(460e) Using Representative Days for the Design of Renewable-Based Utility Plants

The design of renewable-based utility plants requires a modeling approach that integrates long-term decisions (i.e. investment decisions) with the short-term dynamics of the renewables resources and utility demands, avoiding, at the same time, a very large CPU burden [Tejeda-Arango 2018]. Some examples of these types of models include the Regional energy deployment system (ReEDS) [Short et al. 2011], and the Integrated Planning Model (IPM) [EPA, 2015]. In both works, the authors proposed to approximate the annual operation of an electrical grid by using time slices that represent a wide range of electricity demands and trends of renewable resources [Nahmmarcher et al. 2016; Mallapragada et al. 2018]. The same methodology has been used by other authors to predict the capacity expansion of electrical grids [Heuberger et al. 2017; García-Cerezo et al. 2020]. However, one limitation of these models is that the processing routes of the energetic resources were already fixed, thus, they cannot be used for design purposes. Therefore, the objective of this work is to integrate the use of representative days to the design of renewable-based utility plants. We aim at studying different methods to represent the dynamic behavior of renewable resources (i.e. representing the whole year by a given number of days or by using four seasons) and evaluate its effects on the optimal design.

The utility plant couples different technologies (lignocellulosic biomass gasification using direct and indirect gasifiers and syngas use in gas turbines, biomass burning in a boiler to produce steam, different waste digestion and biogas use in a gas turbine, concentrated solar power, wind turbines) to process each renewable resource within a steam and power network. A superstructure was developed in order to select the best technology to process biomass, waste, solar radiation, and wind. The problem was coded and solved in GAMS and it was formulated as a two-stage stochastic MILP model with an hourly-time resolution. We first calculate different sets of representative days to approximate the whole year by using the k-means method and then study their effects on the first and second stage decisions. Thereafter, in-sample and out-sample analyses are conducted in order to calculate the expected value of the objective function. In a second step, the data is separated into four groups, corresponding to each season, and a set of representative days is calculated for each of them. Subsequently, the same sampling method is carried out to calculate the expected value of the objective function. Finally, we compare the results obtained from both approaches.

The proposed approach was applied to design a renewable-based utility plant located in the south-west of Mexico. Results showed significant changes in the plant topology when considering different sets of representative days. For instance, it was observed that the expected value of the investment cost increased from 284.5 $MMUSD (when considering three representative days) to 557.5 $ MMUSD (when considering twenty-one representative days). We also observed that two types of solutions were obtained when considering more than twelve representative days. Namely, a design that integrates a biomass boiler and another that integrates a syngas technology. The input sample analysis revealed that the solution that integrates the biomass boiler was the optimal one for only 25% of the samples taken. Moreover, the results obtained from the out-sample analysis showed that even though the solution that integrates a biomass boiler needs a higher amount of imported electricity, it remains cheaper than the second design.

References

1.García-Cerezo, Á., Baringo, L., & García-Bertrand, R. (2020). Representative Days for Expansion Decisions in Power Systems. Energies, 13(2), 335.

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5.Short, W., Sullivan, P., Mai, T., Mowers, M., Uriate, C., Blair, N., Heimiller, D., Martinez. A. (2011). Regional Energy Deployment System (ReEDS). National Renewable Energy Laboratory, Golden, CO.

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