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IEEE PES General Meeting

作者： Soares, Murilo Pereira Street, Alexandre Valladao, Davi Michel Pontificia Univ Catolica Rio de Janeiro Dept Elect Engn BR-22451900 Rio De Janeiro RJ Brazil

ISBN: (纸本)9781479964154

In the Brazilian energy operation planning, stochastic dual dynamic programming (SDDP) determines hydrothermal planning decisions based on auto-regressive (AR) models for associated risk factors. In this work we show that using AR models to generate scenarios leads to an undesirable drawback on SDDP: the variability of the solutions increases with respect to changes in the AR initial conditions. We propose a modified version of the risk averse SDDP algorithm aimed at reducing decisions and marginal costs variability induced by the use of AR models. We show that it is possible to obtain results with less variability and with the same characteristics of the ones obtained by traditional approach. Moreover, we argue that the proposed approach is more flexible since it is not restricted to linear models as in the original SDDP algorithm.

关键词： Hydrothermal operation planning OR in energy stochastic programming stochastic dual dynamic programming Risk averse

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Optimal microgrid operation considering battery degradation using stochastic dual dynamic programming 2

Optimal microgrid operation considering battery degradation ...

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2nd International Conference on Smart Energy Systems and Technologies (SEST)

作者： Aaslid, Per Belsnes, Michael M. Fosso, Olav B. SINTEF Energy Res Dept Energy Syst Trondheim Norway Norwegian Univ Sci & Technol Dept Elect Power Engn Trondheim Norway

ISBN: (纸本)9781728111568

Intermittent energy sources demand temporal storages to balance generation and load, and batteries stand out as an alternative. However, the lifetime is limited, and cycling depth affects the battery degradation rate. Current stochastic multi-stage methods lack proper representation of battery degradation. This paper proposes a stochastic multi-stage model for optimizing battery operation in a microgrid considering battery degradation with a piece-wise linear cost function with uncertain wind power production and load. The model is solved using stochastic dual dynamic programming (SDDP) and is demonstrated on a 4-bus test case with limited import and export capacity to illustrate the battery degradation cost's impacts on the battery cycling strategy. The results show that the importance of a stochastic method is more pronounced when battery degradation is modelled.

关键词： stochastic dual dynamic programming battery degradation microgrid arbitrage

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Twenty Years of Application of stochastic dual dynamic programming in Official and Agent Studies in Brazil - Main Features and Improvements on the NEWAVE Model 20

Twenty Years of Application of Stochastic Dual Dynamic Progr...

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Power Systems Computation Conference (PSCC)

作者： Maceira, M. E. P. Penna, D. D. J. Diniz, A. L. Pinto, R. J. Melo, A. C. G. Vasconcellos, C. V. Cruz, C. B. CEPEL Elect Energy Res Ctr Rio De Janeiro Brazil UERJ State Univ Rio de Janeiro Rio De Janeiro Brazil

ISBN: (纸本)9781910963104

The objective of NEWAVE model, based on stochastic dual dynamic programming algorithm, is to solve the long term operation planning problem of large scale interconnected systems, such as the Brazilian one. The year of 1998 is the milestone for the first official use of the NEWAVE model in Brazil, whose development has started in 1993 by the Brazilian Electric Energy Research Center (CEPEL). This paper outlines the key features and recent improvements that allowed keeping the NEWAVE model in the state-of-the art and its broad range of practical applications in official and agent studies and decision making in the Brazilian electrical sector along these twenty years.

关键词： Operation and expansion planning stochastic optimization stochastic dual dynamic programming risk aversion parallel computing hydrothermal systems

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Single cut and multicut stochastic dual dynamic programming with cut selection for multistage stochastic linear programs: convergence proof and numerical experiments

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COMPUTATIONAL MANAGEMENT SCIENCE 2021年第2期18卷 125-148页

作者： Bandarra, Michelle Guigues, Vincent FGV Sch Appl Math Praia De Botafogo RJ Brazil

We introduce a variant of Multicut Decomposition Algorithms, called CuSMuDA (Cut Selection for Multicut Decomposition Algorithms), for solving multistage stochastic linear programs that incorporates a class of cut selection strategies to choose the most relevant cuts of the approximate recourse functions. This class contains Level 1 (Philpott et al. in J Comput Appl Math 290:196-208, 2015) and Limited Memory Level 1 (Guigues in Eur J Oper Res 258:47-57, 2017) cut selection strategies, initially introduced for respectively stochastic dual dynamic programming and dual dynamic programming. We prove the almost sure convergence of the method in a finite number of iterations and obtain as a by-product the almost sure convergence in a finite number of iterations of stochastic dual dynamic programming combined with our class of cut selection strategies. We compare the performance of Multicut Decomposition Algorithms, stochastic dual dynamic programming, and their variants with cut selection (using Level 1 and Limited Memory Level 1) on several instances of a portfolio problem. On these experiments, in general, stochastic dual dynamic programming is quicker (i.e., satisfies the stopping criterion quicker) than Multicut Decomposition Algorithms and cut selection allows us to decrease the computational bulk with Limited Memory Level 1 being more efficient (sometimes much more) than Level 1.

关键词： stochastic programming stochastic dual dynamic programming Multicut decomposition algorithm Portfolio selection

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On the Decision-Hazard Approach for the stochastic dual dynamic programming Applied to Hydrothermal Operation Planning

On the Decision-Hazard Approach for the Stochastic Dual Dyna...

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作者： Andre Lawson Pedral Sampaio Pontificia Universidade Catolica do Rio de Janeiro

学位级别：硕士

stochastic dual dynamic programming (SDDP) is currently one of the most employed methods for hydrothermal planning. All previous works on this subject are based on a hazard-decision approach, whereas reality is more closely related to a decision-hazard process. This dissonance between planning and implementation is a source of time-inconsistency, as future planned decisions under the same conditions may not be put into prac- tice. If on the one hand the hazard-decision modeling framework allows a scenario-decomposable efficient solution methodology, on the other hand the decision-hazard structure provides a more robust (pessimistic) solution as it does not rely on anticipativity assumptions. In this work, we measure the inconsistency-gap related to the current methodology and propose an alternative approach for hydrothermal planning that utilizes an information- revelation structure and decision process based on a decision-hazard frame- work, thereby approximating the planning model to realistic operational actions. Instead of relying on non-anticipativity constraints, which would prevent the scenario decomposition of each two-stage stochastic subprob- lem, the proposed methodology considers first-stage decisions as state vari- ables to be optimized through the SDDP procedure. In this framework, the complexity and time required to find a solution is considerably reduced yet ensuring the decision-hazard decision structure and non-anticipativity of the first-stage decisions. Results based on the Brazilian power system indicate that this inconsistency may considerably increase generation of more expensive thermal units, leading to spikes in energy market spot prices and an increase in overall operational costs. Therefore, the proposed decision-hazard approach and augmented-state solution methodology con- stitute timely and relevant contributions to both industry practices and state of the art literature on the subject of hydrothermal operation plan- ning under uncertai

关键词： stochastic dual dynamic programming Time inconsistency Hydrothermal Operation Planning Hazard-decision Decision-hazard

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Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using stochastic dual dynamic programming (SDDP): A Case Study in Mexico

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ENERGIES 2023年第17期16卷 6288页

作者： Marin Cruz, Ignacio Badaoui, Mohamed Mota Palomino, Ricardo Inst Politecn Nacl Escuela Super Ingn Mecan & Elect Ave Luis Enrique Erro S-N Mexico City 07738 DF Mexico

This article aims to obtain and evaluate medium-term operating policies for the hydrothermal scheduling problem by using the stochastic dual dynamic programming (SDDP) approach. To this end, to feed the mathematical model and build the probability distribution functions that best fit each month of the actual inflow volume, monthly inflow data recorded from 1938 to 2018 for the Infiernillo reservoir located in Mexico were employed. Moreover, we simulated inflow volume scenarios using the Monte Carlo method for each month of a one-year planning period. The SDDP approach to solving the optimization problem consisted of the simulation of one forward scenario per iteration and the stabilization of the total operating cost as a convergence criterion, which results in an operating policy. We then assessed its quality by estimating the one-sided optimality gap. It is worth mentioning that the best operation policy required scenario trees of up to 17,000 inflow realizations per stage. Additionally, to study the evolution of the expected value along the planning horizon of the main variables involved in the medium-term hydrothermal scheduling problem, we simulated the best operation policy over 10,000 inflow scenarios. Finally, to show the practical value of the proposed approach, we report its computational complexity.

关键词： stochastic programming medium-term hydrothermal scheduling stochastic dual dynamic programming Infiernillo reservoir

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Optimal and Centralized Reservoir Management for Drought and Flood Protection on the Upper Seine-Aube River System Using stochastic dual dynamic programming

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2019年第3期145卷 1-12页

作者： Raso, L. Chiavico, M. Dorchies, D. Delft Univ Technol Dept Multiactor Syst Jaffalaan 5 NL-2628 BX Delft Netherlands Eni Piazza Ezio Vanoni 1 I-20097 San Donato Milanese MI Italy UMR G EAU Irstea 361 Rue Jean Francois BretonBP 5095 F-34196 Montpellier 5 France

The basin of the Seine River is an extremely important economic region for France and Europe. Four reservoirs are operated to reduce the natural variability of the Seine River, reducing both flood and drought risk. Presently, reservoir operation is not centrally coordinated, and release rules are based on empirical rule curves. This study presents the setting of an optimal and centralized solution to the problem of reservoir operation on the Upper Seine-Aube river system, found by applying the stochastic dual dynamic programming (SDDP) procedure. The novelty of this study lies on the combination of reservoir and hydraulic models in SDDP for flood and drought protection. Including the hydraulic process in SDDP is required for estimating flood and drought at different locations along the river, and for representing the delay between the release from the reservoirs and their effects downstream. The study case covers the Seine basin until the confluence with the Aube River: this system includes two reservoirs, the city of Troyes, France, and, at the confluence of the two rivers, the nuclear power plant at Nogent-Sur-Seine. Results shows that the SDDP solution can be effectively used to optimize the operation of a water system made of multiple reservoirs and multiple hydraulic transfer components, solving a relatively large stochastic dynamic programming problem in an acceptable time. The management obtained from SDDP rules exploits the centralized operation and, compared to the current operational rules, results in more frequent but shorter, less intense, and less severe flood and drought events at Nogent-Sur-Seine.

关键词： Reservoir operation Multireservoir systems Seine river stochastic dual dynamic programming Flood and drought risk

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DISTRIBUTIONALLY ROBUST stochastic dual dynamic programming

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SIAM JOURNAL ON OPTIMIZATION 2020年第4期30卷 2841-2865页

作者： Duque, Daniel Morton, David P. Northwestern Univ Ind Engn & Management Sci Evanston IL 60208 USA

We consider a multistage stochastic linear program that lends itself to solution by stochastic dual dynamic programming (SDDP). In this context, we consider a distributionally robust variant of the model with a finite number of realizations at each stage. Distributional robustness is with respect to the probability mass function governing these realizations. We describe a computationally tractable variant of SDDP to handle this model using the Wasserstein distance to characterize distributional uncertainty.

关键词： distributionally robust optimization multistage stochastic programming stochastic dual dynamic programming

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Spatiotemporal decomposed dispatch of integrated electricity-gas system via stochastic dual dynamic programming-based value function approximation

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ENERGY 2023年第1期282卷

作者： Zhu, Jianquan Liu, Haixin Guo, Ye Chen, Jiajun Zhuo, Yelin Wang, Zeshuang South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China

This paper proposes a novel stochastic dual dynamic programming-based value function approximation approach for the spatiotemporal decomposed dispatch of integrated electricity-gas systems with uncertainties. stochastic dual dynamic programming is employed to decompose the optimal dispatch problem into several subproblems in both spatial and temporal dimensions. Then, Benders cuts are used in the real-time dispatch process to describe the interaction among these subproblems, according to which each subsystem can make decentralized and real-time decisions. In this way, both the information privacy and decision independence of each subsystem can be guaranteed, while the future variability of wind power and loads can be firmed. More-over, the historical information can be used to obtain the Benders cuts offline, which helps to omit the time-consuming iterations in the real-time decision stage, while the near-optimal solutions can still be obtained. The effectiveness of the proposed approach is verified by case studies on a 4-bus-4-node system and a 118-bus-20-node system. Numerical results demonstrate that the proposed method reduces the average error of the operation cost by 1 order of magnitude compared with the traditional real-time methods. Moreover, the computational time is significantly reduced by up to 4 orders of magnitude compared with the traditional decentralized algorithms.

关键词： Integrated electricity and natural gas systems Spatiotemporal decomposition Real-time optimization stochastic dual dynamic programming Uncertainty

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Asynchronous parallel stochastic dual dynamic programming applied to hydrothermal generation planning

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 191卷 106907-106907页

作者： Machado, Felipe D. R. Diniz, Andre Luiz Borges, Carmen L. T. Brandao, Lilian C. Elect Energy Res Ctr Cepel Rio De Janeiro Brazil Fed Univ Rio de Janeiro COPPE UFRJ POB 68504 BR-21941972 Rio De Janeiro RJ Brazil

Optimizing power generation planning is very important to achieve the lowest possible costs while meeting the required power demand along time. High computational resources are required to solve this problem and, in some cases, the use of parallel schemes is imperative. A widely used method to solve long-term energy planning problems is an extension of dual dynamic programming (DDP) called stochastic dual dynamic programming (SDDP) which makes use of sampling techniques to be able to deal with high-dimensional state-spaces. In this work, we propose an asynchronous SDDP parallel scheme (labeled ASDDP) capable of overcoming the intrinsic synchronism of traditional versions of the SDDP method, by dividing subproblems by time steps instead of forward samples. This parallelization strategy, which includes a variant called totally asynchronous ASDDP (TASDDP), allows us to better exploit the parallel resources and decreases the overall CPU time to solve the problem. Consistency and performance tests were applied to evaluate the application of the proposed ASDDP and TASDDP algorithms in the real Brazilian system.

关键词： Decomposition Hydrothermal coordination Parallel computing stochastic dual dynamic programming

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