Optimizing the management of multireservoir systems under shifting flow regimes

Over the past few decades, significant research efforts have been devoted to the development of tools and techniques to improve the operational effectiveness of multireservoir systems. One of those efforts focuses on the incorporation of relevant hydrologic information into reservoir operation models. This effort is particularly relevant in regions characterized by low-frequency climate signals, where time series of river discharges exhibit regime-like behavior. Failure to properly capture such regime-like behavior yields suboptimal operating policies, especially in systems characterized by large storage capacity such as large multireservoir systems. Hidden Markov Modeling is a class of hydrological models that can accommodate both overdispersion and serial dependence in time series, two essential hydrological properties that must be captured when modeling a system where the climate is switching between different states (e.g., dry, normal, and wet). In terms of reservoir operation, Stochastic Dual Dynamic Programming (SDDP) is one of the few optimization techniques that can accommodate both system and hydrologic complexity, that is, a large number of reservoirs and diverse hydrologic information. However, current SDDP formulations are unable to capture the long-term persistence of the streamflow process found in some regions. In this paper, we present an extension of the SDDP algorithm that can handle the long-term persistence and provide reservoir operating policies that explicitly capture regime shifts. Using the Senegal River Basin as a case study, we illustrate the potential gain associated with reservoir operating policies tailored to climate states

Detailed long-term hydro-thermal scheduling for expansion planning in the Nordic power system

acceptedVersio

Hydropower Scheduling with State-Dependent Discharge Constraints: An SDDP Approach

Environmental constraints in hydropower systems serve to ensure sustainable use of water resources. Through accurate treatment in hydropower scheduling, one seeks to respect such constraints in the planning phase while optimizing the utilization of hydropower. However, many environmental constraints introduce state-dependencies and even nonconvexities to the scheduling problem, making them challenging to represent in stochastic hydropower scheduling models. This paper describes how the state-dependent maximum discharge constraint, which is widely enforced in the Norwegian hydropower system, can be embedded within the stochastic dual dynamic programming (SDDP) algorithm for hydropower scheduling without compromising computational time. In this work, a combination of constraint relaxation and time-dependent auxiliary lower reservoir volume bounds is applied, and the modeling is verified through computational experiments on two different systems. The results demonstrate that the addition of an auxiliary lower bound on reservoir volume has significant potential for improved system operation, and that a bound based on the minimum accumulated inflow in the constraint period is the most efficient.Hydropower Scheduling with State-Dependent Discharge Constraints: An SDDP ApproachpublishedVersio

Probabilistic streamflow forecasts in hydropower systems operation

Au Canada, comme dans de nombreux pays de l'OCDE (Organisation de Coopération et de Développement Économiques), l'exploitation plus efficace des actifs hydroélectriques existants devient de plus en plus pertinente. Le fonctionnement optimal d'un système hydroélectrique est un problème de prise de décision séquentielle. Une séquence de décisions de soutirage d'eau doit être déterminée sur une période de planification donnée en tenant compte de diverses contraintes physiques et écologiques. Étant donné que cette période de planification peut s'étendre sur un futur plus ou moins lointain, les décisions de soutirage sont influencées par la disponibilité de prévisions hydrologiques fiables, y compris les systèmes de prévisions hydrologiques d'ensemble (H-EPS). Les hydrologues s'appuient souvent sur des scores statistiques pour évaluer la fiabilité et l'exactitude du H-EPS, mais ces scores ne donnent aucune indication sur la valeur économique des prévisions. Cette étude cherche à identifier les attributs les plus pertinents des prévisions hydrologiques d'ensemble en production hydroélectrique. Pour ce faire, un large ensemble de prévisions est construit à partir de 20 modèles hydrologiques et de prévisions météorologiques d'ensemble de 50 membres sur une période de 6 ans (2011-2016). De ce large ensemble, plusieurs H-EPS sont ensuite produits (configurés) et utilisés par un modèle d'optimisation hydroélectrique. La gestion du système hydrique est ensuite simulée en horizon roulant sur une période de 6 ans (2011-2016). Les résultats de la simulation indiquent qu'il existe une tendance entre la qualité globale et la valeur de la prévision en termes deproduction d'énergie, mais que cette relation n'est pas directement proportionnelle (1 :1). La configuration multimodèle fonctionne un peu mieux que les autres configurations. De plus, les résultats de la simulation montrent que les prévisions d'ensemble à court terme (CT) ont dela valeur, mais la marge d'amélioration se situe clairement dans les prévisions à moyen terme(MT, saisonnières), car un grand réservoir en amont contrôle la disponibilité de l'eau dans tout le système. Par ailleurs, les prévisions probabilistes donnent de meilleures performances que les déterministes, car elles donnent des informations sur l'incertitude du modèle d'optimisation.Enfin, les prévisions CT ont de la valeur tandis que les modèles d'optimisation CT-MT sont couplés.In Canada, like in many OECD (Organization for Economic Co-operation and Development) countries, the more efficient use of existing hydropower assets is becoming increasingly relevant.The optimal operation of a hydroelectric system is a sequential decision making problem. A sequence of release decisions must be determined over a given planning period taking into account a variety of physical and ecological constraints. Since this planning period may extend over a more or less distant future, release decisions are influenced by the availability of reliable hydrologic forecasts, including hydrological ensemble prediction systems (H-EPS).Hydrologists often rely on statistical scores to assess the reliability and accuracy of H-EPS,but those scores do not give any indication of the economic value of the forecasts. This studyseeks to identify the most relevant attributes of ensemble hydrological forecasts in hydropower production. To do this, a large set of forecasts is built from 20 hydrological models and ensemble meteorological forecasts of 50 members over a period of 6 years (2011-2016). From this large set, several H-EPS are then produced (configured) and used by a hydroelectric optimization model. The management of the water system is then simulated on a rolling horizon over a period of 6 years (2011-2016). The simulation results indicate that there is a trend between the overall quality and the value of the forecast in terms of energy production,but that this relationship is not directly proportional (1: 1). The multi-model setup works a bit better than the other setups. In addition, the simulation results show that the ensemble forecast at short-term (ST) has value, but the room for improvement is clearly in the forecastat mid-term (MT, seasonal), as a large reservoir upstream controls the availability of water throughout the system. In addition, probabilistic forecasts give better performance than determinists, because they provide information on the uncertainty of the optimization model.Finally, ST forecasts have value while ST-MT optimization models are coupled

Parallel discrete differential dynamic programming for multireservoir

Author name used in this publication: Chau, Kwok-Wing.2014-2015 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Inferring efficient operating rules in multireservoir water resource systems: A review

[EN] Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water-stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation-optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization-simulation-optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > MethodsThe study has been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion Universidades (MICINN) of Spain, and by the postdoctoral program (PAID-10-18) of the Universitat Politecnica de Valencia (UPV).Macian-Sorribes, H.; Pulido-Velazquez, M. (2019). Inferring efficient operating rules in multireservoir water resource systems: A review. Wiley Interdisciplinary Reviews Water. 7(1):1-24. https://doi.org/10.1002/wat2.1400S12471Aboutalebi, M., Bozorg Haddad, O., & Loáiciga, H. A. (2015). Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII. 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The value of hydrological information in multireservoir systems operation

La gestion optimale d’un système hydroélectrique composé de plusieurs réservoirs est un problème multi-étapes complexe de prise de décision impliquant, entre autres, (i) un compromis entre les conséquences immédiates et futures d’une décision, (ii) des risques et des incertitudes importantes, et (iii) de multiple objectifs et contraintes opérationnelles. Elle est souvent formulée comme un problème d’optimisation, mais il n’existe pas, à ce jour, de technique de référence même si la programmation dynamique (DP) a été souvent utilisée. La formulation stochastique de DP (SDP) permet la prise en compte explicite de l’incertitude entourant les apports hydrologiques futurs. Différentes approches ont été développées pour incorporer des informations hydrologiques et climatiques autres que les apports. Ces études ont révélé un potentiel d’amélioration des politiques de gestion proposées par les formulations SDP. Cependant, ces formulations sont applicables aux systèmes de petites tailles en raison de la célèbre « malédiction de la dimensionnalité ». La programmation dynamique stochastique duale (SDDP) est une extension de SDP développée dans les années 90. Elle est l’une des rares solutions algorithmiques utilisées pour déterminer les politiques de gestion des systèmes hydroélectriques de grande taille. Dans SDDP, l’incertitude hydrologique est capturée à l’aide d’un modèle autorégressif avec corrélation spatiale des résidus. Ce modèle analytique permet d’obtenir certains des paramètres nécessaires à l’implémentation de la technique d’optimisation. En pratique, les apports hydrologiques peuvent être influencés par d’autres variables observables, telles que l’équivalent de neige en eau et / ou la température de la surface des océans. La prise en compte de ces variables, appelées variables exogènes, permet de mieux décrire les processus hydrologiques et donc d’améliorer les politiques de gestion des réservoirs. L’objectif principal de ce doctorat est d’évaluer la valeur économique des politiques de gestion proposées par SDDP et ce pour diverses informations hydro-climatiques. En partant d’un modèle SDDP dans lequel la modélisation hydrologique est limitée aux processus Makoviens, la première activité de recherche a consisté à augmenter l’ordre du modèle autorégressif et à adapter la formulation SDDP. La seconde activité fut dédiée à l’incorporation de différentes variables hydrologiques exogènes dans l’algorithme SDDP. Le système hydroélectrique de Rio Tinto (RT) situé dans le bassin du fleuve Saguenay-Lac-Saint-Jean fut utilisé comme cas d’étude. Étant donné que ce système n’est pas capable de produire la totalité de l’énergie demandée par les fonderies pour assurer pleinement la production d’aluminium, le modèle SDDP a été modifié de manière à considérer les décisions de gestion des contrats avec Hydro Québec. Le résultat final est un système d’aide à la décision pour la gestion d’un large portefeuille d’actifs physiques et financiers en utilisant diverses informations hydro-climatiques. Les résultats globaux révèlent les gains de production d’énergie auxquels les opérateurs peuvent s’attendre lorsque d’autres variables hydrologiques sont incluses dans le vecteur des variables d’état de SDDP.The optimal operation of a multireservoir hydroelectric system is a complex, multistage, stochastic decision-making problem involving, among others, (i) a trade-off between immediate and future consequences of a decision, (ii) considerable risks and uncertainties, and (iii) multiple objectives and operational constraints. The reservoir operation problem is often formulated as an optimization problem but not a single optimization approach/algorithm exists. Dynamic programming (DP) has been the most popular optimization technique applied to solve the optimization problem. The stochastic formulation of DP (SDP) can be performed by explicitly considering streamflow uncertainty in the DP recursive equation. Different approaches to incorporate more hydrologic and climatic information have been developed and have revealed the potential to enhance SDP- derived policies. However, all these techniques are limited to small-scale systems due to the so-called curse of dimensionality. Stochastic Dual Dynamic Programming (SDDP), an extension of the traditional SDP developed in the 90ies, is one of the few algorithmic solutions used to determine the operating policies of large-scale hydropower systems. In SDDP the hydrologic uncertainty is captured through a multi-site periodic autoregressive model. This analytical linear model is required to derive some of the parameters needed to implement the optimization technique. In practice, reservoir inflows can be affected by other observable variables, such snow water equivalent and/or sea surface temperature. These variables, called exogenous variables, can better describe the hydrologic processes, and therefore enhance reservoir operating policies. The main objective of this PhD is to assess the economic value of SDDP-derived operating policies in large-scale water systems using various hydro-climatic information. The first task focuses on the incorporation of the multi-lag autocorrelation of the hydrologic variables in the SDDP algorithm. Afterwards, the second task is devoted to the incorporation of different exogenous hydrologic variables. The hydroelectric system of Rio Tinto (RT) located in the Saguenay-Lac-Saint-Jean River Basin is used as case study. Since, RT’s hydropower system is not able to produce the entire amount of energy demanded at the smelters to fully assure the aluminum production, a portfolio of energy contacts with Hydro-Québec is available. Eventually, we end up with a decision support system for the management of a large portfolio of physical and financial assets using various hydro-climatic information. The overall results reveal the extent of the gains in energy production that the operators can expect as more hydrologic variables are included in the state-space vector

Evaluating the utility of short-term hydrological forecasts in a hydropower system

Le fonctionnement optimal d'un système de réservoirs est un processus décisionnel complexe impliquant, entre autres, l'identication d'un compromis temporel concernant l'utilisation de l'eau : la dernière unité d'eau doit-elle être conservée ou plutôt utilisée pour un usage immédiat? La variabilité des apports hydrologiques complique encore davantage ce processus décisionnel puisque la recherche de ce compromis doit être effectuée sans une connaissance parfaite des conditions futures. De manière générale, l'équilibre optimal entre les utilisations immédiates et futures de l'eau nécessite l'intégration de règles de gestion à court et à long terme. Si les règles à court terme conduisent à des décisions à courte vue, les stratégies opérationnelles à long terme ne sont pas appropriées pour gérer des événements à court terme tels que les inondations. Nous proposons un cadre de modélisation basé sur l'approche de décomposition temporelle (DT) : Les stratégies à moyen/long terme sont tout d'abord déterminées puis utilisées comme limites pour l'optimisation des stratégies à court terme. Le modèle d'optimisation à moyen terme capture la persistance temporelle trouvée dans le processus des apports hydrologiques hebdomadaires, alors que les prévisions hydrologiques d'ensemble (PHE) sont utilisées pour piloter le modèle à court terme sur un pas de temps journalier. Plus spécifiquement, la programmation dynamique stochastique duale (SDDP) génère les fonctions des bénéces de valeur hebdomadaires qui sont ensuite imposées à un modèle de programmation linéaire implémenté sur chaque membre des PHE de 14 jours. Ce cadre de modélisation est mis en oeuvre selon un mode de gestion en horizon roulant sur une cascade de centrales hydroélectriques dans le bassin de la rivière Gatineau dans la province du Québec au Canada. À l'aide de ce cadre de modélisation, nous analysons la relation entre la valeur économique et les caractéristiques statistiques des PHE. Les résultats montrent que l'énergie générée par le système hydroélectrique augmente avec la précision et la résolution de la prévision, mais que la relation n'est pas univoque. En effet, d'autres facteurs semblent contribuer à l'utilité de la prévisionThe optimal operation of a system of reservoirs is a complex decision-making problem involving, among others, the identification of a temporal trade-offs regarding the use of water. Should the last unit of water be kept in storage or rather be released for use downstream? The variability of natural inflows further complicates this decision-making problem: at any given point in space and time, this trade-off must be made without a perfect knowledge of future reservoir in flows. Generally speaking, the optimal balance between immediate and future uses of water requires the integration of short- and long-term policies. If short-term policies lead to shortsighted decisions, long-term operational strategies are not appropriate to handle short-term events such as floods. We propose a modeling framework based on the time decomposition (TD) approach: mid/long-term policies are determined first and then used as boundary conditions for the optimization of short-term policies. The mid-term optimization model captures the temporal persistence found in the weekly streamflow process whereas Ensemble Streamflow Forecasts (ESF) are used to drive the short-term model on a daily time step. More specifically, a Stochastic Dual Dynamic Programming (SDDP) generates the weekly benefit-to-go functions that are then imposed to a linear programming model implemented on each 14-days member of the ESF. This modelling framework is implemented in a rolling-horizon mode on a cascade of hydropower stations in the Gatineau River basin, Quebec, Canada. Using this modelling framework, we analyze the relationship between the economic value of different sets of short-term hydrologic forecasts. The results show that the energy generated by the hydropower system increases with the forecast's accuracy and resolution but that the relationship is not univocal; other factors seem to contribute to the forecast's utility

Hydropower Aggregation by Spatial Decomposition – an SDDP Approach

The balance between detailed technical description, representation of uncertainty and computational complexity is central in long-term scheduling models applied to hydro-dominated power system. The aggregation of complex hydropower systems into equivalent energy representations (EER) is a commonly used technique to reduce dimensionality and computation time in scheduling models. This work presents a method for coordinating the EERs with their detailed hydropower system representation within a model based on stochastic dual dynamic programming (SDDP). SDDP is applied to an EER representation of the hydropower system, where feasibility cuts derived from optimization of the detailed hydropower are used to constrain the flexibility of the EERs. These cuts can be computed either before or during the execution of the SDDP algorithm and allow system details to be captured within the SDDP strategies without compromising the convergence rate and state-space dimensionality. Results in terms of computational performance and system operation are reported from a test system comprising realistic hydropower watercourses.Hydropower Aggregation by Spatial Decomposition – an SDDP ApproachacceptedVersio