stochastic dual dynamic programming (SDDP) is one of the few methods available to solve multipurpose-multireservoir operation problems in a stochastic environment. This algorithm requires that the one-stage optimizati...
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stochastic dual dynamic programming (SDDP) is one of the few methods available to solve multipurpose-multireservoir operation problems in a stochastic environment. This algorithm requires that the one-stage optimization problem be a convex program so that the efficient Benders decomposition scheme can be implemented to handle the large state-space that characterizes multireservoir operation problems. When working with hydropower systems, one usually assumes that the production of hydroelectricity is dominated by the release term and not by the head (storage) term to circumvent the nonlinearity of the hydropower production function. Although this approximation is satisfactory for high head power stations for which the difference between the maximum and the minimum head is small compared to the maximum head, it may no longer be acceptable when a significant portion of the energy originates from low and/or medium head power plants. Recent developments improve the representation of the nonlinear hydropower function through a convex hull approximation of the true hydropower function. A network of hydropower plants and irrigated areas in the Nile Basin is used to illustrate the difference between the two SDDP formulations on the energy generation and the allocation decisions. DOI: 10.1061/(ASCE)WR.1943-5452.0000117. (C) 2011 American Society of Civil Engineers.
Generation and transmission investment planning in deregulated markets faces new challengesparticularly as deregulation has introduced more uncertainty to the planning problem. Tradi-tional planning techniques and pro...
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Generation and transmission investment planning in deregulated markets faces new challengesparticularly as deregulation has introduced more uncertainty to the planning problem. Tradi-tional planning techniques and processes cannot be applied to the deregulated planning problemas generation investments are profit driven and competitive. Transmission investments mustfacilitate generation access rather than servicing generation choices. The new investment plan-ning environment requires the development of new planning techniques and processes that canremain flexible as uncertainty within the system is *** optimisation technique of stochastic dual dynamic programming (SDDP) has been success-fully used to optimise continuous stochasticdynamic planning problems such as hydrothermalscheduling. SDDP is extended in this thesis to optimise the stochastic, dynamic, mixed integerpower system investment planning problem. The extensions to SDDP allow for optimisation oflarge integer variables that represent generation and transmission investment options while stillutilising the computational benefits of SDDP. The thesis also details the development of a math-ematical representation of a general power system investment planning problem and applies it toa case study involving investment in New Zealand’s HVDC link. The HVDC link optimisationproblem is successfully solved using the extended SDDP algorithm and the output data of theoptimisation can be used to better understand risk associated with capital investment in *** extended SDDP algorithm offers a new planning and optimisation technique for deregulatedpower systems that provides a flexible optimal solution and informs the planner about investmentrisk associated with uncertainty in the power system
This paper describes a risk management tool for hydropower generators and its application to Norway's second-largest generation company and largest electricity consumer, Norsk Hydro ASA. The tool considers both op...
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This paper describes a risk management tool for hydropower generators and its application to Norway's second-largest generation company and largest electricity consumer, Norsk Hydro ASA. The tool considers both operations scheduling and the utilization of financial contracts for risk management. Financial risks are accounted for by penalizing incomes below a reference income. The risk management problem is solved by a combination of stochastic dual dynamic programming and stochasticdynamicprogramming. Simulations demonstrate that lower income scenarios improve when risk aversion is introduced. Copyright (C) 2005 John Wiley & Sons, Ltd.
This paper describes an integrated framework to evaluate short-run marginal costs (SRMC) in hydrothermal systems, taking into account the chronological aspects of reservoir operation, transmission constraints, equipme...
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This paper describes an integrated framework to evaluate short-run marginal costs (SRMC) in hydrothermal systems, taking into account the chronological aspects of reservoir operation, transmission constraints, equipment failures, hydrological variation and load uncertainty. The resulting SRMC values are used to calculate circuit revenues, which are then compared with investment requirements. It is shown that the representation of these probabilistic factors substantially increases revenues, in contrast with the widely reported under-recovery found in studies which only represent normal operating conditions. Case studies with the Brazilian North-Northeastern system are presented and discussed. (C) 1997 Published by Elsevier Science S.A.
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