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Abstract:As a developing country, extensive carbon and sulfur emissions are associated with China's rapid social and economic development. Chief among them are the emissions from coal and oil consumption. This paper focuses on the demand side, attempting to regulate the range of relative price of oil to coal at the consumption level. Through the adjustment of the relative price, the goal of reducing the emissions of carbon and sulfur could be achieved in the market of energy consumption. Data regression is applied to investigate the functional relationship between emissions and energy prices. The results indicate that when the coal price is less than 300, the higher relative price leads to less carbon and sulfur emissions; when the coal price is more than 300 and less than 500, there exists an optimal relative price which has the least carbon emissions, and this value is not more than 11.5; when the coal price is more than 500, the smaller relative price is beneficial to decline carbon and sulfur emissions. The changed trend of relative price-sulfur emissions is very similar to relative price-carbon emissions. Compared to the present energy situation in China, the relative price of oil to coal still need to be reduced especially when coal price is more than 500.

Abstract：This paper proposes a hybrid optimization to solve the scheduling of household power consumption forStep and Time-of-Use (TOU) tariff system. The target function is the cost of electricity, and the optimization object is total instantaneous power within a billing period. The control variables are starting moments of each household appliance. The optimization procedure is divided into two stages. Firstly, the prerequisite for minimal cost is calculated through mathematical analysis and generalized function theory. Secondly, the solution is obtained by using a heuristic algorithm in which the result of the first stage is considered to reduce the searching space. And an evaluation methodology is deduced to evaluate the optimization. The computer simulation demonstrates that the proposed approach can reduce the cost of electricity evidently in the sense of probability. The approach shows great value for embedded applications.In the age of smart grid, Step and Time-of-Use (TOU) tariff system for pricing electric energy facilitates the reduction of carbon emission and the enhancement of reliability and asset utilization ratio of power gird [1][2][3][4][5][6][7] . InStep and TOU tariff system, the economy of power consumption means to consume power at proper time and in proper quantity, rather than to simply reduce power consumption as before. For the optimization of cost of electricity (CE), the control variables are starting moments which are continuous within certain constraint. Therefore, the integer programming and combinatorial optimization are improper [8][9][10][11] . The relationship between CE and control variables is also beyond the scope of nonlinear programming [12,13] . Some researchers took advantage of functional analysis to study the optimization problems [14][15][16] successfully, and some discussed an integral optimization problem free of boundary constraint [17] . The problem of immediate consumption was also transformed to a smooth nonlinear problem [18] . Since the tariff index is a step function whose partial derivatives consist of Dirac functions [19,20] , the usage of methods mentioned above are limited. This paper employs a hybird optimization method to solve the CE optimization. At the first stage, the prerequisite for minimal CE is deduced to reduce the searching space. Thus, at the second stage, the efficiency of heuristic searching is improved. The evaluation of results reveals that the hybird method can reduce CE evidently in the sense of probability.

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