708 research outputs found
Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty
No full textOver recent years, mathematical models have largely been used to support planning and management of water resources systems. Yet, the increasing uncertainties in their inputs - due to increased variability in the hydrological regimes - are a major challenge to the optimal operations of these systems. Such uncertainty, boosted by projected changing climate, violates the stationarity principle generally used for describing hydro-meteorological processes, which assumes time persisting statistical characteristics of a given variable as inferred by historical data. As this principle is unlikely to be valid in the future, the probability density function used for modeling stochastic disturbances (e.g., inflows) becomes an additional uncertain parameter of the problem, which can be described in a deterministic and set-membership based fashion.
This study contributes a novel method for designing optimal, adaptive policies for controlling water reservoir systems under climate-related uncertainty. The proposed method, called scenario-based Fitted Q-Iteration (sFQI), extends the original Fitted Q-Iteration algorithm by enlarging the state space to include the space of the uncertain system’s parameters (i.e. the uncertain climate scenarios). As a result, sFQI embeds the set-membership uncertainty of the future inflow scenarios in the action-value function and is able to approximate, with a single learning process, the optimal control policy associated to any scenario included in the uncertainty set. The method is demonstrated on a synthetic water system, consisting of a regulated lake operated for ensuring reliable water supply to downstream users. Numerical results show that the sFQI algorithm successfully identifies
adaptive solutions to operate the system under different inflow scenarios, which outperform the control policy designed under historical conditions. Moreover, the sFQI policy generalizes over inflow scenarios not directly experienced during the policy design, thus alleviating the risk of mis-adaptation, namely the design of a solution fully adapted to a scenario that is different from the one that will actually realize
Integrating operation design into infrastructure planning to foster robustness of planned water systems
No full textOver the past years, many studies have looked at the planning and management of water infrastructure systems as two separate problems, where the dynamic component (i.e. operations) is considered only after the static problem (i.e. planning) has been resolved. Most recent works have started to investigate planning and management as two strictly interconnected faces of the same problem, where the former is solved jointly with the latter in an integrated framework. This brings advantages to multi-purpose water reservoir systems, where several optimal operating strategies exist and similar system designs might perform differently on the long term depending on the considered short-term operating tradeoff. An operationally robust design will be therefore one performing well
across multiple feasible tradeoff operating policies.
This work aims at studying the interaction between short-term operating strategies and their impacts on long-term structural decisions, when long-lived infrastructures with complex ecological impacts and multi-sectoral demands to satisfy (i.e. reservoirs) are considered. A parametric reinforcement learning approach is adopted for nesting optimization and control yielding to both optimal reservoir design and optimal operational policies for water reservoir systems. The method is demonstrated on a synthetic reservoir that must be designed and operated for ensuring reliable water supply to downstream users. At first, the optimal design capacity derived is compared with the ‘no-fail storage’ computed through Rippl, a capacity design function that returns the minimum storage needed to satisfy specified water demands without allowing supply shortfall. Then, the optimal reservoir volume is used to simulate the simplified case study under other operating objectives than water supply, in order to assess whether and how the system performance changes. The more robust the infrastructural design, the smaller the difference between the performances of different operating strategies
Robust Infrastructure Sequencing and Management for Growing Food Energy and Water Demands in the Zambezi River Basin
No full textFast population growth and economic development in several African countries is driving large infrastructure investments for growing energy, food and water demands which will likely strain existing ecosystem services. To minimize negative impacts and guarantee long-term success and sustainability of these investments, careful management and temporal planning of existing and new infrastructure is required. Our study focuses on the Zambezi River Basin (ZRB), a transboundary system supporting key economic growth and poverty reduction across its multiple riparian countries, while sustaining essential ecosystem services. The ZRB currently encompasses five hydropower dams, with three additional dams planned. The goal of this study is to generate efficient pathways that allow the temporal sequencing of planned dam projects along with robust management strategies that balance food, energy and environmental demands. A participatory approach is adopted at an early stage by running a Negotiation Simulation Lab (NSL) to elicit stakeholders’ preferences and concerns supporting both model development and formulation of the optimization problem. Specifically, the pathway design is structured in three stages: first, optimal control policies are generated using Evolutionary Multi-objective Direct Policy Search for all possible combinations of dams projects; the time of construction is subsequently optimized, including the update of the system operation when a new dam is built, by balancing the benefits and the costs of additional infrastructure investments which are activated by projections of population growth triggering higher water and energy demands, finally promising policies are tested under a broad set of irrigation demand and streamflow scenarios. Our analysis shows that the rising demands cause all the planned dams to be built within the planning horizon from 2020-2060. The study also indicates that the operational preferences are key since they dictate the system’s performance across multiple objectives and this behavior prevails under a larger suite of plausible future scenarios. Overall, our study provides a novel approach that integrates infrastructure investment planning that can be coupled with cooperative operations to meet growing regional demands while involving stakeholders in crucial stages of the decision making process
Dam reoperation for controlling water-related diseases: the potential of floating solar for compensating hydropower losses
No full textInformed water infrastructure design: improving coupled dam sizing and operation by streamflow forecasts
No full textThere is a large body of recent research that is capitalizing on the improved skill of state-of-the-art hydroclimatic services for investigating their value in informing water reservoir operations. Yet, the potential value of these services in informing infrastructure design is still unexplored. In this work, we investigate the added value of hydroclimatic services in the planning of water reservoirs, composed of the joint design of the infrastructure’s size and its operations informed by streamflow forecasts. We demonstrate the potential of our approach through an ex-post design analysis of the Kariba dam in the Zambezi river basin, which is the largest man-made reservoir in Africa. The reservoir is operated for hydropower production and irrigation supply. Specifically, we search for flexible operating policies informed by streamflow forecasts that allow the design of smaller and less costly reservoirs with respect to solutions that do not rely on forecast information. This requires selecting the most informative forecast lead times to use in the dam design phase, which depends on both infrastructural reservoir characteristics and tradeoffs across performance objectives. After estimating the value of perfect forecasts, we analyze its sensitivity with respect to using imperfect synthetic forecasts characterized by different biases. The results show that informing the infrastructure design with perfect streamflow forecasts allows reducing capital costs by 20% with respect to a baseline solution not informed by any forecast, while maintaining the same performance in terms of hydropower production and water supply. Forecast overestimation results in the most critical synthetic forecast bias, reducing their value by 8%. Moreover, our analysis show that forecast value is highly sensitive to reservoir size and operational tradeoffs, ultimately representing a valuable tool for supporting the ongoing planning of 3,700 major dams worldwide
Integrated water-energy system modelling for optimal hydropower production planning under changing climate in the Zambezi River
No full textISSN:1029-7006ISSN:1607-7962ISSN:1607-796
Draunara, installation, Federica Cellini, 2014
No full text Draunara, Federica Cellini, Ana B.K, widewalls.ch , 26/05/2014 "Washed away onto the shores of the island, migrants from Africa keep arriving to Lampedusa, a small island just off the coast of Sicily. (...) The Unstoppable Tempest Draunara takes its name from the local term for a storm that swoops over the island coming from the sea. The author of the piece draws a parallel between the tempest and the overwhelming number of people arriving from the same direction. Although the arrivals ar..
correction idelalisib exposure before allogenic stem cell transplantation in patients with follicular lymphoma an EBMT survey
No full textThe article “Idelalisib exposure before allogeneic stem cell transplantation in patients with follicular lymphoma: an EBMT survey”, written by Leopold Sellner, Johannes Schetelig, Linda Koster, Goda Choi, Didier Blaise, Dietrich Beelen, Fabrizio Carnevale Schianca, Jakob Passweg, Urs Schanz, Emmanuel Gyan, Federica Sora, Nicolaus Kröger, Gerald. G. Wulf, Gwendolyn Van Gorkom, Jiri Mayer, Corentin Orvain, Jean Henri Bourhis, Pavel Jindra, Victoria Potter, Francesco Zallio, Elisabeth Vandenberghe, Stephen Robinson, Patrick J. Hayden, Ibrahim Yakoub-Agha, Silvia Montoto, Peter Dreger, on behalf of the European Society for Blood and Marrow Transplantation Lymphoma and Chronic Malignancies Working Parties, was originally published Online First without Open Access. After publication in volume 55, issue 12, page 2335–2338, the author decided to opt for Open Choice and to make the article an Open Access publication. Therefore, the copyright of the article has been changed to © The Author(s) 2020 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder
An inverse nested approach to optimize planning and operation of water reservoir systems
No full textISSN:1029-7006ISSN:1607-7962ISSN:1607-796
- …
