Reservoir ecological operation has become an important means of protection and restoration. The reservoirs on sediment-laden rivers with water supply, sediment transport, flood control, wetland protection, power generation as the primary objectives is a challenge in resources management. Currently, most studies reservoir models involve single objective, inadequacy corresponding constraint condi...

In order to explore the optimal mining strategy of a fractured Enhanced Geothermal System (EGS) reservoir, we numerically investigated influence seven factors on heat production and conducted an optimization analysis multi-factor multi-level combination by orthogonal test based geological data at Qiabuqia geothermal field. Seven were considered, including five reservoir (fracture spacing, fract...

Traditional optimal operation of hydropower station usually has two problems. One is that the optimal algorithm hasn’t high efficiency, and the other is that the optimal operation model pays little attention to ecology. And with the development of electric power market, the generated benefit is concerned instead of generated energy. Based on the analysis of time-varying electricity price policy...

In this study, an integrated system for the comprehensive analysis of climate change, runoff, reservoir operation, and power generation was developed. In order to apply reliable climate change scenarios to the Geum River Basin, we applied representative concentration pathway (RCP) scenarios adopted by the Intergovernmental Panel on Climate Change for its fifth assessment report in 2014 to the s...

Decision problems in water resources management are usually stochastic, dynamic and multidimensional. MDP models have been used since the early fties for the planning and operation of reservoir systems because the natural water in ows can be modeled using Markovian stochastic processes and the transition equations of mass conservation for the reservoir storages are akin to those found in invent...

Optimal operation of hydropower reservoir systems is a classical optimization problem high dimensionality and stochastic nature. A key challenge lies in improving the interpretability strategies, i.e., cause–effect relationship between system outputs (or actions) contributing variables such as states inputs. This paper reports for first time new deep reinforcement learning (DRL) framework optim...