Integrated Intelligent Energy

Study on the impact of re-electrification on the path to carbon peaking and carbon neutralization in China

XIE Dian, GAO Yajing, LIU Tianyang, ZHAO Liang

2022, 44(3): 1-8. doi:10.3969/j.issn.2097-0706.2022.03.001

Asbtract ( 369 )

HTML ( 19)

PDF (1727KB) ( 355 )

Figures and Tables | References | Related Articles | Metrics

Seeing that re-electrification is the most feasible way for energy transformation in terms of technology and economy, it plays a key role in achieving carbon peaking and carbon neutralization in China as scheduled. In order to systematically evaluate the impact of re-electrification, the re-electrification path in China is comprehensively analysed from three prospects, economy,society and environment. A multi-dimensional evaluation index system is built to evaluate its impact on economic development, industrial transformation, employment and environment protection, giving a quantitative analysis result of the influence on socio-economy. The research results show that re-electrification is of great significance to China's carbon neutralization since it facilitates the construction of an electricity-centred modern energy system, promotes the innovation and industrial application of technologies related to electrification and low carbon, stimulates open employment, secures human health and reduces costs of carbon reduction.

Business analysis on integrated energy services of power generation enterprises under the new circumstances

WANG Xiaohai, XU Jingjing, HU Yongfeng, LIU Guangyu, WANG Youtian

2022, 44(3): 9-16. doi:10.3969/j.issn.2097-0706.2022.03.002

Asbtract ( 217 )

HTML ( 18)

PDF (1508KB) ( 425 )

Figures and Tables | References | Related Articles | Metrics

Under the strategic guidance of "carbon neutrality and carbon peaking" and "building a new power system with new energy as the main body",integrated energy services are of great significance to energy saving,efficiency increasing and local consumption of renewable energy.The transformation of power generation enterprises from producers to integrated energy service providerd has become an irreversible global trend.After the discussion on the connotation,business types and models of integrated energy services,the development status and existing problems of the services in China are analyzed,and the prospects are made.According to the requirements of the new situation,the SWOT analysis of the integrated energy services were made and the strategic method to deal with the threats is proposed.Based on the results of strategic analysis,six key types of integrated energy services of power generation enterprises are put forward,which provides guidance for them to deploy integrated energy service business.

Research on innovative business models and development strategies of integrated energy services for power generation companies

ZHAO Jing, XING Zheng, HUANG Baole, LI Peng, ZHANG Pan, ZHANG Tingyu

2022, 44(3): 17-22. doi:10.3969/j.issn.2097-0706.2022.03.003

Asbtract ( 181 )

HTML ( 11)

PDF (1655KB) ( 437 )

Figures and Tables | References | Related Articles | Metrics

Since China is advancing the process of the "carbon neutrality and carbon peaking" and constructing a new power system with new energy as the main body,providing integrated energy services has become an important countermeasure to improve energy utilization efficiency,reduce energy utilization costs and promote green and low-carbon development of the energy system.Power generation companies are transforming from the suppliers of electricity,heat,cold,steam and hot water to novel customer-oriented integrated energy service providers.At present,there is a lack of phrased strategies for exploring and developing this innovative business model.Therefore,an integrated energy service business model framework for power generation companies is built based on the Business Model Canvas （BMC）,according to the actual demand of customers.The framework is analyzed comprehensively.The analysis provides important theoretical support for power generation companies in their transformation into integrated energy services providers,and puts forward relevant suggestions and reflections on the phased strategies of power generation enterprises to develop their integrated energy services.

Applications of blockchain technology in carbon trading

HE Qingsu, HAN Qingzhi, LIU Zhiyuan, ZHANG Zhaoshi

2022, 44(3): 23-28. doi:10.3969/j.issn.2097-0706.2022.03.004

Asbtract ( 335 )

HTML ( 11)

PDF (1336KB) ( 293 )

Figures and Tables | References | Related Articles | Metrics

At present,carbon emissions trading mainly relies on a centralized system to manage the transactions.Due to the lack of transparent trading information and excessive administrative intervention,the carbon emissions trading market is inactive.In order to boost the vitality of carbon market and advance the realization of carbon peaking and carbon neutrality in China,a carbon emissions trading framework based on a two-level hybrid blockchain network is built. Firstly, the development status and relevant policies of China’s carbon market are introduced,and the existing problems in the market are summarized.Then,the carbon trading mechanism is expounded. Finally,a carbon emission trading framework based on blockchain technology is proposed to solve the problems.The framework consists of public blockchain and consortium blockchain.Carbon allowances are allocated in public blockchain,which is transparent and traceable.Only carbon emissions data can be shared in consortium blockchain to protect the private data of enterprises.In addition,the framework can transit information between public blockchain and consortium blockchain based on Polkadot protocol.The carbon emissions trading framework can reduce the system operation cost,and improve the trading efficiency and information transparency,which provides a reference for accelerating the marketization of carbon trading.

Multi-stage energy management strategy for smart buildings with BESS

LIU Jing, SHI Mengge, HU Yongfeng

2022, 44(3): 29-37. doi:10.3969/j.issn.2097-0706.2022.03.005

Asbtract ( 201 )

HTML ( 7)

PDF (1748KB) ( 231 )

Figures and Tables | References | Related Articles | Metrics

Smart building is an indispensable part of new power system. Dealing with the uncertain renewable energy output and load demand, a smart building system has to reasonably schedule the internal energy to realize the reliable and economical operation. To manage the energy optimal scheduling in smart buildings, a multi-stage energy management strategy for smart buildings including battery energy storage system（BESS） and electric vehicle charging stations is proposed. In day-ahead stage, a robust optimization strategy is designed to deal with the uncertainty of renewable power output and load demand, considering the time coupling constraints of the BESS. In intra-day stage, weighted model predictive control method is taken to adjust the optimization strategy in day-ahead stage, and rolling optimization and feedback correction methods are employed to dynamically control the active output of each controllable power supplier, the power charged and discharged from BESS, electric vehicle charging strategy and power transaction with the main power grid in order to adapt to the real-time fluctuation of energy output and load demand. The simulation results show that the proposed multi-stage energy management structure of the smart building embedded BESS can effectively reduce the operation cost of the smart building,lower the tie-line power and reduce the influence of the randomness and volatility brought by renewable energy and load demand on the system operation.

Analysis method of electric energy substitution potential based on time series and BP neural network

LIAO Minle, HUANG Chongyang, DAI Chengcheng, LI Hualin, FAN Gaosong

2022, 44(3): 38-43. doi:10.3969/j.issn.2097-0706.2022.03.006

Asbtract ( 114 )

HTML ( 8)

PDF (1529KB) ( 175 )

Figures and Tables | References | Related Articles | Metrics

A prediction method based on time series and BP neural network is proposed.The time series model based on cubic exponential smoothing method is used to predict the electric energy substitution,and the prediction results are corrected by BP neural network.The prediction and comparative analysis on energy consumption are made according to the data from National Bureau of Statistics.The results of a case study show that the combination forecast method based on time series and BP neural network can make a more accurate prediction on electric energy substitution than a single prediction method,which provides certain guidance for the analysis on electric energy substitution potential.

Application of the secondary network intelligent balance system based on big data analysis

ZUO Wendong, LI Biao, GUO Baogang, GAO Xiaoyu, GU Jihao, WANG Weihe, DONG Zhipeng

2022, 44(3): 44-49. doi:10.3969/j.issn.2097-0706.2022.03.007

Asbtract ( 126 )

HTML ( 2)

PDF (1798KB) ( 337 )

Figures and Tables | References | Related Articles | Metrics

A secondary network intelligent balance system for a residential district in Tianjin was built. Based on big data analysis,heat can be accurately supplied to each dwelling house,and a "station-load linkage" intelligent regulation strategy aiming at providing comfortable room temperature for thermal power users was established to guide the energy-saving and optimal operation of the thermal power station.On the premise of ensuring the heating quality,the strategy realizes the heating on demand,energy-saving and consumption reduction.By comparing the heating operation data before and after the optimization,it can be seen that the temperature difference between the supply and return water of the secondary network,the dispersion of the household return water temperature,the compliance rate of indoor temperature,the operation frequency of the circulating water pump,the power consumption of the circulating pump and the heat consumption per unit area changed significantly.The research results showed that after taking the secondary network intelligent balance system,the heat consumption per unit area and power consumption per unit area in this residential area were reduced by 14% and 23% respectively,and the compliance rate of indoor temperature reached 99%.Moreover,the heating quality was improved significantly while the energy-saving effect was remarkable.The study on this project can provide reference for the transformation and design of similar projects.

Intelligent prediction model of CFB boiler bed temperature based on parallel control theory

LIU Wenhui, YAN Bowen, WU Jiang, REN Yijun, KONG Weizheng, CHEN Jiyu

2022, 44(3): 50-57. doi:10.3969/j.issn.2097-0706.2022.03.008

Asbtract ( 112 )

HTML ( 2)

PDF (2047KB) ( 220 )

Figures and Tables | References | Related Articles | Metrics

To pursue the carbon peaking and carbon neutrality,with the accelerating development of renewable energy oriented power system and rising of environment protection awareness, thermal power units have to operate under extreme operating conditions. But the traditional mechanism modelling can hardly realize the accurate prediction on CFB boiler bed temperature. Based on parallel control theory,a real system can operate following the guidance of its virtual counterpart based on computational experiments. The virtual system takes Temporal Pattern Attention for Multivariate Time Series Forecasting （TPA-LSTM）model which can improve the traditional LSTM model's recognition ability of the temporal segments in industrial process by introducing temporal attention as the attention mechanism. Taking gray correlation analysis method to screen the data of the real system can improve the accuracy of the computational experiments of the virtual system. The analysis results show that the mean absolute deviation and mean absolute percent error of the predicted bed temperature can be reduced to 0.131 7 ℃ and 0.014 29%. The model realizes the accurate prediction on CFB boiler bed temperature.

Prediction on tube wall temperatures of boiler heating surfaces based on artificial intelligence

YAN Xinchun, CAO Huan, HUA Yunpeng

2022, 44(3): 58-62. doi:10.3969/j.issn.2097-0706.2022.03.009

Asbtract ( 133 )

HTML ( 1)

PDF (1608KB) ( 269 )

Figures and Tables | References | Related Articles | Metrics

In order to accurately predict the wall temperature of a boiler superheater at its outlet, the influencing factors for the heating surface temperature of the supercritical unit are analyzed. Then, according to the gray correlation analysis on the correlation degree between the influencing factors and metal wall temperature through, ten variables with correlation degree over 0.7 are chosen as the training samples. According to the variation characteristics of the data samples from the thermal power plant, through sliding window data judgment, multiple input variables in the extracted stable load sections are clustered to obtain cleaned data samples. The prediction model of the metal wall temperature is constructed through LSTM neural network. Making prediction on the heating surface temperature of a 350 MW supercritical unit, the maximum error between the predicted result and the measured value is 4.9 ℃, which proves the effectiveness of the model.

Data-driven modeling for SO₂ mass concentration of CFB units under variable load conditions

LI Caixia, ZHAO Jun, LI Jianwei, WANG Wei, WANG Jie, YU Haoyang

2022, 44(3): 63-69. doi:10.3969/j.issn.2097-0706.2022.03.010

Asbtract ( 121 )

HTML ( 2)

PDF (1711KB) ( 183 )

Figures and Tables | References | Related Articles | Metrics

Due to the lack of effective guidance for pollutant generation and reduction modelling in the dynamic process of circulating fluidized bed（CFB）units,the load adjusting capacity is restricted by pollutant emission to a certain extent.A data-driven SO₂ concentration dynamic model for CFB units is established based on an extreme learning machine.According to the generation and reduction mechanisms of pollutants from CFBs,appropriate input variables are selected.The model improved by genetic algorithms is of higher accuracy and better modelling results under dynamic conditions.The model can provide effective guidance for SO₂ concentration control systems.At the same time,a parallel system integrating a real system with its virtual counterpart is made on the basis of the proposed model under the framework of intelligent parallel control theory.The proposed intelligent parallel control for the SO₂ control systems can provide references for the SO₂ emission control of the following CFBs for boosting their load adjusting capacities to a certain extent.

MPPT for PV systems appended with centripetal attribute based on improved PSO algorithm

LI Xujiong, SUN Linhua, YANG Guoming

2022, 44(3): 70-76. doi:10.3969/j.issn.2097-0706.2022.03.011

Asbtract ( 120 )

HTML ( 2)

PDF (4867KB) ( 210 )

Figures and Tables | References | Related Articles | Metrics

In the study on the flocking behavior of birds,it is observed that birds fly randomly at the initial stage,but the peripheral ones show the tendency to move to the core of the flock as time goes.Combining this attribute with the traditional particle swarm algorithm,the improved PSO algorithm is of better global exploitation and local exploration capacities.The performances of different algorithms are measured and compared based on the PV array modeling under various illumination conditions.The results demonstrate that the proposed algorithm can find the global maximum power point quickly and stably under partial shading and changing illumination conditions, and solve the problem that traditional maximum power point tracking （MPPT）are prone to falling into local optima.

Bayesian network analysis of unplanned shutdown of generating units

MA Dongliang, CHEN Hui, ZHU Yanhai, JIANG Yuan

2022, 44(3): 77-82. doi:10.3969/j.issn.2097-0706.2022.03.012

Asbtract ( 108 )

HTML ( 1)

PDF (1770KB) ( 210 )

Figures and Tables | References | Related Articles | Metrics

To ensure the safe and stable operation of generator sets,it is necessary to analyze the causes of their failures intensively.Based on the analysis report on unplanned shutdowns of a power plant,the influencing factors of the unplanned outage events are summarized,and the Bayesian network diagram for predicting the unplanned outages is made.According to Bayesian network analysis on the unplanned shutdowns of the power plant,the causal inference analysis on the impacts of various factors on unplanned shutdowns is carried out.The results show that when the aging status evaluation of equipment is insufficient,the probability of outage events caused by equipment failure will be significantly increased.Placing equipment in an unfavorable environment for a long time will boost the probability of aging faults.Through Bayesian network analysis,the impact probabilities of various influencing factors on unplanned outage events are clarified,which provides reference for energy big data analysis of power generation enterprises and will improve the operation reliability and safety of units.

Table of Content