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Microgrid is a promising way to integrate renewable distributed generation into the electric power sector, which has received widespread attention. In this paper, a uniform capacity-constrained optimization model under different power purchase and sale price contracts is developed to explore the optimal green investment strategy for grid-connected microgrid (GCM) with three configuration types. The impact of renewable energy source endowment and incentive policy is focused when macrogrid's generators emit more pollution than GCM's non-renewable generators, such as in China and India. The results show that: (1) GCM with renewable generation can only be invested in a site with relatively abundant renewable endowment; (2) carbon tax effectively promotes the deployment of GCM with renewable generation and achieves carbon emission abatement, but excessive carbon tax cannot incentive investment in the most desired GCM type; (3) a subsidy trap is found under feed-in tariff (FIT) subsidy, which implies that the fade-out or exit of FIT subsidy is beneficial for microgrid's healthy development; (4) and the comparisons under different price contracts emphasize the importance of macrogrid providing non-discriminatory access to feed-in and feed-out services to microgrid. • Investment strategy is explored for grid-connected microgrid (GCM) with three types. • Renewable source endowment and incentive policy determine the investment for GCM. • Excessive carbon tax cannot incentive investment in the most desired renewable GCM. • The fade-out or exit of FIT subsidy is beneficial for healthy GCM deployment due to the subsidy trap. • Emphasizing the importance of macrogrid providing non-discriminatory services to GCM. [ABSTRACT FROM AUTHOR]

An accurate and objective prediction of coal consumption is important for stabilizing the coal market, ensuring the operating results of coal enterprises. This paper starts with the background value of the traditional GM(1,1) model, extends the model by the extrapolation method, proposes an optimized grey prediction model, deduces the time response equation, studies the relationship between model parameters and model accuracy. Then the model is optimized by simulated annealing algorithm. Three cases verify the validity of model, the results show that its lowest prediction error percentage is 3.1286%, which is better than two classical grey prediction models, Finally, the model is applied to forecast the next five years' consumption of China, India and the United States, which are the world's top three coal consumers. The errors of the proposed models are all about 5%, which is obviously better than the comparison models. The results show that coal consumption in China and India will continue to rise over the next five years, while the United States will decline. These findings are consistent with the current development of the three countries; therefore, the optimized prediction model can effectively predict the coal consumption of the three countries. • The highlights of this article are as follows: • An optimized grey prediction model that can be applied to various data situations is proposed. • The traditional GM(1,1) model is optimized by a simulated annealing algorithm. • The validity of the model is verified by theoretical deduction and case analysis. • The generality and wide applicability of the proposed method are discussed. [ABSTRACT FROM AUTHOR]