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Journal articles on the topic 'Power transmission planning'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Karaki, Sami, Mithqal Sartawi, Anan Hamdan, and Najah Al-Hafi. "Computer aided power transmission planning." Electric Power Systems Research 9, no. 2 (September 1985): 133–39. http://dx.doi.org/10.1016/0378-7796(85)90030-6.

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2

Qu, Gang, Haozhong Cheng, Liangzhong Yao, Zeliang Ma, and Zhonglie Zhu. "Transmission surplus capacity based power transmission expansion planning." Electric Power Systems Research 80, no. 1 (January 2010): 19–27. http://dx.doi.org/10.1016/j.epsr.2009.08.001.

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3

Li, Wenyuan, and Paul Choudhury. "Probabilistic Transmission Planning." IEEE Power and Energy Magazine 5, no. 5 (2007): 46–53. http://dx.doi.org/10.1109/mpe.2007.904765.

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4

Huang, Yixin, Xinyi Liu, Zhi Zhang, Li Yang, Zhenzhi Lin, Yangqing Dan, Ke Sun, Zhou Lan, and Keping Zhu. "Multi-Stage Transmission Network Planning Considering Transmission Congestion in the Power Market." Energies 13, no. 18 (September 18, 2020): 4910. http://dx.doi.org/10.3390/en13184910.

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The uncertainty of generation and load increases in the transmission network in the power market. Considering the transmission congestion risk caused by various uncertainties of the transmission network, the optimal operation strategies of the transmission network under various operational scenarios are decided, aiming for the maximum of social benefit for the evaluation of the degree of scenario congestion. Then, a screening method for major congestion scenario is proposed based on the shadow price theory. With the goal of maximizing the difference between the social benefits and the investment and maintenance costs of transmission lines under major congestion scenarios, a multi-stage transmission network planning model based on major congestion scenarios is proposed to determine the configuration of transmission lines in each planning stage. In this paper, the multi-stage transmission network planning is a mixed integer linear programming problem. The DC power flow model and the commercial optimization software CPLEX are applied to solve the problem to obtain the planning scheme. The improved six-node Garver power system and the simplified 25-node power system of Zhejiang Province, China are used to verify the effectiveness of the proposed multi-stage planning model.

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5

Reis, F. S., P. M. S. Carvalho, and L. A. F. M. Ferreira. "Reinforcement Scheduling Convergence in Power Systems Transmission Planning." IEEE Transactions on Power Systems 20, no. 2 (May 2005): 1151–57. http://dx.doi.org/10.1109/tpwrs.2005.846073.

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6

Levi, V. A., and D. S. Popovic. "Integrated methodology for transmission and reactive power planning." IEEE Transactions on Power Systems 11, no. 1 (1996): 370–75. http://dx.doi.org/10.1109/59.486120.

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7

Hsu, Yuan-Yih, and Wah-Chun Chan. "Optimal transmission expansion planning for electric power systems." Electric Power Systems Research 9, no. 2 (September 1985): 141–48. http://dx.doi.org/10.1016/0378-7796(85)90031-8.

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8

Franken, Marco, Hans Barrios, Alexander B. Schrief, and Albert Moser. "Transmission expansion planning via power flow controlling technologies." IET Generation, Transmission & Distribution 14, no. 17 (September 4, 2020): 3530–38. http://dx.doi.org/10.1049/iet-gtd.2019.1897.

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9

Hooshmand, Rahmat-Allah, Reza Hemmati, and Moein Parastegari. "Combination of AC Transmission Expansion Planning and Reactive Power Planning in the restructured power system." Energy Conversion and Management 55 (March 2012): 26–35. http://dx.doi.org/10.1016/j.enconman.2011.10.020.

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10

Gbadamosi, Saheed Lekan, and Nnamdi I. Nwulu. "Optimal planning of renewable energy systems for power loss reduction in transmission expansion planning." Journal of Engineering, Design and Technology 18, no. 5 (February 7, 2020): 1209–22. http://dx.doi.org/10.1108/jedt-11-2019-0291.

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Purpose The purpose of this study is to address the efficiency of power losses representation while still reducing the computational burden of an optimal power flow (OPF) model in transmission expansion planning (TEP) studies. Design/methodology/approach A modified TEP model is formulated with inclusions of linearized approximation of power losses for a large-scale power system with renewable energy sources. The multi-objectives function determines the effect of transmission line losses on the optimal power generation dispatch in the power system with and without inclusion of renewable energy sources with emphasis on minimizing the investment and operation costs, emission and the power losses. Findings This study investigates the impact of renewable energy sources on system operating characteristics such as transmission power losses and voltage profile. Sensitivity analysis of the performance for the developed deterministic quadratic programming models was analyzed based on optimal generated power and losses on the system. Research limitations/implications In the future, a comparison of the alternating current OPF and direct current (DC) OPF models based on the proposed mathematical formulations can be carried out to determine the efficiency and reduction of computation process of the two models. Practical implications This paper proposed an accurate way of computing transmission losses in DC OPF for a TEP context with a view of achieving a minimal computation time. Originality/value This paper addresses the following objectives: develop a modified DC OPF with a linearized approximation of power losses in TEP problem with large integration of RES. Investigate the impact of RES on system operating characteristics such as transmission power losses and voltage profile.

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11

Yang, Xiuyu, Qi Guo, Jianzhong Gui, Renyong Chai, and Xueyuan Liu. "A Storage and Transmission Joint Planning Method for Centralized Wind Power Transmission." Computers, Materials & Continua 68, no. 1 (2021): 1081–97. http://dx.doi.org/10.32604/cmc.2021.016375.

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12

Sharan, Ishan, and R. Balasubramanian. "Generation Expansion Planning with High Penetration of Wind Power." International Journal of Emerging Electric Power Systems 17, no. 4 (August 1, 2016): 401–23. http://dx.doi.org/10.1515/ijeeps-2015-0186.

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Abstract Worldwide thrust is being provided in generation of electricity from wind. Planning for the developmental needs of wind based power has to be consistent with the objective and basic framework of overall resource planning. The operational issues associated with the integration of wind power must be addressed at the planning stage. Lack of co-ordinated planning of wind turbine generators, conventional generating units and expansion of the transmission system may lead to curtailment of wind power due to transmission inadequacy or operational constraints. This paper presents a generation expansion planning model taking into account fuel transportation and power transmission constraints, while addressing the operational issues associated with the high penetration of wind power. For analyzing the operational issues, security constrained unit commitment algorithm is embedded in the integrated generation and transmission expansion planning model. The integrated generation and transmission expansion planning problem has been formulated as a mixed integer linear problem involving both binary and continuous variables in GAMS. The model has been applied to the expansion planning of a real system to illustrate the proposed approach.

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13

Soleimani, K., and J. Mazloum. "Considering FACTS in Optimal Transmission Expansion Planning." Engineering, Technology & Applied Science Research 7, no. 5 (October 19, 2017): 1987–95. http://dx.doi.org/10.48084/etasr.1358.

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The expansion of power transmission systems is an important part of the expansion of power systems that requires enormous investment costs. Since the construction of new transmission lines is very expensive, it is necessary to choose the most efficient expansion plan that ensures system security with a minimal number of new lines. In this paper, the role of Flexible AC Transmission System (FACTS) devices in the effective operation and expansion planning of transmission systems is examined. Effort was taken to implement a method based on sensitivity analysis to select the optimal number and location of FACTS devices, lines and other elements of the transmission system. Using this method, the transmission expansion plan for a 9 and a 39 bus power system was performed with and without the presence of FACTS with the use of DPL environment in Digsilent software 15.1. Results show that the use of these devices reduces the need for new transmission lines and minimizes the investment cost.

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14

Rider, M. J., A. V. Garcia, and R. Romero. "Power system transmission network expansion planning using AC model." IET Generation, Transmission & Distribution 1, no. 5 (2007): 731. http://dx.doi.org/10.1049/iet-gtd:20060465.

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15

Kishore, T. S., and S. K. Singal. "Optimal economic planning of power transmission lines: A review." Renewable and Sustainable Energy Reviews 39 (November 2014): 949–74. http://dx.doi.org/10.1016/j.rser.2014.07.125.

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16

Bent, Russell, G. Loren Toole, and Alan Berscheid. "Transmission Network Expansion Planning With Complex Power Flow Models." IEEE Transactions on Power Systems 27, no. 2 (May 2012): 904–12. http://dx.doi.org/10.1109/tpwrs.2011.2169994.

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17

Duan, Gang, and Yixin Yu. "Problem-specific genetic algorithm for power transmission system planning." Electric Power Systems Research 61, no. 1 (February 2002): 41–50. http://dx.doi.org/10.1016/s0378-7796(01)00191-2.

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18

Adetona, Sunday, Raifu Salawu, and Frank Okafor. "Simulink model for planning a new power transmission investment." SIMULATION 90, no. 2 (April 9, 2013): 171–81. http://dx.doi.org/10.1177/0037549713481694.

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19

Wang, Xiaoming, Xudan Gou, Hanlin Chen, Xuexia Zhang, Bo Sun, and Zixuan Yu. "MOEAD based transmission network planning with wind power generation." IOP Conference Series: Earth and Environmental Science 153, no. 2 (May 2018): 022030. http://dx.doi.org/10.1088/1755-1315/153/2/022030.

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20

Passos, Diego, Felipe Rolim e Souza, and Célio Albuquerque. "Linear mesh network planning for power transmission line management." Transactions on Emerging Telecommunications Technologies 27, no. 10 (June 28, 2016): 1396–408. http://dx.doi.org/10.1002/ett.3064.

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21

Qiu, Jing, Junhua Zhao, and Zhao Yang Dong. "Probabilistic transmission expansion planning for increasing wind power penetration." IET Renewable Power Generation 11, no. 6 (May 2017): 837–45. http://dx.doi.org/10.1049/iet-rpg.2016.0794.

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22

Sharaf, T. A. M., and G. J. Berg. "Reliability evaluation in power-system transmission planning: practical considerations." IEEE Transactions on Reliability 37, no. 3 (1988): 274–79. http://dx.doi.org/10.1109/24.3754.

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23

Makvand, Saeid Biglary, Bala Venkatesh, Daniel Cheng, and Peng Yu. "Probabilistic evaluation and planning of power transmission system reliability." IET Generation, Transmission & Distribution 11, no. 5 (March 30, 2017): 1119–25. http://dx.doi.org/10.1049/iet-gtd.2016.0693.

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24

Wu, Shengyu, and Xufeng Song. "A Framework to Include Wind-Thermal Bundled Power Transmission Pattern in Multi-region Generation Expansion Planning Model." Journal of Clean Energy Technologies 5, no. 2 (2017): 159–62. http://dx.doi.org/10.18178/jocet.2017.5.2.362.

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25

Garcia-Bertrand, Raquel, and Roberto Minguez. "Dynamic Robust Transmission Expansion Planning." IEEE Transactions on Power Systems 32, no. 4 (July 2017): 2618–28. http://dx.doi.org/10.1109/tpwrs.2016.2629266.

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26

Haghighat, Hossein, and Bo Zeng. "Bilevel Conic Transmission Expansion Planning." IEEE Transactions on Power Systems 33, no. 4 (July 2018): 4640–42. http://dx.doi.org/10.1109/tpwrs.2018.2835663.

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27

Gilles, M. L., and J. Meisel. "Optimum HVAC-transmission expansion planning." International Journal of Electrical Power & Energy Systems 9, no. 1 (January 1987): 29–44. http://dx.doi.org/10.1016/0142-0615(87)90023-8.

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28

Berg, G. J., and T. A. M. Sharaf. "Dynamic transmission planning under uncertainty." Electric Power Systems Research 8, no. 2 (March 1985): 131–36. http://dx.doi.org/10.1016/0378-7796(85)90042-2.

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29

Luo, Kui, Wen Hui Shi, and Hao Zha. "Optimal Wind Power Planning Considering Power System Adaptability and Economy." Applied Mechanics and Materials 672-674 (October 2014): 246–50. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.246.

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Wind power planning towards large-scale accommodation of wind power while satisfying technical and economical constraints, it should consider power system adaptability and economy when giving full play to the wind benefits. This paper considers the relationship among wind power accommodation, construction of transmission lines and conventional unit operation costs and proposes an economic and reasonable wind power planning approach aiming at accommodating wind power efficiently and effectively. Combined the constrains of peak load regulation and network construction together, optimal wind power planning model is established, and based on the system operation simulation, a series of wind power planning evaluation index are obtained, which can estimate the wind power planning scheme from multiple angles. Finally, the feasibility and reasonability of the proposed planning approach has been verified by a numerical test system.

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30

GUO, Weibin, Hongguang WANG, Yong JIANG, and Aihua LIU. "Obstacle Navigation Planning for a Power Transmission Line Inspection Robot." Robot 34, no. 4 (2013): 505. http://dx.doi.org/10.3724/sp.j.1218.2012.00505.

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31

Yen, Jerome, Yonghe Yan, Javier Contreras, Pai-Chun Ma, and Felix F. Wu. "Multi-agent approach to the planning of power transmission expansion." Decision Support Systems 28, no. 3 (May 2000): 279–90. http://dx.doi.org/10.1016/s0167-9236(99)00092-5.

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32

Leite da Silva, Armando M., Luiz Antônio da Fonseca Manso, Warlley de Sousa Sales, Silvan A. Flavio, George J. Anders, and Leonidas Chaves de Resende. "Chronological Power Flow for Planning Transmission Systems Considering Intermittent Sources." IEEE Transactions on Power Systems 27, no. 4 (November 2012): 2314–22. http://dx.doi.org/10.1109/tpwrs.2012.2203830.

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33

Orfanos, George A., Pavlos S. Georgilakis, and Nikos D. Hatziargyriou. "Transmission Expansion Planning of Systems With Increasing Wind Power Integration." IEEE Transactions on Power Systems 28, no. 2 (May 2013): 1355–62. http://dx.doi.org/10.1109/tpwrs.2012.2214242.

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34

Liu, Zhuan, Yi-Zhe Wang, You-Fei Liu, and Juan Liu. "An Improved Hybrid Planning Model for Wind Power Transmission Channels." IOP Conference Series: Earth and Environmental Science 186 (October 11, 2018): 012029. http://dx.doi.org/10.1088/1755-1315/186/5/012029.

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35

Javadi, Mohammad Sadegh. "Sustainable generation and transmission expansion planning in competitive power markets." Indian Journal of Science and Technology 5, no. 2 (February 20, 2012): 1–7. http://dx.doi.org/10.17485/ijst/2012/v5i2.3.

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36

Gouveia, Eduardo M., Paulo Moisés Costa, Alireza Soroudi, and Andrew Keane. "DC constrained fuzzy power flow for transmission expansion planning studies." International Transactions on Electrical Energy Systems 27, no. 9 (May 15, 2017): e2361. http://dx.doi.org/10.1002/etep.2361.

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37

Sauma, Enzo, and Chung-Li Tseng. "Multiple Perspectives in Power Transmission Expansion Planning under Environmental Awareness." Journal of Energy Engineering 135, no. 3 (September 2009): 53–54. http://dx.doi.org/10.1061/(asce)0733-9402(2009)135:3(53).

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38

Singh, Kanwardeep, Narayana Prasad Padhy, and Jaydev Sharma. "Transmission Expansion Planning Including Reactive Power Procurement in Deregulated Environment." Electric Power Components and Systems 39, no. 13 (August 24, 2011): 1403–23. http://dx.doi.org/10.1080/15325008.2011.584110.

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39

Liu, Dundun, Shenxi Zhang, Haozhong Cheng, Lu Liu, Jianping Zhang, and Xiaohu Zhang. "Reducing wind power curtailment by risk-based transmission expansion planning." International Journal of Electrical Power & Energy Systems 124 (January 2021): 106349. http://dx.doi.org/10.1016/j.ijepes.2020.106349.

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40

de Paula, A. N., E. J. de Oliveira, L. W. Oliveira, and C. A. Moraes. "Reliability-constrained dynamic transmission expansion planning considering wind power generation." Electrical Engineering 102, no. 4 (July 3, 2020): 2583–93. http://dx.doi.org/10.1007/s00202-020-01054-y.

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41

Akhavizadegan, Faezeh, Lizhi Wang, and James McCalley. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning." Energies 13, no. 5 (March 5, 2020): 1203. http://dx.doi.org/10.3390/en13051203.

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Reliable transmission expansion planning is critical to power systems’ development. To make reliable and sustainable transmission expansion plans, numerous sources of uncertainty including demand, generation capacity, and fuel cost must be taken into consideration in both spatial and temporal dimensions. This paper presents a new approach to selecting a small number of high-quality scenarios for transmission expansion. The Kantorovich distance of social welfare distributions was used to assess the quality of the selected scenarios. A case study was conducted on a power system model that represents the U.S. Eastern and Western Interconnections, and ten high-quality scenarios out of a total of one million were selected for two transmission plans. Results suggested that scenarios selected using the proposed algorithm were able to provide a much more accurate estimation of the value of transmission plans than other scenario selection algorithms in the literature.

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42

Wang, Yi, Haozhong Cheng, Chun Wang, Zechun Hu, Liangzhong Yao, Zeliang Ma, and Zhonglie Zhu. "Pareto optimality-based multi-objective transmission planning considering transmission congestion." Electric Power Systems Research 78, no. 9 (September 2008): 1619–26. http://dx.doi.org/10.1016/j.epsr.2008.02.004.

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43

Alayo, Jorge Hans. "A Least Cost Transmission Planning Model Considering Operation Cost." International Journal of Emerging Electric Power Systems 15, no. 2 (April 1, 2014): 121–28. http://dx.doi.org/10.1515/ijeeps-2013-0124.

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Abstract Existing transmission planning models consider basic aspects of the problem. In practice, a transmission utility needs to model other important details such as operation cost of the power system. In this article, a least cost transmission expansion model is proposed considering the operation cost in order to model the trade-off between building new transmission capacity and increasing the power system’s operation cost. The proposed model is transformed into a mixed integer linear programming problem using linearization techniques and solved with CPLEX. Finally, results of the model for the Garver test system and IEEE 24-bus test system are shown.

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44

Dzikowski, Rafal. "DSO–TSO Coordination of Day-Ahead Operation Planning with the Use of Distributed Energy Resources." Energies 13, no. 14 (July 10, 2020): 3559. http://dx.doi.org/10.3390/en13143559.

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Growing penetration of uncoordinated Distributed Energy Resources (DERs) in distribution systems is contributing to the increase of the load variability to be covered at the transmission system level. Forced, fast and substantial changes of power plants’ output powers increase the risk of their failures, which threatens the reliable and safe delivery of electricity to end users in the power system. The paper handles this issue with the use of DERs and proposes a bilevel coordination concept of day-ahead operation planning with new kind of bids to be submitted by Distribution System Operators (DSOs) to the Transmission System Operator (TSO). This concept includes the extension of the Unit Commitment problem solved by TSO and a new optimization model to be solved by DSO for planning a smoothed power profile at the Transmission–Distribution (T–D) interface. Both optimization models are described in the paper. As simulations show, the modified 24-h power profiles at T–D interfaces result in a reduction of the demand for operation flexibility at the transmission system level and, importantly, result in a decrease of the number of conventional power plants that are required to operate during a day. Additionally, it has been proved that the modified profiles reduce the congestions in the transmission network. Hence, the concept presented in the paper can be identified as an important step towards the transformation of power systems to low-emission and reliable systems with high share of DERs.

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45

Wei, Ming Yue. "Impact of Large Scale Grid Connected Wind Power on Power System." Applied Mechanics and Materials 543-547 (March 2014): 685–88. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.685.

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The randomness and fluctuation of wind results in wind power generation with strong uncertainty. To ensure the power system operate safely and reliably, research on the impact of grid connected wind power on power system is necessary. From the perspective of system relay protection, system peaking, transmission capacity planning and reliability planning, this paper introduces the impact of centralized wind power integration on power system.

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46

Choi, Jaeseok, Timothy D. Mount, and Robert J. Thomas. "Transmission Expansion Planning Using Contingency Criteria." IEEE Transactions on Power Systems 22, no. 4 (November 2007): 2249–61. http://dx.doi.org/10.1109/tpwrs.2007.908478.

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47

de la Torre, SebastiÁn, Antonio J. Conejo, and Javier Contreras. "Transmission Expansion Planning in Electricity Markets." IEEE Transactions on Power Systems 23, no. 1 (February 2008): 238–48. http://dx.doi.org/10.1109/tpwrs.2007.913717.

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48

Taylor, Joshua A., and Franz S. Hover. "Linear Relaxations for Transmission System Planning." IEEE Transactions on Power Systems 26, no. 4 (November 2011): 2533–38. http://dx.doi.org/10.1109/tpwrs.2011.2145395.

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49

Haghighat, Hossein, and Bo Zeng. "Bilevel Mixed Integer Transmission Expansion Planning." IEEE Transactions on Power Systems 33, no. 6 (November 2018): 7309–12. http://dx.doi.org/10.1109/tpwrs.2018.2865189.

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50

Khaldi, Mohamad R. "Power Systems Analysis Toolbox: Planning and Contingency." Advanced Materials Research 433-440 (January 2012): 3884–89. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3884.

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Outages and planning primarily account for the removal and addition of new buses, generating power plants, transmission lines, loads, and control devices, respectively. They occur regularly in power systems operation and restoration, and hence a power system is constantly changing its topology. Therefore, there is a need for a software package to emulate these changes. Power System Analysis Toolbox (PSAT) is designed and developed in Matlab environment to simulate contingencies and expansion of power systems. The IEEE 14-bus power system is used to illustrate the effectiveness of the proposed work.

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