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Greenhouse gas emission is a vital problem affecting the fate of mankind. To reduce the emission, passenger cars with various types of power system has been introduced in China. However, there is an uncertainty that whether these vehicles can reduce greenhouse gas emissions and how much emissions can be reduced, especially in terms of life cycle aspect. In this paper, a calculation method has been established to study their life cycle carbon emissions, with consideration of various power system, model class and electric driving range. It is found that most NEVs and Hybrid Electric Vehicle (HEVs) have emission reduction effects compared to internal combustion engine vehicles (ICEVs). The emission reduction rate of vehicles is between 6.56% and 44.4% in 2020, 13.97%–53.39% in 2025 and 19.65%–57.49% in 2030. It is also found that this reduction rate is significantly different between different vehicle class, power system and electric driving range. Battery electrical vehicle (BEVs) in 2020 with electric driving range over 700 km are even worse in carbon emission, the worst is −33.08%. The results could be useful for GHG emission prediction and power system selection for companies. Policies can be formulated to guide enterprise choosing power system with lower carbon emission. • List three main factors influencing the life cycle carbon emissions of passenger car. • Analyze the sensitivity of various vehicle models to three influencing factors. • Analyze the carbon reduction effect of three main factors between 2020 and 2030. [ABSTRACT FROM AUTHOR]

China is pursuing aggressive action to reduce greenhouse gas emissions from its coal-dominated electric power system. Two key strategies are power market reform and carbon pricing. This paper investigates the combined effects of these two strategies in reducing CO 2 emissions from power system operations. We develop an economic dispatch model to simulate hourly power supply system operation in China Southern Power Grid in 2018 under fifteen carbon pricing scenarios; these reflect a wide range of policy ambition, from today's carbon prices to much higher ones that aim to instigate aggressive emission mitigation. Our results show that moderate carbon pricing alone is insufficient to effectively reduce CO 2 emissions without concurrent power market reform. With power market reform and as carbon prices increase, large coal units supplemented by energy storage witness higher use rates as they supplant smaller coal-fired generators, until a carbon price of 300 RMB/TCO 2 phases out coal use in favor of natural gas. Only at carbon prices higher than 300 RMB/TCO 2 do emissions begin to decrease appreciably. Geographic disparities emerge among the five provinces that comprise the Southern Power Grid, with Guangdong witnessing the most CO 2 emission reduction at high carbon prices, while emission reductions in other provinces are negligible. Carbon pricing also dramatically increases total power system costs, even at low carbon prices. Our results show the necessity of introducing power market reform and carbon pricing policies concurrently if the goal is to reduce CO 2 emissions, with the ultimate goal being a carbon price significantly greater than 300 RMB/TCO 2. Because both options will be necessary, our research maps a path to deep emission reductions in China Southern Power Grid for investors, analysts, and policy makers as discussions regarding both reforms accelerate. • We assess combined effects of carbon pricing and power market reform in China. • Carbon pricing cannot reduce CO 2 emissions without power market reform. • CO 2 reductions from carbon pricing are negligible until prices of 300 RMB/TCO 2. • Even with low carbon prices, power supply system cost increase appreciably. • Existing system can only achieve shallow decarbonization, even at 1000RMB/TCO 2. [ABSTRACT FROM AUTHOR]

Accurate wind speed forecasting is increasingly essential for improving the operating efficiency of electric power systems. Numerous models have been proposed to obtain the accurate and stable wind speed forecasting results. However, previous proposed models are limited by single predictive model or cannot deal with complex nonlinear data characteristic, which resulted in poor and unstable prediction results. In this paper, a novel forecasting model that combines noise processing, statistical approaches, deep learning frameworks and multi-objective optimization algorithm is proposed. Multi-objective optimization algorithms can take advantage of the merits of benchmark prediction models to address nonlinear characteristics of wind speed series. The 10-min real wind speed data from three Sites in China are adopted for verifying the effectiveness of this proposed model. The experimental results of multi-step prediction show that the model achieves MAPE 1-step = 2.2109%, MAPE 2-step = 3.0309%, and MAPE 3-step = 4.2536% at Site 1; MAPE 1-step = 2.4586%, MAPE 2-step = 3.2034%, and MAPE 3-step = 4.6843% at Site 2; MAPE 1-step = 2.3180%, MAPE 2-step = 3.0846%, and MAPE 3-step = 4.4193% at Site 3. Therefore, the forecasting performance of this model is excellent, and it is beneficial to the dispatching and planning of power grid. • An advanced combined model is proposed for short-term wind speed prediction. • The latest data processing approach is applied to obtain stable sequences. • Two deep learning methods are involved in this forecasting system. • A modified multi-objective optimization is applied to find the optimal weights. [ABSTRACT FROM AUTHOR]

China is accelerating the formulation of a renewable portfolio standard to promote the development and utilization of renewable energy. The implementation of the standard and corresponding tradable green certificates will change the scheduling procedures of thermal power and other renewable energy generators in the electric power system. This paper formulates a multi-objective dynamic economic emission dispatch model for wind-solar-hydro power under tradable green certificates. Weibull, Beta and Gumbel distributions are used to simulate the random behavior of wind, solar and hydro power, respectively, thus taking into account the overestimation and underestimation costs caused by the randomness of renewable energy. Moreover, a new multi-objective moth-flame optimization (MOMFO) based solution is proposed according to the characteristics of the dispatch model. The MOMFO is based on the external elite reservation and Pareto dominance. To improve the search capability, the strategy of average entropy initialization, Lévy flight and variable scale chaotic strategy are introduced. A heuristic dynamic relaxation method is proposed, which is used to handle the complex constraints of the model. Finally, the feasibility of the proposed scheduling model and the effectiveness of the algorithm are illustrated using examples of different scheduling situations based on IEEE 39-bus standard system. • A new dynamic environmental economic dispatching model is proposed for hybrid energy. • A multi-objective moth-flame optimization (MOMFO) is proposed for dispatching model. • MOMFO is effective and competitive compared with others multi-objective algorithms. • An efficient heuristic dynamic relaxation method is developed for complex constraints. • The influence of different trading systems on scheduling results in model is analyzed. [ABSTRACT FROM AUTHOR]