Abstract Recently, employing environmentally-friendly devices such as Energy Storage Systems (EESs) and Renewable Energy Resources (RERs) has been one of the remarkable ways to reduce electricity generation cost as well as environmental issues. Due to the stochastic nature of injected power through the RERs resulting from variable weather conditions, serving the devices and systems to the electrical grid in order to alleviate the output fluctuations of these resources should be taken into consideration. Installation of energy storage units can be one of the applicable ways that lessens the power variations of RESs by exchanging the required real power into the network through a day. In the current paper, the day-ahead scheduling of ESS in the presence of wind farm uncertainty has been obtained by implementing the proposed stochastic Mixed Integer Linear Programming (MILP)-based bi-objective optimization approach. The suggested objective functions are the daily electricity generation cost and emission pollutants released through the thermal power plants. Based on the presented framework, a simultaneous cost-emission minimization scheme is carried out by deriving Pareto optimal solutions by epsilon-constraint technique. It is noteworthy that one strategy is required to determine optimal ESS operation according to the decision maker's point of view. Thus, the Fuzzy satisfying method as a selection criterion has been exploited to obtain the appropriate solution by compromising between the objective functions. The case study is the IEEE-30BUS system. According to simulation results derived from implementing the proposed framework, it has been concluded that during off-peak periods of the day, the hourly electricity generation cost and emission are increased. On the other hand, the hourly cost and emission have been reduced during on-peak hours. The daily cost and emission are reduced by employing the energy storage unit. Moreover, peak-shaving and peak-shifting resulting from the suitable ESS operation are illustrated in this paper.

中文翻译：

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基于随机 MILP 的双目标优化方法对电网集中式储能系统的日前调度

摘要 近年来，采用诸如储能系统 (EES) 和可再生能源 (RER) 等环保设备已成为降低发电成本和环境问题的显着方法之一。由于天气条件变化导致通过 RER 注入电力的随机性，应考虑将设备和系统提供给电网以减轻这些资源的输出波动。安装储能单元可以作为一种适用的方式，通过将一天所需的有功功率交换到网络中来减少可再生能源的功率变化。在目前的论文中，通过实施所提出的基于随机混合整数线性规划 (MILP) 的双目标优化方法，可以在存在风电场不确定性的情况下获得 ESS 的日前调度。建议的目标函数是日发电成本和通过火力发电厂排放的污染物排放量。基于所提出的框架，通过使用 epsilon 约束技术推导出帕累托最优解来执行同步成本排放最小化方案。值得注意的是，需要一种策略来根据决策者的观点来确定最佳 ESS 操作。因此，已经利用模糊满足方法作为选择标准，通过在目标函数之间进行折衷来获得适当的解决方案。案例研究是 IEEE-30BUS 系统。根据实施该框架的模拟结果，得出结论：在一天的非高峰时段，每小时发电成本和排放量增加。另一方面，高峰时段的每小时成本和排放量有所降低。通过采用储能单元，降低了日常成本和排放。此外，本文还说明了由合适的 ESS 操作产生的削峰和峰移。通过采用储能单元，降低了日常成本和排放。此外，本文还说明了由合适的 ESS 操作产生的削峰和峰移。通过采用储能单元，降低了日常成本和排放。此外，本文还说明了由合适的 ESS 操作产生的削峰和峰移。