Stochastic optimisation and economic analysis of combined high temperature superconducting magnet and hydrogen energy storage system for smart grid applications. (1st July 2023)

• Record Type: Journal Article
• Authors: Patel, Ismail; Shah, Adil; Shen, Boyang; Wei, Haigening; Hao, Luning; Hu, Jintao; Wang, Qi; Coombs, Tim
• Is Part Of: Applied energy. Volume 341(2023)
• Publication Date: 2023-07-01
• Subjects: HTS SMES -- Energy storage -- Economic analysis -- Energy management -- Capacity credit; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2023.121070
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Economic and cashflow analysis of HTS SMES and hydrogen in smart grids. Energy storage fleet run using novel algorithm with maximal time to fail. Time evolution, reliability and capacity credit using Monte Carlo model. Results show a capacity credit and reliability improvement of 30 % and 9 %. Abstract: High Temperature Superconducting (HTS) Magnetic Energy Storage (SMES) devices are promising high-power storage devices, although their widespread use is limited by their high capital and operating costs. This work investigates their inclusion in smart grids when used in tandem with hydrogen fuel cells and other energy storage devices using a novel two-stage model. The first stage presents a stochastic allocation algorithm to optimally size the smart grid assets with the maximum expected profit. The next stage models the time system evolution with these optimal capacities using a dynamic Monte Carlo model to investigate the system reliability, capacity credit and loss of load expectation. A novel energy management algorithm is also presented that maximises the time with which a fleet of energy storage devices can fulfil a stochastically unknown power request. The analysis shows that from a purely economic standpoint, HTS SMES and hydrogen energy do not achieve high penetration levels (up to 10 %) due to their high costs, although the penetration levels can be improved with higher power imbalance penalties in day ahead markets. The novel capacity credit and reliability investigation shows that they can improve the reliability and capacity credit of wind turbines by approximately 30 %, thereby improving potential wind penetration levels. The optimal energy management algorithm improves the fleet lifespan and reliability by up to 9 % depending on the connected capacity.