This paper presents the implementation of stochastic dual dynamic programming (SDDP) to generate water-value functions for operations planning of the British Columbia (BC) Hydro system. An inflow model is developed to...
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This paper presents the implementation of stochastic dual dynamic programming (SDDP) to generate water-value functions for operations planning of the British Columbia (BC) Hydro system. An inflow model is developed to generate stochastic seasonal volumes forecasts and monthly inflows for the Peace River and upper Columbia River in Canada. A model is developed to model storage spaces for flood control and storage account operations to comply with the Columbia River Treaty (CRT) and subsequent agreements between Canada and the United States. Two novel algorithms are developed. The first uses SDDP and an updating process to generate the end of planning horizon water-value function, and the second uses SDDP to generate monthly water-value functions with a new stopping criterion for SDDP. Analysis results of the water-value functions illustrate the importance of globally optimizing reservoirs and storage accounts and modeling stochastic inflows. Results of simulation studies show significant benefits of using water-value functions over currently used methods and the need for modeling inflow uncertainties and storage account operations. (c) 2017 American Society of Civil Engineers.
In this paper, a novel modeling framework is proposed, the multi-horizon modeling approach. This approach allows a very detailed and transparent modeling of many problems in hydro power planning by simultaneously bein...
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In this paper, a novel modeling framework is proposed, the multi-horizon modeling approach. This approach allows a very detailed and transparent modeling of many problems in hydro power planning by simultaneously being computationally very efficient. The model is applied to a complex pumped storage hydro power plant in a liberalized market environment in order to give decision support for the self-scheduling of it. The modeling framework is compared to three alternative state-of-the-art modeling approaches. The results suggest that multi-horizon models are especially valuable for the modeling of hydro power plants with different types of reservoirs.
A plug-in electric vehicle (PEV) can be used for load shifting household demand using an optimal control strategy to minimize the overall cost of the owner. The PEV can provide initial charge, final desired charge and...
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ISBN:
(纸本)9781538626993
A plug-in electric vehicle (PEV) can be used for load shifting household demand using an optimal control strategy to minimize the overall cost of the owner. The PEV can provide initial charge, final desired charge and charging time data to the charging station when plugged in, and the information can be used in a decision-making model to charge/discharge the PEV storage unit in a cost effective manner. In this paper, a multi-stage stochastic optimization model is presented to improve on this decision-making process under demand uncertainty. In this framework, a coordinated control between PEV storage and household load is presented. The stochastic dual dynamic programming (SDDP) algorithm is applied to define the optimal charging/discharging profile for minimizing the household daily operation costs.
Among the many sources of uncertainty in mining are production incidents: these can be strikes, environmental issues, accidents, or any kind of event that disrupts production. In this work, we present a strategic mine...
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Among the many sources of uncertainty in mining are production incidents: these can be strikes, environmental issues, accidents, or any kind of event that disrupts production. In this work, we present a strategic mine planning model that takes into account these types of incidents, as well as random prices. When confronted by production difficulties, mines which have contracts to supply customers have a range of flexibility options including buying on the spot market, or taking material from a stockpile if they have one. Earlier work on this subject was limited in that the optimization could only be carried out for a few stages (up to 5 years) and in that it only analyzed the risk-neutral case. By using decomposition schemes, we are now able to solve large-scale versions of the model efficiently, with a horizon of up to 15 years. We consider decision trees with up to 615 scenarios and implement risk aversion using Conditional Value-at-Risk, thereby detecting its effect on the optimal policy. The results provide a "roadmap" for mine management as to optimal decisions, taking future possibilities into account. We present extensive numerical results using the new *** library, written in the Julia language, and discuss policy implications of our findings.
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 compa...
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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.
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