Table of Content

25 July 2023, Volume 45 Issue 7

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Integrated Energy System Optimal Operation and Control Power Trading and Management

Integrated Energy System

Researches on data center integrated energy systems based on knowledge graph

WANG Yongzhen, HAN Yibo, HAN Kai, HAN Juntao, SONG Kuo, ZHANG Lanlan

2023, 45(7):  1-10.  doi:10.3969/j.issn.2097-0706.2023.07.001

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As a key infrastructure in digital economy era， the energy consumption and carbon emissions of data centers are becoming increasingly prominent， and a series of research has been conducted by global scholars in the past decade. To comprehensively reveal the progress of global data center energy consumption researches， this article proposes a perspective based on integrated energy system and a metric method based on knowledge graph， and conducts a review of the latest research on the integrated energy system of data center from various aspects such as research trends， theoretical method and engineering cases. Firstly， a qualitative and quantitative analysis is conducted on international collaboration， characteristic distribution， and research hotspots in the field of global data center energy research based on scientific publications. Based on this， the article focuses on the research of data center integrated energy systems， which involve energy cascading utilization， multi-energy complementarity， and source-grid-load-storage integration. It analyzes the planning and design， operational evaluation， computational solution methods， and key points of data center integrated energy systems， revealing that the energy systems of data centers are constantly moving towards multidimensional global optimization that includes economic and low-carbon aspects. Finally， the article discusses the inapplicability of traditional PUE in data center integrated energy systems and preliminarily proposes evaluation indicators and systems for data center energy systems.

Research on optimal scheduling of distributed integrated energy systems in load-intensive areas considering demand response

CAO Zilin, WANG Wenjing, ZHAO Wei, KANG Ligai, GAO Xiaofeng, YANG Yang, WANG Jinzhu

2023, 45(7):  11-21.  doi:10.3969/j.issn.2097-0706.2023.07.002

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To coordinative and balanced schedule resources on source and load side in a load intensive area，a distributed integrated energy system considering demand response and coupled with an energy storage system to supplement power supply is established，and the mathematical model for this system is constructed. The system evaluation indexes are divided into economy，energy consumption and environmental protection indexes， and a multi-objective optimization model is established considering the three types of indicators above. The weights of these indexes are determined by Analytic Hierarchy Process （AHP），and the comprehensive performance index is derived and taken as the optimization objective of the system. The building models in a load-intensive area include models for office buildings，hotels，residential buildings and shopping malls，and the monthly cumulative loads of the four types of buildings are simulated. In addition，the capacity configuration and performance index of the load-intensive area before and after taking demand response into consideration are compared，and monthly balanced scheduling capacity of buildings in the load-intensive area before and after considering demand response is analyzed. The results show that， with or without taking demand response into consideration，the performance of the system taking an integrated energy supply system is better than that taking independent energy supply systems for the four types of buildings separately.

Diverse modeling methods for energy hubs in integrated energy systems and their typical applications

LI Yizhe, WANG Dan, JIA Hongjie, ZHOU Tianshuo, CAO Yitao, ZHANG Shuai, LIU Jiawei

2023, 45(7):  22-29.  doi:10.3969/j.issn.2097-0706.2023.07.003

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Under the background of "dual carbon"， the planning and operation of an integrated energy system （IES） is faced with new requirements on various evaluation indicators， such as effective energy utilization rate， energy unavailability and carbon emission level. In this context， selecting appropriate analytical elements and establishing their mechanism models has become an important task in IES research. An energy hub plays a key role in energy transmission and conversion in an IES， determining the distribution of energy and profoundly affecting the energy supply of the system from the perspective of multidimensional evaluation. Therefore， how to construct a multi-element model of an energy hub has become a key issue in IES analysis. The modeling methods of energy hubs with four modeling elements are analyzed， and their mechanisms and the applicability to match the development of energy systems are studied. The study provides references for following theoretical researches and practical applications.

Analysis of material and energy flows in biomass resource utilization under industrial symbiosis system

WU Tong, WANG Shouxin, CHENG Xingxing, LIU Kunkun

2023, 45(7):  30-39.  doi:10.3969/j.issn.2097-0706.2023.07.004

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Under the growing global shortage of fossil energy，bioenergy represented by biomass power and biofuels has been widely applied. However，its industry scale is hindered by the difficulty in raw material collecting and the economic feasibility. Industrial symbiosis is a sustainable development way to efficiently use various resources. From the perspective of industrial symbiosis，taking biomass cogeneration plants，bioethanol plants，anaerobic digestion（AD） plants，bio-oil refineries and cement plants as collaborative objects，the material and energy flows in biomass resource utilization are studied under four synergistic modes. Energy efficiency and economy analyses are carried out based on the study. The results show that the industrial symbiosis system is beneficial to resource reuse in biomass industry and waste reduction. In a case study， with a biomass straw supply at 13.73 t/h，the plants above could yield 660 L/h bioethanol，1.01 t/h bio-oil，275 m³/h biogas and 67.68 t/h cement.Through the reuse of wastes and by-products，the industrial symbiosis system cogenerated 0.36，2.93，3.47 h or 6.54 MW·h electric power，and 1 365.8，4 548.8，2 883.2 or 29 489.4 MJ heat under four synergistic modes. The energy utilization efficiencies of the system in four synergistic modes differed from that of the plants in independent operation mode by -2.66%，11.96%， 4.15% and 9.40%，respectively. The cost of raw material collection and transportation were saved by around 454 000，3 692 000，4 372 000 and 82 40 000 Yuan per year，respectively. This study provides reference data for the synergistic development of biomass industries.

Performance analysis on high temperature air source heat pump coupling cycle based on industrial waste heat

SUN Jian, QIN Yu, HAO Junhong, YANG Yongping

2023, 45(7):  40-47.  doi:10.3969/j.issn.2097-0706.2023.07.005

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Industrial waste heat，with a wide range of temperature，can be hardly utilized by conventional ways. Heat pumps can recovery medium and low temperature waste heat effectively， safely and environmental-friendly with low energy consumption. However， traditional absorption heat pumps and compressive heat pumps can only work in a narrow temperature range due to the limitations of thermodynamic cycle， thermodynamic properties of their working mediums and temperature and pressure resistance of their compressors， which cannot meet the requirements of "high heating temperature" and " wide temperature range heat transfer" of industrial waste heat recovery. To solve the problems above， an ultra-high temperature air source heat pump unit based on absorption and compression coupling cycle is proposed. The unit can recover heat from industrial waste steam（120 ℃） and air to produce 160 ℃ hot water（vapor）. The proposed coupling cycle is modelled and simulated by Engineering Equation Solver（EES）. The results show that the COP of the heat pump unit peaks at 1.600 under the optimal working condition under which hot water temperature is 130 ℃ and outdoor temperature is 30 ℃. When the hot water temperature rises to 160 ℃， the COP of the heat pump unit will be 1.400. The coupling cycle greatly broadens the working temperature range of heat pumps and improves their heating temperature. The study is of certain reference value for heat pumps in industrial waste heat recovery， and can significantly improve the utilization rate of primary energy in industrial field.

Optimal Operation and Control

Power system transient stability assessment method based on multiple STA-GLN ensemble models

YANG Bo, LI Chengyun, LYU Haoxuan, ZHOU Bowen, LI Guangdi, GU Peng

2023, 45(7):  48-60.  doi:10.3969/j.issn.2097-0706.2023.07.006

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With the continuous access of high-proportion renewable energy to the power grid and advancing of power electronization， power systems are becoming increasingly complex in structure， which threats of systems' stability. To address the poor topology adaptability， difficulty in learning instability samples， and long model training time of the transient stability assessment （TSA） method based on artificial intelligence（AI），an ensemble TSA method based on Spatial-Temporal Attention Mechanism， Graph Convolution and Long Short-Term Memory Network（STA-GLN） is proposed. A power system simulation model is built， in which different line faults are set under three topologies， full connection， N-1 disconnection and N-2 disconnection， and the original sample sets are obtained. The TSA method based on STA-GLN shows stronger adaptability and accuracy to the variation of the system's topologies. Then， Adaptive Boosting （AdaBoost） and transfer learning are integrated into the multi-task TSA model based on STA-GLN， which reduces the false judgment and accelerates the response speed of the model. The effectiveness of the method is verified by the simulation analysis of a New England 10-generator 39-node system.

Optimized control method for flexible load of a building complex based on MADDPG reinforcement learning

BAO Yixin, XU Luoyun, YANG Qiang

2023, 45(7):  61-69.  doi:10.3969/j.issn.2097-0706.2023.07.007

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The power grid dispatch environment and information organization environment have become more complex， and the difficulty of power grid regulation has gradually increased. Since deep reinforcement learning technology is of effective perception on complex system operation statuses，strong adaptability and good scalability，a distribution network optimization scheduling method based on deep reinforcement learning is proposed. Based on the simulated source-network-load-storage integrated distribution network model of a building complex，Multi-Agent Deep Deterministic Policy Gradient（MADDPG）algorithm was statically optimized from its principle.The model and real data were input into a multi-agent reinforcement learning framework suitable for grid-level objectives，and the optimized algorithm was tried to regulate the voltage of the distribution network system. The results show that the algorithm basically eliminates the abnormal peak voltages and reduces the overall voltage deviation.The optimized multi-objective oriented algorithm reduces the load-generated power difference while levelling the voltage off at a low level. The optimized control method for building complex flexible load based on reinforcement learning is proven to be effective.

Short-term prediction on integrated energy loads considering temperature-humidity index and coupling characteristics

JIN Li, ZHANG Li, TANG Yang, TANG Qiao, REN Juguang, YANG Kun, LIU Xiaobing

2023, 45(7):  70-77.  doi:10.3969/j.issn.2097-0706.2023.07.008

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To address the vulnerability of integrated energy load prediction to meteorological factors and the complexity and low accuracy of prediction models caused the coupling characteristics of heterogeneous energy， a short-term load forecasting model considering temperature-humidity index and coupling characteristics is proposed. Excavating the coupling characteristics of multiple loads， the input variables considering the influence of temperature-humidity index and multiple factors are constructed. Ensuring that the information is valid，kernel principal component analysis （KPCA） is used to complete the dimensional reduction of prediction input space， and the prediction model is built based on gated recurrent unit （GRU） neural network. Attention mechanism is introduced into the model to extract important differentiated features. Finally， the electric and cooling load data of a practical system are selected for simulation， and the results show that the root mean square errors and mean absolute percentage errors of the electric and cooling load predicted by KPCA-GRU-Attention model are 1 025 kW， 2.7% and 2 167 kW， 2.9 %， respectively. The accuracy has been significantly improved. The proposed model effectively improves the short-term prediction accuracy of integrated energy loads by considering the influence of multiple factors， realizing the accurate perception on energy demand.

Research on power distribution strategy of an RSOC-based wind-photovoltaic-hydrogen energy system

LI Jing, DOU Zhenlan, WANG Jiaxiang, ZHANG Chunyan, LU Tao, NI Yaobing

2023, 45(7):  78-86.  doi:10.3969/j.issn.2097-0706.2023.07.009

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As a new hydrogen storage technology， a reversible solid oxide cell（RSOC）has a promising application prospects in renewable integrated energy systems. Low-temperature hydrogen storage technologies take stack power as system power in power allocation strategy making. A RSOC system is equipped with a large power consumption auxiliary system， Balance of Plant （BOP）， to maintain its high-temperature operation， and the power control rate is limited by the safety temperature. Therefore， the power allocation strategy for the RSOC is decided by the power consumption of the BOP and power control rate of the RSOC hydrogen-water energy conversion system. The modelling of an RSOC-based wind-photovoltaic-hydrogen integrated energy system should make developing the power model for the RSOC hydrogen-water energy conversion system with BOP the priority. To optimize the power distribution in the RSOC-based wind-photovoltaic-hydrogen integrated energy system， a power distribution strategy considering the constraints of the RSOC power control rate and the capacities of subsystems is established， with the goals of minimizing the system daily operating cost and maximizing the consumption of the wind and photovoltaic power. The optimization is solved by multi-objective particle swarm optimization （MOPSO） algorithm. Compared with the general operation strategies， the optimization strategy proposed provides the system with more benefits. Moreover， the participation of the power grid and the storage battery increases the flexibility of the power regulation and reduces the overall operating power of the system.

Power Trading and Management

Pricing strategy in district-level integrated energy market based on deep reinforcement learning

HU Ze, ZHU Ziqing, BU Siqi, CHAN Jiarong, WEI Xiang

2023, 45(7):  87-96.  doi:10.3969/j.issn.2097-0706.2023.07.010

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Integrated energy market （IEM）， being able to integrate multiple forms of energy transactions and promote the efficient use of energy， is growing and gradually taking place the traditional energy markets. District integrated energy market （DIEM）， which serves as a link between the supply and demand side， is crucial for energy transaction and pricing， and affects the operation of integrated energy systems. Given this context， a DIEM transaction structure is constructed to optimize the pricing strategy for Energy Service Providers （IESPs） and the demand response mechanism for Integrated Energy Consumers （IECs）. The double-layer decision-making optimization takes into account the elasticity of the energy demand， the uncertainty of the output of renewable energy sources， and privacy protection comprehensively. The optimal pricing of the IESP can be obtained by Deep Deterministic Policy Gradient （DDPG），which is compared with the pricing strategy made by Deep-Q-Learning（DQN） in a simulation case. The simulation analyzes the coupling relationship of energy prices in DIEM and the interaction between integrated energy pricing strategy and demand elasticity， showing that the revenue of the Integrated energy system obtained by DDPG is 6.8% higher than that made based on DQN.

Integrated energy system optimization scheduling considering improved stepped carbon trading mechanism and demand responses

GE Leijiao, YU Weikun, ZHU Ruoyuan, WANG Guantao, BAI Xingzhen

2023, 45(7):  97-106.  doi:10.3969/j.issn.2097-0706.2023.07.011

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The integrated energy system （IES） is an important approach to pursue the "dual carbon" target and achieve low-carbon energy transformation of China. In order to facilitate the carbon emission reduction of the IES and improve its economic benefits， an IES optimization scheduling model considering improved stepped carbon trading mechanism and demand response mechanism is proposed. Firstly， a carbon flow model is introduced in the energy hub framework to reflect the flow of carbon dioxide in the system， and an improved stepped carbon trading mechanism is proposed to facilitate carbon reduction of the system. Then， the multi-energy demand response mechanism on user side is introduced to drive the transformation of energy usage pattern motivated by pricing mechanism，so as to promote the consumption of renewable energy. Considering decision maker preference， with the improved stepped carbon trading mechanism as the connection point， a low-carbon IES economic optimization scheduling model is established. The optimization scheduling is guided by the carbon emission index and user comfort， and CPLEX solver is used to solve the scheduling model. The proposed model and mechanisms are verified under five scenarios， whose results prove that the cooperation of the improved carbon trading mechanism， demand response mechanism and optimization scheduling model can effectively lower the carbon emissions and improve the economy of IESs.