In this paper, a stochastic multi-objective structure is introduced in joint energy and reserve market to allow energy generation companies participating in the short-term hydro-thermal self-scheduling with uncertainties. To solve this problem, an antlion optimization (ALO) algorithm is used. In addition, uncertainties including energy price, spinning and non-spinning reserve prices, output power of the wind, photovoltaic and small hydro units are mentioned. In this study, two methods are used to generate stochastic multi-objective scenarios, namely lattice monte-carlo simulation and roulette wheel mechanism (RWM). After that, the main purpose of the study is described, i.e. making GENCOs able to achieve the maximum profit and the minimum emission by using a multi-objective function considering a stochastic process. To reach this aim, the mixed integer programming (MIP) which includes a set of multi stage deterministic scenarios is employed. However, some special cases should be introduced in the formulation structure of the presented scheduling regarding hydro-thermal units to make the SMO-HTSS problem with wind, photovoltaic and small hydro units similar to the real time modeling. Since optimal solutions are produced in this method, one can allude to the application of the ε-constraint. Nevertheless, in order to select one of the most appropriate solutions among Pareto solutions obtained, the utilization of fuzzy method has been presented. In the end, as shown in this paper, the ALO algorithm is limited to the ε-constraint; some tests are carried out on an IEEE 118-bus test system to verify the accuracy and validity of the proposed method.

在本文中，在联合能源和储备市场中引入随机多目标结构，允许能源发电公司参与具有不确定性的短期水热自调度。为了解决这个问题，使用了蚁狮优化（ALO）算法。此外，还提到了能源价格、旋转和非旋转储备价格、风电、光伏和小水电机组输出功率等不确定性。在这项研究中，使用两种方法来生成随机多目标场景，即格子蒙特卡洛模拟和轮盘赌机制（RWM）。之后，描述了研究的主要目的，即通过使用考虑随机过程的多目标函数使GENCOs能够实现最大利润和最小排放。为了达到这个目标，采用了包含一组多阶段确定性场景的混合整数规划（MIP）。然而，应该在所提出的水热机组调度的公式结构中引入一些特殊情况，以使风能、光伏和小水电机组的 SMO-HTSS 问题类似于实时建模。由于最佳解决方案是在这种方法中产生的，因此可以提及应用ε -约束。然而，为了在获得的 Pareto 解中选择最合适的解之一，提出了模糊方法的使用。最后，如本文所示，ALO算法受限于ε-约束；在IEEE 118总线测试系统上进行了一些测试，以验证所提方法的准确性和有效性。