No abstract

Achieving carbon neutrality targets requires substantial financial support. Effective utilization of renewable energy is an essential means of promoting energy‐saving and emission reduction to realize a green and circular economy. This paper combines carbon trading mechanisms with the operational mode of Chinese micro‐grid enterprises. This method uses the real options theory to construct an investment decision model for photovoltaic (PV) micro‐grid enterprises. The study aims to analyze the impact of carbon trading revenue on a numerical simulation of PV micro‐grid enterprise investment decisions. The findings demonstrate that when projected growth rates, volatility standard deviations, and risk‐free interest rates rise in the carbon trading market, the carbon price threshold rises, causing businesses to seek out investment opportunities with higher future carbon prices. The increase of micro‐grid generation technology acceptance probability, the availability of resources to meet generation investment requirements, and the generation project cycle will decrease the carbon price threshold and accelerate enterprise investment. Studying the influence of carbon trading prices on micro‐grid investment has specific guidance and practical significance for future government subsidy policies and the investment strategies of grid enterprises.

With the increasing influence of climate change, green development has become an important trend. Since manufacturing represents approximately one-half of total energy consumption, reducing the amount of energy consumed within this industry is imperative. This study provides a hybrid flow shop scheduling issue under a time-of-use and ladder electricity price system to reduce total energy consumption without compromising maximum completion time. An improved non-dominated sorting genetic algorithm II with some optimization strategies is proposed to solve the problem. First, an enhanced constructive heuristic algorithm is used to improve the quantity of initial solution in the initialization. Besides, an adaptive genetic operation is introduced, aiming to avoid the emergence of locally optimal solutions. In addition, the right-shift approach is developed to reduce the total energy consumption without affecting completion time. By maintaining the production efficiency and reducing the energy consumption cost by 4.33%. A trade-off proposal is made between productivity and sustainability in view of the calculation results.