【Purposes】The new energy generation represented by wind power is the most realistic strategic choice to achieve the goals of carbon peaking and carbon neutrality. To absorb renewable energy electricity in power grid...【Purposes】The new energy generation represented by wind power is the most realistic strategic choice to achieve the goals of carbon peaking and carbon neutrality. To absorb renewable energy electricity in power grids, a new probabilistic evaluation method for available transmission capacity in transmission systems is proposed based on joint cumulants, and a decision model for risk available transmission capacity based on expected quantiles is proposed accordingly. As a vital component of available transmission capacity(ATC) calculation, the transmission reliability margin(TRM), as a reserved transmission capacity, reflects the impact of uncertainty factors on transmission capacity. However, in traditional calculation methods, TRM is determined through deterministic or probabilistic methods, which is difficult to reflect the risks brought by large-scale wind power consumption to ATC and cannot meet the requirements for transmission capacity risk management. 【Methods】Firstly, to address the issue that the cumulative method requires variables to be independent of each other and cannot consider the correlation of wind power output, a joint cumulative method combined with FGM Copula function is proposed to characterize the correlation of wind power output;Secondly, for the probabilistic assessment of available transmission capacity, a probabilistic assessment model for available transmission capacity is established by combining the partition integration method and the Comish Fisher expansion;Finally, in response to the problem that decision methods based on value at risk only consider the probability achieved at the tail of the probability distribution and cannot describe the risks generated throughout the distribution, a risk available transmission capacity index based on expected quantiles is proposed, and its evaluation process is proposed. 【Results】Verify the feasibility and practicality of the proposed indicators and models through case analysis.展开更多
文摘【Purposes】The new energy generation represented by wind power is the most realistic strategic choice to achieve the goals of carbon peaking and carbon neutrality. To absorb renewable energy electricity in power grids, a new probabilistic evaluation method for available transmission capacity in transmission systems is proposed based on joint cumulants, and a decision model for risk available transmission capacity based on expected quantiles is proposed accordingly. As a vital component of available transmission capacity(ATC) calculation, the transmission reliability margin(TRM), as a reserved transmission capacity, reflects the impact of uncertainty factors on transmission capacity. However, in traditional calculation methods, TRM is determined through deterministic or probabilistic methods, which is difficult to reflect the risks brought by large-scale wind power consumption to ATC and cannot meet the requirements for transmission capacity risk management. 【Methods】Firstly, to address the issue that the cumulative method requires variables to be independent of each other and cannot consider the correlation of wind power output, a joint cumulative method combined with FGM Copula function is proposed to characterize the correlation of wind power output;Secondly, for the probabilistic assessment of available transmission capacity, a probabilistic assessment model for available transmission capacity is established by combining the partition integration method and the Comish Fisher expansion;Finally, in response to the problem that decision methods based on value at risk only consider the probability achieved at the tail of the probability distribution and cannot describe the risks generated throughout the distribution, a risk available transmission capacity index based on expected quantiles is proposed, and its evaluation process is proposed. 【Results】Verify the feasibility and practicality of the proposed indicators and models through case analysis.
