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作者： Ma, Zhan Gao, Feng Gu, Xin Li, Nan Wu, Qiang Wang, Xiaohui Wang, Xiaolong Key Lab of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China State Grid Jinan Power Supply Company Jinan China Electric Power Research Institute State Grid Tianjin Electric Power Company Tianjin China School of Information Science and Engineering Shandong University Jinan China

It is preferable for the retired batteries to balance their states-of-health (SOH) in the battery energy storage system (BESS) since it can prolong the system lifetime and reduce the maintenance burden. So far, the corresponding balancing techniques mainly focus on either the SOH balancing among packs or the SOH balancing of cells inside a pack. This article further proposes the multilayer SOH equalization scheme to equalize all cells' SOHs of large-scale BESS by comprehensively combining the pack SOH balancing strategy and the commercial cell equalization techniques. It is noted that the balancing schemes for pack and cell cannot be simply superimposed since packs and cells have totally different connection modes and are equipped with different types of balancing circuits. In specific, the modular multilevel converter (MMC) is assumed to coordinate the pack and cell SOH equalization schemes, where the charging/discharging power per submodule is properly adjusted according to the extent of cell SOH deviation and the extent of pack SOH deviation together. MATlab simulation and experimental results of a 10-kWh MMC-BESS prototype verified the performance of the proposed method. © 1986-2012 IEEE.

关键词： power converters

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Optimize globally, control locally: Coordinated optimal local voltage control in hybrid AC/DC microgrid

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INTERNATIONAL JOURNAL OF ELECTRICAL power & ENERGY systemS 2020年第0期118卷 105734-000页

作者： Sun, Xianzhuo Zhang, Wen Chen, Jian Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

The increasing penetration of photovoltaic (PV) generation leads to voltage violations in hybrid AC/DC microgrids. Due to the uncertainty and volatility of PV outputs, fast-responding voltage control devices are obliged to coordinate with each other and regulate nodal voltages in real time. This paper follows the concept of optimizing globally and controlling locally to propose a combined central and local voltage control strategy based on a two-stage voltage control framework. In the first stage, minutely optimal power flow based on predicted PV outputs within one hour is performed in microgrid central controller (MGCC) to calculate voltage and optimal power settings for each controllable device. The uncertainty of PV is considered using chance constraint optimal power flow (CCOPF) method. In the second stage, according to minutely voltage and power settings, local controller (LC) generates improved voltage control curves based on curve fitting method. With minutely measurements of nodal voltages, power outputs of PVs, smart loads (SLs) energy storage systems (ESSs) and VSC are cooperated based on improved local control curves to minimize power losses without communication requirements. In order to coordinate the active and reactive power of SLs ESSs, and VSC, a combined P(V) and Q(V) curve is proposed. The piecewise linear curve fitting method is used to generate optimal voltage control curves due to its higher fitting accuracy compared with other fitting methods. Simulations are performed to verify the effectiveness of the proposed voltage control strategy in mitigating voltage rise problems and reducing power losses.

关键词： Two-stage voltage control framework hybrid AC/DC microgrids Improved voltage control curves Chance constraint optimal power flow Curve fitting method

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Solarsam: Building-Scale Photovoltaic Potential Assessment Based on Segment Anything Model (Sam) and Remote Sensing for Emerging City

SSRN

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SSRN 2024年

作者： Li, Guanglei Wang, Guohao Luo, Tengqi Hu, Yuxiao Wu, Shouyuan Gong, Guanghui Song, Chenchen Guo, Zhiling Liu, Zhengguang Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan250061 China Department of Power and Electrical Engineering Northwest A&F University Yangling712100 China The Hong Kong Polytechnic University Hong Kong Eastern Institute of Technology Ningbo315200 China Higher Information Industry Technology Research Institute Beijing Information Science and Technology University Beijing100192 China Department of Chemical Engineering The University of Manchester ManchesterM13 9PL United Kingdom

Driven by advancements in photovoltaic (PV) technology, solar energy has emerged as a promising renewable energy source, due to its ease of integration onto building rooftops, facades, and windows. For the emerging cities, the lack of detailed street-level data presents a challenge for effectively assessing the potential of building-integrated photovoltaic (BIPV). To address this, this study introduces SolarSAM, a novel BIPV evaluation method that leverages remote sensing imagery and deep learning techniques, and an emerging city in northern China is utilized to validate the model performance. During the process, SolarSAM segmented various building rooftops using text prompt guided semantic segmentation. Separate PV models were then developed for Rooftop PV, Facade-integrated PV, and PV windows systems, using this segmented data and local climate information. The potential for BIPV installation, solar power generation, and city-wide power self-sufficiency were assessed, revealing that the annual BIPV power generation potential surpassed the city’s total electricity consumption by a factor of 2.5. Economic and environmental analysis were also conducted, including levelized cost of electricity and carbon reduction calculations, comparing different BIPV systems across various building categories. These findings demonstrated the model’s performance and reveled the potential of BIPV power generation in the future. © 2024, The Authors. All rights reserved.

关键词： Solar power generation

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Maximum power Point Tracking Technology of Photovoltaic Array under Partial Shading Based On Adaptive Improved Differential Evolution Algorithm

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ENERGIES 2020年第5期13卷 1254页

作者： Zhang, Peng Sui, Huibin Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China

In the case of partial shading conditions, there will be more than one maximum power point (MPP) in photovoltaic (PV) array. The traditional maximum power point tracking (MPPT) methods are easy to get in the local maximum power point (LMPP) and fail. Based on the standard differential evolution (DE) algorithm, the mutation strategy, scaling factor F, and cross factor CR of the algorithm are optimized. Also, the population position variance delta 2 is used to prevent falling into LMPP. Finally, the conditions for algorithm termination and restart are set. It is verified by simulation that the method has fast convergence speed, high accuracy, and can adapt well to changes in the external environment. The improved DE algorithm has a great advantage in MPPT.

关键词： MPPT differential evolution algorithm photovoltaic array DC-DC converter

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Dynamic Reconfiguration of Distribution Network Based on Temporal Constrained Hierarchical Clustering and Fireworks Algorithm

Dynamic Reconfiguration of Distribution Network Based on Tem...

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2020 IEEE/IAS Industrial and Commercial power system Asia, I and CPS Asia 2020

作者： Zhang, Yumin Ji, Xingquan Xu, Jiabin Yin, Ziyang Han, Xueshan Zhang, Chao Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao China Dongying Power Supply Company State Grid Dongying China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Jinan China

ISBN: (纸本)9781728143033

For the dynamic reconfiguration problem of distribution networks, the handling of switching cost constraint and the division of time period are the two key aspects that affect the performance of the reconfiguration algorithm. In this paper, an improved temporal constrained hierarchical clustering algorithm for dynamic reconfiguration of distribution networks is proposed. Firstly, the improved hierarchical clustering approach considering the temporal constraint is used to divide the operating status of the distribution network into multiple time intervals according to the similarity of loads and the output power of distributed generations (DGs). The dynamic reconfiguration of distribution networks is then transformed into multiple single-stage static reconfiguration problems, and the cluster center is used to approximately represent the load state and DG output state of the period. In order to reduce the number of infeasible solutions and poor solutions, a coding method based on heuristic rules to reduce the solution space of distribution network reconfiguration is proposed. An improved fireworks algorithm based on heuristic rules is proposed to solve the reconfiguration problem. Finally, a modified IEEE-33 test system is used to verify the validity of the proposed method. © 2020 IEEE.

关键词： Clustering algorithms

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Sequential Disaster Recovery Model for Distribution systems With Co-Optimization of Maintenance and Restoration Crew dispatch

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IEEE TRANSACTIONS ON SMART GRID 2020年第6期11卷 4700-4713页

作者： Zhang, Gang Zhang, Feng Zhang, Xin Meng, Ke Dong, Zhao Yang Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China Cranfield Univ Sch Water Energy & Environm Energy & Power Theme Cranfield MK43 0AL Beds England Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

To efficiently restore electricity customers from a large-scale blackout, this paper proposes a novel mixed-integer linear programing (MILP) model for the optimal disaster recovery of power distribution systems. In the proposed recovery scheme, the maintenance crews (MCs) are scheduled to repair damaged components, and the restoration crews (RCs) are dispatched to switch on the manual switches. Then, the MC and RC dispatch models are integrated into the disaster recovery scheme, which will generate an optimal sequence of control actions for distributed generation (DG), controllable load, and remote/manual switches. Besides, to address the time scale related challenges in the model formulation, the technical constraints for system operation are investigated in each energization step rather than time step, hence the co-optimization problem is formulated as an "event-based" model with variable time steps. Consequently, the disaster recovery, MC dispatch and RC dispatch are properly cooperated, and the whole distribution systems can be restored step by step. Last, the effectiveness of the co-optimization model is validated in the modified IEEE 123 bus test distribution system.

关键词： Maintenance engineering Switches Indexes Computational modeling power distribution Reactive power Mathematical model Disaster recovery distribution system switching sequence maintenance crew restoration crew resilience

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Optimization of Wind Farm Self-Discipline Interval and Energy Storage system Configuration

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IEEE ACCESS 2020年 8卷 79114-79123页

作者： Yu, Xiaodong Zhang, Wen Dong, Xiao Liu, Shulin Pang, Shaopeng Zang, Hongzhi Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Qilu Univ Technol Sch Elect Engn & Automat Shandong Acad Sci Jinan 250353 Peoples R China State Grid Shandong Elect Power Co Econ & Technol Res Inst Jinan 250001 Peoples R China

The uncertain and stochastic output of the wind farm results in a lot of problems when it is connected to the power grid. In order to improve the wind power's friendship to the grid, the wind farm should has a certain self-discipline level. In this paper, it is studied from the perspective of the wind farm self-discipline interval. First, the concept of wind farm self-discipline interval is proposed, followed by a comprehensive index which is used to evaluate the wind farm self-discipline level by comprehensively considering the interval width and the interval accuracy. Second, an optimization method is discussed to obtain the optimal self-discipline interval. This method has general applicability, not only suitable for normal distribution and other known distributions but also for arbitrary distributions (such as non-symmetric, multi-peak distributions). Finally, the size of the energy storage system (ESS) in the wind farm is optimized to guarantee a suitable wind farm self-discipline level. Simulation results show that the proposed method not only effectively improves the self-discipline level of the wind farm but also has general applicability.

关键词： Wind forecasting Wind power self-discipline energy storage system (ESS) optimization

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Multi-stage dynamic optimal allocation for battery energy storage system in distribution networks with photovoltaic system

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY systemS 2020年第12期30卷 1-20页

作者： Chen, Jian Jiang, Xinyi Li, Jiaqi Wu, Qiuwei Zhang, Yicheng Song, Guanyu Lin, Da Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250061 Peoples R China Tech Univ Denmark Dept Elect Engn Ctr Elect Power & Energy Lyngby Denmark Nanyang Technol Univ Sch Elect & Elect Engn Jurong West Singapore Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin Peoples R China Zhejiang Elect Power Corp Res Inst Hangzhou Peoples R China

In this article, a multi-stage optimal allocation method for battery energy storage system (BESS) in distribution networks with photovoltaic (PV) system is proposed, which is to obtain its optimal installation location, capacity, power, and investment time. This method takes the time value of money, the PV growth rate, and the load increase during the investment period into consideration. Firstly, genetic algorithm and gravity search algorithm are combined to solve a single-stage static optimal allocation problem and the BESS installation location and parameters in target year are obtained. Then, the obtained parameters are regarded as constraints and a heuristic multi-path forward dynamic programming method is used to determine the specific investment time of each equipment. Finally, a case study is used to verify the effectiveness and the economy of the proposed method. The research provides a theoretical basis for the optimal allocation of BESS in distribution networks.

关键词： battery energy storage system multi-stage dynamic GA-GSA optimal allocation

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Investigation on the Impact of the HPPC Profile on the Battery ECM Parameters' Offline Identification

Investigation on the Impact of the HPPC Profile on the Batte...

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2020 Asia Energy and Electrical Engineering Symposium

作者： Li, Zeyu Shi, Xiaohan Shi, Miaoyan Wei, Chuanzhi Di, Feng Sun, Hao Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China Shandong Goldencell Elect Technol Co Ltd Zaozhuang City Shandong Peoples R China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167824

Hybrid Pulse power Characterization (HPPC) test can provide the input data for the identification of the battery equivalent circuit model (ECM) parameters and is becoming a standard test for offline parameters' identification. However, the HPPC was originally designed for determining the dynamic power capability of the tested battery using a profile that incorporates both discharge and regen pulses, which is quite different from the parameters' identification, therefore it is meaningful to make the profile fit the parameters' identification. Aiming at this, this paper investigates the impact of the different HPPC profile on the identification accuracy from two aspects: the pulse width of the simulating current and the rest time. Firstly, the process of the HPPC test as well as parameters' identification is given. Then, identification results of the HPPC profile with different pulse width and rest time are compared both by simulation and experiments. The results show that both the pulse width and rest time have a significant impact on the accuracy of the parameters' identification. If the pulse width and rest time are both small, increasing either of them can improve the identification accuracy significantly, while if the pulse width or the rest time is large enough, the identification accuracy can't be further improved evidently by prolonging any of them.

关键词： lithium battery equivalent circuit model (ECM) offline parameter identification HPPC test

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Hierarchical Active power control of DFIG-Based Wind Farm With Distributed Energy Storage systems Based on ADMM

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2020年第3期11卷 1528-1538页

作者： Huang, Sheng Wu, Qiuwei Guo, Yifei Rong, Fei Tech Univ Denmark DTU Dept Elect Engn Ctr Elect Power & Energy CEE DK-2800 Lyngby Denmark Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China Hunan Univ Coll Elect & Informat Engn Dept Elect Engn Changsha 410082 Hunan Peoples R China

A hierarchical active power control (HAPC) scheme based on the alternating direction method of multipliers (ADMM) is proposed for doubly-fed induction generator (DFIG)-based wind farms with distributed energy storage systems (ESSs). The wind farm controller optimizes the active power references for DFIG-based wind turbines (WTs) and ESSs inside the wind farm, and the aim is to minimize fatigue loads by minimizing variations of thrust force and shaft torque of WTs while tracking the dispatch command from the transmission system operator (TSO). Moreover, ESSs management is considered in the central controller and WT controllers. The solution method based on the ADMM with fast convergence is used to solve the model predictive control (MPC) optimization problem in a hierarchical manner without loss of optimality. With the ADMM, the control task is distributed to WT controllers, and the computation burden of the central wind farm controller can be efficiently reduced. A wind farm with 30 DFIG WTs was used to validate the control performance of the proposed HAPC scheme.

关键词： Wind farms Fatigue Doubly fed induction generators Convex functions power control Wind power generation Optimization ADMM DFIG ESS fatigue loads hierarchical MPC wind farm

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