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Water resources management: a bibliometric analysis and future research directions

ANNALS OF OPERATIONS RESEARCH 2024年 1-56页

作者： Favereau, Marcel Babonneau, Frederic Lorca, Alvaro Univ Andres Bello Dept Engn Sci Antonio Varas 880 Santiago 7500971 Chile Pontificia Univ Catolica Chile Dept Ind & Syst Engn Ave Vicuna Mackenna 4860 Santiago 7820436 Chile KEDGE Business Sch Dept Operat Management Supply Chain Informat Syst Cr Liberat 680 F-33405 Bordeaux France ORDECSYS 4 Pl EtrierChene Bougeries CH-1224 Geneva Switzerland Pontificia Univ Catolica Chile Dept Elect Engn Ave Vicuna Mackenna 4860 Santiago 7820436 Chile

The stochastic dual dynamic programming (SDDP) algorithm introduced by Pereira and Pinto in 1991 has sparked essential research in the context of water resources management, mainly due to its ability to address large-scale multistage stochastic problems. This paper aims to provide a review of 32 years of research since the publication of the SDDP algorithm. A systematic academic literature search identified 174 scientific papers on water resource management published in 96 different journals. A bibliometric analysis is conducted to identify the main methods used to tackle this type of problem and to determine recent and future research trends. Our analysis reveals that stochastic dynamic programming, which was initially the most used approach, has now been replaced by multistage stochastic programming. Risk-averse and robust approaches are also gaining strength in recent years due to uncertainty related to climate change. Water inflows have been the main source of uncertainty considered in the literature by far, followed by, e.g., electricity demand, electricity prices, fuel costs, and renewable energy availability. In addition, as computational capacity continues to increase, aspects of nonlinearities, disaggregated networks, and different water management strategies are increasingly considered to make modeling more realistic. This work suggests there is still a need for tractable stochastic optimization models for large-scale power and water systems that deal with multiple uncertainty sources and nonlinearity approximations.

关键词： Water resources management Hydrothermal scheduling Optimization under uncertainty stochastic dual dynamic programming Bibliometric approach

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Structure of risk-averse multistage stochastic programs

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OR SPECTRUM 2015年第3期37卷 559-582页

作者： Dupacova, Jitka Kozmik, Vaclav Charles Univ Prague Fac Math & Phys Dept Probabil & Math Stat Prague Czech Republic

We deal with risk-averse multistage stochastic programs with coherent risk measures such as multiperiod extensions of conditional value at risk or polyhedral risk measures. Their basic properties are discussed and applied to scenario-based input data. Using the contamination technique we quantify the influence of changes in the structure of the scenario-based approximation to the optimal value of the problem. stochastic dual dynamic programming algorithm is used to provide illustrative numerical comparisons for different choices of risk measures and changes of input data for a simple multistage risk-averse stock allocation problem with scenario trees based on log-normal distribution of the asset returns.

关键词： Multistage stochastic programming Multiperiod risk measures stochastic dual dynamic programming Contamination

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Optimal scheduling of the energy storage system in a hybrid micro-grid considering uncertainties, using the stochastic quasi-gradient method

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IET SMART GRID 2023年第5期6卷 480-491页

作者： Shirvan, Masoud Ghazipour Abardeh, Mohamad Hosseini Hojjat, Mehrdad Islamic Azad Univ Dept Elect & Comp Engn Shahrood Branch Shahrood Iran Islamic Azad Univ Shahrood Branch Daneshgah Blvd Shahrood Iran

Energy storage and renewable sources play a unique role in the future advances of smart grids. In this article, the optimal scheduling of the energy storage system in a hybrid microgrid is presented considering the uncertainties of the renewable generations and the load. The optimisation problem in this article is non-linear and non-convex, therefore conventional optimisation methods such as linear programming (LP) are unable to solve this problem. On the other hand, because of parameters uncertainty, special considerations are required to simulate these parameters. In this regard, a new optimisation algorithm that can solve the non-linearity and non-convexity of the objective function is proposed based on the stochastic Quasi-Gradient optimisation Method (SQGM). Moreover, the uncertainties of the wind, PV generation, and the load are modelled. Different optimisation algorithms: the conventional stochastic dynamic programming (SDP), the stochastic dual dynamic programming (SDDP) and the proposed SQGM are compared. A 9-bus benchmark system with distributed generation units is used to evaluate the optimisation strategies.

关键词： energy storage system load uncertainties optimal scheduling renewable generations uncertainties stochastic dual dynamic programming stochastic dynamic programming stochastic quasi-gradient optimisation method

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A combined SDDP/Benders decomposition approach with a risk-averse surface concept for reservoir operation in long term power generation planning

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ANNALS OF OPERATIONS RESEARCH 2020年第2期292卷 649-681页

作者： Diniz, Andre Luiz Maceira, Maria Elvira P. Vasconcellos, Cesar Luis V. Penna, Debora Dias J. CEPEL Brazilian Elect Energy Res Ctr Rio De Janeiro Brazil Univ Estado Rio De Janeiro UERJ Rio De Janeiro Brazil

Power generation planning in hydrothermal systems is a complex optimization task, specially due to the high uncertainty in the inflows to hydro plants. Since it is impossible to traverse the huge scenario tree of the multistage problem, stochastic dual dynamic programming (SDDP) is the leading technique to solve it, originally from an expected-cost minimization perspective. However, there is a growing need to apply risk-averse/robust formulations to protect the system from critical hydrological scenarios. This is particularly important for predominantly hydro systems, because environmental issues prevent the construction of large reservoirs, thus reducing their water regulating capability. This paper proposes a two-level SDDP/Benders decomposition approach to include a new risk averse surface (RAS) concept for reservoir operation in power generation planning. The upper level problem is a SDDP solving strategy with expected-cost minimization criterion, where recourse functions for each time step are built through forward/backward passes. The second level consists in multi-period deterministic optimization subproblems for each node of the scenario tree, which are solved to ensure a desired level of protection from a set of given critical scenario several months ahead. An inner iterative procedure for each SDDP stage/scenario is applied, where feasibility cuts are included in the upper level subproblems to derive the RAS surface, which are multidimensional rule curves for reservoir operation. Such curves ensure that the policy provided by the SDDP algorithm yields storage levels in the reservoirs that are high enough to protect the system against such critical scenarios. A "max-type" time-linking penalization scheme for violation of RAS constraints is also proposed, which avoids the multiple application of the penalty value for the same violation in consecutive time steps, which may result in large marginal costs. Results are presented for the large-scale Brazilian sys

关键词： OR in energy Power generation planning stochastic dual dynamic programming Benders decomposition Large-scale linear programming Risk aversion

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Long-Term Generation Scheduling: A Tutorial on the Practical Aspects of the Problem Solution

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2022年第3期33卷 806-821页

作者： Pedrini, R. Finardi, E. C. Univ Fed Santa Catarina LabPlan Florianopolis SC Brazil INESC P&D Santos SP Brazil

Managing power production for hydro-dominated electrical energy systems in the long-term horizon is a challenging task that requires a trade-off between costs and electricity shortage risks. Because of the inflow uncertainty, this trade-off can be assessed by an operating policy that determines how much water should be used to generate power at the beginning of each time-step and how much should be stored for the future. The long-term generation scheduling (LTGS) is often solved by system operators using multistage stochastic optimization and stochastic dual dynamic programming (SDDP) approaches. Although the literature related to the LTGS problem is vast, several practical aspects used in SDDP are not demonstrated clearly, which can prevent the interest of young researchers and hinder the advances and new applications usually employed by senior professionals in industry and universities. Thus, this paper provides a tutorial on (but not limited to) (i) the application of SDDP for solving the LTGS problem, including water inflow inter-stage dependence, (ii) assessment of the impacts of hydropower function in the policy and (iii) inclusion of a risk measure to obtain a more reliable operation in unfavorable (dry) inflow scenarios. We present the main ideas behind each aspect's inclusion in the SDDP algorithm, using simple (but comprehensive) numerical examples. The overview is intended to understand these main characteristics, presenting them as powerful tools to investigate the LTGS problem in detail, searching for a good or even (near) optimal operating policy.

关键词： Long-term generation scheduling problem Multistage stochastic programming stochastic dual dynamic programming Hydropower function Risk-averse model

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Assessing policy quality in a multistage stochastic program for long-term hydrothermal scheduling

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ANNALS OF OPERATIONS RESEARCH 2017年第2期253卷 713-731页

作者： de Matos, Vitor L. Morton, David P. Finardi, Erlon C. Plan4 Engn Rua Lauro Linhares2055Sala 707 BR-88036003 Florianopolis SC Brazil Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Univ Fed Santa Catarina Lab Planejamento Sistemas Energia Eletr Campus Univ BR-88040900 Florianopolis SC Brazil

We consider a multistage stochastic linear program in which we aim to assess the quality of an operational policy computed by means of a stochastic dual dynamic programming algorithm. We perform policy assessment by considering two strategies to compute a confidence interval on the optimality gap: (i) using multiple scenario trees and (ii) using a single scenario tree. The first approach has already been considered in several applications, while the second approach has been discussed previously only in a two-stage framework. The second approach is useful in practical applications in order to more quickly assess the quality of a policy. We present these ideas in the context of a multistage stochastic program for Brazilian long-term hydrothermal scheduling, and use numerical instances to compare the confidence intervals on the optimality gap computed via both strategies. We further consider the relative merits of using naive Monte Carlo sampling, randomized quasi Monte Carlo sampling, and Latin hypercube sampling within our framework for assessing the quality of a policy.

关键词： Hydrothermal scheduling stochastic programming stochastic dual dynamic programming

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stochastic model for energy commercialisation of small hydro plants in the Brazilian energy market

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COMPUTATIONAL MANAGEMENT SCIENCE 2015年第1期12卷 111-127页

作者： de Matos, Vitor L. Sierra, Mauro A. G. Finardi, Erlon C. Decker, Brigida U. Milanezi, Andre A. S. Plan4 Engn Rua Lauro Linhares2055 Sala 707 BR-88036003 Florianopolis SC Brazil Univ Fed Santa Catarina LabPlan BR-88040900 Florianopolis SC Brazil CELESC BR-88034900 Florianopolis SC Brazil

This paper presents a stochastic model for energy commercialisation strategies of small hydro plants (SHPs) in the Brazilian electricity market. The model aims to find the maximum expected revenue of the generation company, considering the main energy market regulations in Brazil, such as the penalty for insufficient energy certificates, the seasonality of energy certificates and the stochastic processes of future energy prices and plant generation. The problem is formulated as a multi-stage linear stochastic programming model, where the stochastic variables are the energy future prices, the system hydro generation and the SIIP generation in the portfolio. Because of the large number of time steps in this model, methods with sampling strategies are necessary to identify a good solution. Therefore, we apply the stochastic dual dynamic programming algorithm. A case example is presented to analyse certain results of the model, which considers a generator company with a set of SHPs that can sell energy through contracts with periods of 6-24 months.

关键词： Brazilian energy market Small hydro power plants stochastic programming stochastic dual dynamic programming

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A Partial Nested Decomposition Approach for Remanufacturing Planning Under Uncertainty

A Partial Nested Decomposition Approach for Remanufacturing ...

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IFIP WG 5.7 International Conference on Advances in Production Management Systems (APMS)

作者： Quezada, Franco Gicquel, Celine Kedad-Sidhoum, Safia Sorbonne Univ LIP6 F-75005 Paris France Univ Santiago de Chile USACH Ind Engn Dept LDSPS Santiago Chile Univ Paris Saclay LRI F-91190 Gif Sur Yvette France CEDRIC CNAM F-75003 Paris France

ISBN: (纸本)9783030859022;9783030859015

We seek to optimize the production planning of a threee-chelon remanufacturing system under uncertain input data. We consider a multi-stage stochastic integer programming approach and use scenario trees to represent the uncertain information structure. We introduce a new dynamic programming formulation that relies on a partial nested decomposition of the scenario tree. We then propose a new extension of the recently published stochastic dual dynamic integer programming algorithm based on this partial decomposition. Our numerical results show that the proposed solution approach is able to provide near-optimal solutions for large-size instances with a reasonable computational effort.

关键词： stochastic lot-sizing with remanufacturing Multistage stochastic integer programming stochastic dual dynamic programming

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Optimal Trading of a Fixed Quantity of Power in an Illiquid Continuous Intraday Market

Optimal Trading of a Fixed Quantity of Power in an Illiquid ...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Bertrand, Gilles Papavasiliou, Anthony CORE UCLouvain Louvain La Neuve Belgium

ISBN: (纸本)9781665435970

The recent integration of renewable resources in electricity markets has increased the need for producers to correct their trading position close to real time in order to avoid volatile real-time prices. The last market to close before delivery is the Continuous Intraday Market. Therefore, this market is an interesting outlet for renewable units that aim at covering their forecast errors. As a starting point for tackling this problem, we characterize an optimal policy for trading a fixed quantity in a simplified market model. We use this analytical solution as a basis for developing an Approximate dynamic programming algorithm and an alternative stochastic dual dynamic programming that can trade under a more realistic set of assumptions.

关键词： stochastic optimal control continuous intraday market approximate dynamic programming stochastic dual dynamic programming

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Evaluation of scenario reduction methods for stochastic inflow in hydro scheduling models

Evaluation of scenario reduction methods for stochastic infl...

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PowerTech, 2015 IEEE Eindhoven

作者： Larsen, Camilla Thorrud Doorman, Gerard L. Mo, Birger NTNU Dept Elect Power Engn Trondheim Norway SINTEF Energy Res Ctr Trondheim Norway

ISBN: (纸本)9781479976959

The long-term hydropower scheduling problem is inherently stochastic due to uncertainty in future reservoir inflow. We use stochastic dual dynamic programming (SDDP) to solve this problem. This work evaluate and compare three scenario reduction methods used to construct a multistage scenario tree which represents the underlying stochastic inflow process in the SDDP model. A case study is carried out to numerically assess the performance of the different scenario reduction methods. The performance is measured using out-of-sample simulation, simulating the solution strategies obtain with the various scenario models on an exogenously given set of inflow scenarios. Our results show that the choice of scenario reduction method impacts the solution to a hydropower operation planning problem substantially.

关键词： Hydropower scenario reduction stochastic dual dynamic programming stochastic inflow

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