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Communication Resources Allocation for Time Delay Reduction of Frequency Regulation Service in High Renewable Penetrated Power System

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 468-480页

作者： Hongjie He Ning Zhang chongqing kang Song Ci Fei Teng Goran Strbac State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Electrical and Electronic Engineering Imperial College LondonSW72BULondonU.K

The high renewable penetrated power system has severe frequency regulation problems.Distributed resources can provide frequency regulation services but are limited by com-munication time delay.This paper proposes a communication resources allocation model to reduce communication time delay in frequency regulation service.Communication device resources and wireless spectrum resources are allocated to distributed resources when they participate in frequency regulation.We reveal impact of communication resources allocation on time delay reduction and frequency regulation performance.Besides,we study communication resources allocation solution in high renewable energy penetrated power systems.We provide a case study based on the HRP-38 system.Results show communication time delay decreases distributed resources'ability to provide frequency regulation service.On the other hand,allocating more communication resources to distributed resources'communica-tion services improves their frequency regulation performance.For power systems with renewable energy penetration above 70%,required communications resources are about five times as many as 30%renewable energy penetrated power systems to keep frequency performance the same.Index Terms-Communication resources allocation,commun-ication time delay,distributed resource,frequency regulation,high renewable energy penetrated power system.

关键词： Communication resources allocation communication time delay distributed resource frequency regulation high renewable energy penetrated power system

Low-Carbon Dispatching for Virtual Power Plant with Aggregated Distributed Energy Storage Considering Spatiotemporal Distribution of Cleanness Value

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 346-358页

作者： Hongchao Gao Tai Jin Guanxiong Wang Qixin Chen chongqing kang Jingkai Zhu State Key Laboratory of Power System Operation and Control Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Sichuan Energy Internet Research Institute Tsinghua University Chengdu 200016China Power Dispatching and Control Center of Shanghai Municipal Electric Power Company Shanghai 200122China IEEE

The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness.To address this issue,the concept of cleanness value of distributed energy storage(DES)is proposed,and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness.Based on this,an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator.Then,an optimal low-carbon dispatching for a virtual power plant(VPP)with aggregated DES is constructed,where-in energy value and cleanness value are both considered.To achieve the goal,a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network(DN)and DES behavior,but as a cost,it brings multiple nonlinear relationships.Subsequently,a solution method based on the convex envelope(CE)linear re-construction method is proposed for the multivariate nonlinear programming problem,thereby improving solution efficiency and feasibility.Finally,the simulation verification based on the IEEE 33-bus DN is conducted.The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond.Meanwhile,resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.

关键词： Distributed energy storage virtual power plant(VPP) spatiotemporal distribution low-carbon dispatching

A Distributed Computing Algorithm for Electricity Carbon Emission Flow and Carbon Emission Intensity

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Protection and Control of Modern Power Systems 2024年第2期9卷 138-146页

作者： Xingping Wu Wei Yang Ning Zhang Chunlei Zhou Jinwei Song chongqing kang Big Data Center of China Network Chinese Society of Electrical Engineering State Network Big Data Center Department of Electrical Engineering Tsinghua UniversityBeijingChina 不详 Big Data Center State Grid Corporation of ChinaBeijing 100052China Department of Electrical Engi-neering Tsinghua UniversityBeijingChina

The calculation of the indirect carbon emis-sion is essential for power system policy making,carbon market development,and power grid planning.The em-bedded carbon emissions of the electricity system are commonly calculated by carbon emission flow theory.However,the calculation procedure is time-consuming,especially for a country with 500-1000 thousand nodes,making it challenging to obtain nationwide carbon emis-sions intensity precisely.Additionally,the calculation procedure requires to gather all the grid data with high classified levels from different power grid companies,which can prevent data sharing and cooperation among different companies.This paper proposes a distributed computing algorithm for indirect carbon emission that can reduce the time consumption and provide privacy protection.The core idea is to utilize the sparsity of the nodes’flow matrix of the nationwide grid to partition the computing procedure into parallel sub-procedures exe-cuted in multiple terminals.The flow and structure data of the regional grid are transformed irreversibly for pri-vacy protection,when transmitted between terminals.A 1-master-and-N-slave layout is adopted to verify the method.This algorithm is suitable for large grid compa-nies with headquarter and branches in provinces,such as the State Grid Corporation of China.

关键词： Carbon emission flow cooperative computing carbon emission intensity matrix block par-tition power flow tracing parallel computing privacy protection

An Efficient Method for Identifying the Inactive Transmission Constraints in a Network-Constrained Unit Commitment

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2366-2373页

作者： Ziming Ma Haiwang Zhong Qing Xia chongqing kang Qiang Wang Xin Cao the State Key Laboratory of Power Systems and the Department of Electrical Engineering Tsinghua UniversityBeijing 100084China State Grid Hebei Electric Power Company Shijiazhuang 050000China

Network-constrained unit commitment(NCUC)is one of the most widely used applications in power system and electricity market operations.According to empirical evidence,some of the transmission constraints in a NCUC are inactive.Identifying and eliminating these inactive constraints can improve the efficiency.In this paper,an efficient method is first proposed for identifying the inactive transmission constraints.The physical and economic insights of NCUC are carefully considered and utilized.Both the generating costs and power transfer distribution factor(PTDF)are considered.Not only redundant constraints but also non-binding constraints can be identified via the proposed method.An acceleration method that combines relaxation-based neighborhood search and improved relaxation inducement is proposed for further reducing the computation time.The case study shows that the proposed method can significantly reduce the number of transmission constraints and substantially improve the efficiency of NCUC without impacting the optimality.

关键词： Inactive transmission constraints improved relaxation inducement(IRI) network-constrained unit commitment(NCUC) relaxation-based neighborhood search(RBNS)

Texas Electric Power Crisis of 2021 Warns of a New Blackout Mechanism

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CSEE Journal of Power and Energy Systems 2022年第1期8卷 1-9页

作者： Guanglun Zhang Haiwang Zhong Zhenfei Tan Tong Cheng Qing Xia chongqing kang State Key Laboratory of Power Systems the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

The Texas electric power crisis that occurred in February 2021 has drawn great attention internationally due to its severity and for not having been foreseen.In this rapid communication,we classify the 2021 Texas electric power crisis as an energy insufficiency-caused power crisis,which alerts of a new blackout mechanism.Different from capacity insufficiencycaused power crises in the past,the Texas electric power crisis of 2021 directly resulted from the long-duration extreme cold weather as well as fundamentally from the insufficiency of sustainable supply capability of energy.We begin this paper with a brief retrospect of the event and its consequences.Definitions of energy/capacity insufficiency-caused power crises are given,as well as an overview of the supply and demand during the event,based on realistic operation data.Quantitative simulations are then conducted to reveal the underlying reasons for the power crisis and reveal how to better prepare for the future.Finally,several insights and suggestions on handling the new mode of blackout in the future are proposed and discussed.

关键词： Blackout capacity insufficiency energy insufficiency power crisis Texas electric power crisis 2021

Embedding based quantile regression neural network for probabilistic load forecasting

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Journal of Modern Power Systems and Clean Energy 2018年第2期6卷 244-254页

作者： Dahua GAN Yi WANG Shuo YANG chongqing kang Department of Electrical Engineering

Compared to traditional point load forecasting,probabilistic load forecasting(PLF) has great significance in advanced system scheduling and planning with higher reliability. Medium term probabilistic load forecasting with a resolution to an hour has turned out to be practical especially in medium term energy trading and can enhance the performance of forecasting compared to those only utilizing daily information. Two main uncertainties exist when PLF is implemented: the first is the temperature fluctuation at the same time of each year; the second is the load variation which means that even if observed indicators are fixed since other observed external indicators can be responsible for the variation. Therefore, we propose a hybrid model considering both temperature uncertainty and load variation to generate medium term probabilistic forecasting with hourly resolution. An innovative quantile regression neural network with parameter embedding is established to capture the load variation, and a temperature scenario based technique is utilized to generate temperatureforecasting in a probabilistic manner. It turns out that the proposed method overrides commonly used benchmark models in the case study.

关键词： Probabilistic load forecasting Feature embedding Artificial neural network Quantile regression Machine learning

Feature selection for probabilistic load forecasting via sparse penalized quantile regression

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Journal of Modern Power Systems and Clean Energy 2019年第5期7卷 1200-1209页

作者： Yi WANG Dahua GAN Ning ZHANG Le XIE chongqing kang Department of Electrical Engineering Tsinghua UniversityBeijingChina Department of Electrical and Computer Engineering Texas A&M UniversityUvaldeTXUSA

Probabilistic load forecasting(PLF)is able to present the uncertainty information of the future loads.It is the basis of stochastic power system planning and operation.Recent works on PLF mainly focus on how to develop and combine forecasting models,while the feature selection issue has not been thoroughly investigated for PLF.This paper fills the gap by proposing a feature selection method for PLF via sparse L1-norm penalized quantile regression.It can be viewed as an extension from point forecasting-based feature selection to probabilistic forecasting-based feature selection.Since both the number of training samples and the number of features to be selected are very large,the feature selection process is casted as a large-scale convex optimization problem.The alternating direction method of multipliers is applied to solve the problem in an efficient manner.We conduct case studies on the open datasets of ten areas.Numerical results show that the proposed feature selection method can improve the performance of the probabilistic forecasting and outperforms traditional least absolute shrinkage and selection operator method.

关键词： Probabilistic load forecasting Feature selection Alternating direction method of multipliers(ADMM) Quantile regression L1-norm penalty

Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model:an ERCOT case study

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Journal of Modern Power Systems and Clean Energy 2018年第2期6卷 281-291页

作者： Ziming MA Haiwang ZHONG Le XIE Qing XIA chongqing kang Department of Electrical Engineering Tsinghua University Department of Electrical/Computer Engineering Texas A&M University

With the deregulation of the electric power industry, electricity price forecasting plays an increasingly important role in electricity markets, especially for retailors and investment decision making. Month ahead average daily electricity price profile forecasting is proposed for the first time in this paper. A hybrid nonlinear regression and support vector machine(SVM) model is proposed. Offpeak hours, peak hours in peak months and peak hours in off-peak months are distinguished and different methods are designed to improve the forecast accuracy. A nonlinear regression model with deviation compensation is proposed to forecast the prices of off-peak hours and peak hours in off-peak months. SVM is adopted to forecast the prices of peak hours in peak months. Case studies based on data from ERCOT validate the effectiveness of the proposed hybrid method.

关键词： Electricity price forecasting Month ahead average daily electricity price profile Nonlinear regression model Support vector machine(SVM) Electric Reliability council of Texas(ERCOT)

Transmission network expansion planning with embedded constraints of short circuit currents and N-1 security

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Journal of Modern Power Systems and Clean Energy 2015年第3期3卷 312-320页

作者： Jianxiao WANG Haiwang ZHONG Qing XIA chongqing kang Control and Simulation of Power Systems and Generation Equipments Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

An approach of transmission network expan-sion planning with embedded constraints of short circuit currents and N-1 security is proposed in this paper.The problem brought on by the strong nonlinearity property of short circuit currents is solved with a linearization method based on the DC power flow.The model can be converted to a mixed-integer linear programming problem,realizing the optimization of planning model that considers the constraints of linearized short circuit currents and N-1 security.To compensate the error caused by the assump-tions of DC power flow,the compensation factor is pro-posed.With this factor,an iterative algorithm that can compensate the linearization error is then presented.The case study based on the IEEE 118-bus system shows that the proposed model and approach can be utilized to:opti-mize the construction strategy of transmission lines;ensure the N-1 security of the network;and effectively limit the short circuit currents of the system.

关键词： Short circuit current Embedded constraints of N-1 security Transmission network planning Linearization Compensation factor