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

Hydropower Scheduling Toolchains:Comparing Experiences in Brazil, Norway,and USA and Implications for Synergistic Research

While hydropower scheduling is a well-defined problem, there are institutional differences that need to be identified to promoteconstructive and synergistic research. We study how established toolchains of computer models are organized to assist operational hydro-power scheduling in Brazil, Norway, and the United States’Colorado River System (CRS). These three systems have vast hydropowerresources, with numerous, geographically widespread, and complex reservoir systems. Although the underlying objective of hydropowerscheduling is essentially the same, the systems are operated in different market contexts and with different alternative uses of water, where thestakeholders’objectives clearly differ. This in turn leads to different approaches when it comes to the scope, organization, and use of modelsfor operational hydropower scheduling and the information flow between the models. We describe these hydropower scheduling toolchains,identify the similarities and differences, and shed light on the original ideas that motivated their creation. We then discuss the need to improveand extend the current toolchains and the opportunities to synergistic research that embrace those contextual differences.Hydropower Scheduling Toolchains:Comparing Experiences in Brazil, Norway,and USA and Implications for Synergistic ResearchacceptedVersio

Hydropower sheduling in basins with heavy ecological and human restrictions

The problem of water resources management aims to calculate the optimal energy bids of a set of hydro plants and to estimate costs for consumptive and nonconsumptive volumes of water, when meeting European and local regulations, consumption requirements and basin rights of use, respecting environmental flows, possible congestions in the electric transmission system and other important concerns. The goal of this thesis is to advance in the development of an effective tool for the management of hydro basins with different economic, social, policy, normative, restrictions and resources characteristics. In first case, an optimisation problem for calculating the best offers of a set of hydro power plants is proposed, considering ecological flows and social consumptions. In the simulations, the costs related to the social consumptions and ecological requirements are compared in a relatively small real Spanish basin, for short-term (24-hour) planning. In second case, an improved representation of the market and the optimization of the hydro plants are integrated in a nested algorithm, to calculate local prices and optimal energy bids in a congested electrical system. The algorithm is applied to a real basin in Italy. In a third case, uncertainties in the resources, improved representations of the hydro plants and environmental constraints are integrated in a large basin, in southern Spain. Stochastic scenarios are used to evaluate the significance of uncertainties in a 72-hours horizon. The study provides a new tool for the coordinated management of large basins, complying with ecological restrictions and governmental regulation on water resource allocation and considering the technical characteristics of hydropower plants and the hydropower production profits.El problema de gestión de recursos hídricos busca determinar las ofertas óptimas de energía para un conjunto de centrales hidroeléctricas y también una estimativa de los costos del agua para los volúmenes consutivos y no consuntivos, cumpliendo normativas europeas y locales, las necesidades de consumo y los derechos de uso de las cuencas, las posibles congestiones en el sistema de transmisión eléctrica y otras cuestiones relevantes. El objetivo de esta tesis es avanzar en el desarrollo de una herramienta eficaz para la gestión de cuencas con diferentes características económicas, sociales, políticas, normativas, restrictivas y de recursos. En el primer caso estudiado, se propone un problema de optimización para el cálculo de las ofertas óptimas de un conjunto de centrales hidroeléctricas, en una cuenca española relativamente pequeña, considerando los flujos ecológicos y los consumos sociales. En las simulaciones de planificación a corto plazo (24 horas), se comparan los costes relacionados con los consumos sociales y los requisitos ecológicos. En el segundo caso de estudio, se integran en un algoritmo iterativo una representación mejorada del mercado y la optimización de las centrales hidroeléctricas, a fin de calcular precios locales y ofertas óptimas de energía en un sistema eléctrico congestionado. El algoritmo es aplicado a una cuenca real en el Norte de Italia. En el tercer caso de estudio, las incertidumbres asociadas a los recursos y una representación mejorada de las centrales hidroeléctricas, junto con las limitaciones ambientales, se integran en un modelo que representa una cuenca real de tamaño significativo en el sur de España. Se utilizan escenarios estocásticos para evaluar la influencia de las incertidumbres en un horizonte de 72 horas. El estudio proporciona así una nueva herramienta para la gestión coordinada de grandes cuencas, cumpliendo con las restricciones ecológicas y la regulación gubernamental sobre asignación de recursos hídricos, teniendo en cuenta las características técnicas de las centrales hidroeléctricas y los beneficios de producción de la energía hidroeléctrica.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Luis Fernández Beites.- Secretario: María Ángeles Moreno López de Saa.- Vocal: Juan Ignacio Pérez Día

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

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

acceptedVersio

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