Dataset for Scheduling Strategies for Microgrids Coupled with Natural Gas Networks

Muhammad Yousif, Qian Ai, Yang Gao, Waqas Ahmad Wattoo, Ran Hao and Ziqing Jiang
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Muhammad Yousif: Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, Shanghai 200240, China
Qian Ai: Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, Shanghai 200240, China
Yang Gao: Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, Shanghai 200240, China
Waqas Ahmad Wattoo: Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, Shanghai 200240, China
Ran Hao: Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, Shanghai 200240, China
Ziqing Jiang: Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, Shanghai 200240, China

Data, 2019, vol. 4, issue 1, 1-4

Abstract: Datasets are significant for researchers to test the functionality of their proposed strategies for the microgrid dispatch. This article presents a dataset to help researchers in testing their algorithms related to the dispatch problem of microgrids coupled with natural gas networks. This preliminary release of a microgrid dispatch dataset contains data related to microgrid components (like solar PV, wind turbine, fuel cell and batteries) and natural gas network elements connected with the microgrid (e.g., micro gas turbine). It also includes the data associated with the authors’ proposed scheduling strategy and its dispatch results. The provided dataset can be used to reproduce the authors’ proposed strategy. The presented dataset further can be used for comparisons of other researchers’ proposed strategies. These comparisons will make a strategy’s features more evident.

Keywords: power scheduling strategy data; distribution networks; renewable energy sources; optimal power dispatch; dataset (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
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