[1] Rana, M.M., et al., A review on peak load shaving in microgrid potential benefits, challenges, and future trend. Energies, 2022. 15(6): p. 2278.
[2] Zhang, Y., et al., The impact of non-renewable energy production and energy usage on carbon emissions: evidence from China. Energy & Environment, 2024. 35(4): p. 2248-2269.
[3] Koutsopoulos, I. and L. Tassiulas, Challenges in demand load control for the smart grid. Ieee Network, 2011. 25(5): p. 16-21.
[4] Gajowniczek, K., R. Nafkha, and T. Ząbkowski. Electricity peak demand classification with artificial neural networks. in 2017 Federated Conference on Computer Science and Information Systems (FedCSIS). 2017. IEEE.
[5] Cerna, F.V., et al., Load factor improvement of the electricity grid considering distributed energy resources operation and regulation of peak load. Sustainable Cities and Society, 2023. 98: p. 104802.
[6] Hu, L., et al., Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization. Sustainability, 2023. 16(1): p. 57.
[7] Jonban, M.S., et al., Intelligent fault tolerant energy management system using first-price sealed-bid algorithm for microgrids. Sustainable Energy, Grids and Networks, 2024. 38: p. 101309.
[8] Ebrahimi, A. and A. Hamzeiyan, An ultimate peak load shaving control algorithm for optimal use of energy storage systems. Journal of Energy Storage, 2023. 73: p. 109055.
[9] Hossain, J., et al., Optimal peak-shaving for dynamic demand response in smart Malaysian commercial buildings utilizing an efficient PV-BES system. Sustainable Cities and Society, 2024. 101: p. 105107.
[10] Mirzaei, M.A., et al., Techno‐economic, environmental and risk analysis of coordinated electricity distribution and district heating networks with flexible energy resources. IET Renewable Power Generation, 2023. 17(12): p. 2935-2949.
[11] Bibak, B. and H. Tekiner-Mogulkoc, The parametric analysis of the electric vehicles and vehicle to grid system’s role in flattening the power demand. Sustainable Energy, Grids and Networks, 2022. 30: p. 100605.
[12] Thangaraj, A., S.A.E. Xavier, and R. Pandian, Optimal coordinated operation scheduling for electric vehicle aggregator and charging stations in integrated electricity transportation system using hybrid technique. Sustainable Cities and Society, 2022. 80: p.103768.
[13] Huang, Z., et al., Economic-environmental scheduling of microgrid considering V2G-enabled electric vehicles integration. Sustainable Energy, Grids and Networks, 2022. 32: p. 100872.
[14] Xia, L., et al. Valley filling estimation of coordinated electric vehicle charging on distribution networks. in 3rd International Conference on Control Theory and Applications (ICoCTA 2023). 2023. IET.
[15] Ghafoori, M., M. Abdallah, and S. Kim, Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system. Applied Energy, 2023. 340: p. 121052
[16] Liu, L. and K. Zhou, Electric vehicle charging scheduling considering urgent demand under different charging modes.
Energy, 2022. 249: p. 123714.
[17] Wang, S., et al. A Strategy of Charging and Discharging for Electric Vehicle Aggregate Considering the Correction
Coefficient of Load Peak and Load Valley. in 2023 5th International Conference on Power and Energy Technology
(ICPET). 2023. IEEE.
[18] Rezaei, P. and M.A. Golkar. Economic load curve flattening by evs charge and discharge scheduling in the smart grid
considering machine learning-based forecasted load. in 2021 11th Smart Grid Conference (SGC). 2021. IEEE.
[19] Yin, W., L. Jia, and J. Ji, Energy optimal scheduling strategy considering V2G characteristics of electric vehicle. Energy, 2024. 294: p. 130967.
[20] Liu, Q., et al., Peak shaving potential and its economic feasibility analysis of V2B mode. Journal of Building
Engineering, 2024. 90: p. 109271.
[21] Prakash, K., et al., Bi-level planning and scheduling of electric vehicle charging stations for peak shaving and congestion
management in low voltage distribution networks. Computers and Electrical Engineering, 2022. 102: p. 108235.
[22] Jonban, M.S., et al., A reinforcement learning approach using Markov decision processes for battery energy storage control within a smart contract framework. Journal of Energy Storage, 2024. 86: p. 111342.
[23] Salari, A., M. Zeinali, and M. Marzband, Model-free reinforcement learning-based energy management for plug-in
electric vehicles in a cooperative multi-agent home microgrid with consideration of travel behavior. Energy, 2024. 288: p. 129725.
[24] C. Guille and G. Gross, "A conceptual framework for the vehicle-to-grid (V2G) implementation," Energy policy, vol. 37,
no. 11, pp. 4379-4390, 2009.
[25] G. Langer, "ABC News Poll: Traffic in the United States," ABC News, 2005.
[26] C. Chan and K. Chau, Modern electric vehicle technology. Oxford University Press on Demand, 2001.
[27] L. Sanna, "Driving the Solution," EPRI journal, 2005.
[28] C. S. Ioakimidis, D. Thomas, P. Rycerski, and K. N. Genikomsakis, "Peak shaving and valley filling of power
consumption profile in non-residential buildings using an electric vehicle parking lot," Energy, vol. 148, pp. 148-158, 2018.
[29] H. Hong, "An efficient point estimate method for probabilistic analysis," Reliability Engineering & System Safety, vol. 59, no. 3, pp. 261-267, 1998.
[30] P. Rezaei, S. Jadid, and A. Jalilian, "Probabilistic Optimization of Active and Reactive Power in Smart Grid Considering Vehicleto-Grid and the Uncertainty of Electricity Price," in 2021 11th Smart Grid Conference (SGC), 2021: IEEE, pp. 1-6.