Dergiler / Electrica / 2021 / Cilt: 21 - Sayı: 3

Utilizing RDPSO Algorithm for Economic-Environmental Load Dispatch Modeling Considering Distributed Energy Resources

Sayfa
444–457
Yayın Tarihi
2022-10-12
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The effects of DERs have been analyzed on power loss, voltage profile, and static voltage stability of the test system.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:There are many factors such as long construction time, high investment cost, and low competition between energy providers to justify penetrating the distributed energy resources (DERs) such as wind turbines (WTs) and demand response programs (DRPs) in power systems.
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Temel Bulgular & Veri:The uncertainties about WTs’ power output have increased the complexity of the economic-environmental load dispatch (EELD) problem.
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Sonuç & Katkı:The effects of DERs have been analyzed on power loss, voltage profile, and static voltage stability of the test system.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

There are many factors such as long construction time, high investment cost, and low competition between energy providers to justify penetrating the distributed energy resources (DERs) such as wind turbines (WTs) and demand response programs (DRPs) in power systems. The uncertainties about WTs’ power output have increased the complexity of the economic-environmental load dispatch (EELD) problem. Because it is very difficult to predict the output power of wind farms, additional costs are imposed to the EELD problem. The mean amount of wind energy density has been used to define the storage and additional costs in the developed model. In addition, the DRPs have been facing the problem in reduction of cost. Demand response programs have been considered in two approaches; in the first approach, a certain percentage of the buses’ load determines the maximum amount of participation of the DRPs and in the second one, a certain capacity of these programs determines the maximum amount of their participation. The efficiency of the developed model has been analyzed by simulation results on multi-area IEEE 118 bus test system. The operational constraints in the test system, including lines limit, supply–demand balance and the generation limit of generators, WTs, and DRPs have been considered in the EELD problem. Multi-objective Random Drift Particle Swarm Optimization (MORDPSO) has been used in this study to analyze the model. The effects of DERs have been analyzed on power loss, voltage profile, and static voltage stability of the test system.

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Bu Makaleye Atıf Yap

HABIBI, F., KHOSRAVI, F., KHARRATI, S., KARIMI, S. (2022). Utilizing RDPSO Algorithm for Economic-Environmental Load Dispatch Modeling Considering Distributed Energy Resources. Electrica, Cilt: 21 - Sayı: 3, 444–457. https://ajindex.com/articles/499916

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Kaynakça (13)

  1. P. Wang, Y. Xiao and Y. Ding, “Nodal market power assessment in electricity markets,” IEEE Trans. Power Syst., vol. 19, no. 3, pp. 1373–1379, 2004
  2. A. Nourai, V. I. Kogan and C. M. Schafer, "Load leveling reduces T&D line losses," IEEE Trans. Power Deliv., vol. 23, no. 4, pp. 2168–2173, 2008
  3. R. Shaw, M. Attree, T. Jackson and M. Kay, "The value of reducing distribution losses bu domestic load shifting: A network perspective," Energy Policy, vol. 37, no. 8, pp. 3159–3167, 2009
  4. X. Qi, Z. Ji, H. Wu, J. Zhang and L. Wang, "Short-term reliability assessment of generating systems considering demand response reliability," IEEE Access, vol. 8, pp. 74371–74384, 2020
  5. M. Vahedipour-Dahraie, H. Rashidizadeh-Kermani, A. AnvariMoghaddam and J. M. Guerrero, "Stochastic risk-constrained scheduling of renewable-powered autonomous microgrids with demand response actions: Reliability and economic implications," IEEE Trans. Ind. Appl., vol. 56, no. 2, pp. 1882–1895, 2020
  6. G. Artac, D. Flynn, B. Kladnik, M. Pantos, A. F. Gubina and R. Golob, "A new method for determination the demand response offer function," Electr. Power Syst. Res., vol. 100, pp. 55–64, 2013
  7. E. Shayesteh, A. Yousefi and M. P. Moghaddam, "A probabilistic risk based approach for spinning reserve provision using day ahead demand response program," Energy, vol. 35, pp. 1655–1663, 2010
  8. N. I. Nwulu and X. Xia, “Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs,” Energy Convers. Manag., vol. 89, pp. 963–974, 2015
  9. M. Alipour, K. Zare and B. M. Ivatloo, “Short-term scheduling of combined heat and power generation units in the presence of demand response programs,” Energy, vol. 71, pp. 289–301, 2014
  10. H. C. Gils, “Assessment of the theoretical demand response potential in Europe,” Energy, vol. 67, pp. 1–18, 2014
  11. D. K. Critz, S. Busche and S. Connors, “Power systems balancing with high penetration renewables: The potential of demand response in Hawaii,” Energy Convers. Manag., vol. 76, pp. 609–619, 2013
  12. S. Agrawal, et. al., “Modeling and power quality analysis of wind energy generation arrangement with SMR MPPT,” International Conference of Signal Processing and Integrated Networks, 2020
  13. Y. Zhou, Q. Zhai, M. Zhou and X. Li, “Generation scheduling of self-generation power plant in enterprise microgrid with wind power and gateway power bound limits,” IEEE Trans. Sustain. Energy, vol.