4.7 Article

Development of deterministic-stochastic model to integrate variable renewable energy-driven electricity and large-scale utility networks: Towards decarbonization petrochemical industry

Journal

ENERGY
Volume 238, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.122006

Keywords

Variable renewable electricity; Large-scale utility network; Mathematical programming; Petrochemical industry; Renewable energy policy; Climate change

Funding

  1. National Research Foundation of Korea (NRF) [NRF-2019H1D8A2105994, NRF-2021R1A2C2007838]
  2. Korea Ministry of Environment (MOE) as Graduate school specialized in Climate Change

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This study developed a mathematical model of electricity based on renewable energy and large-scale utility networks, with a focus on the petrochemical industry in South Korea through techno-economic and environmental assessment. The proposed model consists of a deterministic model and a stochastic model, optimizing utility networks and constructing clean electricity networks, while considering carbon capture and storage systems to reduce greenhouse gas emissions.
This paper aims to develop the mathematical model of electricity based on renewable energy and large-scale utility (eRELU) networks to achieve a low-carbon economy. Huge petrochemical industries allocated in South Korea are considered to evaluate the proposed model by techno-economic and environmental assessment subject to Korean renewable energy policy. The suggested mathematical model consists of two parts: the deterministic model to optimize industrial-scale utility networks and the stochastic model to construct clean electricity networks using variable renewable energy coupled with energy storage systems to provide feasible quantities of renewable electricity required from the optimized utility network. The resulting model is complemented by carbon capture and storage (CCS) systems in doing so the inevitable amount of greenhouse gases from boilers in utility networks can be significantly captured. Diverse scenarios under the uncertain parameters such as facility investment/ operating costs and capacity factors of renewable energy are applied to the developed model, and the results show that the best scenario-based eRELU-CCS network reduces 16% of the total costs and capture/mitigate 114 tCO(2)/d comparing to the base case. It is expected that the proposed model will play an essential role in advancing the country's energy transition. (C) 2021 Elsevier Ltd. All rights reserved.

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