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power flow optimization and Control Strategy for Energy Router in Dual Mode Traction power Supply System

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第11期24卷 12284-12293页

作者： Lai, Junhong Chen, Minwu Dai, Xianfeng Liu, Li Zhao, Ning Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China Univ Birmingham Birmingham Ctr Railway Res & Educ Sch Engn Birmingham B15 2TT England

A dual mode traction power supply system (TPSS), as a high-efficiency transportation approach, is composed of a mainline railway (AC traction power supply system) and an urban railway (DC traction power supply system). However, due to the neutral sections, the power from the two systems has been isolated, resulting in a low utilization rate of regenerative braking energy (RBE). The power flow from the two systems can not be managed cooperatively, causing high electricity bills. Therefore, this paper proposes a dual mode based energy router (DMER), which provides a path for energy interaction between two systems through multi-terminal converters. The proposed DMER integrates a hybrid energy storage system (HESS) and an energy feedback system (EFS) to recycle RBE. Moreover, a hierarchical control strategy is presented. The upper control layer is an optimized operation model, which takes the state of charge (SoC), energy conservation, and the other parameters as constraints to utilize more RBE and achieve the minimum daily operation cost of substations. The lower control layer identifies the control strategy for multiple converters to ensure the stable operation and dynamic response of DMER. Finally, the effectiveness of the topology and control strategy is verified using field load data and experiments.

关键词： ~Dual mode traction power supply system energy router power flow optimization railway energy management StarSim

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TCPST Control Strategy Applicable to Transmission Cross-Section for power flow optimization 3

TCPST Control Strategy Applicable to Transmission Cross-Sect...

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3rd International Conference on Energy and Electrical power Systems (ICEEPS)

作者： Bai, Jinzhuo Zhang, Qianwei Hao, Zhiguo Fan, Dengbo Ma, Yingxin Xi An Jiao Tong Univ Xian Peoples R China State Grid Jibei Elect Power Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350375145;9798350375138

Transmission sections are critical components of regional interconnected power grids, and the reasonable distribution of power flow on these sections is significant for the stable and secure operation of the system. The Thyristor Controlled Phase Shifting Transformer (TCPST), as a novel Flexible AC Transmission System (FACTS) device, can effectively improve power flow distribution on transmission sections. This paper focuses on the application of TCPST in optimizing power flow on transmission sections, selecting the load balancing degree and section losses as the control objectives for TCPST. A control strategy for optimizing power flow on transmission sections using TCPST is proposed and its effectiveness is verified through simulations on the PSCAD/EMTDC platform. The simulation results demonstrate that the proposed control strategy provides reasonable control objectives for TCPST applications in transmission sections, thereby improving power flow distribution and enhancing the reliability and safety of section operation.

关键词： thyristor controlled phase shifting transformer Transmission section power flow optimization load balancing degree section losses

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Study on New Energy AC/DC Hybrid Grid power flow optimization Based on Opportunity Constraints 3

Study on New Energy AC/DC Hybrid Grid Power Flow Optimizatio...

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3rd International Conference on New Energy and power Engineering, ICNEPE 2023

作者： Liu, Tingxiang Yang, Libin Zhou, Wanpeng Ma, Junxiong Wang, Kai Jing, Kun Economic and Technical Research Institute of State Grid Qinghai Electric Power Company Xining China Shenyang University of Technology School of Electrical Engineering Shenyang China

ISBN: (纸本)9798350393224

In the optimal power flow problem of AC-DC hybrid power system including wind power and other new energy sources, if the probability distribution information of uncertain variables can be obtained, the stochastic optimization method can be used to model the optimal power flow problem. In this paper, the optimization problem of AC-DC hybrid power system with wind power is studied. Under the condition of known probability distribution of uncertain variables, the optimal power flow model considering N-1 safety criterion is established based on the chance constraint optimization theory. The model and method proposed in this paper are verified by simulation in a hybrid AC DC power system modified by IEEE-14 node system. © 2023 IEEE.

关键词： Opportunity constraints power flow optimization Stochastic optimization methods

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High penetration PV active distribution network power flow optimization and loss reduction based on flexible interconnection technology

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ELECTRIC power SYSTEMS RESEARCH 2024年 226卷

作者： Yan, Xiangwu Wang, Qingao Bu, Jinyong North China Elect Power Univ Hebei Key Lab Distributed Energy Storage & Microgr Baoding 071003 Peoples R China

In order to reduce the huge network loss due to power flow back-feed and transfer under a high percentage of photovoltaic (PV) access, a comparative study on the impact of Soft Open Point (SOP), Rotary power flow Controller (RPFC), Capacitor Banks (CB), and flexible load access on the distribution network is carried out. Firstly, the second-order cone model for active distribution network coordination optimization based on SOP or RPFC flexible interconnection is established with the lowest network loss of distribution network as the goal;secondly, the second-order cone model of distribution network under different configuration schemes is solved separately under the improved IEEE 33-bus distribution network;finally, the optimal loss reduction strategy is obtained by quantitative analysis.

关键词： Flexible interconnection Photovoltaic Active distribution network power flow optimization Rotary power flow controller Soft open point

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Enhancing Steady-State power flow optimization in smart grids with a hybrid converter using GBDT-HBA technique

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 258卷

作者： Jose, Rajendran Arul Paulraj, Ebby Darney Rajesh, Paulthurai Raja Rajeswari Coll Engn Dept Elect & Elect Engn Bangalore Karnataka India Raja Rajeswari Coll Engn Dept Elect & Elect Engn Bangalore India Xpertmindz Innovat Solut Pvt Ltd Dept Elect & Elect Engn Kuzhithurai Tamil Nadu India

Optimizing steady-state power flow (PF) in Smart Grids (SGs) poses a significant challenge due to the complex interplay of various energy sources, converters, and control mechanisms. Conventional optimization methods often face limitations in terms of efficiency, flexibility, and steady-state error reduction. This manuscript proposes a hybrid technique to optimize steady-state power flow within Smart Grids (SGs) by combining DC/DC converters with DC/AC inverters. The proposed method combines the gradient boosting decision tree (GBDT) and Honey Badger Algorithm (HBA) commonly known as the GBDT-HBA method. The HBA is used to improve the control parameters of the hybrid converter. The GBDT is used to predict the control parameters. Among the generation source and microgrid (MG) system, the energy flow is assessed with energy routers. The proposed technique shows a higher efficiency of 96%, low steady-state error of 0.513 %, and less THD of 2.52 % compared to other existing Salp Swarm Algorithm (SSA), Ant Lion optimization (ALO), particle swarm optimization (PSO), techniques.

关键词： Gradient Boosting Decision Tree Honey Badger Algorithm Energy Management System (EMS) Smart Grids power flow optimization and Hybrid Converter

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A Data-Driven Genetic Algorithm for power flow optimization in the power System With Phase Shifting Transformer

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FRONTIERS IN ENERGY RESEARCH 2022年 9卷

作者： Li, Zuohong Li, Feng Liu, Ruoping Yu, Mengze Chen, Zhiying Xie, Zihao Du, Zhaobin Grid Planning & Res Ctr Guangdong Power Grid Corp Guangzhou Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

Phase-shifting transformer (PST) is one of the flexible AC transmission technologies to solve the problem of uneven power transmission. Considering that PST can also be used as a regulation means for the economic operation of the system, it is necessary to study the power flow optimization of power systems with PST. In order to find a more efficient power flow optimization method, an improved genetic algorithm including a data-driven module is proposed. This method uses the deep belief network (DBN) to train the sample set of the power flow and obtains a high-precision proxy model. Then, the calculation of the DBN model replaces the traditional adaptation function calculation link which is very time-consuming due to a great quantity of AC power flow solution work. In addition, the sectional power flow reversal elimination mechanism in the genetic algorithm is introduced and appropriately co-designed with DBN to avoid an unreasonable power flow distribution of the grid section with PST. Finally, by comparing with the traditional model-driven genetic algorithm and traditional mathematical programming method, the feasibility and the validity of the method proposed in this paper are verified on the IEEE 39-node system.

关键词： phase-shifting transformer power flow optimization genetic algorithm data-driven deep belief network

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power flow optimization for DC distribution grid with distributed energy access based on Newton-Raphson method through upper level control

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第4-Sup期22卷 S8999-S9006页

作者： Han, Bing Li, Yonggang North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China

DC distribution grids with distributed energy integration have been gaining a continually increasing interest in recent years both in academia and industry, power flow of the grids is a key issue to make use of these energy effectively and reliably, paper aims at optimizing the power flow of DC distribution through the upper level control, by considering the factors of distributed energy permeability and DC grid topology, without hardware investment increase nor using complex algorithm. firstly, the change rule of the network loss and voltage distribution are studied under different distributed energy permeability values and DC grid topology, Secondly, one OPF mathematical model of DC distribution network is established taking the index of network loss and voltage imbalance degree as objective function. According to the change of system flow, the optimized instruction value is provided by the system through calculation for each AC/DC, DC/DC converter;an optimization method of DC power flow is presented by controlling converter instruction. At last, two typical cases are demonstrated by simulation of PSCAD/EMTDC, indexes of the network loss and voltage imbalance degree of the DC distribution network are compared before and after the optimization, Simulation results show that the presented method is feasible to promote DC Distribution Grid.

关键词： Distributed energy DC distribution grid Network loss Newton-Raphson method power flow optimization

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A new swarm intelligence optimization approach to solve power flow optimization problem incorporating conflicting and fuel cost based objective functions

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2022年 2卷

作者： Jebaraj, Luke Sakthivel, Sithankathan Department of Electrical and Electronics Engineering M.I.E.T. Engineering College Tamil Nadu Tiruchirappalli India Department of Electrical and Electronics Engineering Nehru Institute of Engineering and Technology Coimbatore India

optimization of power flow (OPF) is a notable key tool pertinent to power system process, in both setting up and working phases and it is structured for a specific objective to optimize over power system variables, based on definite constraints. A new method of optimization based on swarm intelligence named Sparrow Search Algorithm (SSA), is proposed in this article to resolve the OPF problem in a most efficient way. The different equipped constraints like power balance, generator capacity, bus voltage limit and line flow, were taken into account. Shunt reactive power compensating elements and tap changing transformer settings were also incorporated in the problem formulation as control variables. The proposed SSA based OPF was constructed by several constraints, formulations and objective functions, scrutinized with higher number of cases (33 cases), for the first time, on the three well-liked IEEE networks (IEEE 30-, 57- and 118-bus) via single and weighted amount multi-objectives. The simulation result was examined and the performance and preeminence of the obtainable method was evaluated against other well-constructed recent optimization studies, reported in the literature. The percentage reduction of fuel rate, active transmission loss and deviation in voltage, were examined and compared with some most important recent studies, specified in the literature. Percentage improvement of voltage stability was also evaluated with recent studies, accounted in the literature. © 2022 The Author(s)

关键词： Fuel rate power flow optimization Sparrow search algorithm Transmission loss Voltage profile Voltage stability index

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Double Deep Q-learning Based Approach for power flow optimization in Distribution Networks

Double Deep Q-learning Based Approach for Power Flow Optimiz...

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2020 Asia Energy and Electrical Engineering Symposium

作者： Cao, Di Hu, Weihao Xu, Xiao Huang, Qi Chen, Zhe Univ Elect Sci & Technol China Chengdu Peoples R China Aalborg Univ Aalborg Denmark

ISBN: (纸本)9781728167824

With the increasing concerns on the environment and fossil resources, more and more renewable energies and energy storage systems (ESS) are embedded in active distribution networks (ADN). The stochastic nature of renewable energies brings great challenges to the power system operation. In this context, a double deep Q-learning based method is proposed to optimize the power flow of ADN considering the randomness of the load demand, wind speed. Experiment results validate that the proposed approach can master the optimal management strategy of ADN under constrains. Comparison results demonstrate that the proposed data-driven method can achieve better results when dealing with the uncertainty environment.

关键词： active distribution wind power power flow optimization deep reinforcement learning

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A Robust Decentralized power flow optimization for Dynamic PV System

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IEEE ACCESS 2019年 7卷 63789-63800页

作者： Alsafasfeh, Qais Saraereh, Omar A. Khan, Imran Choi, Bong Jun Tafila Tech Univ Dept Elect Power & Mechatron Tafila 11183 Jordan Hashemite Univ Dept Elect Engn Zarka 13133 Jordan Univ Engn & Technol Peshawar Dept Elect Engn Peshawar Pakistan Soongsil Univ Sch Comp Sci & Engn Seoul 06978 South Korea

This paper proposes a robust scheme for optimizing the power flow in a photovoltaic system. The scheme utilizes distributed saddle point dynamics and a decentralized approach to solve the power flow problem. It converts the convex optimization problem of the dynamic system control into the asymptotically stable dynamic systems and employs a linear approximation of power flow equations;speciflcally, a quadratic programming model is deployed with the aim of minimizing real-power losses to guarantee a globally optimal solution. Then, the photovoltaic inverters and electric networks are analyzed independently in a decentralized manner to exchange injection power among nodes while maintaining their independence to support the plug-and-play feature. A case study and the experimental results show that the proposed scheme achieves higher optimization accuracy and are more economical than the existing state-of-the-art schemes.

关键词： Renewable energy power flow optimization real-time control photovoltaic system linearization

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