum peak-valley load difference and the maximum new energy consumption as objective functions, and considering the constraints of distribution network operation and flexible resource regulation, a system level flexible resource adjustable capacity evaluation model is established. Finally, a case study is conducted in the Portugal 54-node system, and the results show that the proposed method can effectively quantify the adjustment ability of multi-type flexible resource clusters in the distribution network.

Robust Improvement Strategy for Power Grid Hosting Capacity with Integration of High Proportion of Renewable Energy

DAN Yangqing, WANG Lei, ZHENG Weimin, WU Jiahui, WANG Chenxuan, YU Gaowang

2023, 56(9): 104-111. DOI: 10.11930/j.issn.1004-9649.202305103

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The high proportion of renewable energy integration into the grid poses challenges to the hosting capacity of the power grid. This paper proposes a robust improvement strategy for the hosting capacity of the power grid with integration of high proportion of renewable energy. Firstly, considering the line expansion cost, energy storage device cost, and demand response capacity cost required for load, a deterministic improvement strategy model is constructed for renewable energy output and load under given scenarios. Secondly, based on the improved k-means clustering algorithm, multiple typical scenarios considering the wind-PV-load correlation are obtained, and the uncertainty of the load is described using the uncertainty interval centered with the typical scenarios. And then, based on the two-stage robust optimization theory, a strategy model for improving the hosting capacity of the power grid is constructed, and the column and constraint generation (C&CG) algorithm is used to solve the model. Finally, the effectiveness of the proposed model and solution method is verified by the case study.

Operation Optimization Method for Flexible Distribution Network Considering the Integration of Mobile Energy Storage

XU Jing, ZHAO Liang, ZHANG Liang, QIAN Guangchao, MA Chiyuan, SONG Guanyu, LI Peng

2023, 56(9): 112-119. DOI: 10.11930/j.issn.1004-9649.202210111

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The high penetration of distributed generators (DGs) exacerbates the operation volatility of distribution networks, which seriously challenges the economic and safety operation of distribution networks. In order to avoid the problems caused by high penetration of DG integration, a novel operation optimization strategy for flexible distribution network (FDN) is proposed considering the applications of soft open point (SOP) and mobile energy storage (MES). Firstly, the model of FDN with multi-type devices is described, which can use the potential of controllable resources for flexibility improvement. The MES is accessed to the distribution network through the DC link of SOP, and the SOP model with mobile energy storage access is established. Secondly, the optimal operation strategy is proposed to facilitate the flexible operation of FDN, considering the power flow constraints, flexible devices operation constraints and MES operation constraints. Finally, the modified IEEE 33-node system is used to verify the effectiveness of the proposed method, which shows that the network losses are effectively reduced and the voltage profile is improved due to the coordination of SOP and MES.

An Edge Container Migration Optimization Method for Multi-service Intelligent Resource Scheduling of Distribution Networks

LI Shuai, XU Di, WEN Xiangyu, ZHANG Jiaxin

2023, 56(9): 120-126. DOI: 10.11930/j.issn.1004-9649.202302062

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Edge container migration technology can achieve intelligent scheduling of service data from resource-constrained servers to resource-sufficient servers. Based on the container migration technology, the container migration capacity, delay gain model and the weighted delay gain maximization problem are formulated for the distribution network multi-service resource scheduling scenarios. An edge container dynamic matching migration optimization method is proposed, which can realize the perception of the service priority, migration capacity, and latency requirements through the adaptive adjustment of the price increment. The simulation results demonstrate that compared with traditional algorithms, the proposed method can validly improve the data processing delay performance for distribution network.

Adversarial Reinforcement Learning-Based Converged Communication Efficiency Improvement Method for Power Distribution Network

PENG Linyu, LIU Xu, TANG Wei, LIU Qing, FANG Hao, ZHANG Guanghui

2023, 56(9): 127-133. DOI: 10.11930/j.issn.1004-9649.202210068

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In order to satisfy the diversified communication requirements of terminal source nodes in power distribution network, it is necessary to optimize the communication orchestration in power distribution unified communication network. Firstly, we construct the joint optimization problem of data transmission delay and energy consumption. Then, the joint optimization problem is modeled as a multi-armed bandit problem, and an adversarial reinforcement learning-based communication orchestration algorithm for power distribution unified communication network is proposed, which uses the historical orchestration information and the perceived adversary between source nodes to dynamically learn the communication orchestration strategy. Finally, the superior performance of the proposed algorithm is verified through simulation.

Method for Identifying Abnormal Data in Distribution Network Operation

LIN Yuhuan, HAO Fangzhou, LI Baixin, HUANG Bo

2023, 56(9): 134-139. DOI: 10.11930/j.issn.1004-9649.202302051

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In order to improve the recognition accuracy of abnormal data of distribution network operation, a novel method for identifying abnormal data of distribution network operation is proposed based on k-means improved firefly algorithm. Firstly, the distribution network operation data is collected in the distribution network information system, and such treatments as data cleansing, consolidation and characterization are performed. Then, the optimal clustering of the improved firefly algorithm based on k-means is used to obtain the sample characteristic curves and sample classification, and the band-pass matrix is obtained to determine the abnormal data points, so the identification of abnormal data of distribution network operation is completed. The results show that the proposed method has a higher accuracy in identifying abnormal operating data for different combinations of electrical equipment states.

Hybrid Petri-net Based Voltage Hybrid Control for Multi-level Nodes in Distribution Networks

GUO Wei, AN Jiakun, LIU Yang, SHAO Hua, YANG Shuqiang, DOU Chunxia

2023, 56(9): 140-148,167. DOI: 10.11930/j.issn.1004-9649.202303108

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The development and utilization of distributed renewable energy (DRE) is beneficial to achieving the goal of “double carbon”. However, due to the strong volatility and uncertainty of DRE, its high-proportion penetration to distribution networks may bring operation challenges such as voltage violation. In this regard, a hybrid control method based on hybrid Petri-net is proposed to address the serious overvoltage/undervoltage issues. The method includes two layers, where the upper layer performs an intelligent transformer gear switching control strategy triggered by node voltage exceeding conditions, and the lower layer adjust the active power of virtual power plants based on power voltage sensitivity. In this way, the voltage would be adjusted in the coarse-fine hybrid manner. The proposed method could tap into the potential of utilizing the low-voltage distribution network's own transformers and distributed adjustable resources for aggregation and regulation. It is of great significance in promoting the large-scale consumption of DRE.

Optimal Scheduling of Source-Load Complementation Based on Green Certificate-Step Carbon Trading Interaction

MENG Yuxiang, MA Gang, LI Hao, LI Weikang, XU Jianwei, LI Tianyu

2023, 56(9): 149-156. DOI: 10.11930/j.issn.1004-9649.202302063

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Aiming at the low-carbon economic operation of regional integrated energy systems (RIES) coupled with multi-energy, a source-load complementary optimal scheduling model is proposed based on green certificate-step carbon trading interaction. Firstly, a green certificate-step carbon trading interactive mechanism is introduced to improve the consumption level of source-side renewable energy and reduce system carbon emissions. Secondly, the incentive-based demand response and peak-shaving benefits with consideration of user satisfaction are introduced on the load side to realize “peak-shaving and valley-filling” of thermoelectric loads. Finally, with the goal of minimizing the running cost of RIES, the model is solved using the Cplex toolbox. The results of the case study show that the green certificate-step carbon trading interactive mechanism is helpful to give full play to the optimization ability of incentive demand response, realize the coordinated reduction of carbon at the source and load side, reduce the system operation cost, and effectively improve the green, low-carbon and economic operation of the system.

Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model

WANG Yaoping, LI Te, JIANG Kaihua, LI Wenhui, WU Qiang, WANG Yu

2023, 56(9): 157-167. DOI: 10.11930/j.issn.1004-9649.202303084

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In order to obtain the pollution condition of transmission line insulators in time, a method of insulator equivalent salt deposit density (ESDD) prediction based on meteorological data is proposed in this paper. The meteorological features that are more closely related to insulator pollution degree are mined, and the importance of each meteorological feature is evaluated by the random forest algorithm. Combined with the sequential forward search method, the optimal subset of meteorological features for ESDD prediction model could be determined. Based on the natural pollution test data of Taizhou City, the basic ESDD prediction model was established by using extreme learning machine (ELM), and its initial weights and thresholds were optimized by the mind evolution algorithm (MEA). Then the AdaBoost algorithm was applied to further improve the accuracy of the model. The results show that the average absolute error of ESDD prediction of AdaBoost-MEA-ELM model is 0.0032 mg/cm², which is 58.97% lower than that of the original ELM model. Compared with other common models, the performance of the proposed model and the rationality of the combination of these three algorithms are verified. The variation of prediction error when training data changed was obtained by k-fold verification method, which further prove the generalization performance and stability of the model.

Research on Electric Field Distribution and Breakdown Characteristics of GIS Double-fracture Disconnect Switch under Non-outage Experiment

DONG Zifan, REN Jieshuai, YIN Jiangang, CHEN Jun, WEN Yaqin, LI Jinbin

2023, 56(9): 168-177. DOI: 10.11930/j.issn.1004-9649.202305009

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Due to the breakdown of the test-side fracture during the non-outage voltage-withstand test, the double-fracture disconnect switch (DDS) with a common chamber structure has been exposed to the risk of affecting the insulation performance of the operating-side fracture. Therefore, it is necessary to study the electric field distribution and breakdown characteristics of the DSS during the design phase. In this paper, the newly developed 110 kV three-phase common box type GIS DDS was taken as an example. The finite element method was employed to simulate the electric field, as well as to obtain the internal electric field strength distribution during non-outage voltage-withstand test at the actual site. Based on the electric field calculation results and the breakdown criteria deduced by Thomson discharge theory, the breakdown characteristics of the two fractures were studied during non-outage voltage-withstand test. It was proven that the breakdown of the test-side fracture would not affect the insulation performance of the operating-side in the non-outage voltage-withstand testing process of the DDS. The results provide theoretical support for non-outage expansion and on-site insulation test during the second-phase expansion, and also provide a more detailed theoretical basis for insulation verification in the future development of the next-generation DDS equipment.

Ultra-Short-Term Power Forecasting Method for Wind-Solar-Hydro Integration Based on Improved GRU-CNN

WU Xiaogang, YAN Jie, GE Chang, TANG Yajie, NI Chouwei, JI Qingfeng

2023, 56(9): 178-186,205. DOI: 10.11930/j.issn.1004-9649.202209120

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The models of wind, solar, and hydro energy systems are very different, and there are multiple uncertainties among them. High-precision power forecasting technology for wind, solar, and hydro is an important prerequisite for giving full play to the complementary characteristics of wind, solar, and hydro. To this end, an integrated ultra-short-term power forecasting method is proposed based on gated recurrent units (GRUs) and convolutional neural networks (CNNs), which can consider the temporal and spatial correlation characteristics of heterogeneous energy sources. Firstly, the correlation characteristics of different data of different stations in the area are analyzed, and then, by introducing a temporal attention mechanism, the mapping relationship between historical meteorological/power data and future power data is established based on the improved GRU-CNN network, which realizes the multi-station integrated ultra-short-term forecasting. The calculation example results show that the forecasting method proposed in this paper can realize the integrated high-precision ultra-short-term power forecasting of regional wind, solar, and hydro power stations, and the model effect is better than the single-field forecasting method and general integrated forecasting method, with higher modeling efficiency.

Short-Term Photovoltaic Power Prediction Based on Standard Clear Sky Set Defined by No Climbing Event

GUO Hongwu, CHE Jianfeng, YAN Yixun, WANG Lijie

2023, 56(9): 187-195. DOI: 10.11930/j.issn.1004-9649.202212013

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Photovoltaic power output is influenced by season, weather conditions, and other factors with randomness and uncertainty. It is difficult to predict the power output under bad weather with strong volatility. Therefore, this paper proposed a short-term photovoltaic power prediction model based on the standard clear sky set with no climbing event definition. The sample points with no climbing events in one day were extracted by the climbing definition and defined as a standard clear sky set, and the difference between them and the historical actual power was made. The obtained difference was used as the output target variable, and the numerical weather forecast was used as the input variable. The long-short term memory (LSTM) model was used to model and forecast the difference. Finally, the predicted photovoltaic output power was obtained indirectly by making the difference between the standard clear sky set and the predicted difference. Through the simulation of a photovoltaic power station and comparison of calculation examples, the short-term photovoltaic power prediction accuracy of the proposed model was improved by 2%~4%. In severe weather, this method can reduce mean absolute error (MAE) and root-mean-square error (RMSE) by about 3%, which verifies the prediction performance and effectiveness of the proposed model.

Adaptive Event-Triggered Secondary Frequency Control in Islanded Microgrids with Auxiliary Energy Storage Systems

XUE Fei, LI Hongqiang, TIAN Bei, MA Xin

2023, 56(9): 196-205. DOI: 10.11930/j.issn.1004-9649.202210130

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Under the high penetration of new energy, the frequency stability of islanded microgrids is challenged by the uncertain output of new energy and the random fluctuation of load. The existing secondary frequency control schemes with auxiliary energy storage systems mainly adopt periodic trigger control schemes, which have the disadvantage of high network bandwidth occupancy. Therefore, an adaptive event-triggered secondary frequency control strategy considering auxiliary energy storage systems and uncertain delays is proposed. First, an adaptive participation mechanism considering the state-of-charge (SoC) of energy storage devices is designed to avoid overcharge and overdischarge without affecting the control structure. Second, in order to reduce the network bandwidth occupation, an adaptive event-triggered control (ETC) strategy is designed. Only when the deviations of the secondary frequency control system exceed the trigger threshold, the statuses and control instructions can be transmitted. Finally, the design criteria of ETC parameters are derived based on Lyapunov stability theory. Simulation results show that compared with the periodic trigger control scheme, the proposed method reduces the network bandwidth occupancy by 95.3%, and the absolute value integral of frequency deviation is reduced by 8.98%.

Optimization of Cooling Channel Structure and Numerical Simulation of Heat Transfer with Flow for CPC Collector

GENG Zhi, LU Xiangwu, WANG Jianli, SHI Tianqing, CHANG Xucheng, GU Yujiong

2023, 56(9): 206-214. DOI: 10.11930/j.issn.1004-9649.202210093

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It is an effective way of comprehensive utilization of solar energy to realize the integrated application of photovoltaic and photothermal by using compound parabolic concentrator. Aiming at the core problem of surface cooling of photovoltaic components, this paper conducted theoretical simulation from the perspectives of cooling channel layout and inlet section optimization. Two different cooling channel modes of traditional straight pipe and new serpentine elbow were proposed. Combined with the control equation, the temperature field and velocity field in each channel were simulated by using Fluent software. The results showed that under the same boundary conditions, the comprehensive heat transfer performance of coiled tubes was better than that of traditional straight tubes. On this basis, three kinds of inlet section schemes with different shapes, namely, rectangular, semicircular and trapezoidal inner tubes, were proposed, and the heat transfer flow characteristics of channels with different inlet sections and the thermoelectric efficiency of the system were compared. The results showed that the comprehensive performance of rectangular section was better when the mass flow rate was small. While when the mass flow rate was large, the semicircular section was better.

Research on the Day-Ahead Operation Decision Making Method for the Electric-Thermal System Based on Grey Fuzzy Evaluation

WANG Zhe, YANG Xu, ZHU Di, LI Xianglong, XING Qijing, WANG Liyong, WANG Linyu

2023, 56(9): 215-225. DOI: 10.11930/j.issn.1004-9649.202210055

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The day-ahead operation decision of the electric-thermal system affects the economy and reliability of the next day's system operation. Therefore, it is significant to develop a reasonable operation decision plan. To solve problems of the traditional decision making method, such as the insufficient rationality of index weighting and the failure to consider the grayness and fuzziness of information, a day-ahead operation decision making method based on grey fuzzy evaluation is proposed. To improve the rationality of index weights, the minimum-cross entropy method is used to obtain comprehensive weights that consider the intrinsic connection between subjective and objective weights. To reduce the negative impact of information grayness and expert scoring ambiguity on the solution scoring, this paper constructs a grey fuzzy evaluation model for the electric-thermal system and designs a day-ahead operation decision algorithm based on this model. Finally, this algorithm is used to develop a day-ahead operation decision scheme for an electric-thermal system located at an industrial park. The results show that the operation decision scheme can improve the economy and reliability of the system.

Prediction of Provincial Energy Consumption Intensity and Estimation of Carbon Emission Reduction Potential Based on PSO-GWO

DONG Fugui, XIA Meijuan, LI Wanying

2023, 56(9): 226-234. DOI: 10.11930/j.issn.1004-9649.202302055

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Accurate estimation of provincial energy saving and carbon reduction potential is the basis for policy formulation and adjustment, but the current methods for estimating provincial carbon reduction potential still has limitations, which make it difficult to guide practice. Therefore, a new method was proposed by combining subjective and objective approaches. An energy intensity learning curve was constructed, which contains such three factors as economy, technology input and scale effect, and the grey wolf algorithm was used to improve the particle swarm optimization algorithm to optimize the fitting curves. An accounting framework for emission reduction potential was constructed with full consideration of carbon sink technologies. Taking the Province S as an example, 12 combination scenarios were set for the empirical study. The results show that optimizing the industrial structure and adjusting the energy mix are the main means for reducing carbon emissions and ensuring the realization of the ‘peak carbon’ target; zero-carbon and carbon-negative technologies can make a relatively small contribution to emissions reduction at this stage, but can facilitate the process of reaching the peak carbon target.

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