Hybrid Energy Storage System (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction. (15th December 2019)

• Record Type: Journal Article
• Authors: Shi, Jie; Wang, Luhao; Lee, Wei-Jen; Cheng, Xingong; Zong, Xiju
• Is Part Of: Applied energy. Volume 256(2019)
• Publication Date: 2019-12-15
• Subjects: Very short-term generation scheduling -- Hybrid Energy Storage System -- Wind power -- Output fluctuation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113915
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Based on extracting wind power feature the HESS construction algorithm is proposed. The sizing results of HESS illustrate economic cost compared to the single ESS. MPC and MOCE are combined for control and optimization for generation scheduling. The proposed MMEMA performs better than NEMA through indexes of PD, HSOC and MSE. Abstract: Incorporating Energy Storage System (ESS) with wind farm to establish Wind-Storage Combined Generation System is a promising solution to improve the dependability of integrated wind power. Hybrid Energy Storage System (HESS) is designed based on wind power fluctuation and ESS features. The optimization of system sizing and very short-term generation scheduling are the key points affecting system effectiveness and reliability of wind power. This paper proposes a novel real-time model prediction control (MPC) -multi objective cross entropy (MOCE) based energy management algorithm (MMEMA) to coordinate an HESS based on power output feature extraction. The proposed algorithm includes the SOC regulation strategies considering practical issues including charge/discharge power. Firstly, based on Wavelet Package Decomposition (WPD) and Hilbert Huang Transform (HHT) respectively, the fluctuation feature of real-time wind power output is studied to propose a HESS model aiming to obtain the economic capacity as well as maximum charging/discharging power in every generation scheduling period (10 min). Thus, according to case study, the sizing approach can reduce invest cost by 25.7–47.0%. Then, HESS data is taken as parameters and constraints for the proposed model prediction control (MPC) –multi objective cross entropy (MOCE) algorithm to minimize the deviation between generation scheduling plan and real-time integrated power. The optimization results from case wind farm show that the proposed MMEMA algorithm performs better in smoothing out the fluctuation and managing the SOC of HESS than Non-dominated Sorting Genetic Algorithm II based method (NEMA) by means of evaluating the indexes PD, HSOC and MSE.