With integration of large-scale renewable energy, new controllable devices, and required reinforcement of power grids, modern power systems have typical characteristics such as uncertainty, vulnerability and openness, which makes operation and control of power grids face severe security challenges. Application of artificial intelligence (AI) technologies represented by machine learning in power grid regulation is limited by reliability, interpretability and generalization ability of complex modeling. Mode of hybrid-augmented intelligence (HAI) based on human-machine collaboration (HMC) is a pivotal direction for future development of AI technology in this field. Based on characteristics of applications in power grid regulation, this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence (HHI) system for large-scale power grid dispatching and control (PGDC). First, theory and application scenarios of HHI are introduced and analyzed; then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed. Key technologies are discussed to achieve a thorough integration of human/machine intelligence. Finally, state-of-the-art and future development of HHI in power grid regulation are summarized, aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.
{"title":"Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control","authors":"Shixiong Fan;Jianbo Guo;Shicong Ma;Lixin Li;Guozheng Wang;Haotian Xu;Jin Yang;Zening Zhao","doi":"10.17775/CSEEJPES.2023.00940","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00940","url":null,"abstract":"With integration of large-scale renewable energy, new controllable devices, and required reinforcement of power grids, modern power systems have typical characteristics such as uncertainty, vulnerability and openness, which makes operation and control of power grids face severe security challenges. Application of artificial intelligence (AI) technologies represented by machine learning in power grid regulation is limited by reliability, interpretability and generalization ability of complex modeling. Mode of hybrid-augmented intelligence (HAI) based on human-machine collaboration (HMC) is a pivotal direction for future development of AI technology in this field. Based on characteristics of applications in power grid regulation, this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence (HHI) system for large-scale power grid dispatching and control (PGDC). First, theory and application scenarios of HHI are introduced and analyzed; then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed. Key technologies are discussed to achieve a thorough integration of human/machine intelligence. Finally, state-of-the-art and future development of HHI in power grid regulation are summarized, aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.01280
Zexin Zhao;Weijiang Chen;Zhichang Yang;Guoliang Zhao;Bin Han;Yunfei Xu;Nianwen Xiang;Shulai Wang
The modular multilevel matrix converter (M3C) is a potential frequency converter for low-frequency AC transmission. However, capacitor voltage control of high-voltage and large-capacity M3C is more difficult, especially for voltage balancing between branches. To solve this problem, this paper defines sequence circulating components and theoretically analyzes the influence mechanism of different sequence circulating components on branch capacitor voltage. A fully decoupled branch energy balancing control method based on four groups of sequence circulating components is proposed. This method can control capacitor voltages of nine branches in horizontal, vertical and diagonal directions. Considering influences of both circulating current and voltage, a cross decoupled control is designed to improve control precision. Simulation results are taken from a low-frequency transmission system based on PSCAD/EMTDC, and effectiveness and precision of the proposed branch energy balancing control method are verified in the case of nonuniform parameters and an unbalanced power system.
{"title":"Fully Decoupled Branch Energy Balancing Control Method for Modular Multilevel Matrix Converter Based on Sequence Circulating Components","authors":"Zexin Zhao;Weijiang Chen;Zhichang Yang;Guoliang Zhao;Bin Han;Yunfei Xu;Nianwen Xiang;Shulai Wang","doi":"10.17775/CSEEJPES.2023.01280","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.01280","url":null,"abstract":"The modular multilevel matrix converter (M3C) is a potential frequency converter for low-frequency AC transmission. However, capacitor voltage control of high-voltage and large-capacity M3C is more difficult, especially for voltage balancing between branches. To solve this problem, this paper defines sequence circulating components and theoretically analyzes the influence mechanism of different sequence circulating components on branch capacitor voltage. A fully decoupled branch energy balancing control method based on four groups of sequence circulating components is proposed. This method can control capacitor voltages of nine branches in horizontal, vertical and diagonal directions. Considering influences of both circulating current and voltage, a cross decoupled control is designed to improve control precision. Simulation results are taken from a low-frequency transmission system based on PSCAD/EMTDC, and effectiveness and precision of the proposed branch energy balancing control method are verified in the case of nonuniform parameters and an unbalanced power system.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.05850
Jinliang He;Zhifei Han;Jun Hu
The ubiquitous power Internet of Things (UPIoT) uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system. UPIoT has the characteristics of comprehensive state perception and efficient information processing, and has broad application prospects for transformation of the energy industry. The fundamental facility of the UPIoT is the sensor-based information network. By using advanced sensors, Wireless Sensor Networks (WSNs), and advanced data processing technologies, Internet of Things can be realized in the power system. In this paper, a framework of WSNs based on advanced sensors towards UPIoT is proposed. In addition, the most advanced sensors for UPIoT purposes are reviewed, along with an explanation of how the sensor data obtained in UPIoT is utilized in various scenarios.
{"title":"Advanced Sensors Towards Ubiquitous Power Internet of Things","authors":"Jinliang He;Zhifei Han;Jun Hu","doi":"10.17775/CSEEJPES.2023.05850","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05850","url":null,"abstract":"The ubiquitous power Internet of Things (UPIoT) uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system. UPIoT has the characteristics of comprehensive state perception and efficient information processing, and has broad application prospects for transformation of the energy industry. The fundamental facility of the UPIoT is the sensor-based information network. By using advanced sensors, Wireless Sensor Networks (WSNs), and advanced data processing technologies, Internet of Things can be realized in the power system. In this paper, a framework of WSNs based on advanced sensors towards UPIoT is proposed. In addition, the most advanced sensors for UPIoT purposes are reviewed, along with an explanation of how the sensor data obtained in UPIoT is utilized in various scenarios.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141304111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.05970
Yonghua Song;Ge Chen;Hongcai Zhang
Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks (ADNs) to facilitate integration of distributed renewable generation. Due to unavailability of network topology and line impedance in many distribution networks, physical model-based methods may not be applicable to their operations. To tackle this challenge, some studies have proposed constraint learning, which replicates physical models by training a neural network to evaluate feasibility of a decision (i.e., whether a decision satisfies all critical constraints or not). To ensure accuracy of this trained neural network, training set should contain sufficient feasible and infeasible samples. However, since ADNs are mostly operated in a normal status, only very few historical samples are infeasible. Thus, the historical dataset is highly imbalanced, which poses a significant obstacle to neural network training. To address this issue, we propose an enhanced constraint learning method. First, it leverages constraint learning to train a neural network as surrogate of ADN's model. Then, it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical dataset. By incorporating historical and synthetic samples into the training set, we can significantly improve accuracy of neural network. Furthermore, we establish a trust region to constrain and thereafter enhance reliability of the solution. Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.
{"title":"Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data","authors":"Yonghua Song;Ge Chen;Hongcai Zhang","doi":"10.17775/CSEEJPES.2023.05970","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05970","url":null,"abstract":"Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks (ADNs) to facilitate integration of distributed renewable generation. Due to unavailability of network topology and line impedance in many distribution networks, physical model-based methods may not be applicable to their operations. To tackle this challenge, some studies have proposed constraint learning, which replicates physical models by training a neural network to evaluate feasibility of a decision (i.e., whether a decision satisfies all critical constraints or not). To ensure accuracy of this trained neural network, training set should contain sufficient feasible and infeasible samples. However, since ADNs are mostly operated in a normal status, only very few historical samples are infeasible. Thus, the historical dataset is highly imbalanced, which poses a significant obstacle to neural network training. To address this issue, we propose an enhanced constraint learning method. First, it leverages constraint learning to train a neural network as surrogate of ADN's model. Then, it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical dataset. By incorporating historical and synthetic samples into the training set, we can significantly improve accuracy of neural network. Furthermore, we establish a trust region to constrain and thereafter enhance reliability of the solution. Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The complexity and uncertainty in power systems cause great challenges to controlling power grids. As a popular data-driven technique, deep reinforcement learning (DRL) attracts attention in the control of power grids. However, DRL has some inherent drawbacks in terms of data efficiency and explainability. This paper presents a novel hierarchical task planning (HTP) approach, bridging planning and DRL, to the task of power line flow regulation. First, we introduce a three-level task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes (TP-MDPs). Second, we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task units. In addition, we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist HTP. Experimental results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization, a state-of-the-art deep reinforcement learning (DRL) approach, improving efficiency by 26.16% and 6.86% on both systems.
{"title":"Hierarchical Task Planning for Power Line Flow Regulation","authors":"Chenxi Wang;Youtian Du;Yanhao Huang;Yuanlin Chang;Zihao Guo","doi":"10.17775/CSEEJPES.2023.00620","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.00620","url":null,"abstract":"The complexity and uncertainty in power systems cause great challenges to controlling power grids. As a popular data-driven technique, deep reinforcement learning (DRL) attracts attention in the control of power grids. However, DRL has some inherent drawbacks in terms of data efficiency and explainability. This paper presents a novel hierarchical task planning (HTP) approach, bridging planning and DRL, to the task of power line flow regulation. First, we introduce a three-level task hierarchy to model the task and model the sequence of task units on each level as a task planning-Markov decision processes (TP-MDPs). Second, we model the task as a sequential decision-making problem and introduce a higher planner and a lower planner in HTP to handle different levels of task units. In addition, we introduce a two-layer knowledge graph that can update dynamically during the planning procedure to assist HTP. Experimental results conducted on the IEEE 118-bus and IEEE 300-bus systems demonstrate our HTP approach outperforms proximal policy optimization, a state-of-the-art deep reinforcement learning (DRL) approach, improving efficiency by 26.16% and 6.86% on both systems.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SF 6 degradation mechanism in non-thermal plasma (NTP) systems is not fully understood due to the formation of a complex physico-chemical reaction network, especially when reactive gases and packing materials are involved. In this work, we conduct a combined experimental and theoretical study to unravel the SF 6 degradation path in a γ-Al 2 O 3 packed plasma in the presence of H 2 O or O 2 . Our experimental results show that both H 2 O and O 2 have a synergetic effect with γ-A1 2 O 3 packing on promoting SF 6 degradation, leading to higher stable gas yields than typical spark or corona discharges. HO or O 2 addition promotes SO 2 or SO 2 F 2 selectivity, respectively. Density functional theory (DFT) calculations reveal that SO 2 generation corresponding with the highest activation barrier is the most critical step toward SF 6 degradation. Radicals like H and O generated from H 2 O or O 2 discharge can significantly promote the degradation process via Eley-Rideal mechanism, affecting key reactions of stable product generation, advancing degradation efficiency. The results of this work could provide insights on further understanding SF 6 degradation mechanism especially in packed-bed plasma systems.
{"title":"Investigation on Degradation Path of SF6 in Packed-Bed Plasma: Effect of Plasma-generated Radicals","authors":"Zhaolun Cui;Chang Zhou;Amin Jafarzadeh;Xiaoxing Zhang;Peng Gao;Licheng Li;Yanpeng Hao","doi":"10.17775/CSEEJPES.2022.05910","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.05910","url":null,"abstract":"SF\u0000<inf>6</inf>\u0000degradation mechanism in non-thermal plasma (NTP) systems is not fully understood due to the formation of a complex physico-chemical reaction network, especially when reactive gases and packing materials are involved. In this work, we conduct a combined experimental and theoretical study to unravel the SF\u0000<inf>6</inf>\u0000 degradation path in a γ-Al\u0000<inf>2</inf>\u0000O\u0000<inf>3</inf>\u0000packed plasma in the presence of H\u0000<inf>2</inf>\u0000O or O\u0000<inf>2</inf>\u0000. Our experimental results show that both H\u0000<inf>2</inf>\u0000O and O\u0000<inf>2</inf>\u0000 have a synergetic effect with γ-A1\u0000<inf>2</inf>\u0000O\u0000<inf>3</inf>\u0000 packing on promoting SF\u0000<inf>6</inf>\u0000 degradation, leading to higher stable gas yields than typical spark or corona discharges. HO or O\u0000<inf>2</inf>\u0000addition promotes SO\u0000<inf>2</inf>\u0000or SO\u0000<inf>2</inf>\u0000F\u0000<inf>2</inf>\u0000 selectivity, respectively. Density functional theory (DFT) calculations reveal that SO\u0000<inf>2</inf>\u0000 generation corresponding with the highest activation barrier is the most critical step toward SF\u0000<inf>6</inf>\u0000 degradation. Radicals like H and O generated from H\u0000<inf>2</inf>\u0000O or O\u0000<inf>2</inf>\u0000 discharge can significantly promote the degradation process via Eley-Rideal mechanism, affecting key reactions of stable product generation, advancing degradation efficiency. The results of this work could provide insights on further understanding SF\u0000<inf>6</inf>\u0000 degradation mechanism especially in packed-bed plasma systems.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375998","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141304075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fossil fuel depletion and environmental pollution problems promote development of renewable energy (RE) globally. With increasing penetration of RE, operation security and economy of power systems (PS) are greatly impacted by fluctuation and intermittence of renewable power. In this paper, information gap decision theory (IGDT) is adapted to handle uncertainty of wind power generation. Based on conventional IGDT method, linear regulation strategy (LRS) and robust linear optimization (RLO) method are integrated to reformulate the model for rigorously considering security constraints. Then a robustness assessment method based on hybrid RLO-IGDT approach is proposed for analyzing robustness and economic performance of PS. Moreover, a risk-averse linearization method is adapted to convert the proposed assessment model into a mixed integer linear programming (MILP) problem for convenient optimization without robustness loss. Finally, results of case studies validate superiority of proposed method in guaranteeing operation security rigorously and effectiveness in assessment of RSR for PS without overestimation.
{"title":"Robustness Assessment of Wind Power Generation Considering Rigorous Security Constraints for Power System: A Hybrid RLO-IGDT Approach","authors":"Lianyong Zuo;Shengshi Wang;Yong Sun;Shichang Cui;Jiakun Fang;Xiaomeng Ai;Baoju Li;Chengliang Hao;Jinyu Wen","doi":"10.17775/CSEEJPES.2023.05980","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05980","url":null,"abstract":"Fossil fuel depletion and environmental pollution problems promote development of renewable energy (RE) globally. With increasing penetration of RE, operation security and economy of power systems (PS) are greatly impacted by fluctuation and intermittence of renewable power. In this paper, information gap decision theory (IGDT) is adapted to handle uncertainty of wind power generation. Based on conventional IGDT method, linear regulation strategy (LRS) and robust linear optimization (RLO) method are integrated to reformulate the model for rigorously considering security constraints. Then a robustness assessment method based on hybrid RLO-IGDT approach is proposed for analyzing robustness and economic performance of PS. Moreover, a risk-averse linearization method is adapted to convert the proposed assessment model into a mixed integer linear programming (MILP) problem for convenient optimization without robustness loss. Finally, results of case studies validate superiority of proposed method in guaranteeing operation security rigorously and effectiveness in assessment of RSR for PS without overestimation.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375963","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2023.05250
Yunyang Zou;Yan Xu
In a deregulated Var market, market power issue is more serious than in an energy market since reactive power cannot be transmitted over long distances. This letter designs a multi-timescale Var market framework, where market power that may arise in the hourly-ahead Var support service market due to system configuration deficiency and market structure flaws can be eliminated by day-ahead contract-based Var reserve service market. Settlement of day-ahead Var reserve contract is formulated as a two-stage robust optimization (TSRO) model considering worst case of uncertainty realization and potential market power that may arise in hourly-ahead market. TSRO with integer recourses is then solved by a new column and constraint generation algorithm. Results show a robust Var reserve contract can fully eliminate market power, and prevent suppliers from manipulating market prices.
在放松管制的 Var 市场中，由于无功功率不能远距离传输，市场力量问题比能源市场更为严重。本文设计了一个多时间尺度的无功市场框架，通过基于日前合同的无功储备服务市场，可以消除由于系统配置缺陷和市场结构缺陷而可能在小时前无功支持服务市场中产生的市场支配力。考虑到不确定性实现的最坏情况和小时前市场可能出现的潜在市场支配力，将日前变量储备合同的结算制定为两阶段稳健优化（TSRO）模型。然后通过一种新的列和约束生成算法求解了具有整数资源的 TSRO。结果表明，稳健的 Var 储备合同可以完全消除市场力量，防止供应商操纵市场价格。
{"title":"Design of Robust Var Reserve Contract for Enhancing Reactive Power Ancillary Service Market Efficiency","authors":"Yunyang Zou;Yan Xu","doi":"10.17775/CSEEJPES.2023.05250","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.05250","url":null,"abstract":"In a deregulated Var market, market power issue is more serious than in an energy market since reactive power cannot be transmitted over long distances. This letter designs a multi-timescale Var market framework, where market power that may arise in the hourly-ahead Var support service market due to system configuration deficiency and market structure flaws can be eliminated by day-ahead contract-based Var reserve service market. Settlement of day-ahead Var reserve contract is formulated as a two-stage robust optimization (TSRO) model considering worst case of uncertainty realization and potential market power that may arise in hourly-ahead market. TSRO with integer recourses is then solved by a new column and constraint generation algorithm. Results show a robust Var reserve contract can fully eliminate market power, and prevent suppliers from manipulating market prices.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375974","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-28DOI: 10.17775/CSEEJPES.2022.01080
Yating Zhao;Zhi Wu;Wei Gu;Jingxuan Wang;Fujue Wang;Zhoujun Ma;Minqiang Hu
Industrial parks (IPs) play a crucial role in facilitating economic efficiency and comprehensive energy utilization in the industrial age. At the same time, multi-energy coupling and management of various types of energy in IP have become serious challenges. In this paper, combined heat and power unit (CHP) model considering operation mode switching characteristics is formulated by exploring its internal composition to improve output flexibility of the energy supply side. Then, heat and electricity integrated energy system (HE-IES) optimal dispatch and pricing model are established, taking electricity and heat demand response strategy and steam thermal inertia property into account. Based on the above models, a mixed-integer bilinear programming framework is designed to coordinate the day-ahead operation and pricing strategy of the HE-IES in the IP. The scenario study is carried out on a practical industrial park in Southern China. Numerical results indicate the proposed mechanism can effectively improve IP's energy utilization and economic efficiency.
在工业时代，工业园区（IP）在促进经济效益和能源综合利用方面发挥着至关重要的作用。与此同时，工业园区的多能耦合和各类能源的管理也成为严峻的挑战。本文通过探讨热电联产机组的内部组成，建立了考虑运行模式切换特性的热电联产机组模型，以提高能源供应端的输出灵活性。然后，考虑电力和热力需求响应策略以及蒸汽热惯性特性，建立了热电综合能源系统（HE-IES）优化调度和定价模型。在上述模型的基础上，设计了一个混合整数双线性规划框架，以协调 IP 中热电综合能源系统的日前运行和定价策略。在中国南方的一个实际工业园区进行了情景研究。数值结果表明，所提出的机制能有效提高工业园的能源利用率和经济效益。
{"title":"Optimal Dispatch and Pricing of Industrial Parks Considering CHP Mode Switching and Demand Response","authors":"Yating Zhao;Zhi Wu;Wei Gu;Jingxuan Wang;Fujue Wang;Zhoujun Ma;Minqiang Hu","doi":"10.17775/CSEEJPES.2022.01080","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2022.01080","url":null,"abstract":"Industrial parks (IPs) play a crucial role in facilitating economic efficiency and comprehensive energy utilization in the industrial age. At the same time, multi-energy coupling and management of various types of energy in IP have become serious challenges. In this paper, combined heat and power unit (CHP) model considering operation mode switching characteristics is formulated by exploring its internal composition to improve output flexibility of the energy supply side. Then, heat and electricity integrated energy system (HE-IES) optimal dispatch and pricing model are established, taking electricity and heat demand response strategy and steam thermal inertia property into account. Based on the above models, a mixed-integer bilinear programming framework is designed to coordinate the day-ahead operation and pricing strategy of the HE-IES in the IP. The scenario study is carried out on a practical industrial park in Southern China. Numerical results indicate the proposed mechanism can effectively improve IP's energy utilization and economic efficiency.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10375985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional analytical approaches for stability assessment of inverter-based resources (IBRs), often requiring detailed knowledge of IBR internals, become impractical due to IBRs' proprietary nature. Admittance measurements, relying on electromagnetic transient simulation or laboratory settings, are not only time-intensive but also operationally inflexible, since various non-linear control loops make IBRs' admittance models operating-point dependent. Therefore, such admittance measurements must be performed repeatedly when operating point changes. To avoid time-consuming and cumbersome measurements, admittance estimation for arbitrary operating points is highly desirable. However, existing admittance estimation algorithms usually face challenges in versatility, data demands, and accuracy. Addressing this challenge, this letter presents a simple and efficient admittance estimation method for black-boxed IBRs, by utilizing a minimal set of seven operating points to solve a homogeneous linear equation system. Case studies demonstrate this proposed method ensures high accuracy across various types of IBRs. Estimation accuracy is satisfying even when non-negligible measurement errors exist.
{"title":"An Efficient Method to Estimate Admittance of Black-boxed Inverter-based Resources for Varying Operating Points","authors":"Weihua Zhou;Bin Liu;Nabil Mohammed;Behrooz Bahrani","doi":"10.17775/CSEEJPES.2023.07090","DOIUrl":"https://doi.org/10.17775/CSEEJPES.2023.07090","url":null,"abstract":"Traditional analytical approaches for stability assessment of inverter-based resources (IBRs), often requiring detailed knowledge of IBR internals, become impractical due to IBRs' proprietary nature. Admittance measurements, relying on electromagnetic transient simulation or laboratory settings, are not only time-intensive but also operationally inflexible, since various non-linear control loops make IBRs' admittance models operating-point dependent. Therefore, such admittance measurements must be performed repeatedly when operating point changes. To avoid time-consuming and cumbersome measurements, admittance estimation for arbitrary operating points is highly desirable. However, existing admittance estimation algorithms usually face challenges in versatility, data demands, and accuracy. Addressing this challenge, this letter presents a simple and efficient admittance estimation method for black-boxed IBRs, by utilizing a minimal set of seven operating points to solve a homogeneous linear equation system. Case studies demonstrate this proposed method ensures high accuracy across various types of IBRs. Estimation accuracy is satisfying even when non-negligible measurement errors exist.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":7.1,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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