Integrated model for optimal energy management and demand response of microgrids considering hybrid hydrogen-battery storage systems. (15th March 2023)

• Record Type: Journal Article
• Authors: Yousri, Dalia; Farag, Hany E.Z.; Zeineldin, Hatem; El-Saadany, Ehab F.
• Is Part Of: Energy conversion and management. Volume 280(2023)
• Publication Date: 2023-03-15
• Subjects: Battery -- Hydrogen storage -- Demand response -- Energy management systems -- Microgrids -- Multi-objective artificial hummingbird algorithm; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2023.116809
• Languages: English
• ISSNs: 0196-8904

Abstract

Abstract: Hybrid hydrogen-battery (HHB) based energy storage technologies have recently become matters of significant interest to enable sustainable and net zero-emission microgrids characterized with high penetration levels of variable renewable energy sources (RES) such as solar and wind. In this regard, there are multiple objectives and constraints controlling the scheduled decision-making framework that manages these microgrid resources while supplying the demand. As such, this paper introduces an integrated energy management system (IEMS) to concurrently optimize the operation schedule of the HHB storage system and provide demand response (DR) via adequate shifting slots for the elastic loads of a microgrid setup. The proposed IEMS aims to optimally manage the microgrid's energy by minimizing three conflicting objective functions: the electricity and battery degradation costs, customers' discomfort, and peak-to-average ratio (PAR). The proposed IEMS is developed for microgrids containing a mix of variable RES, an HHB storage system, and a local power consumption considering dynamic grid tariff. The IEMS is formulated as a mixed-integer nonlinear problem and is solved using a multi-objective artificial hummingbird optimizer (MOAHA). To account for the variability and uncertainty of RES, the proposed IEMS is tested under four scenarios: good, bad, average weather scenarios, and a forecasted weather profile based on a stochastic model of RES. The performance of the proposed IEMS is evaluated via comprehensive comparative analyses with the conventional energy management strategy (CEMS) without and with consideration of the HHB storage system and DR. Moreover, the performance of MOAHA has been assessed versus the state-of-the-art multi-objective particle swarm optimizer (MOPSO). For the purpose of these comparisons, four quality metrics have been utilized to quantify the benefits of the proposed IEMS: renewable contribution factor (RCF), greenhouse gas (GHG) emission, loss of power supply probability (LPSP), and saving cost (SC). The results show that the proposed IEMS yields better-optimized values that help the microgrid operator identify the sweet spot of the conflicting objectives, where it has increased the customer saving to be (82.95%, 29.65%, 33.33%, and 33.00 %), (73%, 23.94%, 25.49%, and 31.65%) and (10.69%, 3.62%, 1.59%, and 1.67%) compared to the CEMS without/with including HHB. and IEMS based-MOPSO over the four studied scenarios, respectively. Also, the results show the superiority of the proposed IEMS in improving the identified four quality metrics compared to the CEMS without/with considering HHB storage systems and IEMS-based-MOPSO. Accordingly, integrating HHB and implementing optimized IEMS based-MOAHA with considering the DR approach plays a vital role in providing a profit for the customer, protecting the environment from emissions, and enhancing the system's reliability with considering the weather uncertainty. Highlights: Microgrid integrated energy management system and demand response (IEMS) is proposed. The IEMS optimizes the schedule of the hybrid hydrogen-battery storage system. The IEMS accounts for the uncertainty of renewable energy resources. Multi-objective artificial hummingbird optimizer (MOAHA) is adopted to solve IEMS. Minimal greenhouse gas (GHG) emission is achieved based on the optimized IEMS.