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