hydropower plants
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Abstract. Motivated by the challenges induced by the so-called Target Model and the associated changes to the current structure of the energy market, we revisit the problem of day-ahead prediction of power production from Small Hydropower Plants (SHPPs) without storage capacity. Using as an example a typical run-of-river SHPP in Western Greece, we test alternative forecasting schemes (from regression-based to machine learning) that take advantage of different levels of information. In this respect, we investigate whether it is preferable to use as predictor the known energy production of previous days, or to predict the day-ahead inflows and next estimate the resulting energy production via simulation. Our analyses indicate that the second approach becomes clearly more advantageous when the expert's knowledge about the hydrological regime and the technical characteristics of the SHPP is incorporated within the model training procedure. Beyond these, we also focus on the predictive uncertainty that characterize such forecasts, with overarching objective to move beyond the standard, yet risky, point forecasting methods, providing a single expected value of power production. Finally, we discuss the use of the proposed forecasting procedure under uncertainty in the real-world electricity market.

Hydropower plant operation reorganizes the temporal and spatial distribution of water resources to promote the comprehensive utilization of water resources in the basin. However, a lot of uncertainties were brought to light concerning cascade hydropower plant operation with the introduction of the stochastic process of incoming runoff. Therefore, it is of guiding significance for the practice operation to investigate the stochastic operation of cascade hydropower plants while considering runoff uncertainty. The runoff simulation model was constructed by taking the cascade hydropower plants in the lower reaches of the Lancang River as the research object, and combining their data with the copula joint function and Gibbs method, and a Markov chain was adopted to construct the transfer matrix of runoff between adjacent months. With consideration for the uncertainty of inflow runoff, the stochastic optimal operation model of cascade hydropower plants was constructed and solved by the SDP algorithm. The results showed that 71.12% of the simulated monthly inflow of 5000 groups in the Nuozhadu hydropower plant drop into the reasonable range. Due to the insufficiency of measured runoff, there were too many 0 values in the derived transfer probability, but after the simulated runoff series were introduced, the results significantly improved. Taking the transfer probability matrix of simulated runoff as the input of the stochastic optimal operation model of the cascade hydropower plants, the operation diagram containing the future-period incoming water information was obtained, which could directly provide a reference for the optimal operation of the Nuozhadu hydropower plant. In addition, taking the incoming runoff process in a normal year as the standard, the annual mean power generation based on stochastic dynamic programming was similar to that based on dynamic programming (respectively 305.97 × 108kW⋅h and 306.91 × 108kW⋅h), which proved that the operation diagram constructed in this study was reasonable.

We present a case study of a Structural Health Monitoring (SHM) hybrid system based on Brillouin Distributed Fiber Optic Sensors (D-FOS), Vibrating Wire (VW) extensometers and temperature probes for an existing historical water penstock bridge positioned in a mountain valley in Valle d’Aosta Region, Northwestern Italy. We assessed Brillouin D-FOS performances for this kind of infrastructure, characterized by a complex structural layout and located in a harsh environment. A comparison with the more traditional strain monitoring technology offered by VW strain gauges was performed. The D-FOS strain cable has been bonded to the concrete members using a polyurethane-base adhesive, ensuring a rigid strain transfer. The raw data from all sensors are interpolated on a unique general timestamp with hourly resolution. Strain data from D-FOS and VW strain gauges are then corrected from temperature effects and compared. Considering the inherent differences between the two monitoring technologies, results show a good overall matching between strain time series collected by D-FOS and VW sensors. Brillouin D-FOS proves to be a good solution in terms of performance and economic investment for SHM systems on complex infrastructures such as hydropower plants, which involve extensive geometry combined with the need for detailed and continuous strain monitoring.

The study investigated the socio-economic and environmental impact of hydropower projects in Rwanda with a case study of Rubagabaga hydropower Ltd operating from Nyabihu District. It examines the impact of a socio-economic and environmental hydropower plant in Rwanda, identifies the challenge hydropower plants face in Rwanda, and finally investigates the relationship between hydro powers and their socio-economic impact in Rwanda? In this research, the quantitative research design is based on statistical data of the research that was used with quantitative and qualitative methods. Questionnaires were used to collect data. The target population of this study was made up of 252 participants including 154 respondents all from ten different villages surrounding the Rubagabaga plant in Nyabihu District. Data were analyzed using descriptive and correlation analysis and tables that were interpreted to confirm or deny the relevance of the main and specific objectives. Based on results from table no.16 demonstrates that the beta= 0.397 with the t value of 2.333 and the p-value of 0. 021. Since the p-value is less than 0.05, the researcher rejected the null hypothesis and considered it an alternate. There is a strong positive relationship between environmental assessment of hydropower plant projects and socio-environmental sustenance and development. In a nutshell, the researcher has rejected the null hypothesis and considered its alternate. Community structure and dynamics have a positive influence on socio-environmental sustenance and development. Table no.16 shows that beta= 0.341 with the t value of 2.668 as the p-value was 0.009. Since the p-value is less than 0.05. Therefore, the researcher rejected the null hypothesis and considered it an alternate. According to table no.21, the changes in community structure and dynamics of the hydropower plant project cause the increase of 0.341 (34.1%) of the socio-environment sustenance and development. The ratio of beta modal results for the t value expressed t=2.66 hence the probability value is significant on socio-environment sustenance and development noting that sig. =0.009. Carefully, the researcher has rejected the null hypothesis and considered its alternate. With this in mind, community structure and dynamics has a positive influence on socio-environmental sustenance and development. Table no.16 has shown beta= 0.478 with the t value of 4.543 as the p-value was 0.000 which is less than 0.05. According to the findings, the changes in government policies, stability, and support of hydropower plant project causes the increase of 0.478 (47.80%) of the socio-environment sustenance and development. The ratio of beta modal results for the t value expressed t= 4.54 hence the probability value is significant on socio-environment sustenance and development noting that sig. =0.000.

This paper presents changes in the flow of 14 rivers located in the Warta River basin, recorded from 1951 to 2020. The Warta is the third-longest river in Poland. Unfortunately, the Warta River catchment area is one of the most water-scarce regions. It hosts about 150 hydropower plants with a capacity of up to 5 kW. The catchment areas of the 14 smaller rivers selected for the study differ in location, size, land cover structure and geological structure. The paper is the first study of this type with respect to both the number of analyzed catchments, the length of the sampling series and the number of analyzed flow characteristics in this part of Europe. The analysis of changes in the river flows was performed with reference to low minimum, mean and maximum monthly, seasonal and annual flows. Particular attention was paid to 1, 3, 7, 30 and 90-day low flows and durations of the flows between Q50 and Q90%. In addition, the duration of flows between Q50 and Q90% were analysed. Analysis of the direction and extent of particular flow types was performed by multitemporal analysis using the Mann–Kendall (MK) and Sen (S) tests. The analysis of multiannual flow sequences from the years 1951–2020 showed that the changes varied over the time periods and catchments. The most significant changes occurred in the low flows, while the least significant changes occurred in the high flows. From the point of view of the operation of the hydropower sector, these changes may be unfavourable and result in a reduction in the efficiency of run-of-river hydropower plants. It was established that local factors play a dominant role in the shaping of river flows in both positive and negative terms, for the efficiency of the hydropower plants.

Abstract In this paper HEC-ResSim is applied for a complex hydropower development formed by five reservoirs and related hydropower plants. There were considered characteristics of five existing hydropower developments in Romania, for which three reservoirs are with annual regulation and two with daily regulation. The objective function was the realization of a planned energy generation for one year (the mean hydrological year). Obtained results are very close to those obtained applying linear programming, a revised simplex algorithm.

The modelling of electricity systems with substantial shares of renewable resources, such as solar power, wind power and hydropower, requires datasets on renewable resource profiles with high spatiotemporal resolution to be made available to the energy modelling community. Whereas such resources exist for solar power and wind power profiles on diurnal and seasonal scales across all continents, this is not yet the case for hydropower. Here, we present a newly developed open-access African hydropower atlas, containing seasonal hydropower generation profiles for nearly all existing and several hundred future hydropower plants on the African continent. The atlas builds on continental-scale hydrological modelling in combination with detailed technical databases of hydropower plant characteristics and can facilitate modelling of power systems across Africa.