Maintenance, upgrading and extension of the Decision Support System for the management of the Athens water resource system
Duration: October 2008–November 2011
Budget: €72 000
Project director: N. Mamassis
Principal investigator: D. Koutsoyiannis
This research project includes the maintenance, upgrading and extension of the Decision Support System that developed by NTUA for EYDAP in the framework of the research project “Updating of the supervision and management of the water resources’ system for the water supply of the Athens’ metropolitan area”. The project is consisted of the following parts: (a) Upgrading of the Data Base, (b)Upgrading and extension of hydrometeorological network, (c) upgrading of the hydrometeorological data process software, (d) upgrading and extension of the Hydronomeas software, (e) hydrological data analysis and (f) support to the preparation of the annual master plans
A. Efstratiadis, and G.-K. Sakki, Revisiting the management of water–energy systems under the umbrella of resilience optimization, Environmental Sciences Proceedings, 21 (1), 72, doi:10.3390/environsciproc2022021072, 2022.
The optimal management of sociotechnical systems across the water–energy nexus is a critical issue for the overall goal of sustainable development. However, the new challenges induced by global crises and sudden changes require a paradigm shift in order to ensure tolerance against such kinds of disturbance that are beyond their “normal” operational standards. This may be achieved by incorporating the concept of resilience within the procedure for extracting optimal management policies and assessing their performance by means of well-designed stress tests. The proposed approach is investigated by using as proof of concept the complex and highly extended water resource system of Athens, Greece.
Full text: http://www.itia.ntua.gr/en/getfile/2252/1/documents/environsciproc-21-00072.pdf (2203 KB)
See also: https://www.mdpi.com/2673-4931/21/1/72
G.-K. Sakki, I. Tsoukalas, P. Kossieris, C. Makropoulos, and A. Efstratiadis, Stochastic simulation-optimisation framework for the design and assessment of renewable energy systems under uncertainty, Renewable and Sustainable Energy Reviews, 168, 112886, doi:10.1016/j.rser.2022.112886, 2022.
As the share of renewable energy resources rapidly increases in the electricity mix, the recognition, representation, quantification, and eventually interpretation of their uncertainties become important. In this vein, we propose a generic stochastic simulation-optimization framework tailored to renewable energy systems (RES), able to address multiple facets of uncertainty, external and internal. These involve the system’s drivers (hydrometeorological inputs) and states (by means of fuel-to-energy conversion model parameters and energy market price), both expressed in probabilistic terms through a novel coupling of the triptych statistics, stochastics and copulas. Since the most widespread sources (wind, solar, hydro) exhibit several common characteristics, we first introduce the formulation of the overall modelling context under uncertainty, and then offer uncertainty quantification tools to put in practice the plethora of simulated outcomes and resulting performance metrics (investment costs, energy production, revenues). The proposed framework is applied to two indicative case studies, namely the design of a small hydropower plant (particularly, the optimal mixing of its hydro-turbines), and the long-term assessment of a planned wind power plant. Both cases reveal that the ignorance or underestimation of uncertainty may hide a significant perception about the actual operation and performance of RES. In contrast, the stochastic simulation-optimization context allows for assessing their technoeconomic effectiveness against a wide range of uncertainties, and as such provides a critical tool for decision making, towards the deployment of sustainable and financially viable RES.
Full text: http://www.itia.ntua.gr/en/getfile/2229/1/documents/stochasticRES.pdf (6011 KB)
See also: https://www.sciencedirect.com/science/article/pii/S1364032122007687
Other works that reference this work (this list might be obsolete):
| 1. | Woon, K. S., Z. X. Phuang, J. Taler, P. S. Varbanov, C. T. Chong, J. J. Klemeš, and C. T. Lee, Recent advances in urban green energy development towards carbon neutrality, Energy, 126502, doi:10.1016/j.energy.2022.126502, 2022. |
| 2. | Angelakis, A., Reframing the high-technology landscape in Greece: Empirical evidence and policy aspects, International Journal of Business & Economic Sciences Applied Research, 15(2), 58-70, doi:10.25103/ijbesar.152.06, 2022. |
A. Efstratiadis, P. Dimas, G. Pouliasis, I. Tsoukalas, P. Kossieris, V. Bellos, G.-K. Sakki, C. Makropoulos, and S. Michas, Revisiting flood hazard assessment practices under a hybrid stochastic simulation framework, Water, 14 (3), 457, doi:10.3390/w14030457, 2022.
We propose a novel probabilistic approach to flood hazard assessment, aiming to address the major shortcomings of everyday deterministic engineering practices in a computationally efficient manner. In this context, the principal sources of uncertainty are defined across the overall modelling procedure, namely, the statistical uncertainty of inferring annual rainfall maxima through distribution models that are fitted to empirical data, and the inherently stochastic nature of the underlying hydrometeorological and hydrodynamic processes. Our work focuses on three key facets, i.e., the temporal profile of storm events, the dependence of flood generation mechanisms to antecedent soil moisture conditions, and the dependence of runoff propagation over the terrain and the stream network on the intensity of the flood event. These are addressed through the implementation of a series of cascade modules, based on publicly available and open-source software. Moreover, the hydrodynamic processes are simulated by a hybrid 1D/2D modelling approach, which offers a good compromise between computational efficiency and accuracy. The proposed framework enables the estimation of the uncertainty of all flood-related quantities, by means of empirically-derived quantiles for given return periods. Finally, a set of easily applicable flood hazard metrics are introduced for the quantification of flood hazard.
Full text: http://www.itia.ntua.gr/en/getfile/2170/1/documents/water-14-00457.pdf (6083 KB)
See also: https://www.mdpi.com/2073-4441/14/3/457
Other works that reference this work (this list might be obsolete):
| 1. | Tegos, A., A. Ziogas, V. Bellos, and A. Tzimas, Forensic hydrology: a complete reconstruction of an extreme flood event in data-scarce area, Hydrology, 9(5), 93, doi:10.3390/hydrology9050093, 2022. |
| 2. | Afzal, M. A., S. Ali, A. Nazeer, M. I. Khan, M. M. Waqas, R. A. Aslam, M. J. M. Cheema, M. Nadeem, N. Saddique, M. Muzammil, and A. N. Shah, Flood inundation modeling by integrating HEC–RAS and satellite imagery: A case study of the Indus river basin, Water, 14(19), 2984, doi:10.3390/w14192984, 2022. |
| 3. | Vangelis, H., I. Zotou, I. M. Kourtis, V. Bellos, and V. A. Tsihrintzis, Relationship of rainfall and flood return periods through hydrologic and hydraulic modeling, Water, 14(22), 3618, doi:10.3390/w14223618, 2022. |
K.-K. Drakaki, G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data, Advances in Geosciences, 56, 155–162, doi:10.5194/adgeo-56-155-2022, 2022.
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.
Remarks:
The simulation and forecasting models have been developed in the R environment and they are available at: https://github.com/corinadrakaki/Day-ahead-energy-production-in-small-hydropower-plants
Full text: http://www.itia.ntua.gr/en/getfile/2165/1/documents/adgeo-56-155-2022.pdf (217 KB)
See also: https://adgeo.copernicus.org/articles/56/155/2022/
Other works that reference this work (this list might be obsolete):
| 1. | Krechowicz, A., M. Krechowicz, and K. Poczeta, Machine learning approaches to predict electricity production from renewable energy sources, Energies, 15(23), 9146, doi:10.3390/en15239146, 2022. |
| 2. | Ghobadi, F., and D. Kang, Application of machine learning in water resources management: A systematic literature review, Water, 15(4), 620, doi:10.3390/w15040620, 2023. |
G.-K. Sakki, I. Tsoukalas, and A. Efstratiadis, A reverse engineering approach across small hydropower plants: a hidden treasure of hydrological data?, Hydrological Sciences Journal, 67 (1), 94–106, doi:10.1080/02626667.2021.2000992, 2022.
The limited availability of hydrometric data makes the design, management, and real-time operation of water systems a difficult task. Here, we propose a generic stochastic framework for the so-called inverse problem of hydroelectricity, using energy production data from small hydropower plants (SHPPs) to retrieve the upstream inflows. In this context, we investigate the alternative configurations of water-energy transformations across SHPPs of negligible storage capacity, which are subject to multiple uncertainties. We focus on two key sources, i.e. observational errors in energy production and uncertain efficiency curves of turbines. In order to extract the full hydrograph, we also extrapolate the high and low flows outside of the range of operation of turbines, by employing empirical rules for representing the rising and falling limbs of the simulated hydrographs. This framework is demonstrated to a real-world system at Evinos river basin, Greece. By taking advantage of the proposed methodology, SHPPs may act as potential hydrometric stations and improve the existing information in poorly gauged areas.
Additional material:
Other works that reference this work (this list might be obsolete):
| 1. | Garrett, K. P., R. A. McManamay, and A. Witt, Harnessing the power of environmental flows: Sustaining river ecosystem integrity while increasing energy potential at hydropower dams, Renewable and Sustainable Energy Reviews, 173(1), 113049, doi:10.1016/j.rser.2022.113049, 2023. |
G.-F. Sargentis, P. Siamparina, G.-K. Sakki, A. Efstratiadis, M. Chiotinis, and D. Koutsoyiannis, Agricultural land or photovoltaic parks? The water–energy–food nexus and land development perspectives in the Thessaly plain, Greece, Sustainability, 13 (16), 8935, doi:10.3390/su13168935, 2021.
Water, energy, land, and food are vital elements with multiple interactions. In this context, the concept of a water–energy–food (WEF) nexus was manifested as a natural resource management approach, aiming at promoting sustainable development at the international, national, or local level and eliminating the negative effects that result from the use of each of the four resources against the other three. At the same time, the transition to green energy through the application of renewable energy technologies is changing and perplexing the relationships between the constituent elements of the nexus, introducing new conflicts, particularly related to land use for energy production vs. food. Specifically, one of the most widespread “green” technologies is photovoltaic (PV) solar energy, now being the third foremost renewable energy source in terms of global installed capacity. However, the growing development of PV systems results in ever expanding occupation of agricultural lands, which are most advantageous for siting PV parks. Using as study area the Thessaly Plain, the largest agricultural area in Greece, we investigate the relationship between photovoltaic power plant development and food production in an attempt to reveal both their conflicts and their synergies.
Full text: http://www.itia.ntua.gr/en/getfile/2136/1/documents/sustainability-13-08935.pdf (2709 KB)
See also: https://www.mdpi.com/2071-1050/13/16/8935
Other works that reference this work (this list might be obsolete):
| 1. | Abouaiana, A., and A. Battisti, Multifunction land use to promote energy communities in Mediterranean region: Cases of Egypt and Italy, Land, 11(5), 673, doi:10.3390/land11050673, 2022. |
| 2. | Reasoner, M., and A. Ghosh, Agrivoltaic engineering and layout optimization approaches in the transition to renewable energy technologies: a review, Challenges, 13(2), 43, doi:10.3390/challe13020043, 2022. |
| 3. | Bhambare, P. S., and S. C. Vishweshwara, Design aspects of a fixed focus type Scheffler concentrator and its receiver for its utilization in thermal processing units, Energy Nexus, 7, 100103, doi:10.1016/j.nexus.2022.100103, 2022. |
| 4. | Padilla, J., C. Toledo, and J. Abad, Enovoltaics: Symbiotic integration of photovoltaics in vineyards, Frontiers in Energy Research, 10, 1007383, doi:10.3389/fenrg.2022.1007383, 2022. |
| 5. | Garcia, J. A., and A. Alamanos, Integrated modelling approaches for sustainable agri-economic growth and environmental improvement: Examples from Greece, Canada and Ireland, Land, 11(9), 1548, doi:10.3390/land11091548, 2022. |
G.-K. Sakki, A. Efstratiadis, and C. Makropoulos, Stress-testing for water-energy systems by coupling agent-based models, Proceedings of 7th IAHR Europe Congress "Innovative Water Management in a Changing Climate”, Athens, International Association for Hydro-Environment Engineering and Research (IAHR), 2022.
Full text:
V.-E. K. Sarantopoulou, G. J. Tsekouras, A. D. Salis, D. E. Papantonis, V. Riziotis, G. Caralis, K.-K. Drakaki, G.-K. Sakki, A. Efstratiadis, and K. X. Soulis, Optimal operation of a run-of-river small hydropower plant with two hydro-turbines, Proceedings of 7th International Conference on Mathematics and Computers in Sciences and Industry (MCSI), Marathon Beach, Athens, doi:10.1109/MCSI55933.2022.00020, 2022.
The operation of a small hydroelectric power plant (HPS) with two hydro turbines of different types and power is usually done following a hierarchical rule, which is not necessarily the most efficient. Alternatively, other synergetic rules have been proposed that improve the delivered energy. In this paper, the operation of the two turbines is systematized by examining all possible operation combinations of the turbines, depending on the incoming water flow, its distribution (in the case of operation of both hydro turbines, at the optimal power mode) and the formation of a suitable lookup table for the optimal operation of an HPS. The implementation of the method is easily achieved using a quadratic equation efficiency-flow curve. In this way, the total efficiency of the two-turbine system is optimized.
Full text:
A. Efstratiadis, and G.-K. Sakki, Revisiting the management of water-energy systems under the umbrella of resilience optimization, e-Proceedings of the 5th EWaS International Conference, Naples, 596–603, 2022.
The optimal management of sociotechnical systems across the water-energy nexus is critical issue towards the overall goal of sustainable development. However, the new challenges induced by global crises and sudden changes require a paradigm shift, in order to ensure tolerance against such kinds of disturbance that are beyond their “normal” operational standards. This may be achieved by incorporating the concept of resilience within the procedure for extracting optimal management policies and assessing their performance, by means of well-designed stress-tests. The proposed approach is investigated by using as proof of concept the complex and highly-extended water resource system of Athens, Greece.
Full text:
P. Pagotelis, Κ. Tsilipiras, Α. Lyras, Α. Koutsovitis, G.-K. Sakki, and A. Efstratiadis, Design of small hydropower plants under uncertainty: from the hydrological cycle to energy conversion, European Geosciences Union General Assembly 2023, Vienna, Austria & Online, EGU23-15407, doi:10.5194/egusphere-egu23-15407, 2023.
We investigate the design of small hydropower plants under multiple sources of uncertainty and contrast it with the conventional deterministic practice that leads to a unique solution. In particular, we emphasize three sources of uncertainty, referring to: (a) the rainfall process, (b) the rainfall-runoff transformation, and (c) the flow-energy conversion. The first is due to the natural (i.e., hydroclimatic) variability, and is represented through stochastic approaches. Regarding the rainfall-runoff uncertainty, this arises from inherent structural shortcomings and poor parameter identifiability across the calibration procedure. In fact, hydrological model parameterizations using only historical data are often insufficient for accurately predicting catchment behavior over the long term, as they may not capture the full range of hydroclimatic conditions that the catchment may be subjected to. To address this issue, we use synthetic time series as drivers to parameterize the model and validate it against observed data. This approach preserves the probabilistic properties and dependence structure of the observed data while also providing a much wider range of hydroclimatic conditions for model training. In addition, it allows for assessing and quantifying the total model uncertainty. The final source of uncertainty is depicted by means of probabilistic efficiency curves. This Monte Carlo simulation-optimization framework is formalized as a modular procedure, where the different sources of uncertainty, as well as the full context, is tested through the design of a small hydropower plant in Epirus, Western Greece.
Full text: http://www.itia.ntua.gr/en/getfile/2272/1/documents/EGU23-15407-print.pdf (288 KB)
See also: https://meetingorganizer.copernicus.org/EGU23/session/45335
A. Zisos, M.-E. Pantazi, Μ. Diamanta, Ι. Koutsouradi, Α. Kontaxopoulou, I. Tsoukalas, G.-K. Sakki, and A. Efstratiadis, Towards energy autonomy of small Mediterranean islands: Challenges, perspectives and solutions, EGU General Assembly 2022, Vienna, Austria & Online, EGU22-5468, doi:10.5194/egusphere-egu22-5468, European Geosciences Union, 2022.
The energy autonomy of small non-interconnected islands in the Mediterranean, taking advantage of their high renewable energy potential, has been a long-standing objective of local communities and stakeholders. This is also in line with the recently implemented European Green Deal, which has set the goal of increasing the renewable energy penetration in European countries’ power systems. However, the islands have further challenges than the large-scale inland areas. On the one hand, their population fluctuates significantly across seasons, as result of tourism, which is their key economic activity. The footprint of tourism is a substantial stress to all associated resources and infrastructures during the summer period. On the other hand, most of these areas suffer from both water and land scarcity. These features raise several challenges regarding the development of really autonomous energy systems, based on renewables and essential storage works to regulate the energy surpluses and deficits in the long run. Taking as example the Cycladic island of Sifnos, Greece, we investigate the design of a hybrid power system, combining wind, solar and hydroelectric energy. A major component of the proposed layout is the pumped-storage system. Due to the limited surface water resources of the island, we configure an upper tank at an elevation of 320 m, recycling seawater. This peculiarity introduces a significant level of uncertainty in hydraulic calculations, as well as various technical challenges, such as the erosion of pipes and the electromechanical equipment, and the waterproofing of the tank. An additional challenge is raised by the peculiar wind regime of the island, that makes essential to choose a hub height of turbines to minimize the frequency of power cut-offs. The basis of a rational design procedure for the main system components is the financial optimization that ensures a desirable level of reliability. This is achieved through a stochastic simulation approach that takes into account the stochastic nature of the underlying hydrometeorological drivers (wind velocity and solar radiation) and the energy demand.
Full text:
K.-K. Drakaki, G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, Setting the problem of energy production forecasting for small hydropower plants in the Target Model era, EGU General Assembly 2021, online, EGU21-3168, doi:10.5194/egusphere-egu21-3168, European Geosciences Union, 2021.
The highly-competitive electricity market over EU and the challenges induced by the so-called “Target Model”, introduce significant uncertainties to day-ahead trades involving renewable energy, since most of these sources are driven by non-controllable weather processes (wind, solar, hydro). Here, we explore the case of small hydropower plants that have negligible storage capacity, and thus their production is just a nonlinear transformation of inflows. We discuss different forecasting approaches, which take advantage of alternative sources of information, depending on data availability. Among others, we investigate whether is it preferable to employ day-ahead predictions based on past energy production data per se, or use these data in order to retrieve past inflows, which allows for introducing hydrological knowledge within predictions. Overall objective is to move beyond the standard, yet risky, point forecasting methods, providing a single expected value of hydropower production, thus quantifying the overall uncertainty of each forecasting method. Power forecasts are evaluated in terms of economic efficiency, accounting for the impacts of over- and under-estimations in the real-world electricity market.
Full text:
K. Risva, G.-K. Sakki, A. Efstratiadis, and N. Mamassis, Hydropower potential assessment made easy via the unit geo-hydro-energy index, EGU General Assembly 2021, online, EGU21-4462, doi:10.5194/egusphere-egu21-4462, European Geosciences Union, 2021.
The design of hydropower works typically follows a top-down approach, starting from a macroscopic screening of the broader region of interest, to select promising clusters for hydroelectric exploitation, based on easily retrievable information. Manual approaches are very laborious and may fail to detect sites of significant hydropower potential. In order to facilitate this kind of studies, we provide a novel geomorphological approach to assess the hydropower potential across river networks. The method is based on the discretization of the stream network into segments of equal length, thus providing a background layer of head differences between potential abstraction and power production sites. Next, at each abstraction point, we estimate the so-called unit geo-hydro-energy index (UGHE), which is a key concept of our approach. UGHE is defined as the ratio of annual potential energy divided by the upstream catchment area, the head difference, and the unit annual runoff of the catchment, which is set equal to 1000 mm. The method is further expanded, to estimate the actual hydropotential, if spatially distributed runoff data are available. All analyses are automatized by taking advantage of the high-level interpreted programming language Python and the open-source QGIS tool. The proposed framework is demonstrated at the regional scale, involving the siting of run-of-river hydroelectric works in the Peneios river basin.
Full text:
G.-K. Sakki, I. Tsoukalas, P. Kossieris, and A. Efstratiadis, A dilemma of small hydropower plants: Design with uncertainty or uncertainty within design?, EGU General Assembly 2021, online, EGU21-2398, doi:10.5194/egusphere-egu21-2398, European Geosciences Union, 2021.
Small hydropower plants (SHPPs) are subject to multiple uncertainties and complexities, despite their limited scale. These uncertainties are often ignored in the typical engineering practice, which results in risky design. As this type of renewable energy rapidly penetrates the electricity mix, the impacts of their uncertainties, exogenous and endogenous, become critical. In this vein, we develop a stochastic simulation-optimization framework tailored for small hydropower plants. First, we investigate the underlying multicriteria design problem and its peculiarities, in order to determine a best-compromise performance metric that ensures efficient and effective optimizations. Next, we adjust to the optimal design problem a modular uncertainty assessment procedure. This combines statistical and stochastic approaches to quantify the uncertainty of the inflow process per se, the associated input data, the initial selection of efficiency curves for the turbine mixing in the design phase, as well as the drop of efficiency due to aging effects. Overall, we propose a holistic framework for the optimal design of SHPPs, highlighting the added value of considering the stochasticity of input processes and parameters. The novelty of this approach is the transition from the conventional to the uncertainty-aware design; from the unique value to Pareto-optimality, and finally to the reliability of the expected performance, in terms of investment costs, hydropower production, and associated revenues.
Full text:
G.-K. Sakki, V. Papalamprou, I. Tsoukalas, N. Mamassis, and A. Efstratiadis, Stochastic modelling of hydropower generation from small hydropower plants under limited data availability: from post-assessment to forecasting, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-8129, doi:10.5194/egusphere-egu2020-8129, 2020.
Due to their negligible storage capacity, small hydroelectric plants cannot offer regulation of flows, thus making the prediction of energy production a very difficult task, even for small time horizons. Further uncertainties arise due to the limited hydrological information, in terms of upstream inflow data, since usually the sole available measurements refer to the power production, which is a nonlinear transformation of the river discharge. In this context, we develop a stochastic modelling framework comprising two steps. Initially, we extract past inflows on the basis of energy data, which may be referred to as the inverse problem of hydropower. Key issue of this approach is that the model error is expressed in stochastic terms, which allows for embedding uncertainties within calculations. Next, we generate stochastic forecasting ensembles of future inflows and associated hydropower production, spanning from small (daily to weekly) to meso-scale (monthly to seasonal) time horizons. The methodology is tested in the oldest (est. 1926) small hydroelectric plant of Greece, located at Glafkos river, in Northern Peloponnese. Among other complexities, this comprises a mixing of Pelton and Francis turbines, which makes the overall modelling procedure even more challenging.
Related works:
Full text:
See also: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-8129.html
G. Antonogiannakis, V. Papalamprou, A. G. Pettas, A. Pytharouliou, and G.-K. Sakki, [No English title available], Course work, Department of Water Resources and Environmental Engineering – National Technical University of Athens, 2020.
Full text:
G.-K. Sakki, Disentangling flow-energy transformations for small hydropower plants: from reverse engineering to uncertainty assessment and calibration, Diploma thesis, 98 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, Athens, July 2020.
Due to their negligible storage capacity, small hydroelectric plants cannot offer regulation of flows, thus making the control of energy production a very difficult task, even for small time horizons. Further uncertainties arise due to limited information, both in terms of upstream inflow data and technical characteristics. Usually, the sole available measurements refer to power production, which is a nonlinear transformation of the river discharge. In this thesis we investigate the three configurations of this transformation, named the forward, the inverse and the calibration problem. The major outcome is a generic stochastic framework for the so-called inverse problem of hydroelectricity, i.e. the extraction of streamflow from observed energy data, focusing on two key potential sources of uncertainty, i.e. in energy production (observational error) and the efficiency curve of turbines (parameter error). Key issue of this reverse engineering approach is that the model error is expressed in stochastic terms, which allows for embedding uncertainties within calculations. Another interesting issue is the extrapolation of high and low flows outside of the range of operation of SHPs, which is employed by combining empirical hydrological rules for representing the rising and falling limbs. The methodology is tested in hypothetical problems as well as in a real-world case, i.e. the oldest (est. 1926) small hydroelectric plant of Greece, located at Glafkos river, in Northern Peloponnese. Among other complexities, this comprises a mixing of Pelton and Francis turbines, which makes the overall modelling procedure even more challenging and also requires to extract the efficiency curves of the two turbines through calibration. Our analyses indicate that the proposed framework may be the basis for handling several practical problems and open research questions in the broader area of simulation and optimization of small hydroelectric works.
Related works:
Full text:
A. Efstratiadis, and G.-K. Sakki, Investigation of the water supply system's management for period January-September 2023, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 49 pages, January 2023.
Related project: Modernization of the management of the water supply system of Athens - Update
A. Efstratiadis, N. Mamassis, G.-K. Sakki, I. Tsoukalas, P. Kossieris, P. Dimas, and N. Pelekanos, [No English title available], Modernization of the management of the water supply system of Athens - Update, 141 pages, Department of Water Resources and Environmental Engineering – National Technical University of Athens, June 2022.
Related project: Modernization of the management of the water supply system of Athens - Update
A. Efstratiadis, I. Tsoukalas, and G.-K. Sakki, Investigation of the water supply system's management for period March-September 2022, Modernization of the management of the water supply system of Athens - Update, Contractor: Department of Water Resources and Environmental Engineering – National Technical University of Athens, 49 pages, April 2022.
Related project: Modernization of the management of the water supply system of Athens - Update