This study aimed to apply the Markovian Model (MM) to obtain optimized operating rules for a hydropower reservoir to maximize its efficiency. In this study, a Markovian Control Model with continuous state space (MCM-CSS) is presented to analyze the climatological effects through stochastic integrals. The MCM-CSS compute the optimal policies of hydropower reservoir system over the Grijalva Hydropower System (GHS). The MCM-CSS can be described as follows: a) Historical data review, b) histograms of variables studied, c) state space analysis, d) action space analysis, e) admissible decision rule, f) expected benefit analysis. As a result, the optimal policy graphs were obtained. The result obtained in this research demonstrated the advantage of having applied a continuous space in reservoir inflow and water demand with the MMC-CSS method to avoid the uncertainness and inaccuracies in simulation results with a discrete space. Modified Svanidze’s Method (MSM) is also employed to investigate their capabilities to predict streamflow over the GHS and mean energy generated. The hydrological monthly simulations were calibrated and validated at GHS station for the period 1952–2018. The main objective of the study is to simulate the optimal policies obtained with the MMC-CSS for continuous states using MSM in GHS.

这项研究旨在应用马尔可夫模型（MM）来获得水电水库的优化运行规则，以最大化其效率。在这项研究中，提出了具有连续状态空间的马尔可夫控制模型（MCM-CSS），以通过随机积分分析气候影响。MCM-CSS计算了格里耶尔瓦水电系统（GHS）上水库系统的最优策略。MCM-CSS可以描述如下：a）历史数据回顾，b）研究变量的直方图，c）状态空间分析，d）动作空间分析，e）可接受的决策规则，f）预期收益分析。结果，获得了最佳策略图。这项研究获得的结果证明了采用MMC-CSS方法在水库入水和需水量上应用连续空间的优势，从而避免了离散空间模拟结果的不确定性和不准确性。还采用了改进的Svanidze方法（MSM）来研究其预测GHS上的水流量和产生的平均能量的能力。在1952-2018年期间，在GHS站对水文月度模拟进行了校准和验证。该研究的主要目的是模拟在GHS中使用MSM对连续状态使用MMC-CSS获得的最佳策略。在1952-2018年期间，在GHS站对水文月度模拟进行了校准和验证。该研究的主要目的是模拟在GHS中使用MSM对连续状态使用MMC-CSS获得的最佳策略。在1952-2018年期间，在GHS站对水文月度模拟进行了校准和验证。该研究的主要目的是模拟在GHS中使用MSM对连续状态使用MMC-CSS获得的最佳策略。