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Future high renewable electricity scenarios ??? Insights from mapping the diversity of near least cost portfolios

机译:未来高可再生电力情景???通过绘制成本最低的投资组合的多样性而获得的见解

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This paper reports on future electricity generation scenarios modelled using NEMO, a model that applies a genetic algorithm to optimise a mix of simulated generators to meet hourly demand profiles, to the required reliability standard, at lowest overall industry cost. The modelling examined the least and near least cost technology portfolios for a scenario that limited emissions to approximately one quarter of those from the Australian National Electricity Market (NEM) at present. It was found that all the near least cost solutions (within 15% of the least cost solution) involved wind capacity in the range of 31-51 GW, with 98.8% of these near least cost portfolios having at least 35 GW of wind installed. In contrast, the near least cost solutions consistently involved much lower quantities of PV, with 90% of the near least cost portfolios having less than 4.9 GW of installed PV capacity. This suggests that policies to promote high levels of wind deployment and grid integration are likely to be important for achieving low cost, low emissions outcomes, while policies to promote significant PV deployment may be less warranted in the absence of cost effective supporting technologies, such as battery storage or significant demand side participation.
机译:本文报告了使用NEMO建模的未来发电情景,NEMO是一种模型,该模型应用遗传算法优化模拟发电机的组合,以满足每小时的需求概况,并以最低的整体行业成本达到所需的可靠性标准。该模型研究了一种方案,该方案将排放量限制在目前仅来自澳大利亚国家电力市场(NEM)排放量的四分之一左右的最低和接近最低成本的技术组合。结果发现,所有成本最低的解决方案(在成本最低的解决方案的15%内)涉及的风力发电量为31-51 GW,其中98.8%的成本最低的投资组合中至少安装了35 GW的风。相比之下,成本最低的解决方案始终使用更少的光伏发电,其中成本最低的投资组合中有90%的光伏装机容量不足4.9 GW。这表明,促进高水平风能部署和电网整合的政策对于实现低成本,低排放成果可能很重要,而在缺乏成本有效的支持技术的情况下,促进重大光伏技术部署的政策可能就没有那么必要了。电池存储或大量需求方参与。

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