A hybrid optimization approach to analyze the risk-constrained operation of a residential hybrid energy system incorporating responsive loads. (January 2022)

• Record Type: Journal Article
• Authors: Nourollahi, Ramin; Tabar, Vahid Sohrabi; Zadeh, Saeid Ghassem; Akbari-Dibavar, Alireza
• Is Part Of: Computers & chemical engineering. Volume 157(2022)
• Publication Date: 2022-01
• Subjects: Demand response program -- Residential hybrid energy system -- Robust optimization -- Renewable resources -- Risk management -- Stochastic programming; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2021.107603
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Modeling a residential hybrid energy system composed of various sources, storages and loads. Dividing the uncertain parameters into two classifications and considering different risk levels. Using both stochastic programming and robust optimization methods to model the uncertainties. Analyzing the financial risks of system by robust and conditional value-at-risk approaches. Abstract: Hybrid energy systems are developed recently due to their applicability in small scales. This paper presents a hybrid approach to optimize the operation and handle the uncertainties of a residential energy system composed of photovoltaic, fuel cell, boiler and storage units. In this regard, the uncertain parameters are divided into two categories including badly-behaved and well-behaved parameters and then, robust optimization and stochastic programming are utilized for modeling, respectively. As well, conditional value-at-risk is implemented to evaluate the risk of well-behaved parameters. According to the simulations, it is proved that badly-behaved uncertainties have higher impacts on system operation. Moreover, changing the control parameters of robust optimization and conditional value-at-risk from 0 to 24 and 0 to 1 increase the total cost by 5.2% and 0.47%, respectively. The comparative results also show that the proposed hybrid method takes less conservative decisions to optimize cost.