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Day-Ahead scheduling of centralized energy storage system in electrical networks by proposed stochastic MILP-Based bi-objective optimization approach

机译:基于随机MILP的双目标优化方法提出了电网集中能量存储系统的一天提前调度

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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.
机译:最近,使用诸如能量存储系统(EESS)和可再生能源资源(RERS)的环保设备是降低发电成本以及环境问题的显着方法之一。由于通过可变天气条件产生的RER来注入功率的随机性质,将设备和系统服务于电网,以减轻这些资源的输出波动,应考虑到应考虑。储能单元的安装可以是通过将所需的实际电源交换到网络,通过将所需的实力交换为一天,安装能量存储单元可以是减少RES的功率变化。在目前的论文中,通过实施所提出的随机混合整数线性规划(基于MILP)的双目标优化方法,获得了风电场不确定性存在的最新调度。建议的客观职能是通过热电厂释放的日常发电成本和排放污染物。基于所提出的框架,通过通过epsilon-约束技术推导Pareto最佳解决方案来执行同时成本发射最小化方案。值得注意的是,根据决策者的观点,需要一个策略来确定最佳ESS操作。因此,已经利用了作为选择标准的模糊满足方法以通过在目标函数之间损害来获得适当的解决方案。案例研究是IEEE-30Bus系统。根据仿真结果来源于实施所提出的框架,已经得出结论,在一天的非高峰期间,每小时发电成本和排放增加。另一方面,在高峰时段期间,每小时成本和排放已经减少。通过采用能量储存单元减少了每日成本和排放。此外,本文示出了由合适的ESS操作产生的峰值剃料和峰值。

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