Profitability of Concentrated Solar-Biomass hybrid power plants: Dataset of the stochastic techno-economic assessment

被引:1
|
作者
Gutierrez-Alvarez, R. [1 ,2 ]
Guerra, K. [1 ]
Haro, P. [1 ]
机构
[1] Univ Amer Quito, Postgrad Fac, Ave Granados E12-41 & Colimes Quito, Quito, Ecuador
[2] Univ Seville, Escuela Tecn Super Ingn, Chem & Environm Engn Dept, Camino Descubrimientos S-N, Seville 41092, Spain
来源
DATA IN BRIEF | 2023年 / 48卷
关键词
Concentrated solar power; Biomass energy; Hybrid power system; Techno-economic assessment; Uncertainty assessment; Profitability; Monte Carlo;
D O I
10.1016/j.dib.2023.109096
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
An increasing share of dispatchable renewable generation is required to achieve energy decarbonisation goals and ensure a reliable supply to power grids. Concentrating solar power (CSP) plants hybridised with biomass boilers are promising alternatives to replace part of the peaking and baseload power generated from fossil fuel-based systems. This paper includes data related to the design variables, equations, valuation parameters and detailed results that support the research article "Market profitability of CSP-Biomass hybrid power plants: Towards a firm supply of renewable energy." The profitability assessment is based on integrating the hourly variation of electricity prices in the Iberian day-ahead market (MIBEL) to the results of the techno-economic model through a novel economic metric named Profitability Factor. In addition, stochastic simulations were conducted to capture the uncertainty of relevant input variables on the profitability of the proposed hybrid plants. The resulting datasets presented in this paper will provide insights for researchers looking to address the economic performance of renewable generation concepts from a market profitability approach. Furthermore, the data can be used by investors and policymakers to better understand the risks and implications associated with the profitability potential of these systems. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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页数:17
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