A deep learning-based microgrid market modeling with planning assumptions. (May 2022)

• Record Type: Journal Article
• Authors: Zeng, Yijun; Han, Yihua; Zhang, Duo
• Is Part Of: Computers & electrical engineering. Volume 100(2022)
• Publication Date: 2022-05
• Subjects: Curtailable load -- Shiftable load -- Demand response plan -- Optimum planning -- Resiliency; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107858
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Using the HSRO framework for Modeling the uncertainties of the problem. Develop a robust optimization framework by applying the market price uncertainty for both normal and RO MGs operations. Risk evaluation of the RO planning for the MG for assessing the operating costs within various risk approaches. Abstract: Microgrids (MGs) can be considered as one of the best solutions for the distribution grid's resiliency and reliability. The research study suggests a hybrid stochastic-robust optimization method for determining the optimum schedule of a MG within usual and resilient operation situations. A new optimization method is used to model the effects of main electrical network cost uncertainty on the optimum planning of MGs. A stochastic optimization process is modeled with other dominant uncertainties, such as wind turbine power, photovoltaic power, and reactive/active loads, through creating appropriate case studies for each. MG's resilient operation is improved further by implementing curtailable and shiftable demand response programs.The suggested method enhances the performance of MGs under uncertainties when the network is in its normal and resilient mode. In addition, the paper discusses different approaches for RO planning and evaluates the effectiveness of the suggested structure in a large-scale MG trial system. Graphical abstract: Image, graphical abstract