Review of Hydropower Plant Models

Acakpovi, Amevi; Hagan, Essel Ben; Fifatin, François

doi:10.5120/19014-0541

Cited by 25 publications

(24 citation statements)

References 15 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To convert the simulated river runoffs to the energy output of run-of-river plants, conversion models (e.g. Kishor et al 2007;Acakpovi et al 2014) were used. For the hydro power plants, the corresponding runoff gauging station was assigned by using the nearest station along the river.…”

Section: Hydro Power Model For Run-of-river Plantsmentioning

confidence: 99%

Impacts of climate change on stream flow and hydro power generation in the Alpine region

Wagner

Themeßl²,

Schüppel

et al. 2016

Environ Earth Sci

View full text Add to dashboard Cite

Electricity generated by hydro power is the most widely used form of renewable energy, and as such, its vulnerability to climate change is of great interest. The aim of this work is to estimate the change in river discharge characteristics in the Alpine region due to possible impacts of climate and the related changes in the power generation of run-of-river hydro power plants up to 2050. Four representative bias-corrected climate simulations from the ENSEMBLES project are chosen based on the SRES greenhouse gas emission scenario pathway A1B. Data of these simulations serve as input for a lumped-parameter rainfall-runoff model at a monthly time step, which is calibrated on discharge data of gauging stations along important rivers in the Alpine region. A power plant model fed with runoff data generated by the hydrological model is used to compute changes in the long-term average annual net electrical energy output of hydro power plants for the whole Alpine region; while the model for Austria is based on known technical parameters of the power plants, a more simplified approach is employed elsewhere. The general warming trend observed in all four climate scenarios causes to various degrees a seasonal shift towards earlier runoff. However, more diverse changes in precipitation for the different climate scenarios and time periods result in diverging hydrological projections. Although the annual runoff is found to decrease in some scenarios, the generally observed shift of runoff towards the winter season that typically shows higher energy consumption in the Alpine region suggests that the overall impact for the electricity sector tends to be positive rather than negative. Estimated changes in the average annual electricity generation of runof-river plants are generally found to be within a singledigit percentage range but can be either positive or negative depending on the climate scenario. The estimated ranges reflect the diversity (uncertainty) of the climate models; the total bandwidth of possible changes in the water availability and hydro power generation in the Alpine region up to 2050 is assumed to be even higher, because of other uncertainties in the model chain that are not explicitly considered here. Nevertheless, as the general regional trends and bandwidth of changes in runoff and hydro power production strongly depend on the future changes in precipitation, the results of this work provide reasonable orders of magnitude of expected changes and are seen as a first step towards an improved understanding of climate impacts on hydro power production within the entire Alpine region.

show abstract

Section: Hydro Power Model For Run-of-river Plantsmentioning

confidence: 99%

Impacts of climate change on stream flow and hydro power generation in the Alpine region

Wagner

Themeßl²,

Schüppel

et al. 2016

Environ Earth Sci

View full text Add to dashboard Cite

show abstract

“…There have been presented different mathematical models of other renewable technologies (e.g. hydroelectric power plants [67][68][69][70], wind farms [71,72]) that can be used to characterize the model of power conversion and the efficiency of its main components. Other works have investigated the failure behavior of these systems highlighting the dynamic dependencies and aging effects of the main components (e.g.…”

Section: /( + )mentioning

confidence: 99%

On the use of dynamic reliability for an accurate modelling of renewable power plants

Chiacchio

D’Urso

Famoso

et al. 2018

Energy

View full text Add to dashboard Cite

Renewable energies are a key element of the modern sustainable development. They play a key role in contributing to the reduction of the impact of fossil sources and to the energy supply in remote areas where the electrical grid cannot be reached. Due to the intermittent nature of the primary renewable resource, the feasibility assessment, the performance evaluation and the lifecycle management of a renewable power plant are very complex activities. In order to achieve a more accurate system modelling, improve the productivity prediction and better plan the lifecycle management activities, the modelling of a renewable plant may consider not only the physical process of energy transformation, but also the stochastic variability of the primary resource and the degradation mechanisms that affect the aging of the plant components resulting, eventually, in the failure of the system. This paper presents a modelling approach which integrates both the deterministic and the stochastic nature of renewable power plants using a novel methodology inspired from reliability engineering: the Stochastic Hybrid Fault Tree Automaton. The main steps for the design of a renewable power plant are discussed and implemented to estimate the energy production of a real photovoltaic power plant by means of a Monte Carlo simulation process. The proposed approach, modelling the failure behavior of the system, helps also with the evaluation of other key performance indicators like the power plant and the service availability.

show abstract

“…The optimization problem is defined as in (15) by replacing (14) with (17). Additionally, the terminal state constraint (16) is included.…”

Section: Standard Tracking Mpcmentioning

confidence: 99%

Optimal energy dispatch in a smart micro-grid system using economic model predictive control

Nassourou

Blesa

Puig

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

The problem of energy dispatch in heterogeneous complex systems such as smart grids cannot be efficiently addressed using classical control or ad hoc methods. This article discusses the application of economic model predictive control to the management of a smart micro-grid system connected to an electrical power grid. The considered system is composed of several subsystems, namely, some photovoltaic panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices (batteries). The batteries are charged with the energy from the photovoltaic panels, wind and hydroelectric generators, and they are discharged whenever the generators produce less energy than needed. The subsystems are interconnected via a DC Bus, from which load demands are satisfied. Modeling smart grids components is based on the generalized flow-based networked systems paradigm, and assuming energy generators to be stable, load demands and energy prices are known. This study shows that economic model predictive control is economically superior to a two-layer hierarchical model predictive control.

show abstract

Review of Hydropower Plant Models

Cited by 25 publications

References 15 publications

Impacts of climate change on stream flow and hydro power generation in the Alpine region

Impacts of climate change on stream flow and hydro power generation in the Alpine region

On the use of dynamic reliability for an accurate modelling of renewable power plants

Optimal energy dispatch in a smart micro-grid system using economic model predictive control

Contact Info

Product

Resources

About