IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v188y2017icp456-465.html
   My bibliography  Save this item

Optimal scheduling of aggregated thermostatically controlled loads with renewable generation in the intraday electricity market

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Ryan S. Montrose & John F. Gardner & Aykut C. Satici, 2021. "Centralized and Decentralized Optimal Control of Variable Speed Heat Pumps," Energies, MDPI, vol. 14(13), pages 1-18, July.
  2. Xiaolin Ayón & María Ángeles Moreno & Julio Usaola, 2017. "Aggregators’ Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets," Energies, MDPI, vol. 10(4), pages 1-20, April.
  3. Diaz-Londono, Cesar & Enescu, Diana & Ruiz, Fredy & Mazza, Andrea, 2020. "Experimental modeling and aggregation strategy for thermoelectric refrigeration units as flexible loads," Applied Energy, Elsevier, vol. 272(C).
  4. Vinicius Braga Ferreira da Costa & Gabriel Nasser Doyle de Doile & Gustavo Troiano & Bruno Henriques Dias & Benedito Donizeti Bonatto & Tiago Soares & Walmir de Freitas Filho, 2022. "Electricity Markets in the Context of Distributed Energy Resources and Demand Response Programs: Main Developments and Challenges Based on a Systematic Literature Review," Energies, MDPI, vol. 15(20), pages 1-43, October.
  5. Lee, Donghun & Kim, Kwanho, 2021. "PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information," Renewable Energy, Elsevier, vol. 173(C), pages 1098-1110.
  6. Bomela, Walter & Zlotnik, Anatoly & Li, Jr-Shin, 2018. "A phase model approach for thermostatically controlled load demand response," Applied Energy, Elsevier, vol. 228(C), pages 667-680.
  7. Kai Ma & Chenliang Yuan & Jie Yang & Zhixin Liu & Xinping Guan, 2017. "Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids," Energies, MDPI, vol. 10(7), pages 1-18, July.
  8. Winstead, Christopher & Bhandari, Mahabir & Nutaro, James & Kuruganti, Teja, 2020. "Peak load reduction and load shaping in HVAC and refrigeration systems in commercial buildings by using a novel lightweight dynamic priority-based control strategy," Applied Energy, Elsevier, vol. 277(C).
  9. Zhang, Chenghua & Wu, Jianzhong & Zhou, Yue & Cheng, Meng & Long, Chao, 2018. "Peer-to-Peer energy trading in a Microgrid," Applied Energy, Elsevier, vol. 220(C), pages 1-12.
  10. Chen, Xi & Liu, Boxuan & Qiu, Jing & Shen, Wei & Reedman, Luke & Dong, Zhao Yang, 2021. "A new trading mechanism for prosumers based on flexible reliability preferences in active distribution network," Applied Energy, Elsevier, vol. 283(C).
  11. Vuelvas, José & Ruiz, Fredy & Gruosso, Giambattista, 2018. "Limiting gaming opportunities on incentive-based demand response programs," Applied Energy, Elsevier, vol. 225(C), pages 668-681.
  12. Li, Yang & Feng, Bo & Li, Guoqing & Qi, Junjian & Zhao, Dongbo & Mu, Yunfei, 2018. "Optimal distributed generation planning in active distribution networks considering integration of energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1073-1081.
  13. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
  14. Zhong, Shengyuan & Wang, Xiaoyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Wang, Yongzhen & Deng, Shuai & Zhu, Jiebei, 2021. "Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating," Applied Energy, Elsevier, vol. 288(C).
  15. Wenhao Zhuo & Andrey V. Savkin & Ke Meng, 2019. "Decentralized Optimal Control of a Microgrid with Solar PV, BESS and Thermostatically Controlled Loads," Energies, MDPI, vol. 12(11), pages 1-15, June.
  16. Iria, José & Soares, Filipe & Matos, Manuel, 2018. "Optimal supply and demand bidding strategy for an aggregator of small prosumers," Applied Energy, Elsevier, vol. 213(C), pages 658-669.
  17. Moreno, Blanca & Díaz, Guzmán, 2019. "The impact of virtual power plant technology composition on wholesale electricity prices: A comparative study of some European Union electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 100-108.
  18. Adhikari, Rajendra & Pipattanasomporn, M. & Rahman, S., 2018. "An algorithm for optimal management of aggregated HVAC power demand using smart thermostats," Applied Energy, Elsevier, vol. 217(C), pages 166-177.
  19. Dong, Jingya & Song, Chunhe & Liu, Shuo & Yin, Huanhuan & Zheng, Hao & Li, Yuanjian, 2022. "Decentralized peer-to-peer energy trading strategy in energy blockchain environment: A game-theoretic approach," Applied Energy, Elsevier, vol. 325(C).
  20. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
  21. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
  22. Luciana Marques & Wadaed Uturbey & Miguel Heleno, 2021. "An Integer Non-Cooperative Game Approach for the Transactive Control of Thermal Appliances in Energy Communities," Energies, MDPI, vol. 14(21), pages 1-22, October.
  23. Song, Yuguang & Chen, Fangjian & Xia, Mingchao & Chen, Qifang, 2022. "The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution," Applied Energy, Elsevier, vol. 309(C).
  24. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
  25. Pied, Marie & Anjos, Miguel F. & Malhamé, Roland P., 2020. "A flexibility product for electric water heater aggregators on electricity markets," Applied Energy, Elsevier, vol. 280(C).
  26. Jie Yang & Tongyu Liu & Huaibao Wang & Zhenhua Tian & Shihao Liu, 2019. "Optimizing the Regulation of Aggregated Thermostatically Controlled Loads by Jointly Considering Consumer Comfort and Tracking Error," Energies, MDPI, vol. 12(9), pages 1-17, May.
  27. Diaz, Cesar & Ruiz, Fredy & Patino, Diego, 2017. "Modeling and control of water booster pressure systems as flexible loads for demand response," Applied Energy, Elsevier, vol. 204(C), pages 106-116.
  28. Bahrami, Shahab & Amini, M. Hadi, 2018. "A decentralized trading algorithm for an electricity market with generation uncertainty," Applied Energy, Elsevier, vol. 218(C), pages 520-532.
  29. Ibrahim Ali Kachalla & Christian Ghiaus, 2024. "Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions," Energies, MDPI, vol. 17(2), pages 1-32, January.
  30. Iria, José & Soares, Filipe & Matos, Manuel, 2019. "Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets," Applied Energy, Elsevier, vol. 238(C), pages 1361-1372.
  31. Wang, Haixin & Yang, Junyou & Chen, Zhe & Li, Gen & Liang, Jun & Ma, Yiming & Dong, Henan & Ji, Huichao & Feng, Jiawei, 2020. "Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks," Applied Energy, Elsevier, vol. 267(C).
  32. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Zhang, Zhen, 2018. "Coordination optimization of multiple thermostatically controlled load groups in distribution network with renewable energy," Applied Energy, Elsevier, vol. 231(C), pages 456-467.
  33. Xia, Mingchao & Song, Yuguang & Chen, Qifang, 2019. "Hierarchical control of thermostatically controlled loads oriented smart buildings," Applied Energy, Elsevier, vol. 254(C).
  34. Kong, Xiangyu & Kong, Deqian & Yao, Jingtao & Bai, Linquan & Xiao, Jie, 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning," Applied Energy, Elsevier, vol. 271(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.