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Most available long-term operation models for hydropower stations use deterministic historical data as inputs but cannot be employed to update the decision scheme in real time according to the actual solar radiation and inflow conditions, resulting in a disconnect between the given plan and actual decision-making process. In this study, a multistage rolling reservoir decision model considering the uncertainties in solar radiation and inflow is proposed to guide the formulation of long-term operational schemes for hydro-PV systems. We adopt the solar radiation and inflow series generated by the scenario tree (ST) method as inputs of the reservoir optimal operation model and use a genetic algorithm (GA) to solve the model. In the solution process, the scheme is adjusted according to the actual solar radiation and inflow conditions. Typical wet, normal, and dry years are analysed. The results illustrate that the model can better inform the design of long-term operational schemes for hydro-PV stations relative to the actual operational scheme and the traditional deterministic model. [ABSTRACT FROM AUTHOR]

Increasing the power generation and reducing the sediment deposition in a reservoir are two contradictory and incommensurable objectives. Hence, these two operational objectives rarely are considered simultaneously in reservoir optimization. This study proposes a method to balance sediment desilting and power generation in reservoirs, and to study their quantitative relationship. First, a formula to calculate the suspended sediment transport rate for reservoirs was established. Second, based on that formula, a multiobjective model was built that considers simultaneously, the sediment transporting efficiency and the power generation efficiency. The nondominated sorting genetic algorithm (NSGA-II) along with Pareto optimality was used to find the Pareto front of the model. The proposed method was applied to the Three Gorges project in China. The research results not only provide the Pareto front of the reservoir operation scheme, but also imply that the maximum annual sediment runoff was improved by 6% at the expense of 9% of the annual power generation. [ABSTRACT FROM AUTHOR]

Various studies have demonstrated that the long-term flow regime plays an important role in stimulating fish spawning. The flow regime is altered by the construction and operation of reservoirs. In this study, a flow-regime identification method is proposed, which extracts the rising hydrograph processes with a certain amplitude and duration through multiple passes of denoising and filtering algorithms to filter out random fluctuations with different amplitudes. The identified rising hydrograph processes were then quantitatively characterized using three parameters. A multiobjective optimization model was constructed to mimic the natural flow process in reservoir discharge and to maximize the output value of the power plant using the Nondominated Sorting Genetic Algorithm (NSGA-II). Finally, the proposed model was applied to the planning of a large-scale reservoir on the upper reaches of the Yellow River in China. The derived Pareto front in different hydrological years was analyzed using the analytic hierarchy process (AHP) method, and various operational schemes were evaluated on this basis. [ABSTRACT FROM AUTHOR]

Hydropower can be an ideal compensation for fluctuant photovoltaic (PV) power due to its flexibility. In this study, a multiobjective optimization model considering energy generation and consumption simultaneously for a hydro‐PV hybrid power system is proposed. With the daily mean radiation intensity and temperature, the PV power output is calculated. Taking reservoir release as the decision variable, the total energy generation of the hydro‐PV system is maximized. Meanwhile, the gap between the energy generation and the energy consumption is minimized to reduce the abandoned PV power or hydropower. The proposed multiobjective model is optimized by Non‐dominated Sorting Genetic Algorithms‐II (NSGA‐II). The Longyangxia Project, the largest hydro‐photovoltaic hybrid power system in the world is taken as the study case to estimate the optimal operational strategies for different objectives in wet year, normal year, and dry year, respectively. The optimal operation process of the reservoir is presented, which is instructive for the operation in the future. Both maximizing energy generation and minimizing the gap between energy generation and consumption are taken into account simultaneously. The proposed multiobjective optimization model is optimized by NSGA‐II, and applied to the largest hydro‐PV project in the world. The methodology can be generalized to any other hydro/PV hybrid systems. [ABSTRACT FROM AUTHOR]

A hierarchical optimization method is presented for an assessment of the potential maximum energy output of the Three Gorges Project and Gezhouba cascade hydropower stations in China. The optimization incorporates a detailed description of daily load dispatching among different types of hydropower units. The optimization method is divided into three levels to render feasible the computation of the global optimization problem. In levels 1 and 2, a genetic algorithm is applied to optimize discharge and water head distribution between the upstream and downstream reservoirs. Level 1 selects the water levels at the beginning of each month in a year. Level 2 selects the water levels at the beginning of each day in a month. Level 3 solves a linear programming problem for allocation of water among the heterogeneous types of hydropower generation. Hydrological data of the years 2004 and 2005 are used to perform an optimized power generation process within the operating rules. The maximal annual energy production could be approximately 61 and 87 MWh higher than the actual recorded energy production in 2004 and 2005, respectively. [ABSTRACT FROM AUTHOR]

A new approach for optimization of long-term operation of large-scale reservoirs is presented, incorporating Incremental Dynamic Programming (IDP) and Genetic algorithm (GA) . The immense storage capacity of the large scale reservoirs enlarges feasible region of the operational decision variables, which leads to invalidation of traditional random heuristic optimization algorithms. Besides, long term raised problem dimension, which has a negative impact on reservoir operational optimization because of its non-linearity and non-convexity. The hybrid IDP-GA approach proposed exploits the validity of IDP for high dimensional problem with large feasible domain by narrowing the search space with iterations, and also takes the advantage of the efficiency of GA in solving highly non-linear, non-convex problems. IDP is firstly used to narrow down the search space with discrete d variables. Within the sub search space provided by IDP, GA searches the optimal operation scheme with continuous variables to improve the optimization precision. This hybrid IDP-GA approach was applied to daily optimization of the Three Gorges Project-Gezhouba cascaded hydropower system for annual evaluation from the year of 2004 to 2008. Contrast test shows hybrid IDP-GA approach outperforms both the univocal IDP and the classical GA. Another sub search space determined by actual operational data is also compared, and the hybrid IDP-GA approach saves about 10 times of computing resources to obtain similar increments. It is shown that the hybrid IDP GA approach would be a promising approach to dealing with long-term optimization problems of large-scale reservoirs. [ABSTRACT FROM AUTHOR]