CN

Mingyu YanYAN MINGYU

研究员(自然科学)    Supervisor of Doctorate Candidates    Supervisor of Master's Candidates

  • Professional Title:研究员(自然科学)
  • Gender:Male
  • Status:Employed
  • Department:School of Electrical and Electronic Engineering
  • Education Level:Postgraduate (Doctoral)
  • Degree:Doctoral Degree in Engineering

Paper Publications

Current position: Home > Scientific Research > Paper Publications

DP based multi-stage ARO for coordinated scheduling of CSP and wind energy with tractable storage scheme: Tight formulation and solution technique

Release time:2023-03-14
Hits:
Indexed by:
Journal paper
First Author:
Houbo Xiong
Correspondence Author:
Chuangxin Guo
Co-author:
Mingyu Yan,Yi Ding,Yue Zhou
Journal:
Applied Energy
Included Journals:
SCI
Place of Publication:
United Kingdom
Discipline:
Engineering
First-Level Discipline:
Electrical Engineering
Document Type:
J
Volume:
333
Page Number:
120578
ISSN No.:
0306-2619
Key Words:
Concentrated solar power plants;Wind energ;yPower scheduling;Dynamic programming;Multi-stage robust optimization;Thermal energy storage;Robust dual dynamic programming
DOI number:
10.1016/j.apenergy.2022.120578
Date of Publication:
2023-03-01
Impact Factor:
11.446
Abstract:
The concentrating solar power plants (CSP) have well potential in coordinating with the ever-increasing wind energy during power scheduling. However, the existing studies individually design the day-ahead or intra-day optimization of coordinated scheduling between CSP and wind power, which makes the scheduling decisions not optimal in terms of economic and environmental benefits. Additionally, the non-anticipativity of scheduling decisions are not considered in most of them. This paper proposes a novel dynamic programming (DP) formulated multi-stage robust reserve scheduling (DPMRS) model, which is the first attempt to realize the day-ahead and intra-day joint optimization for coordinated scheduling of CSP and wind power. Under the framework of multi-stage adaptive robust optimization (ARO), DPMRS model enforces the non-anticipativity of scheduling. Besides, a convex modelling technique for thermal energy storage (TES) is presented to ensure the tractability of DPMRS model, whose effectiveness is proved mathematically. Moreover, to efficient solve the DPMRS model, a robust dual dynamic programming with accelerated upper approximation (RDDP-AU) solution methodology is developed, and the mathematical proof for its convergence is provided. Numerical studies on the modified IEEE RTS-79 system and a real-world system in Northwest China validate the effectiveness of the proposed scheduling model and solution methodology. The simulation results demonstrate the DPMRS model brings a 17.22% reduction in scheduling cost, and reduces 57.39% curtailment of renewable energy. Compared with the conventional algorithm, the RDDP-AU significantly reduces the computational consumption by 87.56%, and with the error less than 0.074%.
Links to published journals:
https://www.sciencedirect.com/science/article/pii/S0306261922018359