The deployment of smart technologies such as storage systems is a requirement for the integration of renewable energy sources (RES) in today's grids. The increase in the share of renewable energy, mainly wind and solar power relies on the grid operators' capacity to offset intermittency and enhance the grids' flexibility, for which the most recommended solution is the deployment of energy storage systems (ESS). However, this type of addition to the grid will have consequences on current power sources operation and lead to changes in their environmental impacts. It is no longer possible to rely on temporally aggregated data, linear impact allocation assumptions or averaged emission factors to evaluate the ESS use phase. A more robust environmental assessment tool is therefore required. In light of this limitation, we propose an optimized consequential life cycle assessment (O-C-LCA) methodology applied to the Norman grid (France) for the year 2017. We optimally simulate the operation of lithium-ion batteries as an ESS within the grid by means of an optimization algorithm. The cost of electricity production, including greenhouse gas emissions through a price on carbon, is minimized, and the various generation sources are managed. A near-real ESS operation pattern is obtained as well. Afterward, we assess the environmental impacts of electricity generation using a retrospective consequential LCA. The results highlight the importance of time-variant data in the identification of the system's temporal hotspots. The life cycle optimization analysis illustrates the generation patterns and periods that are most altered by (i) the minimization of electricity generation costs including greenhouse gas (GHG) emissions and (ii) the addition of an ESS. For this case, on average, 53% GHG emissions abatement results from the grid optimized operation and deployment of ESS, along with a total marginal operating costs reduction of 28%. Temporally-differentiated region-specific emission factors (EFs) are also recommended for enhanced assessment results. By including time-variant data and temporally-differentiated EFs, the developed method leads to an appropriate representation and a more accurate evaluation of the ESS use phase. It is therefore considered an effective tool for policy and decision makers regarding the impacts of ESS operation on the environmental profiles of power grids.

诸如存储系统之类的智能技术的部署是当今电网中可再生能源（RES）集成的要求。可再生能源（主要是风能和太阳能）份额的增加依赖于电网运营商抵消间歇性并增强电网灵活性的能力，为此，最推荐的解决方案是部署储能系统（ESS）。但是，这种对电网的添加将对当前电源的运行产生影响，并导致其对环境的影响发生变化。不再可能依靠时间汇总数据，线性影响分配假设或平均排放因子来评估ESS使用阶段。因此，需要一种更强大的环境评估工具。鉴于此限制，我们建议在2017年将优化的结果生命周期评估（OC-LCA）方法应用于法国Norman电网。我们通过优化算法来优化模拟锂离子电池在电网中作为ESS的运行。发电成本（包括通过碳价产生的温室气体排放量）被最小化，并且各种发电源得到管理。还获得了接近真实的ESS操作模式。之后，我们使用回顾性的随之而来的LCA评估发电对环境的影响。结果突出了时变数据在识别系统时间热点中的重要性。生命周期优化分析说明了通过（i）最大限度地降低包括温室气体（GHG）排放在内的发电成本和（ii）添加ESS来最大程度地改变了发电方式和发电周期。对于这种情况，通过电网优化运行和ESS部署，平均可减少53％的温室气体排放，同时边际运营成本降低了28％。为了增强评估结果，还建议使用时区特定的排放因子（EFs）。通过包括随时间变化的数据和随时间变化的EF，所开发的方法导致对ESS使用阶段的适当表示和更准确的评估。