Innovative Thought of Tendering Management Mechanism of Universities and Colleges in China under New Circumstances ——Taking North China Electric Power University as an Example

Since the reform and opening up, the Chinese government has attached growing importance to education, and has invested more resources and funds into higher education. In addition, the government has also invested large amounts of funds and technologies in the infrastructure construction of universities and colleges. The undertakings related to the infrastructure construction of universities and colleges in China are complicated in essence. Therefore, funds and technologies of the highest standards should be introduced. At the same time, external tendering is necessary for some undertakings. Currently, the tendering model adopted by universities and colleges in China is the traditional, which is ridden with some problems to be resolved in the shortest possible period. This paper focuses on the current problems of the tendering model adopted by universities and colleges and their solutions. Taking the tendering model in the undertakings of North China Electric Power University as an example, it notes setbacks of the traditional tendering model, and provides kind of theoretical support for establishing a new tendering model for universities and colleges and the related enterprises in China.

Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression

To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm.First,a swing door trending(SDT)algorithm based on compression result feedback was designed to extract the feature data points of wind power.The gating coefficient of the SDT was adjusted based on the compression ratio and deviation,enabling the acquisition of grid-connected wind power signals through linear interpolation.Second,a novel algorithm called IDOA-KM is proposed,which utilizes the Improved Dingo Optimization Algorithm(IDOA)to optimize the clustering centers of the k-means algorithm,aiming to address its dependence and sensitivity on the initial centers.The EVs were categorized into priority charging,standby,and priority discharging groups using the IDOA-KM.Finally,an two-layer power distribution scheme for EVs was devised.The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals.The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles.The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals,smoothing wind power fluctuations,mitigating EV degradation,and enhancing the SOC balance.

An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.

Phase field model for electric-thermal coupled discharge breakdown of polyimide nanocomposites under high frequency electrical stress

In contrast to conventional transformers, power electronic transformers, as an integral component of new energy power system, are often subjected to high-frequency and transient electrical stresses, leading to heightened concerns regarding insulation failures. Meanwhile, the underlying mechanism behind discharge breakdown failure and nanofiller enhancement under high-frequency electrical stress remains unclear. An electric-thermal coupled discharge breakdown phase field model was constructed to study the evolution of the breakdown path in polyimide nanocomposite insulation subjected to high-frequency stress. The investigation focused on analyzing the effect of various factors, including frequency, temperature, and nanofiller shape, on the breakdown path of Polyimide(PI) composites. Additionally, it elucidated the enhancement mechanism of nano-modified composite insulation at the mesoscopic scale. The results indicated that with increasing frequency and temperature, the discharge breakdown path demonstrates accelerated development, accompanied by a gradual dominance of Joule heat energy. This enhancement is attributed to the dispersed electric field distribution and the hindering effect of the nanosheets. The research findings offer a theoretical foundation and methodological framework to inform the optimal design and performance management of new insulating materials utilized in high-frequency power equipment.

Dispersed Wind Power Planning Method Considering Network Loss Correction with Cold Weather

In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.

Investigating Load Regulation Characteristics of a Wind-PV-Coal Storage Multi-Power Generation System

There is a growing need to explore the potential of coal-fired power plants(CFPPs)to enhance the utilization rate of wind power(wind)and photovoltaic power(PV)in the green energy field.This study developed a load regulation model for a multi-power generation system comprising wind,PV,and coal energy storage using realworld data.The power supply process was divided into eight fundamental load regulation scenarios,elucidating the influence of each scenario on load regulation.Within the framework of the multi-power generation system with the wind(50 MW)and PV(50 MW)alongside a CFPP(330 MW),a lithium-iron phosphate energy storage system(LIPBESS)was integrated to improve the system’s load regulation flexibility.The energy storage operation strategy was formulated based on the charging and discharging priority of the LIPBESS for each basic scenario and the charging and discharging load calculation method of LIPBESS auxiliary regulation.Through optimization using the particle swarm algorithm,the optimal capacity of LIPBESS was determined to be within the 5.24-4.88 MWh range.From an economic perspective,the LIPBESS operating with CFPP as the regulating power source was 49.1% lower in capacity compared to the renewable energy-based storage mode.

Resilience-Oriented Load Restoration Method and Repair Strategies for Regional Integrated Electricity-Natural Gas System

The rising frequency of extreme disaster events seriously threatens the safe and secure operation of the regional integrated electricity-natural gas system(RIENGS).With the growing level of coupling between electric and natural gas systems,it is critical to enhance the load restoration capability of both systems.This paper proposes a coordinated optimization strategy for resilience-enhanced RIENGS load restoration and repair scheduling and transforms it into a mixed integer second-order cone programming(MISOCP)model.The proposed model considers the distribution network reconfiguration and the coordinated repair strategy between the two systems,minimizing the total system load loss cost and repair time.In addition,a bi-directional gas flow model is used to describe the natural gas system,which can provide the RIENGS with more flexibility for load restoration during natural gas system failure.Finally,the effectiveness of the proposed approach is verified by conducting case studies on the test systems RIENGS E13-G7 and RIENGS E123-G20.

Optimal Operation Strategy of Electricity-Hydrogen Regional Energy System under Carbon-Electricity Market Trading

Given the“double carbon”objective and the drive toward low-carbon power,investigating the integration and interaction within the carbon-electricity market can enhance renewable energy utilization and facilitate energy conservation and emission reduction endeavors.However,further research is necessary to explore operational optimization methods for establishing a regional energy system using Power-to-Hydrogen(P2H)technology,focusing on participating in combined carbon-electricity market transactions.This study introduces an innovative Electro-Hydrogen Regional Energy System(EHRES)in this context.This system integrates renewable energy sources,a P2H system,cogeneration units,and energy storage devices.The core purpose of this integration is to optimize renewable energy utilization and minimize carbon emissions.This study aims to formulate an optimal operational strategy for EHRES,enabling its dynamic engagement in carbon-electricity market transactions.The initial phase entails establishing the technological framework of the electricity-hydrogen coupling system integrated with P2H.Subsequently,an analysis is conducted to examine the operational mode of EHRES as it participates in carbon-electricity market transactions.Additionally,the system scheduling model includes a stepped carbon trading price mechanism,considering the combined heat and power generation characteristics of the Hydrogen Fuel Cell(HFC).This facilitates the establishment of an optimal operational model for EHRES,aiming to minimize the overall operating cost.The simulation example illustrates that the coordinated operation of EHRES in carbon-electricity market transactions holds the potential to improve renewable energy utilization and reduce the overall system cost.This result carries significant implications for attaining advantages in both low-carbon and economic aspects.

Combined Optimal Dispatch of Thermal Power Generators and Energy Storage Considering Thermal Power Deep Peak Clipping and Wind Energy Emission Grading Punishment

Peak load and wind energy emission pressure rise more as wind energy penetration keeps growing,which affects the stabilization of the PS(power system).This paper suggests integrated optimal dispatching of thermal power generators and BESS(battery energy storage system)taking wind energy emission grading punishment and deep peak clipping into consideration.Firstly,in order to minimize wind abandonment,a hierarchical wind abandonment penalty strategy based on fuzzy control is designed and introduced,and the optimal grid-connected power of wind energy is determined as a result of minimizing the peak cutting cost of the system.Secondly,considering BESS and thermal power,the management approach of BESS-assisted virtual peak clipping of thermal power generators is aimed at reducing the degree of deep peak clipping of thermal power generators and optimizing the output of thermal power generators and the charging and discharging power of BESS.Finally,Give an example of how this strategy has been effective in reducing abandonment rates by 0.66% and 7.46% individually for different wind penetration programs,and the daily average can reduce the peak clipping power output of thermal power generators by 42.97 and 72.31 MWh and enhances the effect and economy of system peak clipping.

Estimate of China's energy carbon emissions peak and analysis on electric power carbon emissions

China's energy carbon emissions are projected to peak in 2030 with approximately 110%of its 2020 level under the following conditions:1)China's gross primary energy consumption is 5 Gtce in 2020 and 6 Gtce in 2030;2)coal's share of the energy consumption is 61%in 2020 and55%in 2030;3)non-fossil energy's share increases from 15%in 2020 to 20%in 2030;4)through 2030,China's GDP grows at an average annual rate of 6%;5)the annual energy consumption elasticity coefficient is 0.30 in average;and 6)the annual growth rate of energy consumption steadily reduces to within 1%.China's electricity generating capacity would be 1,990 GW,with 8,600 TW h of power generation output in 2020.Of that output 66%would be from coal,5%from gas,and 29%from non-fossil energy.By 2030,electricity generating capacity would reach3,170 GW with 11,900 TW h of power generation output.Of that output,56%would be from coal,6%from gas,and 37%from non-fossil energy.From 2020 to 2030,CO2emissions from electric power would relatively fall by 0.2 Gt due to lower coal consumption,and relatively fall by nearly 0.3 Gt with the installation of more coal-fired cogeneration units.During 2020e2030,the portion of carbon emissions from electric power in China's energy consumption is projected to increase by 3.4 percentage points.Although the carbon emissions from electric power would keep increasing to 118%of the 2020 level in 2030,the electric power industry would continue to play a decisive role in achieving the goal of increase in non-fossil energy use.This study proposes countermeasures and recommendations to control carbon emissions peak,including energy system optimization,green-coal-fired electricity generation,and demand side management.

Supply Chain Cooperation between Coal Enterprise and Electric Power Enterprise in China

Based on the two-stage Stackelberg game method,value creation of supply chain cooperation between coal enterprise and power utilities is studied by formulating profit functions of coal and power enterprises and calculating the maximum profit.According to the analysis,it is found that the profit from supply chain cooperation between coal and power enterprises is more than that of non-cooperation.The cooperation is validated to be beneficial for both units;however,the profit is mainly taken by the power enterprise.Thus,it is necessary to set up the incentive mechanism to distribute cooperation value between coal and power enterprises to promote their continual cooperation.

Dynamic State Forecasting in Electric Power Networks

The real time monitoring and control have become very important in electric power system in order to achieve a high reliability in the system. So, improvement in Energy Management System (EMS) leads to improvement in the monitoring and control functions in the control center. In this paper, DSE is proposed based on Weighted Least Squares (WLS) estimator and Holt’s exponential smoothing to state predicting and Extended Kalman Filter to state filtering. The results viewing the dynamic state the estimator performance under normal and abnormal operating conditions.

New power system development path mechanism design

Carrying out green energy transformation,implementing clean energy power replacement and supply,and developing a new power system are some primary driving forces needed to fulfill China’s carbon-peak and carbon-neutral strategic goals.The construction of new power systems in China’s provinces and cities is developing rapidly,and the lack of a typical model promotes the application.The new power system path design should be based on the actual development of the power grid in different regions,energy use characteristics,and other actual needs to carry out the differentiated path design.In this context,this study analyzes the characteristics of the new domestic power system based on the policy background of the new domestic power system,constructs a new model for power system development stage identification,and proposes the overall design of the new power system development path from the power supply,transmission and distribution,and load sides.It also uses the Hebei South Network as an example to explore the development stage of the Hebei South Grid based on actual development needs.Finally,this study designs a novel power system development path for the entire supply and demand chain for the Hebei South Grid to propose ideas for constructing a new power system in China and to help green energy transformation.

Laser shaping and optical power limiting of pulsed Laguerre–Gaussian laser beams of high-order radial modes in fullerene C60

We study the strong nonlinear optical dynamics of nanosecond pulsed Laguerre–Gaussian laser beams of high-order radial modes with zero orbital angular momentum propagating in the fullerene C60molecular medium. It is found that the spatiotemporal profile of the incident pulsed Laguerre–Gaussian laser beam is strongly reshaped during its propagation in the C60molecular medium. The centrosymmetric temporal profile of the incident pulse gradually evolves into a noncentrosymmetric meniscus shape, and the on-axis pulse duration is clearly depressed. Furthermore, the field intensity is distinctly attenuated due to the field-intensity-dependent reverse saturable absorption, and clear optical power limiting behavior is observed for different orders of the input pulsed Laguerre–Gaussian laser beams before the takeover of the saturation effect;the lower the order of the Laguerre–Gaussian beam, the lower the energy transmittance.

Impact of Electric Vehicle Charging on Power Load Based on TOU Price

Large-scale electric vehicle charging has a significant impact on power grid load, disorderly charging will increase power grid peak load. This article proposes an orderly charging mechanism based on TOU price. To build an orderly charging model by researching TOU price and user price reaction model. This article research the impact of electric vehicle charging on grid load by orderly charging model. With this model the grid’s peak and valley characteristics, the utilization of charging equipment, the economics of grid operation can all be improved.

How Load Aggregators Avoid Risks in Spot Electricity Market:In the Framework of Power Consumption Right Option Contracts

There is uncertainty in the electricity price of spot electricity market,which makes load aggregators undertake price risks for their agent users.In order to allow load aggregators to reduce the spot market price risk,scholars have proposed many solutions,such as improving the declaration decision-making model,signing power mutual insurance contracts,and adding energy storage and mobilizing demand-side resources to respond.In terms of demand side,calling flexible demand-side resources can be considered as a key solution.The user’s power consumption rights(PCRs)are core contents of the demand-side resources.However,there have been few studies on the pricing of PCR contracts and transaction decisions to solve the problem of price forecast deviation and to manage the uncertainty of spot market prices.In addition,in traditional PCR contracts,PCRs are mostly priced using a single price mechanism,that is,the power user is compensated for part of the electricity that was interrupted or reduced in power supply.However,some power users might engage in speculative behaviours under this mechanism.Further,for load aggregators,their price risk avoidance ability has not substantially improved.As a financial derivative,options can solve the above problems.In this article,firstly,the option method is used to build an option pricing optimization model for power consumption right contracts that can calculate the optimal option premium and strike price of option contracts of power consumption rights.Secondly,from the perspective of power users and load aggregators,a simulation model of power consumption right transaction decision-making is constructed.The results of calculation examples show that(1)Under the model in this article,the pricing of option contracts for power consumption rights with better risk aversion capabilities than traditional compensation contracts can be obtained.(2)The decision to sell or purchase the power consumption rights will converge at respective highvalue periods,and option contracts will expedite the process.(3)Option contracts can significantly reduce the loss caused by the uncertainty of spot electricity prices for load aggregators without reducing users’willingness to sell power consumption rights.

A Fuzzy Probability-based Markov Chain Model for Electric Power Demand Forecasting of Beijing, China

In this study, a fuzzy probability-based Markov chain model is developed for forecasting regional long-term electric power demand. The model can deal with the uncertainties in electric power system and reflect the vague and ambiguous during the process of power load forecasting through allowing uncertainties expressed as fuzzy parameters and discrete intervals. The developed model is applied to predict the electric power demand of Beijing from 2011 to 2019. Different satisfaction degrees of fuzzy parameters are considered as different levels of detail of the statistic data. The results indicate that the model can reflect the high uncertainty of long term power demand, which could support the programming and management of power system. The fuzzy probability Markov chain model is helpful for regional electricity power system managers in not only predicting a long term power load under uncertainty but also providing a basis for making multi-scenarios power generation/development plans.

Thermo-Dynamical Analysis on Electricity-Generation Subsystem of CAES Power Plant

Besides pumped hydropower, Compressed Air Energy Storage (CAES) is the other solution for large energy storage capacity. It can balance fluctuations in supply and demand of electricity. CAES is essential part of smart power grids. Linked with the flow structure and dynamic characteristic of electricity generation subsystem and its components, a simulation model is proposed. Thermo-dynamical performance on off-design conditions have been analyzed with constant air mass flux and constant gas combustion temperature. Some simulation diagrams of curve are plotted too. The contrast of varied operation mode thermal performance is made between CAES power plant and simple gas turbine power plant.