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

• Published in: Energy (Oxford) Vol. 238; p. 122006
• Main Authors: Hwangbo, Soonho, Heo, SungKu, Yoo, ChangKyoo
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-01-2022; Elsevier BV
• Subjects: Boilers; Carbon; Carbon sequestration; Clean energy; Climate change; Decarbonization; Economic models; Electricity; Energy; Energy policy; Energy storage; Energy transition; Environmental assessment; Greenhouse gases; Large-scale utility network; Mathematical models; Mathematical programming; Networks; Operating costs; Parameter uncertainty; Petrochemical industry; Petrochemicals industry; Renewable energy; Renewable energy policy; Renewable resources; Stochastic models; Stochasticity; Storage systems; Variable renewable electricity; Petrochemical industry; Climate change; Large-scale utility network; Renewable energy policy; Variable renewable electricity; Mathematical programming
• DOI: 10.1016/j.energy.2021.122006
• EISSN: 1873-6785
• ISSN: 0360-5442

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 tCO2/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.
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•Mathematical model developed for renewable energy and large-scale utility network.•CCS technology is combined with the optimized utility network for low-carbon economy.•A case study of huge petrochemical industries in South Korea were applied.•Scenario-based techno-economic and environmental assessment was conducted.•16% of total economic costs and 114 tCO2/d of GHG emissions were reduced.