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Challenges, Strategies and Key Technologies for Virtual Power Plants in Market Trading AI Introduction
Abstract：With the massive access to renewable power generation and the advancement of the marketization process, the development prospects of virtual power plants that can give full play to the value of flexible resources have gradually emerged. In the process of virtual power plant participation in the market, the formulation of its market trading strategy needs to be challenged by the market mechanism. On the other hand, the market trading is closely related to the optimal scheduling of resources. The adjustable capacity of the internal resources, uncertainty, load behavior and other characteristics bring challenges to the development of trading strategy. This paper focused on the challenges and problems faced in the market transactions of virtual power plants, and summarized the market strategies adapted to the market mechanism. The key technologies required to deal with the formulation of the strategies, such as uncertainty handling, bid analysis methods, and virtual plant management methods were analyzed and reviewed, in order to provide a reference and a direction for the future research on virtual power plants.Keywords：virtual power plant (VPP);market strategy;frequency regulation market;demand response;optimal dispatch;game theory74|927|14<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06Resilience Enhancement Strategy of Combined Heat and Power-Virtual Power Plant Considering Thermal Inertia AI Introduction
Abstract：The increasingly frequent extreme weather has a more serious impact on the electro-thermal coupling system. Resilience is a core indicator that measures the system’s ability to withstand extreme events, reduce the impact of failures,and recover quickly. To enhance the ability of the electro-thermal coupling system to withstand extreme disasters,a two-stage three-layer resilience enhancement strategy of combined heat and power-virtual power plant (CHP-VPP) considering thermal inertia was proposed. In the first stage, the system was reconstructed based on the minimum spanning tree theory with the goal of minimizing the cost of tie switches. In the second stage,aiming at minimizing the operating cost,the optimal decisions under the worst failure scenario was formulated based on distributionally robust optimization theory. The column and constraint generation algorithm was used for iterative solutions. A CHP-VPP test system was built based on an IEEE 33-bus system and a 6-node thermal system. The simulation results show that the proposed method can effectively enhance the resilience of CHP-VPP to cope with extreme disasters.Keywords：combined heat and power-virtual power plant (CHP-VPP);thermal inertia;distributionally robust;resilience enhancement;extreme disaster;electro-thermal coupling system75|393|8<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06Low-Carbon Economic Dispatch Based on Ladder Carbon Trading Virtual Power Plant Considering Carbon Capture Power Plant and Power-to-Gas AI Introduction
Abstract：Under the background of energy complementarity and low-carbon economy, virtual power plant (VPP) is an effective carrier for achieving optimal allocation of regional resources and new energy consumption. At the technical level,the CO2 recycling through carbon capture power plant (CCPP) and power-to-gas (P2G) conversion devices was realized, a CCPP-P2G coupling model was established, and a price based demand response model considering user satisfaction on the load side was introduced. In terms of low-carbon policy, the ladder carbon trading mechanism was introduced into VPP to constrain carbon emissions. Then, with the goal of minimizing the total cost, a low-carbon economic dispatch model for VPP was established. By setting different scheduling scenarios for comparison, the effectiveness of the model in low-carbon economy operation of VPP was verified, and the impact of carbon trading parameters on the carbon emissions and costs of VPP was explored through sensitivity analysis. The results show that the model has guiding significance for VPP low-carbon economic scheduling.Keywords：virtual power plant (VPP);carbon capture power plant (CCPP);power-to-gas (P2G);ladder carbon trading;optimized dispatching49|390|16<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06Multi-Power Coordinated Optimization Operation Strategy Considering Conditional Value at Risk AI Introduction
Abstract：In order to achieve carbon peaking and carbon neutrality goals, the power system presents the trend of extensive access of multiple power sources. A multi-power coordination bi-level optimal operation model was proposed. In the upper level model, photovoltaic, gas turbine, energy storage and other power sources were aggregated into the virtual power plant, and the overall dispatching cost of virtual power plant was minimized through optimization. In addition, the conditional value at risk theory was adopted to measure the risks in view of the uncertainty of photovoltaic output. The lower level model was the system clearing model, which took the minimum total cost of the system as the optimization objective. It can consider the virtual power plant, thermal power units and diesel units to participate in the bidding, and output the actual bidding capacity of the virtual power plant back to the upper level, so that the virtual power plant can adjust the power output according to the actual bidding capacity. In order to solve the constructed model, Karush-Kuhn-Tucker (KKT) condition and strong duality theory were used to transform the bi-level model into a single-level model. Finally, an example was given to verify the validity of the model, the results show that the coordinated operation of multiple power sources can effectively reduce the operating cost of the system and improve the consumption rate of renewable energy.Keywords：virtual power plant;multi-power optimization;bi-level model;conditional value at risk178|275|9<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06Study on Uncertainty Operation Optimization of Virtual Power Plant Based on Intelligent Prediction Model Under Climate Change AI Introduction
Abstract：In order to effectively cope with climate change and promote the healthy development of virtual power plant, an uncertainty operation optimization model of virtual power plant (VPP) adapted to climate change was proposed based on the providing regional climate for impact studies (PRECIS), BP neural network prediction model and interval optimization algorithm. PRECIS was used to simulate the changes in meteorological factors such as temperature,wind speed and radiation under different carbon emission scenarios in 2025. The BP neural network model was used to predict the power generation of photovoltaic power plants based on the simulation results of PRICES. The interval optimization algorithm was coupled with the power generation prediction results to reduce the impact caused by the influence of photovoltaic power generation uncertainty on the simulation results of the optimization model. The results show that the model can not only generate the optimal operation strategy of VPP under climate change, but also reduce operating costs and improve economic benefits.Keywords：virtual power plant (VPP);providing regional climate for impact studies (PRECIS);BP neural network;uncertainty optimization;climate change56|257|5<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06
Virtual Power Plant Planning, Scheduling and Control Technology
Experiment on Multi-Component Synchronous Test of Reducing Atmosphere Adjacent to Water Wall of High Temperature Corrosion Boiler AI Introduction
Abstract：In order to reveal the correlation between H2S concentration and the concentration of CO, H2 and other reducing atmosphere near the water wall of the boiler, a multi-component synchronous test of reducing atmosphere adjacent to water wall was carried out on a 300 MW high temperature corrosion boiler. It is found that when CO volume fraction is between 3.5% and 13%, there is a strong positive correlation between CO concentration and H2S, H2 concentration. The correlation is negative between CO concentration and CO2 concentration. In addition, the positive correlation between CO concentration and H2S concentration is related to the location of the measuring point. With the increase of furnace height, the positive correlation between the two gradually decreases. In the reducing atmosphere, in addition to CO, a large amount of H2 is also observed, with the maximum H2 volume fraction of 4.7%. There is also a strong positive correlation between H2S concentration and H2 concentration. Finally, according to the results of synchronous test, the correlation equations between CO concentration and H2S, H2 concentration were given respectively, which could be used as a reference for operators.Keywords：boiler;high temperature corrosion;low-NOx retrofit;reducing atmosphere;water wall52|175|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06- Abstract：Aiming at the multi-objective optimization of boiler combustion system, on the basis of the established prediction model of boiler combustion system, the weighted-particle swarm algorithm and the multi-objective particle swarm optimization (MOPSO) algorithm were used to optimize the adjustable operating parameters of the boiler, which can realize the operating state of the boiler with high efficiency and low NOx emission. The analysis shows that the operating parameters obtained by the two optimization algorithms are similar, and the trend is consistent with the combustion characteristics analysis and combustion adjustment test results. It indicates that the intelligent algorithm is effective and feasible to optimize the combustion system of the power plant boiler. However, the weighted-particle swarm optimization algorithm has serious subjective dependence. It is difficult to select appropriate weights, and the optimization time is long and the results are few. However, the optimization time of the MOPSO algorithm is far less than the optimization time of the weighted-particle swarm optimization algorithm, the optimization results are more, and the optimization efficiency is higher. Therefore, the MOPSO algorithm is more beneficial to guide the actual operation of the boiler.Keywords：power station boiler;multi-objective optimization;weighted-particle swarm optimization;multi-objective particle swarm optimization (MOPSO)49|22|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06
- Abstract：The shaft seal heater is a heat exchange device in a steam turbine, which plays an important role to the performance of the shaft seal system. Based on limited data, a method for calculating the relevant parameters of the shaft seal heater was proposed by using an iterative algorithm. The algorithm was validated by comparing the calculated basic heat transfer parameters of the shaft seal heater with the results from Aspen Plus software. The discrepancy is within 0.4%, which validates the iterative algorithm presented in this paper. Taking the practical application of the combined cycle steam turbine unit of Huaneng Beijing Thermal Power Co., Ltd. as an example, the liquid phase and gas phase flow at the outlet of the shell side, under the condtion of inlet parameter change, were calculated by using this method. Moreover, the correlation model between the fluctuation of import parameters and the heat transfer parameters of the heater was established to verify the effectiveness of the method.Keywords：steam turbine;sealing heater;import parameters;iterative calculation;correlation model65|270|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06
Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis AI Introduction
Abstract：Fault diagnosis of wind turbines suffers from less training data and noises. A method based on wide deep convolutional neural network with resampling and principal component analysis was presented for the diagnosis of mechanical faults (that is the main fault component of wind turbines). The method adopted a variety of signal preprocessing methods such as resampling wavelet threshold denoising and principal component analysis to increase the information density and ensure the integrity of the information. After being trained with small amount of data, the network which has a powerful feature extraction capability could extract the fault signal in the time domain which will be further used for fault diagnosis. Experimental results were verified based on the real wind turbine data, demonstrating the effectiveness of this method.Keywords：wind turbine;wide deep convolutional neural network;resampling;wavelet threshold denoising;principal component analysis57|129|10<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06Experiment and Dynamic Characteristics Simulation of Primary Frequency Regulation of a 660 MW Nuclear Power Unit AI Introduction
Abstract：The operation mode of nuclear power units is very different from that of thermal power units, and the specific data on the response of relevant nuclear power units to grid frequency changes are relatively lacking at present. In order to deeply study the dynamic response characteristics of the power and grid frequency changes of the 660 MW nuclear power unit, a perturbation test of load change frequency difference was conducted in units 3 and 4 of a domestic nuclear power plant to analyze the changes of relevant parameters during the actual operation of the unit. We verified that the unit had the regulation capability under small grid perturbations. At the same time, the mathematical model of the primary frequency regulation capability of nuclear power units was established by using MATLAB/Simulink software, and the corresponding optimization scheme was proposed through the analysis of the dynamic change characteristics of nuclear power units, which provide important test data support for the participation of the same type of nuclear power units in the primary frequency regulation of the power grid.Keywords：nuclear power unit;primary frequency regulation;dynamic response characteristics63|121|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06Research on Performance Optimization of Semiconductor Thermoelectric Generaor Based on Phase Change Material AI Introduction
Abstract：In recent years, the use of phase change material (PCM) to improve the output performance of semiconductor thermoelectric generator (TEG) and maintain the long-term operation of TEG has been widely concerned. In view of the current situation that the existing PCM-TEG combination methods are complicated and lack of unified understanding, this paper established a PCM-TEG coupling mathematical model, compared the system performance when PCM is arranged on the hot side, cold side and double sides of TEG, and proposed a skeleton with PCM design and verified its effectiveness. The results show that, through the design of PCM, the output capacity of the TEG can be improved by the device thermal management, which can effectively avoid the failure of thermoelectric devices due to its own heat storage capacity. The skeleton with PCM design is superior to the conventional PCM-TEG system performance. The design of hot-side-PCM-TEG on the double-sides-PCM-TEG on double sides can effectively maintain the stable operation of TEG. Enhancing the heat transfer capacity of TEG on the cold side can make up for the defect of insufficient output performance of hot-side-PCM-TEG. The study results can provide a reference for the next research on the relevant application of PCM-TEG.Keywords：thermal management;thermoelectric generator (TEG);phase change material (PCM);enhanced heat transfer;skeleton;failure77|138|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06
Power Generation and Environmental Protection
- Abstract：A reduced order model for subsynchronous oscillation analysis of direct drive wind farm was studied, and a balanced truncation method based on alternating direction implicit (ADI) was proposed. Firstly, the mathematical model of wind farm was built according to the mathematical representation of direct-driven permanent magnet synchronous wind turbine. The ADI method was used to solve the Lyapunov equation iteratively, and the controllable and observable Gramm matrixs were obtained. Then, the balanced truncation method was used to obtain the reduced order model. The time domain simulation waveform, Bode diagram, calculation time and subsynchronous oscillation mode of the full order model and the reduced order model were compared to verify the effectiveness of the proposed method. The simulation results show that the reduced order model has good consistency with the full order model, and the calculation speed is improved and the number of reduced order is greatly reduced.Keywords：direct drive wind farm;alternating direction implicit (ADI);subsynchronous oscillation;model reduction;balanced truncation method48|52|11<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06
Analysis of Syngas Components and Energy in Biomass Gasification Coupled Power Generation System AI Introduction
Abstract：To achieve the carbon emission reduction target, the use of biomass to replace traditional energy is a key measure. The gasification process in the biomass gasification coupled power generation system was modeled and analyzed by Aspen Plus. The influence of equivalence ratio and ambient pressure on the composition of syngas gas was investigated, and the variation law of calorific value and energy of syngas was discussed based on the calculation results. The results show that the volume fraction of H2 shows a monotonically decreasing trend with the increase of the equivalence ratio, from 19.47% to 5.19%. The CO volume fraction reaches a maximum value of about 22% near the equivalence ratio of 0.3. The volume fraction of CH4 shows a monotonically decreasing state with the increase of equivalence ratio, from 1.3% to 0. Relying on the results of theoretical research, the gas calorific value curve shows the same change rule as CO and H2, and reaches the highest value when the equivalence ratio is 0.3. In the range of 0.3-0.35, the overall change is not obvious and then declines. Considering the energy brought into the furnace and the sensible enthalpy of the gas, a synthesis gas energy evaluation method of calorific value + sensible enthalpy was proposed, which reaches the maximum value when the equivalence ratio is about 0.35.Keywords：coupled power generation;biomass gasification;equivalence ratio;calorific value;energy analysis42|75|8<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06
New Energy
Power System Transient Stability Preventive Control Optimization Method Driven by Stacking Ensemble Learning AI Introduction
Abstract：Aiming at the contradiction between the rapidity requirement of online calculation of transient stability preventive control and the computational complexity of time-domain equations, a stacking ensemable learning driven optimization method for power system transient stability preventive control was proposed. Firstly, a transient stability estimator based on a stacking ensemble deep belief network was constructed to replace the nonlinear differential algebraic equation solution process required for transient stability determination. Secondly, the trained transient stability estimator was used as a transient stability constraint discriminator, which was embedded in the iterative optimization process of the Aptenodytes Forsteri optimization algorithm. Finally, with the goal of minimizing the cost of preventive control, a stacking ensemble learning driven power system transient stability preventive control optimization algorithm was established. The algorithm realized the efficient judgment of transient stability constraints in preventive control, and improved the decision-making level of preventive control for power generation rescheduling. Based on the IEEE39 bus system, the proposed preventive control method was verified by experiments. The results show that the method has achieved good results in both evaluation accuracy and calculation efficiency.Keywords：power system;stacking ensemble learning;Aptenodytes Forsteri optimization algorithm;transient stability;preventive control58|256|12<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06- Abstract：Modular multilevel direct current transformer (MMDCT) provides a feasible solution to realize the efficient and stable integration of electric vehicle charging and discharging cluster into urban DC distribution network. However, there are impulse currents caused by capacitor charging and transformer inrush current in its starting process. Therefore, a fast pre-charging strategy based on compound frequency control was proposed. The independent control of direct current component and intermediate frequency component was used to realize simultaneous charging of the primary and secondary sides. The double closed-loop control was adopted in the primary side, while the variable-step phase shift peak current control was adopted in the secondary side, which solved the contradiction between charging speed and inrush current. Furthermore, a quantitative comparative analysis of various pre-charging strategies was carried out. The effectiveness and feasibility of the proposed pre-charging strategy were verified through simulation. The results show that the magnitude of the inrush current and the charging time are effectively controlled by the proposed method.Keywords：electric vehicle;direct current transformer (DCT);pre-charging;compound frequency control49|190|14<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06
- Abstract：The research on the evaluation model of the energy storage power station focuses on the cost model and economic benefit model of the energy storage power station. However, fewer studies consider the social benefits brought by the long-term operation of the energy storage power station. The cost model of energy storage power station was firstly established by considering the construction cost, storage battery rental cost, labor cost, operation and maintenance cost, disposal cost and other costs. Then, the benefit model of energy storage power station was established by comprehensively considering the economic and social benefits. Finally, the comprehensive benefit evaluation model based on the whole life cycle of the energy storage power station was established, and the optimal scale was determined by comparing the comprehensive benefits of the four scale energy storage power stations. This work helps to verify the effectiveness of the comprehensive evaluation model, and provide an intuitive comprehensive evaluation method for the selection of the construction scale of the energy storage power station.Keywords：energy storage power station;full life cycle;comprehensive evaluation model107|92|5<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06
Improved Deep Learning Model for Forecasting Short-Term Load Based on Random Forest Algorithm and Rough Set Theory AI Introduction
Abstract：Accurate power load forecasting is conducive to ensuring the safe and economic operation of the power system. Aiming at the problems of low prediction accuracy and long time consuming of the current prediction algorithms, an improved deep learning (DL) short-term load forecasting model based on random forest (RF) algorithm and rough set theory (RST), namely RF-DL-RST, was proposed. Firstly, based on historical data, the model used RF algorithm to extract the key features that affected the load forecasting. Then, the key features and historical load data were trained as the input and output items of deep neural network (DNN), and the prediction results were corrected by RST. After that, the rough set method was used to revise the prediction results. Finally, the simulation was verified by an example. The results show that the prediction accuracy of the model is higher than that of a single DNN model and a model without RST revised.Keywords：power load forecasting;random forest (RF) algorithm;deep learning (DL);rough set theory (RST)125|73|25<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06Optimal Control of Residents’ Controllable Load Resources Considering Different Demands of Users AI Introduction
Abstract：Optimizing the controllable load resources of residents on the demand side is a major aspect of virtual power plant (VPP). It is an important means to improve the utilization rate of new energy such as wind power and photovoltaic power, to achieve carbon neutrality. The key to control controllable load is to meet the basic power demand of users, and different users have different power demand. Therefore, an optimal control method of residents’ controllable load resources considering user demand was proposed. Considering the different demand of different users, as an intermediate link between the power grid and the load group, VPP manages the controllable load. Firstly, an improved K-means clustering algorithm for optimal k selection was proposed for clustering. Then, according to the demand difference function to quantify the deviation between the power consumption and the original demand of each group after load regulation, a priority division rule based on the demand difference was proposed. Finally, in the regulation, VPP compensated different costs for different priority groups to achieve the regulation goal of maximizing VPP revenue and minimizing changes in user electricity consumption behavior. The simulation results show that the proposed optimal control strategy can effectively cut the peak and fill the valley of the load curve, and ensure the VPP revenue with less impact on the power demand of users.Keywords：virtual power plant (VPP);controllable load;load clustering;user demand;peak shaving and valley filling23|255|4<HTML><L-PDF> <WORD> <Meta-XML>Updated：2025-06-06
Smart Grid
