Analysis of Small Hydropower Generation Potential: (2) Future Prospect of the Potential under Climate Change

Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario

Global climate models (GCMs) are used to analyze future climate change. However, the observed data of a specified region may differ significantly from the model since the GCM data are simulated on a global scale. To solve this problem, previous studies have used downscaling methods such as quantile mapping (QM) to correct bias in GCM precipitation. However, this method cannot be considered when certain variables affect the observation data. Therefore, the aim of this study is to propose a novel method that uses a convolution neural network (CNN) considering teleconnection. This new method considers how the global climate phenomena affect the precipitation data of a target area. In addition, various meteorological variables related to precipitation were used as explanatory variables for the CNN model. In this study, QM and the CNN models were applied to calibrate the spatial bias of GCM data for three precipitation stations in Korea (Incheon, Seoul, and Suwon), and the results were compared. According to the results, the QM method effectively corrected the range of precipitation, but the pattern of precipitation was the same at the three stations. Meanwhile, for the CNN model, the range and pattern of precipitation were corrected better than the QM method. The quantitative evaluation selected the optimal downscaling model, and the CNN model had the best performance (correlation coefficient (CC): 69% on average, root mean squared error (RMSE): 117 mm on average). Therefore, the new method suggested in this study is expected to have high utility in forecasting climate change. Finally, as a result of forecasting for future precipitation in 2100 via the CNN model, the average annual rainfall increased by 17% on average compared to the reference data.

Generalized Stochastic Petri Nets for Planning and Optimizing Maintenance Logistics of Small Hydroelectric Power Plants

Maintenance plays a crucial role in the availability of an asset. In particular, when a company’s assets are decentralized, logistical aspects directly impact maintenance management and, consequently, productivity. In the energy generation sector, this scenario is common in enterprises and projects in which distributed energy resources (DERs), such as small hydroelectric power plants (SHPPs), are considered. Hence, the objective of this work is to propose an application of generalized stochastic Petri nets (GSPN) for the planning and optimization of the maintenance logistics of a DER enterprise with two SHPPs. In the presented case study, different scenarios are modeled considering logistical aspects related to the availability of spare parts and the sharing of maintenance teams between plants. From the financial return resulting from the estimated energy generation and the operating cost of each simulated scenario, the most profitable one can be estimated. The results demonstrate the ability of GSPNs to estimate the influence of the number of spare parts and maintenance teams on the availability of DERs, allowing the optimization of costs related to maintenance logistics.

Assessment of the Current Potential of Hydropower for Water Damming in Poland in the Context of Energy Transformation

The present paper indicates that hydropower, including small hydropower plants (SHPs), may play a very important role in Poland’s energy transformation in the near future. The development of SHPs may also increase water resources in the steppe Poland. Additionally, the aim of the present research is to conduct the PEST analysis of SHPs in Poland, taking into account the SHP potential. For the first time, maps showing the power and location of potential SHPs on the existing dams in Poland are presented. SHPs should be an important element of energy transition in Poland, especially on a local scale—it is stable energy production. Our analysis shows that there are 16,185 such dams in Poland, while the total capacity of potential hydropower plants in Poland would be 523.6 MW, and the total number of new jobs is estimated at 524. It was calculated that the annual avoided carbon dioxide emissions will amount to 4.4 million tons, which will reduce Poland’s emissions by 1.4%. The construction of SHPs can bring significant environmental and economic benefits. As far as the PEST analysis is concerned, the political environment of SHPs in Poland can be described as unfavorable (2.86 points). The economical nature of PEST analysis (3.86 points) should be considered as friendly for the development of SHPs. The social nature of PEST analysis can be considered as neutral (3.36 points). The technological nature of the PEST analysis can be considered as neutral (3.21 points).

Socio-Technical Viability Framework for Micro Hydropower in Group Water-Energy Schemes

Most renewable energy (RE) studies focus on technology readiness, environmental benefits and/or cost savings. The market permeation, viability and adoption of RE technologies such as micro hydropower (MHP), however, require the alignment of other interrelated factors, such as the socio-technical, institutional and political dimensions. This is particularly the case where the energy recovery potential in decentralised water networks is being explored as part of a wholesome sustainability strategy by and for individual and communal prosumers. This study employs a socio-technical approach to understand factors that influence the perceived viability and adoption of MHP in group water-energy schemes. Methods included a progressive literature review to formulate a conceptual framework for the implementation of MHP systems. The framework was validated using survey data from representative stakeholders from groups schemes in Ireland and Spain. These stakeholders were sampled and surveyed at the stage of considering the adoption of MHP in their water networks. The findings highlight the push–pull factors and discusses the opportunities and barriers to the adoption of MHP systems. It confirms that the market, institutional and policy context, cost and financial benefits, social support and collaborative services combine to influence the adoption of MHP technology. Thus, a framework for evaluating the socio-technical viability of MHP systems based on these more realistic integrated, multi-dimensional criteria is proposed.

Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.