Optimizing Low-Carbon Pathway of China’s Power Supply Structure Using Model Predictive Control

Abstract

With the increasing severity of climate change, the power industry, as one of the main sources of carbon emissions, is playing an extremely important role in the process of low-carbon energy transformation. The purpose of this paper is to try to find a general method to solve the optimal path for the low-carbon evolution of the power supply structure so as to meet the challenges faced by the low-carbon transformation of the power industry in the future. This paper first uses the capacity coefficient index (CCI) to represent the power generation ability of different technologies and proposes a forecasting method for the CCI of renewable energy generation. In this paper, a two-layer optimization model considering multiple constraints is established and solved using the MPC method. The results show that China’s installed capacity of renewable power could account for more than 50% in 2030, while the carbon emissions will decrease after reaching a peak in 2023. On the premise of ensuring sufficient reserve adjustment capacity of thermal power units, increasing the proportion of renewable energy generation is an important way to realize emission reduction in the power industry.

Optimization of Power Supply Structure Considering Carbon Emissions and Flexibility

“…Propose a cross-border power optimization planning method considering carbon emission constraints based on the requirements of clean and low-carbon power planning [9] . In addition, predict and evolve the optimal path to address low-carbon evolution in the power supply structure [10] . In terms of practical applications, scholars have conducted research on economically feasible solutions to improve grid flexibility and reduce losses [11] .…”