Low carbon oriented collaborative energy management framework for multi-microgrid aggregated virtual power plant considering electricity trading

Chang, Weiguang; Yang, Qiang

doi:10.1016/j.apenergy.2023.121906

Cited by 15 publications

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, a single VPP lacks diversities and quantities of distributed energy resources. Consequently, it is difficult for the separate operation of VPPs to satisfy changes in load demand and it is averse to their economic benefits [12][13][14]. Considering the complementarity among distributed energy resources of different VPPs, the joint operation of multiple VPPs can be utilized to achieve resource mutualization to adapt to the changes in load demand and to improve the economic revenue of all VPPs.…”

Section: Introductionmentioning

confidence: 99%

Distributed Cooperative Optimal Operation of Multiple Virtual Power Plants Based on Multi-Stage Robust Optimization

Cheng,

Li,

Yang

2024

Sustainability

View full text Add to dashboard Cite

This paper develops a distributed cooperative optimization model for multiple virtual power plant (VPP) operations based on multi-stage robust optimization and proposes a distributed solution methodology based on the combination of the alternating direction method of multipliers (ADMMs) and column-and-constraint generation (CCG) algorithm to solve the corresponding optimization problem. Firstly, considering the peer-to-peer (P2P) electricity transactions among multiple VPPs, a deterministic cooperative optimal operation model of multiple VPPs based on Nash bargaining is constructed. Secondly, considering the uncertainties of photovoltaic generation and load demand, as well as the non-anticipativity of real-time scheduling of VPPs in engineering, a cooperative optimal operation model of multiple VPPs based on multi-stage robust optimization is then constructed. Thirdly, the constructed model is solved using a distributed solution methodology based on the combination of the ADMM and CCG algorithms. Finally, a case study is solved. The case study results show that the proposed method can realize the optimal scheduling of renewable energy in a more extensive range, which contributes to the promotion of the local consumption of renewable energy and the improvement of the renewable energy utilization efficiency of VPPs. Compared with the traditional deterministic cooperative optimal operation method of multiple VPPs, the proposed method is more resistant to the risk of the uncertainties of renewable energy and load demand and conforms to the non-anticipativity of real-time scheduling of VPPs in engineering. In summary, the presented works strike a balance between the operational robustness and operational economy of VPPs. In addition, under the presented works, there is no need for each VPP to divulge personal private data such as photovoltaic generation and load demand to other VPPs, so the security privacy protection of each VPP can be achieved.

show abstract

Section: Introductionmentioning

confidence: 99%