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A Day-Ahead Optimal Scheduling of Regional Integrated Energy System Considering Power to Gas
XIONG Junhua, JIAO Yachun, WANG Mengdi
 doi: 10.19725/j.cnki.1007-2322.2021.0132
[Abstract](201) [FullText HTML](116)
To enhance the economy of regional integrated energy system and improve the accommodation ability of renewable energy sources, a scheduling optimization model containing power-to-gas (abbr. P2G) was proposed. Firstly, the P2G was divided into two operating stages: in the link of water electrolysis the produced hydrogen was added into hydrogen storage tank as the fuel source of hydrogen fuel cell, and the energy conversion of hydrogen energy into electric energy and thermal energy was implemented by hydrogen fuel cell, in the next link the residual hydrogen was input into methanator to reduce the energy loss due to directly methanating all hydrogen. Secondly, the gas turbine units adopted the variable efficiency operation mode, by means of flexibly regulating the efficiency of power supply and thermal supply of gas turbine units the output of heat and electricity became more economic and reasonable. On this basis, taking the minimum daily operating cost composed of system electricity purchasing cost, gas purchasing cost, wind curtailment cost and environmental cost as economic objective, a day-ahead optimization scheduling model for of regional integrated energy system containing P2G was constructed. Finally, this problem was solved by chaotic particle swarm optimization algorithm based on spatial distance. Simulation results show that the proposed scheduling model can effectively promote the reasonable and high-efficient utilization of multi-class energy sources and improve the accommodation ability of renewable energy sources and the economy of system operation.
Electricity Price Risk Assessment Considering Guaranteed Accommodation of Renewable Energy in Electricity Market
HE Jie, JIN Luosong, ZHAO Wen, HUANG Hengzi, LI Siying, SANG Maosheng, DING Yi
 doi: 10.19725/j.cnki.1007-2322.2021.0111
[Abstract](169) [FullText HTML](110)
Due to the uncertainty of the output of renewable energy mainly composed of wind power and photovoltaic generation, the grid-connection of high proportion renewable energy may bring risk to the clearing price of electricity market. For this reason, bringing the guaranteed accommodation of renewable energy composed of wind power and photovoltaic generation into the clearing model of electricity market, an electricity price risk assessment method considering the uncertainty of renewable energy was proposed. Firstly, the multi-scenario technique was used to describe the uncertainty of renewable energy output, and a representative set of limited scenarios was generated. Secondly, an electricity market clearing model, into which the renewable energy participated, was established and a computing method of nodal price was put forward. Thirdly, a price risk assessment index system was proposed to assess the price risk caused by renewable energy uncertainty in the levels of node and system respectively. Finally, the effectiveness of the proposed method was verified by modified IEEE30 bus system. Analysis results show that using the proposed method both system price risk and nodal price risk brought by the uncertainty of renewable energy can be effectively assessed, and this method is available for reference in the relevant research on renewable energy participating in electricity market.
The Congestion Management in Active Distribution Network Based on the Master-Slave Game
ZHANG Xiaodong, AI Xin
Accepted Manuscript  doi: 10.19725/j.cnki.1007-2322.2021.0090
[Abstract](146) [FullText HTML](99)
With continually increase of permeability of demand-side flexible resources in distribution network, its uncoordinated operating mode may lead to line congestion and nodal voltage out-of-limit in distribution network. To cope with these problems, a master-slave game bi-level dispatching framework, in which the nodal marginal price of distribution network was uniformly cleared, was proposed. The problem of minimizing user’s electricity utilization cost under the guidance of the load aggregator, which was the leader of the master-slave game, was solved by the upper framework. The problem of maximizing social welfare by the distribution network system operator, which was the follower of the master-slave game, under the premise of considering power flow security and voltage out-of-limit was solved by the lower framework. By use of the Karush-Kuhn-Tucker optimality condition and the duality theorem, the nonlinear bi-layer problem was translated into monolayer mixed integer linear programming problem to solve. The correctness of the proposed model is verified by simulation result, and its effectiveness of mitigating distribution network congestion as well as the effect of flexible resources in the management of distribution network congestion are analyzed.
Energy Internet
Collaborative Optimal Dispatch of Multi-Station Integration Based on Affine Robust Optimization
ZHANG Xiaoyan, GUO Chuangxin, JIN Guosheng, YIN Kang, GAO Yadong, ZHOU Ying
2022, 39(4): 379-387.   doi: 10.19725/j.cnki.1007-2322.2021.0139
[Abstract](0) [FullText HTML](0) [PDF 2927KB](0)
To cope with the optimal operation and income distribution of multi-energy power stations such as data centers, electric vehicle charging stations, distributed photovoltaic generation, wind power stations and energy storage stations accompanied with uncertain renewable energy output, an affine robust optimization-based a multi-station collaborative optimization strategy was proposed. Considering the fact that the data center and other subsystems belonged to different operators, the modeling for each subsystem of the multi-station integration system, in which the load in the data center possessed the characteristics of shifting load, was respectively conducted. By means of box uncertainty set, the modeling for the uncertainty of the output of wind power stations and the photovoltaic generation was carried out. Taking the maximum of total revenue from the multi-station fusion and coordination as objective function, the proposed models were solved by affine robust optimization method. A shadow price-based income distribution method for energy stations and data centers was proposed. Finally, the effectiveness of the proposed models and the reasonableness of the proposed income distribution method are verified by the results of simulation example.
Chance-Constrained-Based Optimal Operation for Regional Energy Internet
LI Ning, WANG Qi, GE Zhongxin, CHEN Yonghua, YANG Dongmei, YU James
2022, 39(4): 388-396.   doi: 10.19725/j.cnki.1007-2322.2021.0142
[Abstract](179) [FullText HTML](104) [PDF 2489KB](7)
To ensure reliable and economic operation of regional energy Internet, based on chance constraint theory the optimal operation method under uncertain environment was researched. Considering stochastic variables in wind power output forecasting error, the reserve capacity chance constraint condition for gas turbine and the objective function of reserve deviation risk cost were established, and then, a stochastic optimization operation method of regional energy Internet considering reserve capacity was proposed. Simulation results show that the proposed method can effectively control the operation risks caused by wind power integration, improve the reliability of system operation, and ensure the economy of system operation and gas turbine reservation.
Energy Inter
Optimized Operation Mode Considering Cooperation Among Energy Hubs
ZHU Xiping, YAO Xianyi, FU Qian, LUO Jian, LI Zilin, WEN Hong
2022, 39(4): 397-405.   doi: 10.19725/j.cnki.1007-2322.2021.0168
[Abstract](174) [FullText HTML](121) [PDF 2563KB](7)
In regional energy system coordination and cooperation of multi energy hubs (abbr. EH) is beneficial to improve system stability and decrease operational costs of EH themselves. Therefore, multi EHs were built to cooperation alliance and the energy dispatching was conducted in the manner of cooperation. By means of Shapley values the benefit was distributed and related strategy was enacted. At the same time the EH participated in two markets, i.e., the day-ahead market and realtime market, and by means of demand response the load was cut down to further optimize the operation cost. A multi EH model was constructed to expound the transaction mode and the influence of uncertain factors was taken into account, and applying stochastic optimization method the minimized operation cost was taken as the object and this method was adopted to carry out the simulation among multi EH. Simulation results show that by means of the cooperation among multi EH the cost reduction of each individual can be implemented.
New Energy Power System
Stability Analysis of High Permeability Wind Power with Inertial Control Connected to AC-DC Hybrid Transmission System
ZENG Lingquan, WANG Dong, HUANG Yunhui, TANG Ruiyang, DENG Xiangtian, ZHU Guorong
2022, 39(4): 406-413.   doi: 10.19725/j.cnki.1007-2322.2021.0155
[Abstract](0) [FullText HTML](0) [PDF 4151KB](0)
To research the stability under the condition of connecting high-permeability wind power in AC-DC hybrid power transmission system, firstly, a low-frequency oscillation analysis model, in which the doubly-fed induction generator (abbr. DFIG) containing inertial control was connected in the AC-DC hybrid system with frequency limit control (abbr. FCL), was established. Secondly, the influence of the DFIG unit containing inertial control on the inter-area oscillation mode of AC-DC hybrid network under different permeabilities was analyzed. Thirdly, the influence rule of putting frequency limit controller for DC transmission system into operation on both system damping characteristic and frequency characteristic was analyzed. Research results show that adopting inertia control of DFIG and FLC of HVDC transmission system under high permeabilities could increase system damping and suppress low-frequency oscillation. Finally, above-mentioned results of stability analysis are verified by time-domain simulation by Matlab/Simulink software.
Adaptive SPMC Frequency Modulation Strategy for AC/DC Interconnected Grid Including Wind Power
ZHAO Xilin, CHEN Xurong, ZHANG Chengcheng, WU Pengqi
2022, 39(4): 414-421.   doi: 10.19725/j.cnki.1007-2322.2021.0152
[Abstract](119) [FullText HTML](113) [PDF 3618KB](0)
Connecting large-scale wind power into AC/DC interconnected grid put forward higher requirements on the control performance and frequency regulation ability of automatic generation control (abbr. AGC). On the basis of wind power participating power system frequency regulation, a supplementary power modulation controller (abbr. SPMC) based inter-regional power compensation strategy was proposed. Firstly, considering the elastic characteristics of overload rate variation in HVDC link, an adaptive dynamic SPMC strategy, in which the variation of power and frequency was considered simultaneously, was put forward. This strategy could effectively promote the rapidity of power regulation in HVDC when SMPC participated in the system frequency regulation. Secondly, based on the active participation of HVDC link in the system frequency regulation, considering the uncertainty of wind power, the integrated inertia control was utilized to change wind power output to further improve system frequency regulation performance index. Simulation results show that the dynamic power regulation characteristics of the put forward strategy can effectively enhance the pertinence of inter-regional power transmission and improve the effect of power grid frequency regulation.
Reactive Power Optimization of Clustered New Energy Power Stations Considering Active and Reactive Coupling Characteristics of Generating Units
FU Hongjun, SUN Ran, ZHAO Hua, LI Haibo, JIANG Keteng, LEI Yi, WANG Ruizhe
2022, 39(4): 422-430.   doi: 10.19725/j.cnki.1007-2322.2021.0159
[Abstract](0) [FullText HTML](0) [PDF 2590KB](0)
Along with the increasingly grid-connected scale of such new energy as wind power stations and photovoltaic (abbr. PV) stations and the grid-connection of HVDC equipments, the power grid gradually presents the feature of high degree power electronization, and it brings new demands and new challenges to reactive power control. In allusion to this problem, firstly, the dynamic reactive power regulation ability model of doubly fed induction generator (abbr. DFIG), direct-driven wind turbine (abbr. DDWT) and PV were presented. Secondly, a reactive power control strategy, in which the active power was proactively considered to reduce, was proposed, and a reactive power optimization model, in which the active power was considered at the clustered level of new energy stations, was established. Thirdly, by means of combining the Second-order cone-convex relaxation algorithm with piecewise linearization algorithm, a solving method for mixed integer and second-order cone optimization was put forward, by use of this method the difficult nonlinear nonconvex and mixed integer programming problems in the established model were effectively solved. Finally, the simulation-based analysis on the grid-connection system of a certain practical clustered new energy power stations was performed. Simulation results show that adopting this reactive power regulation and control strategy, in which the active power is proactively considered to reduce, the reactive power control ability of the system can be evidently enhanced, and the voltage control effect can be improved, the economy of the system can be ameliorated as well, thus, both the correctness and the economic value of the established model were verified.
Electricity Market
Joint Optimal Operation of Multi Wind-Hydrogen System Based on Multi-Agent Reinforcement Learning
LIU Jianshu, JIANG Yuewen
2022, 39(4): 431-440.   doi: 10.19725/j.cnki.1007-2322.2021.0197
[Abstract](0) [FullText HTML](0) [PDF 2623KB](0)
In allusion to the joint operation of multi wind-hydrogen system, based on multi-agent reinforcement learning a multi wind-hydrogen system joint optimization operation method was proposed to make the multi wind-hydrogen system enable to accommodate wind power effectively and meanwhile to maximize the joint revenue. Firstly, considering the joint operation of wind farm, hydrogen generation and hydrogenation station in the manner of contract transaction, respective operation models for them were constructed. Secondly, taking the maximized joint operation revenue of multi wind-hydrogen system as the objective, a joint optimization operation model was established. Thirdly, to cope with the dimension disaster caused by multi decision variables of multi wind-hydrogen system, the multi agent was led into the reinforcement learning and the method of multi-decision update was adopted to speed up the algorithm convergence. Finally, the reasonableness of the established model and the feasibility of the adopted method are verified by simulation example.
Multi-Frequency Combination Short-term Power Load Forecasting with Convolutional Neural Networks - Bidirectional Gated Recurrent Unit-Multiple Linear Regression based on Variational Mode Decomposition
FANG Na, LI Junxiao, CHEN Hao, LI Xinxin
2022, 39(4): 441-448.   doi: 10.19725/j.cnki.1007-2322.2021.0130
[Abstract](0) [FullText HTML](0) [PDF 2422KB](0)
To effectively improve the accuracy of power load forecasting and in allusion to such characteristics of power load as nonlinearity, non-stationary and time sequence, a multi-frequency combination power load forecasting model, in which the Convolutional Neural Network (abbr. CNN) and the Bidirectional Gated Recurrent Unit (abbr. BiGRU) and the Multiple Linear Regression (abbr. MLR were mixed, was proposed. Firstly, in the proposed model the correlation degree analysis was utilized to obtain similar days and their loads were constituted new data series, meanwhile the variational mode decomposition (abbr. VMD) was used to decompose the obtained data series and reconstruct into high and low frequencies. As for the high-frequency component the CNN-BiGRU model was used for the prediction; and for the low-frequency component the MLR was used. Finally, superposing the predicted results obtained by above mentioned two models the final predicted results could be obtained. Based on the real data of Australia in 2006, a short-term load forecasting was performed. Simulation results show that comparing with other network models, by use of the proposed model the forecasting results possess higher prediction accuracy and fitting ability.
Research on Credit Risk Evaluation Model of Electricity Selling Company Based on Bayesian Best Worst Method and Cloud Model
YANG Yongqi, XUE Wanlei, ZHAO Xin, QI Ze, ZHAO Huiru
2022, 39(4): 449-459.   doi: 10.19725/j.cnki.1007-2322.2021.0148
[Abstract](0) [FullText HTML](0) [PDF 2443KB](0)
The electricity selling company is a brand new role appeared in the new round reform of electricity market, and the evaluation on its credit risk can perfect the credit management system of electricity market. The credit risk evaluation is the important safeguard to promote the ordered operation of electricity market. By means of establishing a multi-level credit risk indicator system containing five dimensions the credit risk of electricity selling company could be identified. After determining the weight of the index by utilizing Bayesian best and worst method, based on normal cloud model a credit risk evaluation model of electricity selling company was constructed. Taking related data of four electricity selling company as the objects, the analysis of examples was performed. Analysis results show that both market trading behavior and financial condition are the key factors impacting the credit risk of electricity selling company, and the constructed model can effective evaluate the credit risk of electricity selling company and provide effective tool for credit risk management of electricity selling company.
Energy Storage
Distributed Photovoltaic Generation and Energy Storage Planning of Distribution Network Based on Multi Scenarios
ZHAO Lijun, ZHANG Xiulu, HAN Liwei, SUN Yonghui, WANG Junsheng, LIU Zifa, YU Puyang
2022, 39(4): 460-468.   doi: 10.19725/j.cnki.1007-2322.2021.0257
[Abstract](0) [FullText HTML](0) [PDF 2443KB](0)
In view of the fact that the planning method based on the maximum operation section of the system was difficult to effectively cope with the uncertainty caused by the grid-connection of distributed photovoltaic (abbr. PV) generation, the joint planning characteristics of distributed PV generation and energy storage was researched. By means of the information entropy-based scenario extraction method the typical operation scenarios of PV generation and loads were generated. Comprehensively considering the economy, environmental protection and reliability, a model for multi scenarios-based site-selection and capacity determination for PV generation and energy storage planning was established and this model was solved by centroid opposition-based learning-particle swarm optimization (abbr. COBL-PSO) algorithm, and the result of the planning was intuitively assessed by the radar chart. Through the contrastive analysis on the computing example, the planning scheme of PV generation and energy storage battery, which can meet the demand of distribution network, was obtained. Thus, both feasibility and correctness of the established model are verified.
Key Technologies of Electric Vehicle
Configuration Model of Electric Vehicle Charging Facilities Considering Demand Response
LÜ Yuan, HE Yongxiu, WANG Kehui, SU Fengyu
2022, 39(4): 469-477.   doi: 10.19725/j.cnki.1007-2322.2021.0144
[Abstract](159) [FullText HTML](106) [PDF 2156KB](7)
With the implementation of policies such as dual-carbon targets and new infrastructure construction, the number of electric vehicles has become an inevitable trend, and electric vehicle users will become one of the main players in demand response. In the process of participating in demand response, users will change the charging time and power of electric vehicles, which will bring about the temporal and spatial migration of charging load demand, and the original planning results of charging facilities are no longer applicable. This article considers the impact of price-based demand response and incentive-based demand response on the results of electric vehicle planning. First, from the user’s point of view, the charging behavior model of electric vehicles under different demand response modes is established with the lowest charging cost of users as the objective function, and electric vehicle demand response strategies under peak and valley electricity prices are proposed to predict the temporal and spatial distribution of charging demand. In the case of the site, from the perspective of investors, a charging facility planning model is established with the goal of maximizing the total profit of construction and operation, and the construction of the charging station and the scale of construction are planned. The calculation example plans the electric vehicle charging facilities without considering demand response, price-type demand response, and incentive-type demand response, and conducts sensitivity analysis on demand response participation and demand response price.
Smart Grid
Temperature Control Load Extraction and Characteristic Analysis Based on Time-dependent Intrinsic Correlation
WANG Min, WU Chao, YANG Xingang, ZHOU Jian
2022, 39(4): 478-486.   doi: 10.19725/j.cnki.1007-2322.2021.0122
[Abstract](201) [FullText HTML](113) [PDF 3471KB](4)
To balance the demand of users’ electricity and the demand of power grid dispatching and to improve the ability of demand response of temperature-controlled load as well as to further master the operation rules of temperature-controlled load so that to prearrange the dispatching plan, a multi time-scale dynamic correlation analysis model based on improved complete set of adaptive noise and time internal relatedness was proposed. By means of analyzing the dynamic association of load and environmental temperature time series under multi time-scale,its complex variation relation within partial time period was seized, thus both peeling off the fluctuant component of temperature-controlled load off from the load curve and the estimation of the proportion of temperature-controlled load were implemented. The application method of the proposed model was illustrated by the analysis of computing examples, meanwhile the effectiveness of the proposed model was verified.
Research on the Home Intelligent Energy Management System Based on Noninvasive Load Monitoring
DING Xun, ZHANG Zhong, XIA Zhaojun, FAN Yangyang, ZHANG Ying, KONG Liang
2022, 39(4): 496-504.   doi: 10.19725/j.cnki.1007-2322.2021.0140
[Abstract](202) [FullText HTML](156) [PDF 2619KB](8)
With the integration of multi-energy networks and the rapid development of energy Internet technology, household energy management plays an important role in solving the problem of supply and demand of each energy network node. Most of the existing household energy consumption management is optimized for the known power load, while the diversification of the types of electrical equipments and the sudden increase of electrical equipments are not considered. On the basis of the noninvasive load monitoring (abbr. NILM) algorithm, the household load electricity consumption law and information that provide data support for household smart energy management can be effectively obtained. A multi-objective optimization model of smart home energy consumption, in which the household electricity cost, temperature, time and comfort level were taken as objective functions, was established, and the controllable load, EV and energy storage system were analyzed and corresponding mathematical models were proposed and solved by particle swarm algorithm. Simulation results show that based on NILM algorithm and only considering electricity cost and comfort level, the home power utilization cost can reduced by 72.5%. When user-controllable power load increases, the control strategy can be updated by NILM in realtime to decrease user’s electricity utilization cost. Results of multiple calculations for different users show that the net cost and computing time fluctuate slightly, thus the rationality and reliability of the NILM algorithm can meet the requirements of family intelligent energy consumption.
An Evaluation Method of Distribution Network Scheduling Mode Based on Comprehensive Weight TOPSIS Model
QU Shaojie, LÜ Yibo, GAO Zhonghui, WANG Tengzhi, LIN Li
2022, 39(4): 487-495.   doi: 10.19725/j.cnki.1007-2322.2021.0164
[Abstract](165) [FullText HTML](103) [PDF 2086KB](2)
The complexity, perception difficulty and scheduling management difficulty of distribution network are far beyond those of main grid and along with the large amount grid-connection of new energy the selection of dispatching and management mode of distribution network is such a work in a need for research urgently. In allusion to the problem of selecting distribution network dispatching and management mode, a dispatching and management mode evaluation method based on the model of technique for order preference by similarity to ideal solution (abbr. TOPSIS) was proposed to implement the evaluation and analysis on dispatching and management modes. Firstly, the current dispatching management modes and the being planned future intensification mode were summarized, and taking the grid-connected capacity of new energy into account and comprehensively considering high efficiency, coordination, economy and sociality an evaluation index system for distribution network dispatching and management was constructed. Secondly, by use of analytic hierarchy process (abbr. AHP) algorithm and entropy weight method, the subjective and objective weights were obtained and the weighted comprehensive weight was obtained, furthermore, a TOPSIS-based evaluation model was established. Finally, taking a certain actual distribution network located in North China for example, the feasibility of the proposed method was verified.


Competent Authorities:

Ministry of Education of the People's Republic of China

Sponsor: North China Electric Power University

Editor and Publisher:

Editorial Department of Modern Electric Power

Editor-in-Chief: LIU Jizhen

Deputy Editor: ZHAO Dongmei

Director of Editorial Department: SONG Shufang

Publication Number:

ISSN 1007-2322 CN 11-3818/TM CODEN XDIIC2

Address: Building D0539,North China Electric Power University,Beijing 102206, China

Tel: +86-10-61772300

Fax: 010-61772726



Agent of Subscriptions Abroad:

China International Book Trading Corporation (Guoji Shudian) P.O.Box 399, Beijing, China

Postal code: BM1745

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