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With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

Improving the efficiency of renewable energy and electricity utilization is an urgent problem for China under the objectives of carbon peaking and carbon neutralization. This paper proposes an optimization scheduling method of electric vehicles (EV) combined with wind and photovoltaic power based on the Frank-Copula-GlueCVaR. First, a joint output model based on copula theory was built to describe the correlation between wind and photovoltaic power output. Second, the Frank-Copula-GlueCVaR index was introduced in a novel way. Operators can now predetermine the future wind-photovoltaic joint output range based on this index and according to their risk preferences. Third, an optimal scheduling model aimed at reducing the group charging cost of EVs was proposed, thereby encouraging EV owners to participate in the demand response. Fourth, this paper: proposes the application of a Variant Roth–Serve algorithm; regards the EV group as a multi-intelligent group; and finds the Pareto optimal strategy of the EV group through continuous learning. Finally, case study results are shown to effectively absorb more renewable energy, reduce the consumption cost of the EV group, and suppress the load fluctuation of the whole EV group, which has a practical significance and theoretical value. [ABSTRACT FROM AUTHOR]

In response to the rising importance of the climate agenda, many countries have restructured their electricity markets to facilitate the utilization of renewable energy. China is an interesting case because it has expanded its utilization of wind and solar energy with unmatched speed. This review starts with an analysis of the 2002 reforms that uncoupled electricity production from transmission. The investigation covers the period leading up to the 2022 proposal, which aimed to build a nationally integrated electricity market. The analysis suggests that a careful alignment of incentives for key market players to produce and consume renewable energy is vital during the process of energy transition. The introduction of feed-in tariffs in 2009 for wind energy, which were subsequently extended to solar energy, stimulated a high growth in installed capacity. However, a high electricity curtailment rate resulted. Since 2018, the Chinese government has resorted to curtailment caps and renewable portfolio standards to increase the utilization of renewable electricity. After the announcement of the "dual carbon goals" in 2020, the Chinese government launched a series of reforms that aimed to nurture growth in the green electricity market and the formation of a nationally integrated electricity market. The removal of interregional trade barriers is a key element of China's current electricity market reforms and will be crucial to determining whether China can achieve its climate goals. [ABSTRACT FROM AUTHOR]

The uncertain output of variable renewables adds significant challenges to the generation of affordable, reliable, and sustainable power sources in a country or region. Therefore, we propose a new stochastic nonlinear multi-objective model to optimize the power generation structure in 31 provinces of China. Considering variable renewable integration, we use Monte Carlo simulation to describe the randomness and uncertainty of renewable power output. The learning curve in the exponential expression is used to describe the nonlinear relationship between generation cost and installed capacity. The optimized results show that China can substitute fossil power with clean power. Renewable power will account for more than 42% of total power in the optimal power generation structure in 2040. In particular, the annual average growth rate of non-hydro renewable generation is expected to be 12.06%, with solar photovoltaic (PV) power growing the most by 17.95%. The share of renewable power exceeds that of thermal power in 14 provinces, and PV power represents the highest proportion at 30.21%. Reducing transmission capacity can promote the development of advantageous power in each region, such as wind power in the Northwest region and PV power in the South region, with the share increasing by 36.33% and 132.59%, respectively. [ABSTRACT FROM AUTHOR]