Uncertainty, a familiar concept for power system operators has been set to be one of important topics in the industry of electricity systems. This circumstance is mainly caused by uncertain behavior of some parameters like price. Since the forecasting techniques are usually unable to guarantee a fixed and accurate value of such parameters therefore uncertainty modeling becomes essential. This work has applied an interval based optimization model for optimal performances of intelligent parking lot (IPL) of electric vehicles (EVs) within severe uncertainty of upper grid price under demand response program (DRP). In fact, DRP is used to enable IPL reduce its daily operation cost by shifting some parts of load demand from peak time intervals to off-peak time intervals. It should be mentioned that interval approach does not solve single objective problem and instead of that it generates a multi-objective optimization problem within which average and deviation costs are minimized as the bi-objective model. To do this, weighted sum and fuzzy approached are applied to solve the bi-objective problem. A sample system containing IPL, local dispatchable generation (LDG) units, non-renewable and renewable generation systems is studied under uncertainty of upper grid price through mentioned techniques and the results proving efficiency of employed techniques are investigated for comparison. According to the compared results, under DRP, average cost of IPL is reduced up to 4.37% while deviation cost representing uncertainty impact is also decreased up to 10.93%.

对于电力系统运营商来说，不确定性已成为电力系统操作员熟悉的概念，这是电力系统操作人员熟悉的概念。这种情况主要是由价格等某些参数的不确定行为引起的。由于预测技术通常无法保证此类参数的固定和准确值，因此不确定性建模变得至关重要。这项工作已经应用了基于间隔的优化模型，以在需求响应程序（DRP）下，在电网价格严重不确定的情况下，电动汽车（EV）的智能停车场（IPL）的最佳性能。实际上，DRP用于通过将部分负载需求从高峰时间间隔转移到非高峰时间间隔来使IPL降低其日常运营成本。应该提到的是，区间方法不能解决单目标问题，而是会产生多目标优化问题，在该问题中，平均成本和偏差成本被最小化为双目标模型。为此，应用加权和和模糊算法来解决双目标问题。通过上述技术，在电网价格不确定的情况下，研究了包含IPL，本地可调度发电（LDG）单元，不可再生和可再生发电系统的样本系统，并研究了所采用技术的结果证明效率，以进行比较。根据比较结果，在DRP下，IPL的平均成本降低了4.37％，而代表不确定性影响的偏差成本也降低了10.93％。