Two-stage energy scheduling optimization model for complex industrial process and its industrial verification

Yusha Hu and Yi Man

Renewable Energy, 2022, vol. 193, issue C, 879-894

Abstract: As complex industries move towards small batches and customization, the uncertainty of the operation time of intermittent electrical devices increases, leading to an increasing fluctuation range of the complex industrial electrical load. To ensure the regular production process, the fossil power plant increases the installed capacity of generators, which can sharply raise carbon emissions. Renewable energy generation has been introduced to reduce carbon emissions and achieve power generation sustainability. However, the instability of renewable energy generation increases the fluctuation range of transmission voltage, leading to highly unsafe electricity transmission. Energy Storage Systems (ESSs) solves the instability problem of renewable energy generation. Thus, this study proposes a two-stage energy scheduling optimization model for complex industrial processes. The first stage proposes a scheduling optimization model for intermittent electrical devices with high electricity consumption. The second stage proposes a scheduling optimization model for the ESS to optimize the transmission capacity proportion of photovoltaic (PV) power plants, ESS, and fossil power plants. The results show that the proposed two-stage scheduling optimization model for the complex industrial process can reduce electricity cost by 7.1%–9.1%, and carbon emissions by 384 tons of standard coal in a year.

Keywords: Energy scheduling; Energy storage system; Two-stage scheduling; Scheduling optimization model; Artificial intelligence (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:193:y:2022:i:c:p:879-894

DOI: 10.1016/j.renene.2022.05.064

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