Collaborative Optimization Decision Support System for Biomass and Smart Energy Based on Virtual Power Plant

[1] Ana, Baringo, Luis, & Baringo. (2017). A stochastic adaptive robust optimization approach for the offering strategy of a virtual power plant. IEEE Transactions on Power Systems, 32(5), 3492–3504. Search in Google Scholar

[2] Shayegan-Rad, A., Badri, A., & Zangeneh, A. (2017). Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties. Energy, 121(FEB.15), 114–125. Search in Google Scholar

[3] Iacobucci, R., Mclellan, B., & Tezuka, T. (2018). The synergies of shared autonomous electric vehicles with renewable energy in a virtual power plant and microgrid. Energies, 11(8), 2016. Search in Google Scholar

[4] Abbasi, & Ehsan. (2017). Coordinated primary control reserve by flexible demand and wind power through ancillary service–centered virtual power plant. International Transactions on Electrical Energy Systems, e2452. Search in Google Scholar

[5] Li, Y., Deng, Y., Wang, Y., Jiang, L., & Shahidehpour, M. (2023). Robust bidding strategy for multi-energy virtual power plant in peak-regulation ancillary service market considering uncertainties. International journal of electrical power and energy systems. Search in Google Scholar

[6] Zhang, T., & Hu, Z. (2022). Optimal scheduling strategy of virtual power plant with power-to-gas in dual energy markets. IEEE Transactions on Industry Applications(2), 58. Search in Google Scholar

[7] Ju, L., Yin, Z., Zhou, Q., Li, Q., Wang, P., & Tian, W., et al. (2022). Nearly-zero carbon optimal operation model and benefit allocation strategy for a novel virtual power plant using carbon capture, power-to-gas, and waste incineration power in rural areas. Applied Energy, 310. Search in Google Scholar

[8] Park, S. W., Park, Y. G., & Son, S. Y. (2017). Local-generator-based virtual power plant operation algorithm considering operation time. Journal of Electrical Engineering and Technology, 12(6), 2127–2137. Search in Google Scholar

[9] Koraki, & Strunz. (2018). Wind and solar power integration in electricity markets and distribution networks through service-centric virtual power plants. IEEE T POWER SYST. Search in Google Scholar

[10] Zhang, G., Jiang, C., Wang, X., Li, B., & Zhu, H. (2017). Bidding strategy analysis of virtual power plant considering demand response and uncertainty of renewable energy. IET Generation, Transmission & Distribution. Search in Google Scholar

[11] Qiu, J., Meng, K., Zheng, Y., & Dong, Z. Y. (2017). Optimal scheduling of distributed energy resources as a virtual power plant in a transactive energy framework. IET Generation, Transmission & Distribution. Search in Google Scholar

[12] Pandey, A. K., Jadoun, V. K., & Ns, J. (2023). Real-time and day-ahead risk averse multi-objective operational scheduling of virtual power plant using modified harris hawk’s optimization. Electric Power Systems Research(Jul.), 220. Search in Google Scholar

[13] Severini, M., Pisacane, O., Fagiani, M., & Squartini, S. (2018). Collaborative energy management in micro-grid environments through multi-objective optimization. IEEE. Search in Google Scholar

[14] Guo, X., Wang, K., Wang, L., Shen, S., & Zhang, C. (2020). Research on Collaborative optimization Decision Method of Source-Grid-Load-Storage. 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). IEEE. Search in Google Scholar

[15] Liao, L., & Ji, C. (2020). Smart grid dispatching optimization for system resilience improvement. Complexity, 2020. Search in Google Scholar

[16] S., W., Jin, Y., P., & Li, et al. (2017). An interactive optimization model for energy systems planning associated with clean-energy development under uncertainty. International Journal of Energy Research. Search in Google Scholar

[17] Hêri Golpra, & Khan, S. A. R. (2019). A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty. Energy. Search in Google Scholar

[18] Ye, X., Li, M., Si, P., Yang, R., Wang, Z., & Zhang, Y. (2023). Collaborative and intelligent resource optimization for computing and caching in iov with blockchain and mec using a3c approach. IEEE Transactions on Vehicular Technology (2), 72. Search in Google Scholar

[19] Majid, M., & Kazem, Z. (2018). Integration of smart energy hubs in distribution networks under uncertainties and demand response concept. IEEE Transactions on Power Systems, PP, 1–1. Search in Google Scholar

[20] Lu, W., Gong, Y., Liu, X., Wu, J., & Peng, H. (2017). Collaborative energy and information transfer in green wireless sensor networks for smart cities. IEEE Transactions on Industrial Informatics, 1–1. Search in Google Scholar