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Perspective of uncertainty and risk from the CVaR-LCOE approach: An analysis of the case of PV microgeneration in Minas Gerais, Brazil

机译:CVAR-LCOE方法的不确定性和风险的视角:巴西Minas Gerais的光伏微观案例分析

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This present study proposes to investigate whether the classification based on deterministic LCOE (Levelized Cost of Electricity) for PV microgeneration in twenty cities in Minas Gerais (Brazil) presents differences in relation to risk classification, based on a stochastic approach, namely CVaR-LCOE (Conditional Value at Risk LCOE). In this stochastic approach, the Conditional Value at Risk (CVaR) is calculated with 99.9% confidence level for 5000 LCOE values for each city, generated by Monte Carlo Simulation. CVaR-LCOE results show a significant difference in ranking in relation to the deterministic approach. Diamantina city presented the best expected LCOE and the best CVaR-LCOE results. However, the cities located in Zona da Mata (Muriae?, Juiz de Fora and Manhua & ccedil;u) are the worst in the deterministic ranking, but in CVaR-LCOE approach, Juiz de Fora and Manhua & ccedil;u results are better than Ipatinga, Governador Valadares, and Po & ccedil;os de Caldas, this last one which presents the worst CVaR-LCOE. It is also clear that in the CVaR-LCOE ranking, the cities in different regions appear in more shuffle positions in the ranking, the opposite was observed in the deterministic ranking. Cities with the lowest LCOE standard deviation had the best classification changes among the two rankings.& nbsp; (c) 2021 Elsevier Ltd. All rights reserved.
机译:本研究建议研究基于Minas Gerais(巴西)的二十个城市的PV微生物的确定性LCOE(电力调平成本)的分类呈现与风险分类有关的差异,即CVAR-LCOE(风险LCoE的条件价值)。在这种随机的方法中,风险(CVAR)的条件值计算每座城市的5000个LCOE值的99.9%的置信水平,由Monte Carlo仿真产生。 CVAR-LCoE结果表现出与确定性方法相关的排名差异。 Diamantina City呈现出最好的LCoE和最佳Cvar-Lcoe结果。然而,位于Zona da Mata(Muriae?,Juiz de Fora和Manhua&Cedil; U)的城市是确定性排名中最糟糕的,但在Cvar-Lcoe方法中,Juiz de Fora和Manhua&Cedil;你的结果更好比Ipatinga,Congonator Varadares和Po和Ccedil; Os De Caldas,最后一个呈现最糟糕的Cvar-Lcoe。还显然,在CVAR-LCOE排名中,不同地区的城市在排名中出现在更多的洗牌位置,在确定性排名中观察到相反。 LCoE标准偏差最低的城市具有两项排名中最佳分类变化。  (c)2021 elestvier有限公司保留所有权利。

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