TY - JOUR
T1 - Optimal energy scheduling of virtual power plant integrating electric vehicles and energy storage systems under uncertainty
AU - Feng, Jie
AU - Ran, Lun
AU - Wang, Zhiyuan
AU - Zhang, Mengling
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/11/15
Y1 - 2024/11/15
N2 - The integration of renewable energy and electric vehicles into the smart grid is transforming the energy landscape, and Virtual Power Plant (VPP) is at the forefront of this change, aggregating distributed energy resources to optimize supply and demand balance. In this study, we propose a two-stage distributionally robust optimization framework for day-ahead energy scheduling and real-time power scheduling in VPP energy management system. Considering the uncertainty of power deviation in renewable energy generation, we design a coordinated charging and discharging strategy which integrates electric vehicles and energy storage systems to maintain a balance between supply and demand. To efficiently solve the tri-level min–max–min optimization problem with mixed-integer recourse variables in the second stage, we develop an improved nested C&CG algorithm to make real-time decisions on energy storage, which exhibits superior computational performance. The numerical results further demonstrate the effectiveness of the optimal energy scheduling strategy and provide some valuable insights. Moreover, our strategy not only proves cost-effective but also outperforms other comparable approaches in achieving superior peak shaving and valley filling effects. By guiding VPP operators to develop a reasonable energy scheduling solution, we can effectively balance economic and environmental sustainability.
AB - The integration of renewable energy and electric vehicles into the smart grid is transforming the energy landscape, and Virtual Power Plant (VPP) is at the forefront of this change, aggregating distributed energy resources to optimize supply and demand balance. In this study, we propose a two-stage distributionally robust optimization framework for day-ahead energy scheduling and real-time power scheduling in VPP energy management system. Considering the uncertainty of power deviation in renewable energy generation, we design a coordinated charging and discharging strategy which integrates electric vehicles and energy storage systems to maintain a balance between supply and demand. To efficiently solve the tri-level min–max–min optimization problem with mixed-integer recourse variables in the second stage, we develop an improved nested C&CG algorithm to make real-time decisions on energy storage, which exhibits superior computational performance. The numerical results further demonstrate the effectiveness of the optimal energy scheduling strategy and provide some valuable insights. Moreover, our strategy not only proves cost-effective but also outperforms other comparable approaches in achieving superior peak shaving and valley filling effects. By guiding VPP operators to develop a reasonable energy scheduling solution, we can effectively balance economic and environmental sustainability.
KW - Multi-energy storage dispatch
KW - Nested C&CG algorithm
KW - Smart charging and discharging
KW - Two-stage distributionally robust optimization
KW - Uncertain renewable energy generation
KW - Virtual power plant (VPP)
UR - http://www.scopus.com/inward/record.url?scp=85203196220&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2024.132988
DO - 10.1016/j.energy.2024.132988
M3 - Article
AN - SCOPUS:85203196220
SN - 0360-5442
VL - 309
JO - Energy
JF - Energy
M1 - 132988
ER -