Artículos de revistas: "DISTRIBUTED GENERATION PLANNING"

1

Dugan, R. C., T. E. McDermott y G. J. Ball. "Planning for distributed generation". IEEE Industry Applications Magazine 7, n.º 2 (2001): 80–88. http://dx.doi.org/10.1109/2943.911193.

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2

Bazrafshan, Mohammadhafez, Likhitha Yalamanchili, Nikolaos Gatsis y Juan Gomez. "Stochastic Planning of Distributed PV Generation". Energies 12, n.º 3 (31 de enero de 2019): 459. http://dx.doi.org/10.3390/en12030459.

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Recent studies by electric utility companies indicate that maximum benefits of distributed solar photovoltaic (PV) units can be reaped when siting and sizing of PV systems is optimized. This paper develops a two-stage stochastic program that serves as a tool for optimally determining the placing and sizing of PV units in distribution systems. The PV model incorporates the mapping from solar irradiance to AC power injection. By modeling the uncertainty of solar irradiance and loads by a finite set of scenarios, the goal is to achieve minimum installation and network operation costs while satisfying necessary operational constraints. First-stage decisions are scenario-independent and include binary variables that represent the existence of PV units, the area of the PV panel, and the apparent power capability of the inverter. Second-stage decisions are scenario-dependent and entail reactive power support from PV inverters, real and reactive power flows, and nodal voltages. Optimization constraints account for inverter’s capacity, PV module area limits, the power flow equations, as well as voltage regulation. A comparison between two designs, one where the DC:AC ratio is pre-specified, and the other where the maximum DC:AC ratio is specified based on historical data, is carried out. It turns out that the latter design reduces costs and allows further reduction of the panel area. The applicability and efficiency of the proposed formulation are numerically demonstrated on the IEEE 34-node feeder, while the output power of PV systems is modeled using the publicly available PVWatts software developed by the National Renewable Energy Laboratory. The overall framework developed in this paper can guide electric utility companies in identifying optimal locations for PV placement and sizing, assist with targeting customers with appropriate incentives, and encourage solar adoption.

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3

Liu, Zi Fa, Gang Liu y Wei Zhang. "Substation Optimization Planning Considering Distributed Generation". Advanced Materials Research 732-733 (agosto de 2013): 1314–19. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.1314.

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This paper established a transformer substation comprehensive optimal planning model considering distribution generation (DG) and different block geographic information factors (GIF), set form, volume, location of existing DG in planning area and transformer substation load-bearing capacity as constraint condition, taking construction cost of distribution transform substation and feeder and operation cost including current supply loss into account, in the meantime, regarding the influence of GIF such as land properties and so on to location and cost of construction with load demand satisfied. Furthermore, influence factors of different block information factor to construction cost were work out by means of interval analytical hierarchy process. On the basis of the established objective function, an particle swarm optimization (PSO) algorithm is proposed to solve the problem in this paper. By empirical study of certain planning area, the proposed model and algorithm are proved to be scientific and effective.

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4

Singh, Bindeshwar y Janmejay Sharma. "A review on distributed generation planning". Renewable and Sustainable Energy Reviews 76 (septiembre de 2017): 529–44. http://dx.doi.org/10.1016/j.rser.2017.03.034.

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5

Rouhani, Ahmad, Seyyed Hadi Hosseini y Mahdi Raoofat. "Composite generation and transmission expansion planning considering distributed generation". International Journal of Electrical Power & Energy Systems 62 (noviembre de 2014): 792–805. http://dx.doi.org/10.1016/j.ijepes.2014.05.041.

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6

Keane, A., Q. Zhou, J. W. Bialek y Mark O'Malley. "Planning and operating non-firm distributed generation". IET Renewable Power Generation 3, n.º 4 (2009): 455. http://dx.doi.org/10.1049/iet-rpg.2008.0058.

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7

Dzamarija, Mario y Andrew Keane. "Autonomous Curtailment Control in Distributed Generation Planning". IEEE Transactions on Smart Grid 7, n.º 3 (mayo de 2016): 1337–45. http://dx.doi.org/10.1109/tsg.2015.2427378.

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8

Kochukov, O. y A. Mutule. "Network-Oriented Approach to Distributed Generation Planning". Latvian Journal of Physics and Technical Sciences 54, n.º 3 (27 de junio de 2017): 3–12. http://dx.doi.org/10.1515/lpts-2017-0015.

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AbstractThe main objective of the paper is to present an innovative complex approach to distributed generation planning and show the advantages over existing methods. The approach will be most suitable for DNOs and authorities and has specific calculation targets to support the decision-making process. The method can be used for complex distribution networks with different arrangement and legal base.

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9

Wu, Lei, Hai Zhang, Zhaojie Hu, Yinghua Wang, Hairong Wang, Hua Yang, Bin Fan y Hao Chang. "Multi-objective distribution network planning method with distributed generation based on non dominated sorting differential evolution algorithm". Journal of Physics: Conference Series 2247, n.º 1 (1 de abril de 2022): 012019. http://dx.doi.org/10.1088/1742-6596/2247/1/012019.

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Abstract Combined with the specific problems of distribution network planning with distributed generation, this paper constructs a multi-objective optimization model of distribution network planning with distributed generation. According to the distributed generation distribution network layout planning with distributed generation, under the condition of uncertain load prediction value of distributed generation distribution network, taking the minimum voltage stability index, minimum network loss and minimum investment cost of distributed generation as sub objectives, a multi-objective programming model is established, and the model is solved by non dominated sorting differential evolution (NSDE) algorithm.

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10

Contreras, Javier y Gregorio Muñoz-Delgado. "Distributed Power Generation Scheduling, Modeling, and Expansion Planning". Energies 14, n.º 22 (19 de noviembre de 2021): 7757. http://dx.doi.org/10.3390/en14227757.

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11

Fathabad, Abolhassan Mohammadi, Jianqiang Cheng, Kai Pan y Feng Qiu. "Data-Driven Planning for Renewable Distributed Generation Integration". IEEE Transactions on Power Systems 35, n.º 6 (noviembre de 2020): 4357–68. http://dx.doi.org/10.1109/tpwrs.2020.3001235.

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12

Singh, Devender, R. K. Misra y Deependra Singh. "Effect of Load Models in Distributed Generation Planning". IEEE Transactions on Power Systems 22, n.º 4 (noviembre de 2007): 2204–12. http://dx.doi.org/10.1109/tpwrs.2007.907582.

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13

Zhao, Jun Hua, John Foster, Zhao Yang Dong y Kit Po Wong. "Flexible Transmission Network Planning Considering Distributed Generation Impacts". IEEE Transactions on Power Systems 26, n.º 3 (agosto de 2011): 1434–43. http://dx.doi.org/10.1109/tpwrs.2010.2089994.

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14

Trebolle, David, Tomás Gómez, Rafael Cossent y Pablo Frías. "Distribution planning with reliability options for distributed generation". Electric Power Systems Research 80, n.º 2 (febrero de 2010): 222–29. http://dx.doi.org/10.1016/j.epsr.2009.09.004.

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15

Mendoza, Jorge E., Miguel E. López, Sebastián C. Fingerhuth, Héctor E. Peña y Claudio A. Salinas. "Low voltage distribution planning considering micro distributed generation". Electric Power Systems Research 103 (octubre de 2013): 233–40. http://dx.doi.org/10.1016/j.epsr.2013.05.020.

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16

Alvarez, Manuel, Sarah K. Rönnberg, Math H. J. Bollen, Rafael Cossent y Jin Zhong. "Regulatory matters affecting distribution planning with distributed generation". CIRED - Open Access Proceedings Journal 2017, n.º 1 (1 de octubre de 2017): 2869–73. http://dx.doi.org/10.1049/oap-cired.2017.0358.

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17

Xiang, Yue, Yong Liu, Junyong Liu, Yilu Liu y Kunyu Zuo. "An Economic Criterion for Distributed Renewable Generation Planning". Electric Power Components and Systems 45, n.º 12 (21 de julio de 2017): 1298–304. http://dx.doi.org/10.1080/15325008.2017.1346727.

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18

Trebolle, David y Tomas Gomez. "Reliability Options in Distribution Planning Using Distributed Generation". IEEE Latin America Transactions 8, n.º 5 (septiembre de 2010): 557–64. http://dx.doi.org/10.1109/tla.2010.5623509.

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19

Shea, J. J. "Distributed power generation planning and evaluation [Book Review]". IEEE Electrical Insulation Magazine 17, n.º 2 (marzo de 2001): 67–68. http://dx.doi.org/10.1109/mei.2001.917549.

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20

Barati, Fatemeh, Shahram Jadid y Ali Zangeneh. "Private investor-based distributed generation expansion planning considering uncertainties of renewable generations". Energy 173 (abril de 2019): 1078–91. http://dx.doi.org/10.1016/j.energy.2019.02.086.

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21

Kumar, Sandeep, Vikas Manjrekar, Vivek Singh y Bhupesh Kumar Lad. "Integrated yet distributed operations planning approach: A next generation manufacturing planning system". Journal of Manufacturing Systems 54 (enero de 2020): 103–22. http://dx.doi.org/10.1016/j.jmsy.2019.12.001.

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22

KAUR, S. y G. B. KUMBHAR. "Incentive Driven Distributed Generation Planning with Renewable Energy Resources". Advances in Electrical and Computer Engineering 14, n.º 4 (2014): 21–28. http://dx.doi.org/10.4316/aece.2014.04004.

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23

Jin, Tongdan, Yu Tian, Cai Wen Zhang y David W. Coit. "Multicriteria Planning for Distributed Wind Generation Under Strategic Maintenance". IEEE Transactions on Power Delivery 28, n.º 1 (enero de 2013): 357–67. http://dx.doi.org/10.1109/tpwrd.2012.2222936.

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24

Ho, W. S., H. Y. Chin, K. C. Wong, Z. A. Muis y H. Hashim. "Grid-connected distributed energy generation system planning and scheduling". Desalination and Water Treatment 52, n.º 4-6 (14 de agosto de 2013): 1202–13. http://dx.doi.org/10.1080/19443994.2013.826785.

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25

Wu, Xiaomeng, Zheng Shi, Guo Feng y Qianyu Wang. "Overview of distributed generation planning in electric distribution networks". Journal of Physics: Conference Series 1634 (septiembre de 2020): 012114. http://dx.doi.org/10.1088/1742-6596/1634/1/012114.

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26

Su, Sheng-Yi, Chan-Nan Lu, Rung-Fang Chang y Guillermo Gutierrez-Alcaraz. "Distributed Generation Interconnection Planning: A Wind Power Case Study". IEEE Transactions on Smart Grid 2, n.º 1 (marzo de 2011): 181–89. http://dx.doi.org/10.1109/tsg.2011.2105895.

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27

Martinez Cesena, Eduardo A., Tomislav Capuder y Pierluigi Mancarella. "Flexible Distributed Multienergy Generation System Expansion Planning Under Uncertainty". IEEE Transactions on Smart Grid 7, n.º 1 (enero de 2016): 348–57. http://dx.doi.org/10.1109/tsg.2015.2411392.

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28

Niu, Getu. "Reliability - Based Distributed Generation Optimization in Demand Response Planning". Journal of Physics: Conference Series 1345 (noviembre de 2019): 052050. http://dx.doi.org/10.1088/1742-6596/1345/5/052050.

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29

Kools, L. y F. Phillipson. "Data granularity and the optimal planning of distributed generation". Energy 112 (octubre de 2016): 342–52. http://dx.doi.org/10.1016/j.energy.2016.06.089.

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30

Vinothkumar, K. y M. P. Selvan. "Fuzzy Embedded Genetic Algorithm Method for Distributed Generation Planning". Electric Power Components and Systems 39, n.º 4 (18 de febrero de 2011): 346–66. http://dx.doi.org/10.1080/15325008.2010.528533.

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31

Cao, Xiaoyu, Jianxue Wang y Bo Zeng. "Distributed Generation Planning Guidance Through Feasibility and Profit Analysis". IEEE Transactions on Smart Grid 9, n.º 5 (septiembre de 2018): 5473–75. http://dx.doi.org/10.1109/tsg.2018.2849852.

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32

Munoz-Delgado, Gregorio, Javier Contreras y Jose M. Arroyo. "Joint Expansion Planning of Distributed Generation and Distribution Networks". IEEE Transactions on Power Systems 30, n.º 5 (septiembre de 2015): 2579–90. http://dx.doi.org/10.1109/tpwrs.2014.2364960.

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33

Zangeneh, Ali, Shahram Jadid y Ashkan Rahimi-Kian. "Uncertainty based distributed generation expansion planning in electricity markets". Electrical Engineering 91, n.º 7 (23 de enero de 2010): 369–82. http://dx.doi.org/10.1007/s00202-010-0146-6.

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34

Zhang, Jietan, Hong Fan, Wenting Tang, Maochun Wang, Haozhong Cheng y Liangzhong Yao. "Planning for distributed wind generation under active management mode". International Journal of Electrical Power & Energy Systems 47 (mayo de 2013): 140–46. http://dx.doi.org/10.1016/j.ijepes.2012.10.024.

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35

Vinothkumar, K. y M. P. Selvan. "Hierarchical Agglomerative Clustering Algorithm method for distributed generation planning". International Journal of Electrical Power & Energy Systems 56 (marzo de 2014): 259–69. http://dx.doi.org/10.1016/j.ijepes.2013.11.021.

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36

AlMuhaini, Mohammad, Abass Yahaya y Ahmed AlAhmed. "Distributed Generation and Load Modeling in Microgrids". Sustainability 15, n.º 6 (8 de marzo de 2023): 4831. http://dx.doi.org/10.3390/su15064831.

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Solar PV and wind energy are the most important renewable energy sources after hydroelectric energy with regard to installed capacity, research spending and attaining grid parity. These sources, including battery energy storage systems, and well-established load modeling have been pivotal to the success of the planning and operation of electric microgrids. One of the major challenges in modeling renewable-based DGs, battery storage, and loads in microgrids is the uncertainty of modeling their stochastic nature, as the accuracy of these models is significant in the planning and operation of microgrids. There are several models in the literature that model DG and battery storage resources for microgrid applications, and selecting the appropriate model is a challenging task. Hence, this paper examines the most common models of the aforementioned distributed energy resources and loads and delineates the mathematical rigor required for characterizing the models. Several simulations are conducted to demonstrate model performance using manufacturers’ datasheets and actual atmospheric data as inputs.

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37

Alarcón Villamil, Jorge Alexander, Sergio Raúl Rivera Rodríguez y Francisco Santamaria Piedrahita. "Planning of power distribution networks in densely populated cities. Through distributed generation and capacitive compensators". Revista vínculos 16, n.º 2 (20 de noviembre de 2019): 209–18. http://dx.doi.org/10.14483/2322939x.15585.

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This paper analyses different options that can be used to solve the problem of the planning of power distribution networks by including capacitive compensation and distributed generation. The methodology for planning aims to determine the size of the units, the bus where the units have to be located, and the year in which investments should be made, in order to minimize the total energy losses on the network during the planning period. The work analyses four different cases: planning using neither capacitive compensation (SC) nor distributed generation (DG), planning using only SC, planning using only DG, and planning using reactive compensation and distributed generation simultaneously. Results show that simultaneous use of SC and DG reduce the total energy losses and improve the voltage profiles on the network, so good results for the planning are obtained.

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38

Saberi, Reza, Hamid Falaghi y Mostafa Esmaeeli. "Resilience-Based Framework for Distributed Generation Planning in Distribution Networks". Iranian Electric Industry Journal of Quality and Productivity 9, n.º 4 (1 de noviembre de 2020): 35–49. http://dx.doi.org/10.29252/ieijqp.9.4.35.

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39

Song, Wen y Qi Qiang Li. "Optimal Planning of Distributed Generation Using Self-Organizing Optimization Algorithm". Advanced Materials Research 852 (enero de 2014): 720–24. http://dx.doi.org/10.4028/www.scientific.net/amr.852.720.

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Recently, distributed generation (DG) has gained lots of attention due to a variety of benefits it can bring to the traditional power produce and distribution system. Identify the optimal location and size of DG in the distribution network is one of the crucial problems of DG integration, because a non-optimal planning might cause some adverse effects. In this paper, an optimization model with the consideration of minimizing energy losses is formulated first, and then an optimization methodology based on the Self-organizing Optimization Algorithm (SOA) is proposed. Finally, a case study is carried out to demonstrate the effectiveness of the proposed procedure.

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40

Daud, Sa’adah, Aida Fazliana Abdul Kadir, Musa Yusup Lada y Chin Kim Gan. "A Review: Optimal Distributed Generation Planning and Power Quality Issues". International Review of Electrical Engineering (IREE) 11, n.º 2 (30 de abril de 2016): 208. http://dx.doi.org/10.15866/iree.v11i2.5806.

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41

陈, 昡姿. "Siting and Sizing of Distributed Generation in Distribution Network Planning". Smart Grid 03, n.º 06 (2013): 153–58. http://dx.doi.org/10.12677/sg.2013.36028.

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42

Muraoka, Yukari y Tsutomu Oyama. "Generation Planning including Distributed Generator under Uncertainty of Demand Growth." IEEJ Transactions on Power and Energy 123, n.º 2 (2003): 162–68. http://dx.doi.org/10.1541/ieejpes.123.162.

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43

Naderi, Ehsan, Hossein Seifi y Mohammad Sadegh Sepasian. "A Dynamic Approach for Distribution System Planning Considering Distributed Generation". IEEE Transactions on Power Delivery 27, n.º 3 (julio de 2012): 1313–22. http://dx.doi.org/10.1109/tpwrd.2012.2194744.

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44

Chandel, A., D. S. Chauhan y D. Singh. "Distributed generation planning in deregulated power market - a bibliographical survey". International Journal of Energy Technology and Policy 8, n.º 3/4/5/6 (2012): 267. http://dx.doi.org/10.1504/ijetp.2012.052121.

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45

Zangeneh, Ali, Shahram Jadid y Ashkan Rahimi-Kian. "Promotion strategy of clean technologies in distributed generation expansion planning". Renewable Energy 34, n.º 12 (diciembre de 2009): 2765–73. http://dx.doi.org/10.1016/j.renene.2009.06.018.

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46

Tan, Wen-Shan, Mohammad Yusri Hassan, Md Shah Majid y Hasimah Abdul Rahman. "Optimal distributed renewable generation planning: A review of different approaches". Renewable and Sustainable Energy Reviews 18 (febrero de 2013): 626–45. http://dx.doi.org/10.1016/j.rser.2012.10.039.

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47

Zangeneh, Ali, Shahram Jadid y Ashkan Rahimi-Kian. "A fuzzy environmental-technical-economic model for distributed generation planning". Energy 36, n.º 5 (mayo de 2011): 3437–45. http://dx.doi.org/10.1016/j.energy.2011.03.048.

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48

Novoa, Clara y Tongdan Jin. "Reliability centered planning for distributed generation considering wind power volatility". Electric Power Systems Research 81, n.º 8 (agosto de 2011): 1654–61. http://dx.doi.org/10.1016/j.epsr.2011.04.004.

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49

Wang, HongHao, Libao Shi y Yixin Ni. "Distribution system planning incorporating distributed generation and cyber system vulnerability". Journal of Engineering 2017, n.º 13 (1 de enero de 2017): 2198–202. http://dx.doi.org/10.1049/joe.2017.0720.

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50

El-Khattam, W., Y. G. Hegazy y M. M. A. Salama. "An Integrated Distributed Generation Optimization Model for Distribution System Planning". IEEE Transactions on Power Systems 20, n.º 2 (mayo de 2005): 1158–65. http://dx.doi.org/10.1109/tpwrs.2005.846114.

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