Abstract
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.
Highlights
With the rapid development of the social economy and the continuous progress of science and technology, people’s demand for electric power resources is increasingly urgent [1]
Based on the above considerations, this paper tries to establish how wind power reliability affects the optimal dispatching solution, how carbon emission reduction and power generation efficiency improvements are influenced by carbon trading price and how to set the rational price of carbon trading according to the specific requirements of carbon emission reduction and power generation efficiency improvement
By virtue of the multi-objective optimization algorithm and multi-attribute decision-making method, this study tries to further study how wind power reliability affects the optimal dispatching solution, how carbon emission reduction and power generation efficiency improvement are influenced by carbon trading price, and how to set the rational price of carbon trading according to the specific requirements of carbon emission reduction and power generation efficiency improvement
Summary
With the rapid development of the social economy and the continuous progress of science and technology, people’s demand for electric power resources is increasingly urgent [1]. Wind power integration, EED and carbon trading are three current approaches beneficial for low-carbon electricity development. For hedging random fuzzy wind power in response to the demand for integrated multi-period economic emission dispatch, Chen et al presented a conditional value-at-credibility model, which proved to be feasible and effective in solving multi-period EED, considering wind power uncertainty [19]. Owing to alternative special emphasis, the above research paid little attention to wind power integration and simultaneous EED and carbon trading, and the relationships between them were ignored, but are quite necessary in low-carbon electricity development. As one of the essential processes in low-carbon electricity development, exploring the effects of wind power integration and carbon trading on EED becomes especially significant. This paper is organized as follows: Section 2 builds a stochastic EED model considering wind power reliability; Section 3 studies the statistical characteristics of wind energy, and the effect of decision-making methods upon carbon trading price; Section 4 presents a case study to investigate the relationships between wind power integration, EED and carbon trading; and Section 5 summarizes the full text and provides some conclusions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
