Nowadays, the power system is faced with some new changes from low-carbon approaches, though these approaches have proved to be effective in developing low-carbon electricity. Specifically, wind power integration and carbon trading influence the traditional economic emission dispatch (EED) mode, allowing for the disturbance of wind power uncertainties and the fluctuation of carbon trading price. Aiming at the above problems, this study firstly builds a stochastic EED model in the form of chance-constrained programming associated with wind power reliability. Next, wind power features are deduced from the statistic characteristics of wind speed, and thus the established model is converted to a deterministic form. After that, an auxiliary decision-making method based on the technique for order preference by similarity to an ideal solution (TOPSIS) is designed to draw the optimal solution based upon the specific requirements of carbon emission control. The simulation results eventually indicate that the minimization of fuel costs and carbon emissions comes at the expense of wind power reliability. Meanwhile, carbon emission reduction can be effectively realized by carbon trading rather than a substantial increase in fuel costs, and carbon trading may help to improve power generation efficiency. Furthermore, carbon trading prices could be determined by the demands of carbon emission reduction and power generation efficiency improvement.

This paper proposes a real options model for evaluating the biomass power generation investment in China. The uncertainties in the market price of electricity, CO₂ price and straw price are considered. Meanwhile the dynamic relationship between installed capacity and fuel cost, as well as the long-term reduction of subsidy are described. Two scenarios, i.e., with the carbon emission trading scheme existent and non-existent, respectively, is built to empirically analyze the investment of a 25-MW straw-based power generation project. The results show that investors should undertake the investment in 2030 under two scenarios. Investment values are 14,869,254.8 and 37,608,727 Chinese Yuan (RMB), respectively. The implementation of the carbon emission trading scheme theoretically helps improve investment value and advance the most likely optimal investment time. However, the current CO₂ price is not sufficient to advance the most likely optimal investment time. The impacts of several factors, including subsidy policy, CO₂ price, straw price, installed capacity, correlation structure and the validity period of investment, on the optimal investment strategy are also examined. It is suggested that governments take some measures, including increasing subsidy, setting the growth pattern of subsidy and establishing and perfecting a nationwide carbon trading market, to improve the investment environment and attract more investments.

Scientific prediction and accurate grasp of the future trend of population change are conducive to the formulation of different population policies at different stages, so as to alleviate the adverse effects of the aging population on society and provide scientific theoretical reference for controlling the population size and making policy. Considering that the population system is affected by many complex factors and the structural relationship among these factors is complex, it can be regarded as a typical dynamic grey system. In this paper, the fractional-order GM (1, 1) model and the fractional-order Verhulst model are established, respectively, based on the statistical data of China's population indices from 2015 to 2019 to forecast the population size and the change trend of population structure of China from 2015 to 2050 in the short-term and medium- to long-term. The forecast results show that China’s population will grow in an inverse S shape from 2015 to 2050, when the total population will reach 1.43 billion. Moreover, during this period, the birth rate and natural growth rate of population will decrease year by year, and the proportion of aging population and the dependency ratio of population will increase year by year. Besides, the problem of aging population is going to become increasingly serious. The application of grey system method to population prediction can mine the complex information contained in the population number series. Meanwhile, the fractional-order accumulation can weaken the randomness of the original data series and reduce the influence of external disturbance factors, so it is a simple and effective population prediction method.

This article presents a new model to solve the dynamic economic emission dispatch problem incorporating uncertainties in the process of power generation. Besides the classic dynamic economic emission dispatch factors, the constraints of both reliability and efficiency are especially considered to restrain the disturbances of uncertainties. Accordingly, the penalty and reserve cost function together with the penalty and reserve emission function are added in the multi-objective function, respectively. To obtain some quantitative results, the characterizations of the different sources of uncertainty are discussed based on statistical theory, and this optimization problem is numerically solved by the improved particle swarm optimization algorithm. Eventually, the simulation results demonstrate that improving the level of reliability will increase the operation costs and emissions of the power system, while increasing the level of efficiency will decrease the operation costs and emissions of the power system. Furthermore, there seems to be no significant linear correlation among the economic and environmental costs and the proportion of wind power generation under the requirements of reliability and efficiency.