Hydropower Plant Investments Research Articles

The multiple services of hydropower plants in the context of sustainable electric sector in Brazil

At the worldwide level, the hydroelectricity corresponds to 54% of all renewable electricity, 15% of total electricity generated and only 3% of all total primary energy. In Brazil, hydropower produces 74% of all renewable electricity, 57% of electrical energy and 11% of all total primary energy (EPE, 2023). As all over the world, the construction and operation of large artificial reservoirs until the mid-twentieth century was seen in Brazil by government and by the general public as important infrastructure investment for economic development and improvement of well-being. Nowdays, investments in hydropower plants are re-gaining importance given not only its ability in supporting the transition of the electricity grid to get rid of dependence on fossil fuels but also in the provision of numerous other services. In particular, Brazilian hydropower plants contribute to multiple water resources management services such as flood and drought control, groundwater stabilization, water quality management, river navigation, irrigation, leisure and tourism and aquaculture around the country. The article discusses the multiple services provided to society by hydroelectric power plants within the context of sustainability in Brazil.

Energy transition paradox: Solar and wind growth can hinder decarbonization

The decarbonization of energy systems requires the deployment of intermittent renewables (wind and solar) with increasingly low costs. The growth of these renewables impacts power prices, which may influence hydropower investments and the adoption of fossil-fuel options for baseload electricity. It thus remains unclear whether deploying more intermittent renewables would reduce the net carbon emissions across energy systems in the absence of well-designed policy. This study first developed a techno-economic optimization model to explore the impacts of future projections of intermittent renewables on long-run electricity prices and the subsequent hydropower investments, using Hubei Province of China as a case study. The carbon emissions associated with the change in hydropower investment were then quantified. Results indicate that the new policy-driven renewables deployment reduces the electricity prices by 36 % on average over 2021–2100, compared to the case with the policy specified in the prior (13th) National Five-Year Plan. Without intervention, the lower electricity price could result in a 42 % reduction in hydropower investments by 2050. With increased intermittent renewables and decreased investments in hydropower, the energy system is expected to deploy more backup fossil-fuel options, which could increase carbon emissions by 45 %. Therefore, deploying more renewable energy would depress the investments in hydropower plants and further hinder decarbonization unexpectedly, suggesting a possible paradox in the energy transition. This possible paradox calls for a new, and robust policy to ensure the benefits of decarbonization are realized regardless of potential changes electricity prices.

An Experimental Private Small Hydropower Plant Investments Selection Classification System

Investment selection problems and models are crucial for humans, communities, and states. Private small hydroelectric power/ hydropower plant investments (PSHPPIs) selection problem is a unique one in those problems and models. Many available scientific methods can be implemented on this research topic. One methods group is type-2 Mamdani’s type fuzzy inference systems (FISs). This study presents an experimental one-node general type-2 4-zslices Mamdani’s type FIS model for PSHPPIs selection. There are 3 input variables (total estimated electricity generation: M1, total estimated cost: M2, change in the average runoff: M3), 1 output variable (O1: selected/unselected, selection classification of PSHPPIs), and 49 rules. M1 is designed in 5 triangular and trapezoidal general type-2 4-zslices fuzzy membership functions (MFs), M2 in 5 triangular and trapezoidal general type-2 4-zslices fuzzy MFs, M3 in 5 Gaussian type-2 4-zslices fuzzy MFs, and O1 in 2 Gaussian type-2 4-zslices fuzzy MFs. All modeling details are finalized by a human-expert decision process. JuzzyOnline V2.0, a browser-based platform of Juzzy (a Java-based library: Juzzy toolkit), is used for all FIS modeling and calculation activities. Five PSHPPI options assumed to be in their very early investment stages in Turkiye are evaluated in an experimental application. It is clear that an improved, tuned, and fine-tuned version of this model will help private investors to take more convenient/satisfying actions/decisions in a very short evaluation period in their PSHPPIs decision processes. An automatic investment selection classification system can be built in an appropriate development period after experiencing many methods and models. This study is one of the open-science humanoid robot, robot and platform development activities entitled Global Power Robots and Platforms: GPRP/GPRAP (Global Power Plants Developers: GP2D/G2PD/GPPD, Global Power Plants Engineers: GP2E/G2PE/ GPPE, Global Power Plants Owners: GP2O/G2PO/GPOO, Global Power Prediction Systems: GP2S/G2PS/GPPS), Global Profile Analyses Systems, and Global Social Network Analyses Systems.

A Proposal And Its Application On The Type-2 Fuzzy Mamdani's Method For The Selection Classification Of The Private Small Hydropower Plant Investments In The Very Early Investment Stages In Turkey

The private small hydroelectric power plant investments (PSHPPIs) differ very much from the other types of the private hydropower plant investments. In most cases, it will be more appropriate for the private electricity generation investors to analyze the alternatives by help of the models proposed only for the PSHPPIs based on the PSHPPIs' own characteristics. This paper pro-poses an online Type-2 Mamdani's fuzzy inference method by help of a Java-based library: Juzzy toolkit, for the selection of the PSHPPIs in the very early investment stages in Turkey. The real-life PSHPPIs' applications have shown that there isn't any clear, detailed, and sharp data and information in the very early investment stages. It is believed that a sophisticated and developed version of this proposed method will help the private investors to take more convenient actions and to make more satisfying decisions in a very short evaluation period in their PSHPPIs processes.

Location and investment factors of hydropower plants

ABSTRACT Location and investment selection features lakes of hydropower plant investments are presented in a proposed expert, event, and data-driven fact-based robot and platform. The traditional magical number 4 scale is tested in simple natural language process clustering at sub-factors, factors, main factors, and metafactors. The traditional Decision-Making Trial and Evaluation Laboratory methodology is tested in complex mathematical natural language process clustering at top metafactors as Political, Economic, Social, and Technological with its extensions. Nine presumptive logarithmic learning curve functions as experience curves are modeled considering time-domain effects. The functions with 0,1; 0,5; and 0,9 learning rates are tested in the current models. They cover almost the whole possibility space. Weighted arithmetic mean and weighted geometric mean as group decision-making approximation methods are tested for decision aggregation on the time domain. In the end, six clustering configurations are compared with each other at the top metafactors level. Six top metafactors, 12 metafactors, 27 main factors, 114 factors, and 10,612 sub-factors are found and clustered in location selection features. There are 11 hierarchical levels. Two top metafactors, five metafactors, 42 main factors, 136 factors, and 4276 sub-factors are found and clustered in investment selection features. There are 10 hierarchical levels.

Climate change impacts and adaptation strategies for a hydro-dominated power system via stochastic optimization

As outlined in the Paris Agreement on climate change, efforts to mitigate and adapt to climate change will require new modes of development of the energy sector including the transformation and expansion of power systems to low-carbon and more resilient designs. However, there is a need for more systematic tools to support decision-making processes in the context of climate change impacts and adaptation strategies for the energy and power sectors. For instance, quantitative approaches should be developed and implemented for the assessment of the impacts and hedging strategies associated with the uncertainties inherent to energy and power planning problems. This study addresses the development and implementation of an integrated model-based system analysis, which uses general circulation models, global sensitivity analysis, and stochastic optimization techniques, for the optimal design and planning of the Colombian power system in view of submitted climate pledges and climate change adaptation. It was found that during the 2015 to 2029 time frame, climate change will likely reduce the capacity factor of hydropower generation by 5.5–17.1%. Additionally, it was established that the independent effects of three key uncertain parameters, i.e., capacity factor of hydropower generation, gas prices, and emission reduction target, account for ∼96% of the variance in the total cost for the required expansion and operation of the power system. Furthermore, when uncertainty is taken into account, the optimal expansion strategy consists of rescheduling of investments in hydropower plants and investing more in carbon management technologies and renewable power plants to compensate for the uncertainty in hydropower generation, climate policy, and gas prices.

The analysis of the hydropower potential in Podkarpackie Voivodship

The paper presents an analysis of the use and potential of hydropower in Podkarpackie (Subcarpathian) Voivodship. The current usage of hydropower in the Region is presented, divided for range of generated power, geographical location and given rivers. Existing and planned artificial water reservoirs are taken into consideration in frame of hydropower development in the Region. Selected case studies related to varied hydropower plants are discussed. Problems connected with development of the hydropower were identified and considered. For each county in the Region theoretical available power possible to install in the main rivers was indicated, taking into account environmental aspects, technical problems with connection to the electrical grid and overall possibility of the facility installation. For each county communities with the best favorable situation are indicated. It is discussed the analysis of the evaluation of the use of hydropower in Podkarpackie Voivodship and provided information on planned and considered investments in hydropower plants. In addition, an analysis of the energy potential of the main rivers of the Podkarpackie Region have been procced to indicating the most favorable areas for location of hydropower plants. Moreover, given the environmental conditions, an analysis of the power estimation of the hydropower plants were made.

Location selection factors of small hydropower plant investments powered by SAW, grey WPM and fuzzy DEMATEL based on human natural language perception

Small hydropower plants (SHPP) are site-specific. Hence, attractiveness of investments is related to sites. Satisfactory SHPP locations can be selected with appropriate SHPP location selection factors. In this study, 1st generation Original Anatolian Honeybees' investment decision support methodology, location selection factors module is followed for finding, defining, identifying and selecting relevant criteria for investment screening stage. Information retrieval process in knowledge gathering and acquisition stage is very detailed. Eligible factors are selected by Simple Additive Weighting approach. Awarded factors are evaluated by Weighted Product approach outfitted by grey theory. This approach can deal with incomplete information. These factors are structured by fuzzy theory supported Decision Making Trial and Evaluation Laboratory method. This approach can handle complexity well. Findings can easily be used in multi criteria and objective research and practical studies. This study is only based on human perception. Future works will cover computerised (e.g., machine learning) methods integrated applications.

Location selection factors of small hydropower plant investments powered by SAW, grey WPM and fuzzy DEMATEL based on human natural language perception

Small hydropower plants (SHPP) are site-specific. Hence, attractiveness of investments is related to sites. Satisfactory SHPP locations can be selected with appropriate SHPP location selection factors. In this study, 1st generation Original Anatolian Honeybees' investment decision support methodology, location selection factors module is followed for finding, defining, identifying and selecting relevant criteria for investment screening stage. Information retrieval process in knowledge gathering and acquisition stage is very detailed. Eligible factors are selected by Simple Additive Weighting approach. Awarded factors are evaluated by Weighted Product approach outfitted by grey theory. This approach can deal with incomplete information. These factors are structured by fuzzy theory supported Decision Making Trial and Evaluation Laboratory method. This approach can handle complexity well. Findings can easily be used in multi criteria and objective research and practical studies. This study is only based on human perception. Future works will cover computerised (e.g., machine learning) methods integrated applications.

A qualitative multi-attribute model for the selection of the private hydropower plant investments in Turkey: By foundation of the search results clustering engine (Carrot2), hydropower plant clustering, DEXi and DEXiTree

Purpose: The electricity demand in Turkey has been increasing for a while. Hydropower is one of the major electricity generation types to compensate this electricity demand in Turkey. Private investors (domestic and foreign) in the hydropower electricity generation sector have been looking for the most appropriate and satisfactory new private hydropower investment (PHPI) options and opportunities in Turkey. This study aims to present a qualitative multi-attribute decision making (MADM) model, that is easy, straightforward, and fast for the selection of the most satisfactory reasonable PHPI options during the very early investment stages (data and information poorness on projects).Design/methodology/approach: The data and information of the PHPI options was gathered from the official records on the official websites. A wide and deep literature review was conducted for the MADM models and for the hydropower industry. The attributes of the model were identified, selected, clustered and evaluated by the expert decision maker (EDM) opinion and by help of an open source search results clustering engine (Carrot2) (helpful for also comprehension). The PHPI options were clustered according to their installed capacities main property to analyze the options in the most appropriate, decidable, informative, understandable and meaningful way. A simple clustering algorithm for the PHPI options was executed in the current study. A template model for the selection of the most satisfactory PHPI options was built in the DEXi (Decision EXpert for Education) and the DEXiTree software.Findings: The basic attributes for the selection of the PHPI options were presented and afterwards the aggregate attributes were defined by the bottom-up structuring for the early investment stages. The attributes were also analyzed by help of Carrot2. The most satisfactory PHPI options in Turkey in the big options data set were selected for each PHPI options cluster by the EDM evaluations in the DEXi.Originality/value: The recommended DEXi PHPI selection model by the search results clustering engine within a country wise case offered the possibility of easy, meaningful and satisfying continental or worldwide applications for the private investors and the international financial institutions such as the African Development Bank, or the World Bank was the main contribution.

A PROMETHEE I, II and GAIA-based approach by Saaty's subjective criteria weighting for small hydropower plant investments in Turkey

Academics, politicians, and researches have underlined that societies in the world cannot reach mutual happiness and peace without satisfying their daily necessities. One of these needs is the electricity. Most of the countries have been working to increase their electricity generation and quality; and some of them have been searching ways to supply electricity to their rural areas. Nowadays, mainly environmental concerns have been shifting the core business strategies of the electricity generation companies, from consuming fossil fuels to renewable energy sources. Small hydropower technology is one of the most widely used renewable energy technologies. The foreign and domestic investors have been looking for the most appropriate private small hydropower plant (SHPP) (PSHPP) projects/investments (PSHPPI) in the privatised electricity generation sector in Turkey. This study shows how the PROMETHEE method can help in this respect at the pre-development investment stages in Turkey.

A COMPARATIVE STUDY OF AHP, ELECTRE III & ELECTRE IV BY EQUAL OBJECTIVE & SHANNON’S ENTROPY OBJECTIVE & SAATY’S SUBJECTIVE CRITERIA WEIGHTING IN A PRIVATE SMALL HYDROPOWER PLANTS INVESTMENTS SELECTION PROBLEM

<p>Private small hydropower plant investments are more challenging than medium and large private hydropower plant investments when considering engineering analysis. One of the necessary tasks is the selection of the most appropriate private small hydropower investment amongst several alternatives. Brokerage, consultancy and private investor activities are a few examples of these kinds of real world activities. There are many multi-criteria decision making (MCDM) or multiple-criteria decision analysis (MCDA) methods, and different researchers prefer different methods This research study investigates three methods at once for the same problem: Analytic Hierarchy Process (AHP), Elimination and Choice Translating Reality (ELECTRE) III and ELECTRE IV. A comparative investigation is conducted on one simple unique selection model for all three methods. This unified model has seven objective factors (catchment area, project runoff, net head, flow rate, firm energy, secondary energy, investment cost). An additional comparison is also made on the criteria weighting amongst equal objective, Shannon’s Entropy objective and Saaty’s subjective criteria weighting. The simplistic unified model is structured in three levels. There are seven alternatives in the pre-development investment stage. The Super Decisions software and the ELECTRE III-IV software are implemented in this study. The pairwise comparisons of factors results in an inconsistency value of 0,09511 which is 7<sup>th</sup> in the Saaty's AHP weighting. The Shannon’s Entropy objective weighting represents a difference in the rate of the expert decision maker’s evaluations ranging between -67% and 671%. The equal weighting represents a rate change between -64% and 626%. Both approaches can't represent human judgments (here only one expert decision maker) well in this model. Fortunately, the same alternative (Alternative 3) ranks first. These findings promise that further studies on this subject can give some clues for the development of an autonomous computer based intelligent decision support system. Some observed pros and cons of these methods are also presented in this study. The observations and critical issues are presented during modeling, application, evaluation and analysis to help researchers, consultants, and readers in the small hydropower investments research and practical fields. </p>

An Experimental Research Study on the Solution of a Private Small Hydropower Plant Investments Selection Problem by ELECTRE III/IV, Shannon’s Entropy, and Saaty’s Subjective Criteria Weighting

Almost all of the today’s modern daily life conditions of humankind depend on the electricity. The countries either by only themselves or sometimes with some international intuitions and/or organizations have been trying to find the best methods, ways, and projects to supply the electricity to their societies. One of the important tools for the countries to increase the amount and quality of the electricity generation is to activate/ignite/initiate the private investment capabilities/opportunities. The electricity generation market in Turkey is a free/open market for both the foreign and domestic private investors. Hence, both the foreign and domestic private investors have been looking for the most suitable electricity generation plant projects. Small hydropower plant (SHPP) investments (SHPPIs) are one of the alternatives in the Turkish electricity generation market especially for the private investors searching for the renewable energy investments. This experimental research study investigates the possibility of using the ELECTRE III/IV, Shannon’s Entropy, and Saaty’s Analytic Hierarchy Process (AHP) subjective weighting (for criteria) methods for the solution of this problem. In the experimental case study, the most appropriate SHPPIs amongst five alternative SHPPIs at the SHPPIs’ predevelopment investment stages in Turkey were evaluated and ranked in order.

An Experimental Research of Small Hydropower Plant Investments Selection in Turkey by Carrot2, DEXi, DEXiTree

Nowadays, human daily lives depend very much to electricity. Hence, electricity generation plays a key role not only in countries' economies, but also in people's welfare and comfort in any region. This situation isn't also different for Turkey. Turkish electricity generation policy gives the opportunity for foreign and domestic private investments plans and schemes. Private investors have many private power plant investment options to invest in Turkey. One of the main groups is hydropower plants. Small hydropower plant investments are grouped under the hydropower plant investments. Their characteristics are very different from other types of hydropower plants. This experimental research study investigates the possibility of using a qualitative Multi Attribute Decision Making (MADM) model based on DEXi (Decision EXpert for Education) and DEXiTree software. Attributes in the current experimental research model are analyzed by help of a text mining tool Carrot2. In the experimental case study, the most appropriate small hydropower plant investments amongst five alternative ones at the predevelopment investment stages (an assumption) in Turkey are evaluated, and prioritized by help of seventeen basic factors (catchment area, project runoff, net head, discharge, firm energy, secondary energy, investment cost, river basin, conveyance structure, community attitude, transportation, topography, geology, security conditions, terrorism conditions, protected areas, substation conditions) based on the experimental DEXi decision model. There are some interesting findings such as Option 5 gets the highest priority in the overall experimental decision model. Similarly, Option 5 has the very good preference in the objective attributes evaluation. Option 3 comes afterwards with the good preference. But, both Option 4 and Option 5 get the good preference in the subjective attributes evaluation. The DEXi model file (*.dxi) is also presented to readers for giving opportunity to readers for preparing their models based on this experimental research model. This experimental research study is seen as another very small research step to deeply understand the nature of the small hydropower plants investments decisions in the way of developing an autonomous computer based intelligent decision support system for the real world cases.