Sentence examples for hydropower optimization from inspiring English sources

The California's Energy-Based Hydropower Optimization Model (EBHOM 2.0) is applied to evaluate the adaptability of California's high-elevation hydropower system to climate warming, considering the warming effects on hydroelectricity supply and pricing.

In some others, it can be risk reduction due to flood events, or optimization of hydropower production along rivers.

And an energy equivalent reservoir model [11] was applied to the stochastic optimization of hydropower production decisions in the Brazilian power system.

In this study, we proposed a stochastic optimization model for hydropower generation reservoirs, in which 1) the transition probability matrix was calculated based on copula functions; and 2) the value function of the last period was calculated by stepwise iteration.

To achieve an optimal shape of the blade paddle that maximizes the hydropower generation capacity, a full-scale optimization study has to be conducted, where the blade number, blade length, and curvature of the blades should be defined as design variables, whose influence on the overall performance of the present hydropower system needs to be examined.

This paper presents a stochastic optimization model to find the optimal hourly schedule of a set of hydraulically coupled hydropower plants to obtain the weekly operation that simultaneously maximizes the expected profit in both energy and regulation reserves markets.

These optimization techniques are vital for existing hydropower plants since it is difficult to predict and model climate and weather precisely.

Short-term hydro generation scheduling (STHGS) of cascade hydropower stations is a typical nonlinear mixed integer optimization problem to minimize the total water consumption while simultaneously meeting the grid requirements and other hydraulic and electrical constraints.

An optimization model to explore the trade-offs between hydropower generation and ice flood control requirements is developed.

This paper proposes a computationally efficient stochastic optimization algorithm to solve the MTHS problem for cascaded hydropower systems.

The improvement of the GA as well as the evaluation method proposed in this paper provide a new approach for choosing and improving optimization algorithms to solve the ELDP of large-scale hydropower plants.