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Intelligent Voltage Control Method in Active distribution Networks Based on Averaged Weighted double deep Q-network Algorithm

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 132-143页

作者： Yangyang Wang Meiqin Mao Liuchen Chang Nikos D.Hatziargyriou Research Center for Photovoltaic System Engineering Ministry of EducationSchool of Electrical Engineering and AutomationHefei University of TechnologyHefei 230009China University of New Brunswick FrederictonNB E3B 5A3Canada National Technical University of Athens Athens 15780Greece IEEE

High penetration of distributed renewable energy sources and electric vehicles(EVs)makes future active distribution network(AdN)highly variable.These characteristics put great challenges to traditional voltage control methods.Voltage control based on the deep Q-network(dQN)algorithm offers a potential solution to this problem because it possesses humanlevel control performance.However,the traditional dQN methods may produce overestimation of action reward values,resulting in degradation of obtained solutions.In this paper,an intelligent voltage control method based on averaged weighted double deep Q-network(AWddQN)algorithm is proposed to overcome the shortcomings of overestimation of action reward values in dQN algorithm and underestimation of action reward values in double deep Q-network(ddQN)algorithm.Using the proposed method,the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process(MdP)model which is solved by the AWddQN algorithm.The designed AWddQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the AdN.The proposed voltage control method is validated using the IEEE 33-bus and 123-bus systems containing renewable energy sources and EVs,and compared with the dQN and ddQN algorithms based methods,and traditional mixed-integer nonlinear program based methods.The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones.

关键词： Averaged weighted double deep Q-network(AWddQN) deep Q learning active distribution network(AdN) voltage control electrical vehicle(EV)

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面向多场景辅助服务的大规模电动汽车聚合可调度容量建模

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电力系统自动化 2023年

作者：王杨洋茆美琴杨铖周堃杜燕 Nikos D.Hatziargyriou 合肥工业大学电气与自动化工程学院国网安徽省电动汽车服务有限公司国网安徽省电力有限公司雅典国立理工大学

大规模电动汽车（electric vehicle EV）聚合可调度容量（aggerated schedulable capacity, ASC）是含EV的虚拟电厂参与多层级多场景电力平衡辅助服务的重要技术指标之一。然而现有电动汽车ASC模型难以适应大规模电动汽车与省级电力调... 详细信息

大规模电动汽车（electric vehicle EV）聚合可调度容量（aggerated schedulable capacity, ASC）是含EV的虚拟电厂参与多层级多场景电力平衡辅助服务的重要技术指标之一。然而现有电动汽车ASC模型难以适应大规模电动汽车与省级电力调度中心互动场景。为此，文中从面向电力系统调峰、调频和调压多级多场景调控新视角，提出了数据驱动和机器学习相结合的双层聚类ASC建模方法。该方法通过构建EV-充电桩广义储能单元的个体可调度容量模型，结合基于密度空间的聚类算法和改进的自组织映射深度聚类算法，从而有效地融合了EV电量的时间分布和充电桩的空间分布特性，构建了面向调峰、调频和调压多场景调控的ASC模型。采用了某省数百万条实际充电纪录数据对提出的方法进行了验证，获得了“早间型”、“中午型”、“晚间型”等多种充电画像类型，实现了时空分布各异的EV广义储能系统的自主聚合和省市级规模EV参与电网不同辅助服务潜能的评估，并为ASC的预测奠定了数据基础。

关键词：电动汽车辅助服务数据驱动广义储能系统 V2G 可调度容量聚合模型双层聚类

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面向多场景辅助服务的大规模电动汽车聚合可调度容量建模

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电力系统自动化 2024年第7期48卷 103-115页

作者：王杨洋茆美琴杨铖周堃杜燕 Nikos D.Hatziargyriou 教育部光伏系统工程研究中心合肥工业大学安徽省合肥市230009 国网安徽省电力有限公司安徽省合肥市230000 国网安徽省电动汽车服务有限公司安徽省合肥市230000 雅典国立理工大学希腊雅典15780

大规模电动汽车聚合可调度容量是含电动汽车的虚拟电厂参与多层级多场景电力平衡辅助服务的重要技术指标之一。然而,现有电动汽车聚合可调度容量模型难以适应大规模电动汽车与省级电力调度中心互动场景。为此,文中从面向电力系统调峰、... 详细信息

大规模电动汽车聚合可调度容量是含电动汽车的虚拟电厂参与多层级多场景电力平衡辅助服务的重要技术指标之一。然而,现有电动汽车聚合可调度容量模型难以适应大规模电动汽车与省级电力调度中心互动场景。为此,文中从面向电力系统调峰、调频和调压多级多场景调控新视角,提出了数据驱动和机器学习相结合的双层聚类电动汽车聚合可调度容量建模方法。该方法通过构建电动汽车-充电桩广义储能单元的个体可调度容量模型,结合基于密度空间的聚类算法和改进的自组织映射深度聚类算法,有效地融合了电动汽车电量的时间分布和充电桩的空间分布特性,构建了面向调峰、调频和调压多场景调控的聚合可调度容量模型。采用了某省实际充电记录数据对提出的方法进行了验证,获得了“早间型”“中午型”“晚间型”等多种充电画像类型,实现了时空分布各异的电动汽车广义储能系统的自主聚合和省市级规模电动汽车参与电网不同辅助服务潜能的评估,并为聚合可调度容量的预测奠定了数据基础。

关键词：电动汽车辅助服务数据驱动广义储能系统电动汽车与电网互动可调度容量聚合模型双层聚类

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Schedulable capacity forecasting for electric vehicles based on big data analysis

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Journal of Modern Power Systems and Clean Energy 2019年第6期7卷 1651-1662页

作者： Meiqin MAO Shengliang ZHANG Liuchen CHANG Nikos D.Hatziargyriou Research Center for Photovoltaic System Engineering School of Electrical Engineering and AutomationHefei University of TechnologyHefei 230009China University of New Brunswick FrederictonNB E3B 5A3Canada National Technical University of Athens 15780 AthensGreece

Fast and accurate forecasting of schedulable capacity of electric vehicles(EVs)plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems.Traditional methods are insufficient to deal with large-scale actual schedulable capacity data.This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales.The time scale of these data analysis comprises the real time of one minute,ultra-short-term of one hour and one-day-ahead scale of 24 hours.The predicted results for different time scales can be used for various ancillary services.The proposed algorithm is validated using operation data of 521 EVs in the field.The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm,the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.

关键词： Electric vehicle(EV) Schedulable capacity Machine learning Big data Multi-time scale

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Guest Editorial: Special Section on Battery Energy Storage Systems for Net-zero Power Systems and Markets

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JOURNAL OF MOdERN POWER SYSTEMS ANd CLEAN ENERGY 2024年第2期12卷 321-322页

作者： Mancarella, Pierluigi hatziargyriou, nikos Kang, Chongqing Univ Melbourne Melbourne Australia Univ Manchester Manchester England Natl Tech Univ Athens NTUA Athens Greece Tsinghua Univ Beijing Peoples R China

Battery energy storage technologies have witnessed both dramatic cost reduction and technical evolution in recent years. This is leading to widespread deployment of battery energy storage systems (BESSs) worldwide, particularly to support operation of power grids with already deep penetration of renewables. Considering the development of new battery technologies with different power-to-energy ratios and for various engineering applications, BESSs could play a strategic role towards a net-zero energy future. New opportunities are emerging for BESSs to participate in several markets, provide different grid services, and perform "value stacking", eventually allowing development of new business cases and improved BESS economics. BESS configurations that are "behind the meter", "in front of the meter", hybrid plants co-located with renewables, and so forth, are only some of the exciting propositions that are being seen in different countries and at different scales, from highly distributed (virtual power plants), to neighbourhood-level (community batteries), to utility scale.

关键词： Meters Costs Battery energy storage system Special issues and sections Net zero Stacking Virtual power plants

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Towards an Internet-like Power Grid

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 1-11页

作者： Iasonas Kouveliotis-Lysikatos nikos hatziargyriou Yanli Liu Felix Wu department of Electrical Engineering and Computer Science KTH-Royal Institute of TechnologyTeknikringen 3310040StockholmSweden School of Electrical and Computer Engineering National Technical University of Athens15773 AthensGreece School of Electrical and Information Engineering Tianjin UniversityTianjin300072China department of Electrical and Electronic Engineering The University of Hong KongHong KongChina department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720USA

The great challenges faced by modern power systems require a fresh look at the conventional operation paradigm. The significant challenges faced by modern power systems require an innovative method for the conventional operation paradigm. We claim that the decarbonization of the power grid and extensive electrification of numerous sectors of human activity can only be fostered by a self-adaptable and smart power grid that manifests similar qualities to those of the Internet. The Internet is constructed on a layered architecture that facilitates technology innovations and its intelligence is distributed throughout a hierarchy of networks. In this paper, the fundamental differences between the network data flows and power flows are examined, and the basic requirements for an innovative operation paradigm are highlighted. The current power grid is operated in a highly inflexible, centralized manner to meet increased security goals. A new highly flexible, distributed architecture can be realized by distributing the operation responsibility in smaller areas or even in grid components that can make autonomous decisions. The characteristics of such a power grid are presented, and the key features and advances for the on-going transition to a sustainable power system are identified. Finally, a case study on distributed voltage control is presented and discussed.

关键词： Smart grid Internet distributed intelligence decentralized architecture

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基于暂态分析和系统辨识的光伏并网逆变器建模

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电工技术学报 2021年第10期36卷 2061-2071页

作者：翁公羽茆美琴张榴晨 nikos hatziargyriou 合肥工业大学教育部光伏系统工程研究中心合肥230009 加拿大新布伦瑞克大学弗雷德里克顿E3B5A3 希腊雅典国立理工大学电气与计算机工程学院雅典15780

建立并网光伏发电系统的模型是研究其与电网交互过程的基础。光伏系统的暂态特性不仅受天气情况、电网工况等环境因素的影响,还取决于商用逆变器的内部结构以及控制策略,然而它们并不对外公开,这给系统的分析带来了困难。为了解决这个问... 详细信息

建立并网光伏发电系统的模型是研究其与电网交互过程的基础。光伏系统的暂态特性不仅受天气情况、电网工况等环境因素的影响,还取决于商用逆变器的内部结构以及控制策略,然而它们并不对外公开,这给系统的分析带来了困难。为了解决这个问题,该文提出一种基于暂态分析和系统辨识的光伏并网逆变器通用建模方法。将光伏逆变器视作内部结构以及控制算法未知的黑箱,通过先后施加扰动和测取响应并结合系统辨识的方法来建立其模型。以一个3kW的商用光伏逆变器为研究对象,搭建硬件在环实验系统来研究并验证这一建模方法。结果显示,在多种扰动对应的暂态过程中,模型的输出均准确地拟合了逆变器的实际输出,证明了这一建模方法的有效性。

关键词：光伏逆变器建模系统辨识传递函数功率硬件在环

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Performance Improvement of Very Short-term Prediction Intervals for Regional Wind Power Based on Composite Conditional Nonlinear Quantile Regression

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 60-70页

作者： Yan Zhou Yonghui Sun Sen Wang Rabea Jamil Mahfoud Hassan Haes Alhelou nikos hatziargyriou Pierluigi Siano College of Energy and Electrical Engineering Hohai UniversityNanjing 210098China department of Electrical Engineering Tishreen UniversityLatakia 2230Syria National Technical University of Athens Athens 15773Greece department of Management&Innovation Systems University of SalernoSalerno 84084Italy

Accurate regional wind power prediction plays an important role in the security and reliability of power systems.For the performance improvement of very short-term prediction intervals(PIs),a novel probabilistic prediction method based on composite conditional nonlinear quantile regression(CCNQR)is proposed.First,the hierarchical clustering method based on weighted multivariate time series motifs(WMTSM)is studied to consider the static difference,dynamic difference,and meteorological difference of wind power time series.Then,the correlations are used as sample weights for the conditional linear programming(CLP)of CCNQR.To optimize the performance of PIs,a composite evaluation including the accuracy of PI coverage probability(PICP),the average width(AW),and the offsets of points outside PIs(OPOPI)is used to quantify the appropriate upper and lower bounds.Moreover,the adaptive boundary quantiles(ABQs)are quantified for the optimal performance of PIs.Finally,based on the real wind farm data,the superiority of the proposed method is verified by adequate comparisons with the conventional methods.

关键词： Regional wind power probabilistic prediction nonlinear quantile regression composite evaluation adaptive boundary quantiles

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Thermal Energy Storage Contribution to the Economic dispatch of Island Power Systems

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CSEE Journal of Power and Energy Systems 2020年第1期6卷 100-110页

作者： Panagiotis Romanos Emmanouil Voumvoulakis Christos N.Markides nikos hatziargyriou School of Electrical and Computer Engineering National Technical University of Athens(NTUA)Zografou 15780Greece Hellenic Electricity distri-bution Network Operator S.A.(HEdNO) Athens 11743Greece Clean Energy Processes(CEP)Laboratory Depart-ment of Chemical EngineeringImperial College LondonLondon SW72AZU.K.

In this paper the provision of flexible generation is investigated by extracting steam from Rankine-cycle power stations during off-peak demand in order to charge thermal tanks that contain suitable phase-change materials(PCMs);at a later time when this is required and/or is economically effective,these thermal energy storage(TES)tanks can act as the heat sources of secondary thermal power plants in order to generate power,for example as evaporators of,e.g.,organic Rankine cycle(ORC)plants that are suitable for power generation at reduced temperatures and smaller scales.This type of solution offers greater flexibility than TES-only technologies that store thermal energy and release it back to the base power station,since it allows both derating but also over-generation compared to the base power-station capacity.The solution is applied in a case study of a 50-MW rated oil-fired power station unit at the autonomous system of Crete.The optimal operation of the TES system is investigated,by solving a modified Unit Commitment-Economic dispatch optimization problem,which includes the TES operating constraints.The results indicate that for most of the scenarios the discounted payback period is lower than 12 years,while in few cases the payback period is 5 years.

关键词： Economic dispatch energy-management energy storage flexibleenergysystem flexiblegeneration generation integrated energystorage phasechangematerials smartgrids

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Nonparametric Probabilistic Prediction of Regional PV Outputs Based on Granule-based Clustering and direct Optimization Programming

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1450-1461页

作者： Yonghui Sun Yan Zhou Sen Wang Rabea Jamil Mahfoud Hassan Haes Alhelou George Sideratos nikos hatziargyriou Pierluigi Siano College of Energy and Electrical Engineering Hohai UniversityNanjing 210098China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China IEEE department of Electrical Engineering Tishreen UniversityLatakia 2230Syria National Technical University of Athens Athens 15773Greece department of Management&Innovation Systems University of SalernoSalerno 84084Italy

Regional photovoltaic(PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals(PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granulebased clustering(GC) and direct optimization programming(dOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction(NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples' utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, dOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.

关键词： Regional photovoltaic output prediction intervals granule-based clustering direct optimization programming nonparametric probabilistic prediction

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