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

Data in Brief 48 (2023) 109096 Contents lists available at ScienceDirect Data in Brief journal homepage: www.elsevier.com/locate/dib Data Article Profitability of Concentrated Solar-Biomass hybrid power plants: Dataset of the stochastic techno-economic assessment R. Gutiérrez-Alvarez a , b , ∗, K. Guerra a , P. Haro a a Postgraduate Faculty, Universidad de las Américas Quito, Avenida de los Granados E12-41 y Colimes Quito, Ecuador b Chemical and Environmental Engineering Department. Escuela Técnica Superior de Ingeniería, Universidad de Sevilla. Camino de los Descubrimientos s/n. 41092 Seville, Spain a r t i c l e i n f o Article history: Received 13 February 2023 Revised 14 March 2023 Accepted 22 March 2023 Available online 29 March 2023 Dataset link: Profitability assessment of CSP-biomass hybrid power plants: Monte-Carlo simulations results (Original data) a b s t r a c t 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 Abbreviations: BC, base case; CSP, concentrated solar power; DNI, direct normal irradiation; FB, full biomass; MB, medium biomass; MIBEL, Iberian electricity market; NB, no biomass; OS(i), operation strategy, i ranges from 1-2; PT, parabolic trough; SAM, system advisor model; SM, solar multiple; ST, solar tower; TES, thermal energy storage; TMY, typical meteorological year. DOI of original article: 10.1016/j.apenergy.2023.120754 ∗Corresponding author. E-mail address: [email protected] (R. Gutiérrez-Alvarez). Social media: @KarlaGuerra25 (K. Guerra), @Pedro_Haro_ (P. Haro) https://doi.org/10.1016/j.dib.2023.109096 2352-3409/© 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/ ) 2 R. Gutiérrez-Alvarez, K. Guerra and P. Haro / Data in Brief 48 (2023) 109096 Keywords: Concentrated solar power Biomass energy Hybrid power system Techno-economic assessment Uncertainty assessment Profitability Monte Carlo 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. ©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/ ) Nomenclature A sf solar field reflection area (m 2 ) HHV Biomass biomass high heating value (MWh th /tonne) I sf solar field incident power (MWh/m 2 /year) M Biomass biomass feed flow (tonnes/year) NC system system installed capacity (kW e ) Ƞ e net electric efficiency (%) Ƞ (boiler) biomass boiler global efficiency to electricity (% HHV) Ƞ (PB) power cycle efficiency (%) Ƞ (Q) part-load efficiency (%) Ƞ (T) temperature variation efficiency (%) Ƞ th (boiler) biomass boiler thermal efficiency (% HHV) Q n plant power output (MWh/year) Q sf solar field power output (MWh/year) Q TES thermal energy storage power output (MWh/year) SM solar multiple TES hours thermal energy storage capacity (hours) TGE Biomass biomass gross thermal energy (MWh th /year) Specifications Table Subject Renewable Energy, Sustainability, and the Environment Specific subject area Hybrid renewable power systems Type of data Table Raw data Equation Figure How data were acquired An annualised dataset of solar irradiation was used to predict the energy yield of the CSP system. In addition, the historical market electricity price dataset was used in the economic model for the profitability assessment. A novel evaluation metric called Profitability Factor was proposed. The resulting datasets related to the profitability assessment were obtained using self-constructed probability distributions for some relevant design parameters of the techno-economic analysis and the Monte-Carlo simulations of the stochastic model (10 0 0 0 iterations). Data format Raw Analysed Filtered ( continued on next page ) R. Gutiérrez-Alvarez, K. Guerra and P. Haro / Data in Brief 48 (2023) 109096 3 Description of data collection The criteria for collecting data on solar irradiation and electricity prices in the market was based on matching both data series on an hourly resolution for the same year. At the same time, the techno-economic design parameters were obtained from current best practices in the sizing of CSP-biomass hybrid plants. 1. Data on solar irradiation and other climatic variables were obtained from the meteorological station of the engineering school (ETSI) of the University of Seville. 2. For modelling the CSP generation system, technical design parameters widely accepted in the literature were used, as well as the specialised software System Advisor Model (SAM) property of the National Renewable Laboratory. 3. The annual electricity price data series was obtained from the Iberian electricity market operator (MIBEL) and “Red Eléctrica de España (REE)”. 4. For modelling the biomass block integration and the economic analysis, a proprietary calculation tool was used, in which the market prices of electricity were also included. Monte-Carlo simulations were carried out through Crystal Ball software. Data source location Country: Spain Primary dataset: “Iberian Electricity Market Operator”, data “Hourly day-ahead market price (EUR/MWh)” from 01 January 2017 to 31 December 2017. Link: https://www.omie.es/es/market-results/daily/daily-market/ daily-hourly-price Data accessibility Repository name: HARVARD Dataverse Data identification number: 10.7910/DVN/ZD2LFV Direct link to the dataset: https://doi.org/10.7910/DVN/ZD2LFV Related research article R. Gutiérrez-Alvarez, K. Guerra, P. Haro, Market profitability of CSP-Biomass hybrid power plants: Towards a firm supply of renewable energy, Appl. Energy. 335 (2023) 120754. https://doi.org/10.1016/j.apenergy.2023.120754 . Value of the Data • These data provide a detailed description of the technical and economic parameters used in modelling various configurations of Concentrated Solar-Biomass hybrid plants for electricity generation. The design parameters, equations and relationships obtained from optimisation analyses and presented in this paper provide valuable insights for researchers and technology developers studying the feasibility of hybrid technologies in other locations beyond the sunbelt. • This article presents for the first-time datasets of stochastic calculations (probability distributions) for the Profitability Factor, a novel economic metric applicable to assess the economic feasibility of renewable generation systems from a market profitability approach. These data help to understand the Profitability Factor’s construction by facilitating its calculations’ reproducibility. • These data can be useful to policymakers as a basis for developing new financial support mechanisms to encourage investment in baseload renewable power systems. In addition, data can be used to assess the effect of targeted subsidies to reward ancillary services supplied to the grid by dispatchable technologies (e.g., stability and rotational inertia). • These data also serve as a reference for researchers seeking to include electricity price variability in feasibility analyses of new energy systems. Accounting for the variations of electricity prices in a day-ahead market (hourly datasets) extends the scope of traditional techno-economic studies towards an approach of profitability and competitiveness under real conditions. • Although these data are specific to Seville (Spain), they serve as a guide for researchers and investors who want to assess the performance of hybrid CSP-biomass systems in other locations. Furthermore, the datasets provide a framework for extending the application of 4 R. Gutiérrez-Alvarez, K. Guerra and P. Haro / Data in Brief 48 (2023) 109096 the Profitability Factor to analyse other renewable technologies and their participation in other electricity markets. 1. Objective Given the progressive withdrawal of feed-in tariff subsidies for newly constructed renewable systems, it is crucial to assess their performance when participating as free bidders in electricity markets. The data included in this paper plot the hourly coupling between the generation of a dispatchable renewable system (i.e., CSP-biomass hybrid plant) and the variation in market electricity prices. Due to the probabilistic approach and the diversity of assessed cases (30 in total), a large number of results (30 0 0 0 0) were generated in the original research study [1] . The datasets included in this paper reflect in detail the iterative process conducted to account for the uncertainty linked to relevant economic design variables and their effect on the profitability of each case. Therefore, allowing for the reproducibility of the methodology used in the study and its potential extension to assess the profitability of other renewable generation concepts. 2. Data Description This paper includes data that supports the profitability assessment of CSP-biomass hybrid power plants and their potential to participate in the electricity market as price-taking technologies. For the calculation of market profitability, a new economic metric proposed by the authors called "Profitability Factor" has been used, whose detailed description and calculation procedure are presented in [1] . Thirty cases were assessed considering different combinations between two CSP technologies (i.e., Parabolic Trough (PT) and Solar Tower (ST)), three operating strategies and five levels of thermal storage. Data of the normal direct irradiation (DNI) for Seville and the data series of hourly electricity prices in the Iberian market have been included. Besides, deterministic and stochastic results (e.g., Monte-Carlo simulations) related to the techno-economic performance of the addressed plant configurations are presented. The data are grouped into several categories, which are summarised as follows: Section 2.1 . shows the generation profiles for different thermal energy storage (TES) and biomass capacities, including the effect of the day-dependent DNI variations. Data related to the annual deterministic performance are presented in Section 2.2 , while the probability distributions related to the profitability factor for all the assessed cases are summarised in Section 2.3 . Finally, a thorough description of the dataset included in a public repository [2] with all the runs performed in the Monte-Carlo simulations is provided in Section 2.3 . 2.1. Generation profiles The intensity of biomass and TES participation in the electricity generation profiles of the entire system are shown using ST technology as an example. Figs. 1 and 2 show the hourly electricity price profiles for operating strategies 1 and 2 (medium and high biomass share), respectively. The variable participation of generation sources is shown for each operation strategy, considering two TES capacities (0 and 20 hours) for three different days according to their DNI level (P0%, P50% and P100%), as detailed in Table 1 . 2.2. Annualised deterministic results Tables 2–8 show the annualised deterministic results according to the fixed point technoeconomic input parameters for all the cases included in the study ( BC - NB : Base case - No biomass, OS1 - MB: Operation strategy 1 - Medium biomass, OS2 - FB : Operation strategy 2 - Full biomass). R. Gutiérrez-Alvarez, K. Guerra and P. Haro / Data in Brief 48 (2023) 109096 5 Fig. 1. Power output profile for Solar Tower with medium biomass operation for various day types according DNI level. (a) P0% without TES, (b) P50% without TES, (c) P100% without TES, (d) P0% with 20 hours of TES, (e) P50% with 20 hours of TES, (f) P100% with 20 hours of TES. Table 1 Days sorted by DNI level. Day 1 Day 2 Day 3 DNI (kWh/m 2 -day) 0.01 6.33 11.69 Percentile P0% P50% P100% Date for 2017 18 th January 25 th November 16 th May Day type Working day Non-Working day Working day Table 2 Capacity factor results for PT and ST. Capacity factor (%) No-TES 5h 10h 15h 20h Parabolic Trough BC - NB 21.31% 32.51% 42.78% 52.97% 59.37% OS1 - MB 59.80% 66.82% 72.29% 77.00% 80.04% OS2 - FB 99.77% 10 0.0 0% 10 0.0 0% 10 0.0 0% 10 0.0 0% Solar Tower BC - NB 23.66% 38.99% 51.82% 63.15% 71.44% OS1 - MB 61.71% 71.30% 78.29% 84.45% 88.97% OS2 - FB 10 0.0 0% 10 0.0 0% 10 0.0 0% 10 0.0 0% 10 0.0 0% 6 R. Gutiérrez-Alvarez, K. Guerra and P. Haro / Data in Brief 48 (2023) 109096 Fig. 2. Power output profile for Solar Tower with full biomass operation for various day types according DNI level. (a) P0% without TES, (b) P50% without TES, (c) P100% without TES, (d) P0% with 20 hours of TES, (e) P50% with 20 hours of TES, (f) P100% with 20 hours of TES. Table 3 Solar share results for PT and ST. Solar share (%) No-TES 5h 10h 15h 20h Parabolic Trough BC - NB 10 0.0 0% 10 0.0 0% 10 0.0 0% 10 0.0 0% 10 0.0 0% OS1 - MB 35.63% 48.65% 59.17% 68.80% 74.18% OS2 - FB 21.36% 32.11% 42.08% 52.52% 59.07% Solar Tower BC - NB 10 0.0 0% 10 0.0 0% 10 0.0 0% 10 0.0 0% 10 0.0 0% OS1 - MB 38.34% 54.68% 66.19% 74.77% 80.29% OS2 - FB 23.55% 37.75% 49.60% 59.84% 67.20% 2.3. Profitability factor probability distributions This section presents the datasets associated with the results of the profitability analysis under uncertainty for CSP-biomass hybrid power plants. PT and ST technologies were considered, each with three operating strategies (no biomass, medium and full biomass) and five cases according to their thermal storage capacity (no storage, 5, 10, 10, 15 and 20 hours). Figs. 3 and 4 show the cumulative distribution functions for all PT and ST cases, respectively. R. Gutiérrez-Alvarez, K. Guerra and P. Haro / Data in Brief 48 (2023) 109096 7 Table 4 Biomass use results for PT and ST. Biomass use (kt/year) No-TES 5h 10h 15h 20h Parabolic Trough BC - NB 0 0 0 0 0 OS1 - MB 118.76 105.84 91.02 74.08 63.74 OS2 - FB 215.25 188.14 161.15 131.06 112.57 Solar Tower BC - NB 0 0 0 0 0 OS1 - MB 111.12 94.32 77.27 62.19 51.20 OS2 - FB 199.20 166.53 136.37 109.74 90.28 Table 5 Net electric efficiency for PT and ST. Net electric efficiency (%) No-TES 5h 10h 15h 20h Parabolic Trough BC - NB 14.48% 14.72% 14.53% 14.40% 13.45% OS1 - MB 19.89% 18.68% 17.55% 16.60% 15.28% OS2 - FB 23.45% 21.81% 20.22% 18.77% 17.12% Solar Tower BC - NB 15.48% 16.03% 15.96% 15.90% 15.50% OS1 - MB 20.81% 19.52% 18.44% 17.68% 16.88% OS2 - FB 24.48% 22.53% 20.85% 19.57% 18.41% Table 6 Investment per capacity for PT and ST. Investment per capacity (USD/kW e ) No-TES 5h 10h 15h 20h Parabolic Trough BC - NB 3010.39 4904.76 6799.13 8693.50 10587.86 OS1 - MB 4180.10 6074.46 7968.83 9863.20 11757.57 OS2 - FB 5326.41 7220.78 9115.14 11009.51 12903.88 Solar Tower BC - NB 3936.89 5271.57 6524.11 7694.53 8782.81 OS1 - MB 5127.67 6462.35 7714.90 8885.31 9973.59 OS2 - FB 6294.63 7629.31 8881.86 10052.27 11140.55 In total, 30 Monte-Carlo simulations were run (1 per case), each with 10 0 0 0 trials. The raw datasets are included in the Harvard repository. The datasets are contained in an Excel file, freely accessible at [2] and consisting of thirteen datasheets. The first sheet (Index) presents a general table of contents to describe the corresponding abbreviations and the order presentation of the other datasheets. Two sheets have been included for each of the six assessed configurations (i.e., considering two technologies and three operating strategies), which individually encompass the five cases according to TES capacities. The first one presents the datasets related to the random combinations of stochastic input variables and the corresponding results of the Profitability Factor . The second sheet shows each case’s probability density and cumulative distribution functions. 8 R. Gutiérrez-Alvarez, K. Guerra and P. Haro / Data in Brief 48 (2023) 109096 Table 7 LCOE for PT and ST. LCOE (USD/kWh) No-TES 5h 10h 15h 20h Parabolic Trough BC - NB 0.214 0.223 0.231 0.236 0.253 OS1 - MB 0.128 0.152 0.174 0.195 0.217 OS2 - FB 0.105 0.126 0.148 0.171 0.194 Solar Tower BC - NB 0.242 0.198 0.185 0.178 0.179 OS1 - MB 0.142 0.148 0.155 0.161 0.169 OS2 - FB 0.114 0.126 0.138 0.150 0.160 Table 8 Profitability factor for PT and ST. Profitability factor (USD/kWh) No-TES 5h 10h 15h 20h Parabolic Trough BC - NB -0.143 -0.152 -0.161 -0.167 -0.184 OS1 - MB -0.060 -0.084 -0.106 -0.126 -0.149 OS2 - FB -0.037 -0.058 -0.080 -0.103 -0.126 Solar Tower BC - NB -0.172 -0.127 -0.115 -0.110 -0.111 OS1 - MB -0.074 -0.079 -0.087 -0.093 -0.101 OS2 - FB -0.046 -0.058 -0.070 -0.082 -0.092 3. Experimental Design, Materials and Methods 3.1. Meteorological data and biomass properties For the study located in Seville – Spain, a single year (2017) solar data with a 10-minute resolution is used. This dataset was provided by the meteorological measurement station of the Universidad de Sevilla (University of Seville) [3] . These data are computed using NREL’s System Advisor Model (SAM) software to obtain datasets related to the performance of the solar block (e.g., net generated electricity, thermal power captured in the reflection field, charging and discharging of the thermal storage, among others). General information related to the measuring station and a summary of the main parameters for the assessed single-year and the typical meteorological year (TMY) datasets is shown in Table 9 . The TMY series was constructed and provided by the thermodynamics and renewable energy group of the University of Seville from data collected at the station during the period 20 0 0-2012 [3] . Table 10 compares accumulated incidence, and daily DNI means for each month of the year regarding both datasets (i.e., this study’s selected year and TMY). Finally, the properties of the feed biomass for the grate boiler included in the model are shown in Table 11 [4] . 3.2. Baseline design parameters The optimal solar multiple (SM) at design for each TES capacity is calculated according to Eqs. 1 and 2 for PT and ST configurations, respectively [5] . Parametric optimisation analysis was conducted by varying the SM from 1.0 to 3.0 with a step of 0.4 and the TES capacity from 0 (implies no TES) to 18 hours with a step of 2 hours as in [6] . The parametric analysis results were used as input variables for the sizing of the hybrid systems. Correlations were obtained R. Gutiérrez-Alvarez, K. Guerra and P. Haro / Data in Brief 48 (2023) 109096 9 Fig. 3. Cumulative distribution function of the profitability factor for Parabolic Trough plants. (a) Base Case, (b) Including biomass. BC: base case (no biomass), OS1: operation strategy 1 (medium biomass); OS2: operation strategy 2 (full biomass). from the combinations between SM and TES where the minimum LCOE is obtained. The results are shown in Fig. 5 . SM = 0 . 10 0 ( T E S hours ) + 1 . 0 0 0 (1) SM = 0 . 106 ( T E S hours ) + 1 . 122 (2) Parameters such as total absorbed power, TES capacity and total reflection area, among others, vary depending on the correlations mentioned above. In addition, other input variables such as tower height (i.e., for ST configurations), receiver area and solar field geometry are calculated using the optimisation algorithm of the System Advisor Model (SAM) software based on the SM and DNI values at design. Details of the specifications used in the sizing of the PT and ST sys- 16 R. Gutiérrez-Alvarez, K. Guerra and P. Haro / Data in Brief 48 (2023) 109096 Table 13 Hourly electricity prices in the Iberian market. Working days electricity prices (USD/kWh) Non-Working days electricity prices (USD/kWh) Mean Max Min Mean Max Min 0 0:0 0 0.064 0.118 0.041 0.065 0.107 0.043 01:00 0.060 0.110 0.027 0.061 0.098 0.035 02:00 0.058 0.102 0.022 0.059 0.091 0.025 03:00 0.057 0.098 0.016 0.057 0.090 0.024 04:00 0.057 0.096 0.014 0.056 0.088 0.021 05:00 0.061 0.097 0.024 0.056 0.091 0.020 06:00 0.067 0.099 0.034 0.057 0.092 0.021 07:00 0.073 0.115 0.049 0.059 0.090 0.022 08:00 0.076 0.120 0.057 0.062 0.095 0.025 09:00 0.075 0.122 0.054 0.064 0.103 0.029 10:00 0.076 0.123 0.049 0.066 0.103 0.025 11:00 0.075 0.123 0.047 0.065 0.098 0.025 12:00 0.075 0.121 0.046 0.066 0.097 0.020 13:00 0.073 0.118 0.047 0.065 0.097 0.019 14:00 0.071 0.116 0.046 0.062 0.097 0.017 15:00 0.070 0.114 0.045 0.060 0.093 0.017 16:00 0.071 0.113 0.046 0.059 0.088 0.019 17:00 0.073 0.126 0.050 0.063 0.094 0.023 18:00 0.076 0.134 0.051 0.069 0.115 0.030 19:00 0.078 0.129 0.053 0.073 0.117 0.037 20:00 0.078 0.129 0.058 0.075 0.115 0.045 21:00 0.075 0.127 0.056 0.073 0.113 0.038 22:00 0.071 0.121 0.052 0.070 0.107 0.027 23:00 0.068 0.118 0.047 0.067 0.104 0.027 Solar share = Q sf + Q T ES Q n ·100 (8) M Biomass = T G E Biomass H H V Biomass (9) ηe = Q n M Biomass ·H H V Biomass + I sf ·A sf (10) Ethical Statement This study meets all the requirements in Elsevier’s publication ethics policy. It did not involve humans or animals and did not use data from social media platforms. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article. Data Availability Profitability assessment of CSP-biomass hybrid power plants: Monte-Carlo simulations results (Original data) (Harvard dataverse). R. Gutiérrez-Alvarez, K. Guerra and P. Haro / Data in Brief 48 (2023) 109096 17 CRediT Author Statement R. Gutiérrez-Alvarez: Conceptualization, Methodology, Data curation, Formal analysis, Software, Writing – original draft; K. Guerra: Investigation, Writing –review & editing; P. Haro: Conceptualization, Methodology, Validation, Supervision, Writing –review & editing. Acknowledgements This work was supported by grant PID2020-114725RA-I00 of the project GH2T funded by MCIN/AEI/10.13039/50110 0 011033 and by the “European Union ”. This work was also supported by the Junta de Andalucía through the grant P18-RT-4512 (Co-funded by European Regional Development Fund/European Social Fund “A way to make Europe”). The Ph.D. grant of K. Guerra from Universidad de Sevilla under VI PPIT-US is acknowledged. References [1] R. Gutiérrez-Alvarez, K. Guerra, P. Haro, Market profitability of CSP-Biomass hybrid power plants: Towards a firm supply of renewable energy, Appl. Energy. 335 (2023) 120754, doi: 10.1016/j.apenergy.2023.120754 . [2] R.E. Gutiérrez, Profitability assessment of CSP-biomass hybrid power plants: Monte-Carlo simulations results, Harvard Datav. (2021), doi: 10.7910/DVN/ZD2LFV . [3] S. Moreno-Tejera, M.A. Silva-Pérez, I. Lillo-Bravo, L. Ramírez-Santigosa, Solar resource assessment in Seville, Spain. Statistical characterisation of solar radiation at different time resolutions, Sol. Energy 132 (2016) 430–441, doi: 10. 1016/j.solener.2016.03.032 . [4] P. Haro, P. Ollero, A.L. Villanueva Perales, A. 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