(342a) Optimal Selection of Renewable Energy Technologies in the Energy Transition. Study of the Spanish Peninsular Electricity System

Global warming has forced nations to make international agreements to change the way we use and transform energy. The use of no sustainable sources must be reduced, and renewable energy technologies must become the leading energy provider in the world to reduce CO₂ emissions and mitigate climate change. For example, the European Union (EU) has committed to reduce greenhouse gas (GHG) emissions 20% by 2020, 40% by 2030, 60% by 2040 and 80% by 2050, as compared to 1990 levels, consistent with the internationally agreed target to limit atmospheric warming to below 2°C. Electricity is becoming one of the main sources of the final energy demand in the world, electricity consumption is increasing in residential and commercial building sectors due to population growth, and increased access to electricity in non-OECD regions [1]. Therefore, energy transition from fossil fuels to renewable energy sources is one of the most important issues to archive climate change objectives. Large-scale electrification of end-use sectors such as buildings, industry and transport, as well as gradual decarbonization of the power sector, are key for the energy transition [2].

In this work, we focus on the energy transition of a national power system. It seeks to make the most of the available renewable resources through the territory in a sustainable way. The characteristics of the existing power plants are considered (those that operate normally and those scheduled for decommissioning). A mixed integer linear programing multi-objective model was developed to address the substitution of decommissioned power plants considering simultaneously technical constrains, availability of renewable energy sources in different places, fresh water consumption in function of type and location of new plants, direct and lifecycle greenhouse gas emissions, and total annual cost. The Spanish electricity system was selected to show the applicability of the model using real data, particularly, the Spanish peninsular electricity system (PES) [3]. The PES was chosen due to the imminent closure of its coal and nuclear thermal plants (which contribute with more than 20% of electricity produced by the Spanish PES) and its ambitious CO₂ reduction targets. The objective is to substitute the above mentioned power producing facilities with the best mix of new renewable power plants (biomass power plants, photovoltaic power stations, concentrated solar power tower systems with energy storage and wind farms) and integrating it with the existing technologies (hydroelectric, combined cycle, wind, solar photovoltaic, thermal solar and cogeneration) taking into account sustainability, flexibility and cost to supply a known electricity demand that varies throughout the year. The mixed integer linear programming model was implemented in the algebraic modeling language GAMS [4]. The model includes 195,261 continuous variables, 141 binary variables and 72,359 constrains.

The results show location, type, capacity and generation structure of the optimal energy mix of existing and new power plants. The proposed energy transition shows a potential reduction up to 37.50% and 49.68% in water consumption and greenhouse gas emission, respectively, with respect to the current system using the multi-objective optimization approach. Additional trade-offs analysis are performed to explore the behavior of the system if we assign different levels of priority to each objective. We demonstrate the importance of considering multiple objectives to achieve environmental benefits and it is shown the utility of the proposed model in the study of energy transition.

References:

[1] EIA. International Energy Outlook 2019 2019. https://www.eia.gov/outlooks/ieo/ (accessed April 21, 2020).

[2] IRENA. Power system flexibility for the energy transition, Part 1:Overview for policy makers. Sbu Dhabi: 2018.

[3] REE. REData | Red Eléctrica de España 2019.

https://www.ree.es/en/datos/generation/generation-structure (accessed April 17, 2020).

[4] Bussieck MR, Meeraus A. General Algebraic Modeling System (GAMS), Springer, Boston, MA; 2004, p. 137–57. https://doi.org/10.1007/978-1-4613-0215-5_8.