Stochastic optimization of carbon mitigation path in Shenzhen based on uncertainty of power demand

Abstract

The core elements of urban carbon emission mitigation optimization path include structural adjustment, low energy supply, technological innovation, and enhanced energy demand management and improvement. How to optimize the combination of these factors to achieve the city's emission mitigation goals at the lowest cost is very important to study the path of urban low-carbon development. Due to many factors involved, it is difficult to solve this problem by building a mathematical optimization model that includes all the elements. This paper minimizes the total cost of emission mitigations in various departments in Shenzhen, combines the uncertainty of parameters and constraints, and uses mathematically standardized method to establish a stochastic optimization model for urban carbon emissions paths. Considering the uncertainty of energy demand, the optimal promotion rate of technical measures of the city's various departments in the stochastic optimization model during the planning period can be obtained, and the optimal solution of the city's lowcarbon development optimization path can be formed.

Two-stage robust optimal scheduling of active distribution network

“…Literature [8] proposes a multi-stage stochastic optimization model in which variables can adjust themselves when uncertainty information changes, which is more in line with the operation requirements of distribution networks under high-voltage permeability. Literature [9] established a stochastic optimization model of urban carbon emission path, considered the uncertainty of energy demand, and obtained the optimal propulsion rate in the stochastic optimization model. The main technical difficulty of stochastic programming is that it is difficult to accurately describe the probability distribution of uncertain parameters.…”