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A hybrid partial and general equilibrium modeling approach to assess the hydro-economic impacts of large dams - The case of the Grand Ethiopian Renaissance Dam in the Eastern Nile River basin

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ENVIRONMENTAL MODELLING & SOFTWARE 2019年 117卷 76-88页

作者： Kahsay, Tewodros Negash Arjoon, Diane Kuik, Onno Brouwer, Roy Tilmant, Amaury van der Zaag, Pieter Addis Ababa Univ Dept Econ Addis Ababa Ethiopia Vrije Univ Amsterdam Inst Environm Studies Amsterdam Netherlands Univ Laval Dept Civil & Water Engn Quebec City PQ Canada Univ Waterloo Dept Econ Waterloo ON Canada Univ Waterloo Water Inst Waterloo ON Canada IHE Delft Inst Water Educ Delft Netherlands Delft Univ Technol Dept Water Management Delft Netherlands

A novel integrated hydro-economic modeling framework that links a bottom-up partial equilibrium (engineering) model with a top-down (economic) general equilibrium model is developed for assessing the regional economic impacts of water resources management and infrastructure development decisions in a transboundary river basin. The engineering model is employed first to solve the water allocation problem for a river system in a partial equilibrium setting. The resulting system-wide changes in optimal water allocation are subsequently fed into the general equilibrium model to provide an economy-wide perspective. This integrated hydro-economic modeling framework is illustrated using the Eastern Nile River basin as a case study. The engineering-based stochastic dual dynamic programming (SDDP) model of the Eastern Nile basin is coupled with the computable general equilibrium (CGE) model GTAP-W to assess the economy-wide impacts of the Grand Ethiopian Renaissance Dam (GERD) on the Eastern Nile economies.

关键词： Computable general equilibrium modeling stochastic dual dynamic programming Grand Ethiopian Renaissance dam Eastern Nile countries

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Energy Cost Distortion Due to Ignoring Natural Gas Network Limitations in the Scheduling of Hydrothermal Power Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2016年第5期31卷 3785-3793页

作者： Toledo, Felipe Sauma, Enzo Jerardino, Samuel Pontificia Univ Catolica Chile Dept Ind & Syst Engn Santiago Chile KAS Ingn Santiago Chile

The use of natural gas as fuel for electricity generation in thermal plants has played a key role in the development of power systems. The development of gas pipelines and other related infrastructure imposes availability constraints, which are often ignored when determining the optimal dispatch of the electric power system. This research proposes the use of an integrated optimization model, based on a stochastic dual dynamic programming methodology, to simulate both the scheduling of a hydrothermal power system and the transportation and availability of natural gas. We illustrate the applicability of the proposed model through the implementation of different case studies in the context of the main Chilean power network. We conclude that it is essential to consider the limitations that occur in natural gas power plant's dispatch due to the fuel availability constraints, because they induce the use of power plants with higher variable costs, directly impacting the total operational costs and marginal costs.

关键词： Hydrothermal power generation marginal costs natural gas network stochastic dual dynamic programming

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Constant Depth Decision Rules for multistage optimization under uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期295卷 223-232页

作者： Guigues, Vincent Juditsky, Anatoli Nemirovski, Arkadi FGV EMAp Sch Appl Math BR-22 25090 Rio De Janeiro Brazil Univ Grenoble Alpes LJK Domaine Univ700 Ave Cent F-38041 St Martin Dheres France Georgia Inst Technol Atlanta GA 30332 USA

In this paper, we introduce a new class of decision rules, referred to as Constant Depth Decision Rules (CDDRs), for multistage optimization under linear constraints with uncertainty-affected right-hand sides. We consider two uncertainty classes: discrete uncertainties which can take at each stage at most a fixed number d of different values, and polytopic uncertainties which, at each stage, are elements of a convex hull of at most d points. Given the depth mu of the decision rule, the decision at stage t is expressed as the sum of t functions of mu consecutive values of the underlying uncertain parameters. These functions are arbitrary in the case of discrete uncertainties and are poly-affine in the case of polytopic uncertain-ties. For these uncertainty classes, we show that when the uncertain right-hand sides of the constraints of the multistage problem are of the same additive structure as the decision rules, these constraints can be reformulated as a system of linear inequality constraints where the numbers of variables and con-straints is O(1)(n +m)d(mu)N(2) with n the maximal dimension of control variables, m the maximal number of inequality constraints at each stage, and N the number of stages. As an illustration, we discuss an application of the proposed approach to a Multistage stochastic Pro-gram arising in the problem of hydro-thermal production planning with interstage dependent inflows. For problems with a small number of stages, we present the results of a numerical study in which optimal CDDRs show similar performance, in terms of optimization objective, to that of stochastic dual dynamic programming (SDDP) policies, often at much smaller computational cost. (C) 2021 Elsevier B.V. All rights reserved.

关键词： stochastic programming Robust optimization Decision rules stochastic dual dynamic programming

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Joint optimization of regional water-power systems

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ADVANCES IN WATER RESOURCES 2016年 92卷 200-207页

作者： Pereira-Cardenal, Silvio J. Mo, Birger Gjelsvik, Anders Riegels, Niels D. Arnbjerg-Nielsen, Karsten Bauer-Gottwein, Peter Tech Univ Denmark Dept Environm Engn Miljoevej B113 DK-2800 Lyngby Denmark SINTEF Energy Res Trondheim Norway DHI Horsholm Denmark

Energy and water resources systems are tightly coupled;energy is needed to deliver water and water is needed to extract or produce energy. Growing pressure on these resources has raised concerns about their long-term management and highlights the need to develop integrated solutions. A method for joint optimization of water and electric power systems was developed in order to identify methodologies to assess the broader interactions between water and energy systems. The proposed method is to include water users and power producers into an economic optimization problem that minimizes the cost of power production and maximizes the benefits of water allocation, subject to constraints from the power and hydrological systems. The method was tested on the Iberian Peninsula using simplified models of the seven major river basins and the power market. The optimization problem was successfully solved using stochastic dual dynamic programming. The results showed that current water allocation to hydropower producers in basins with high irrigation productivity, and to irrigation users in basins with high hydropower productivity was sub-optimal. Optimal allocation was achieved by managing reservoirs in very distinct ways, according to the local inflow, storage capacity, hydropower productivity, and irrigation demand and productivity. This highlights the importance of appropriately representing the water users' spatial distribution and marginal benefits and costs when allocating water resources optimally. The method can handle further spatial disaggregation and can be extended to include other aspects of the water-energy nexus. (C) 2016 Published by Elsevier Ltd.

关键词： Hydropower Irrigation Water-energy models stochastic dual dynamic programming Water-energy nexus

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Partially observable multistage stochastic programming

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OPERATIONS RESEARCH LETTERS 2020年第4期48卷 505-512页

作者： Dowson, Oscar Morton, David P. Pagnoncelli, Bernardo K. Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Univ Adolfo Ibanez Sch Business Santiago Chile

We propose a class of partially observable multistage stochastic programs and describe an algorithm for solving this class of problems. We provide a Bayesian update of a belief-state vector, extend the stochastic programming formulation to incorporate the belief state, and characterize saddle-function properties of the corresponding cost-to-go function. Our algorithm is a derivative of the stochastic dual dynamic programming method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Bayesian Multistage Partially observable stochastic dual dynamic programming stochastic programming

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The economic value of coordination in large-scale multireservoir systems: The Parana River case

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WATER RESOURCES RESEARCH 2013年第11期49卷 7546-7557页

作者： Marques, Guilherme Fernandes Tilmant, A. Ctr Fed Educ Tecnol Minas Gerais Dept Civil Engn BR-30510000 Belo Horizonte MG Brazil Univ Laval Dept Civil & Water Engn Quebec City PQ Canada

The coordination of reservoir operation is critical for water systems' efficiency. Improved coordination requires sharing information, demanding a clear understanding of the potential gains and its distribution among the users to motivate engagement in coordinated operations and bearing of transaction costs. In a multiuser, multireservoir system, the evaluation of the potential coordination gains is not trivial because it requires the simultaneous evaluation of numerous trade offs. This paper presents a methodology to identify the likely upper and lower bounds in multireservoir system benefits, providing a reference framework for analyzing the economic value of coordination. The methodology is applied to a large-scale multireservoir system in Brazil. The methods rely on the comparison between two management scenarios. The first one mimics typical system operation based on individually designed rule curves, which are likely to perform on the lower bound. This is compared with fullscale system-wide optimization through an stochastic dual dynamic programming algorithm to represent fully coordinated reservoir operation (upper bound). For our case study, results indicate that better coordination reduced spills and improved releases timing according to reservoirs characteristics and location, allowing overall gains between 3% and 8% in energy and 7.9% in revenues, with revenues mostly improved by coordination in dry years. Larger reservoirs presented the highest gains in absolute terms, while the smaller ones presented the highest relative increases. By indicating individual gains at each reservoir, valuable information is produced to support future negotiations and benefit sharing among different agents, being water agencies or power companies.

关键词： reservoir operation hydropower optimization stochastic dynamic programming water systems coordination stochastic dual dynamic programming

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Inflow Aggregation and Run-of-the-River Inflow Energy for Reducing Dimensionality in the Long-Term Generation Scheduling Problem

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IEEE ACCESS 2021年 9卷 98542-98560页

作者： Minetto Fredo, Guilherme Luiz Finardi, Erlon Cristian Larroyd, Paulo Vitor Picarelli, Lucas Borges Univ Fed Santa Catarina Dept Elect & Elect BR-88040900 Florianopolis SC Brazil INESC P&D Brasil BR-11055300 Santos SP Brazil Norus BR-88036003 Florianopolis SC Brazil Norte Energia SA BR-70390025 Brasilia DF Brazil

The long-term generation scheduling (LTGS) problem aims to build an operating policy over a multi-year planning horizon, correlating thermal generation and deficit costs with water storage. The LTGS is modeled as a linear multistage stochastic program, whose state-of-art solution is the stochastic dual dynamic programming (SDDP). One critical challenge is to represent the time-dependency of river inflows accurately. Despite recent advances, modeling simplifications are needed to allow the LTGS computational tractability via SDDP since it is necessary to increase the state space to include the time-dependent variables properly. Thus, most works simplify the hydro production function (HPF) to represent the inflows in detail to overcome this negative aspect. However, the literature lacks this trade-off, i.e., finding a balance between stochastic inflow modeling and HPF representation. Thus, this paper proposes an alternative approach to analyzing this trade-off, using the inflow aggregation and run-of-the-river energy inflow instead of individual inflow in SDDP. The proposed approach drastically reduces the number of state variables in SDDP, allowing a detailed HPF representation in the LTGS. We employ a one-sided confidence interval for expected cost estimation to show that our approach provides better performance than the individualized inflows. The analysis is performed in several large-scale computational instances using a power system with 53 geographically widespread hydro plants. The numerical results demonstrate that inflow aggregation provides, on average, a 4 % reduction on the operating cost, whereas for the run-of-the-river inflow energy, the reduction is, on average, 2 %.

关键词： Computational modeling stochastic processes Reservoirs Planning Hydroelectric power generation dynamic programming Analytical models Inflow modeling hydro production function the long-term generation scheduling problem stochastic dual dynamic programming run-of-the-river inflow energy

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The generation of synthetic inflows via bootstrap to increase the energy efficiency of long-term hydrothermal dispatches

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ELECTRIC POWER SYSTEMS RESEARCH 2015年第Jul.期124卷 33-46页

作者： de Castro, Cristina M. B. Marcato, Andre L. M. Souza, Reinaldo Castro Silva Junior, Ivo Chaves Cyrino Oliveira, Fernando Luiz Pulinho, Tales Univ Fed Juiz de Fora Fac Engn Juiz de Fora MG Brazil Pontificia Univ Catolica Rio de Janeiro Dept Elect Engn Rio De Janeiro RJ Brazil Pontificia Univ Catolica Rio de Janeiro Dept Ind Engn Rio De Janeiro RJ Brazil

One of the most important techniques used to study long-term energy operation planning is the stochastic dual dynamic programme (SDDP). In large systems, hydraulic power plants are aggregated in so-called equivalent energy systems, where the inflows into hydro reservoirs are represented by the affluent natural energy CANE) and the stored volumes are represented by the stored energy. The stochasticity of energy inflows is captured by the historical series ANE. Currently, ANEs are studied using the Box-Jenkins methodology to fit periodic autoregressive models (PAR(p)) and their order (p). A three-parameter lognormal distribution is applied to the residuals generated via PAR(p) modelling to generate synthetic hydrological series similar to the original historical series. However, the log-normal transformation incorporates non-linearities that affect the convergence in SDDP. This study incorporates the bootstrap statistical technique to determine the order p of the PAR(p) model to generate synthetic scenarios that will serve as a basis for SDDP application. The results indicate the adherence of the proposed method on the operational planning of hydrothermal systems. The proposed methodology in this article could successfully be applied in hydro-dominated systems such as Brazilian, Canadian and Nordic systems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Hydrothermal planning stochastic dual dynamic programming Scenario generation Model order PAR(p) Bootstrap

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Utilising demand response for distribution service restoration to achieve grid resiliency against natural disasters

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第14期13卷 2942-2950页

作者： Hafiz, Faeza Chen, Bo Chen, Chen de Queiroz, Anderson Rodrigo Husain, Iqbal North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Argonne Natl Lab Div Energy Syst 9700 S Cass Ave Argonne IL 60439 USA North Carolina Cent Univ Sch Business Dept Decis Sci Durham NC USA North Carolina State Univ Civil Construct & Environm Engn Dept Raleigh NC USA

The increased frequency of power outages due to natural disasters in recent years has highlighted the urgency of enhancing distribution grid resilience. The effective distribution service restoration (DSR) is an important measure for a resilient distribution grid. In this work, the authors demonstrate that DSR can be significantly improved by leveraging the flexibility provided by the inclusion of demand response (DR). The authors propose a framework for this by considering integrated control of household-level flexible appliances to vary the load demand at the distribution-grid level to improve DSR. The overall framework of the proposed system is modelled as a three-step method considering three optimization problems to (i) calculate feasible controllable aggregated load range for each bus, (ii) determine candidate buses to perform DR and their target load demand, and (iii) maintain the load level in each house through home energy management during the DSR, considering uncertainties in load and solar generation sequentially. The optimization problems are formulated as linear programming, mixed-integer linear programming, and multistage stochastic programming (solved using the stochastic dual dynamic programming) models. Case studies performed in the IEEE 123-node test feeder show improvements in resilience in terms of energy restored compared to the restoration process without DR.

关键词： optimisation power grids energy management systems power distribution control power system restoration demand side management domestic appliances demand response grid resiliency power outages distribution grid resilience resilient distribution grid integrated control household-level flexible appliances distribution-grid level optimisation problems aggregated load target load demand mixed-integer linear programming distribution service restoration IEEE 123-node test feeder system stochastic dual dynamic programming multistage stochastic programming solar generation home energy management

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Assessing solution quality and computational performance in the long-term generation scheduling problem considering different hydro production function approaches

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RENEWABLE ENERGY 2019年 131卷 45-54页

作者： Minetto Fredo, Guilherme Luiz Finardi, Erlon Cristian de Matos, Vitor Luiz Fed Univ Santa Catarina UFSC Elect Syst Planning Res Lab LabPlan Florianopolis SC Brazil Plan4 Engn Lauro Linhares 2055 Room 201 BR-88036003 Florianopolis SC Brazil

The long-term generation scheduling (LTGS) problem aims at finding a generation policy that minimizes an objective function over a multi-year planning horizon. A crucial aspect of this problem is the Hydropower Production Function (HPF), which relates power with head, turbined outflow, and efficiency of the generating units. Given that the LTGS is a large-scale stochastic optimization problem, the HPF is modeled in a simplified manner. However, considering the high-performance computers currently available and the recent advances in stochastic optimization algorithms, it is possible to enhance the HPF modeling to use the energy resources more efficiently. This paper proposes a piecewise linear model of HPF that considers the plant generation as a function of the volume and the total outflow. Unlike previous works, the HPF also considers the (nonlinear) efficiency function of each generating unit. The paper also presents a comparison between the proposed HPF and a one-dimensional HPF known as constant productivity. The generation policy and the computational burden are analyzed using an optimization simulation process based on stochastic dual dynamic programming algorithm. The computational tests use data of a large-scale electrical power system, which corresponds to about 90% of the Brazilian system. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Hydropower production function Long-term generation scheduling problem Piecewise linear model Constant productivity model stochastic dual dynamic programming

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