Approximate Dynamic Programming I: Modeling

Powell, Warren B.

doi:10.1002/9780470400531.eorms0042

Cited by 17 publications

(10 citation statements)

References 32 publications

(23 reference statements)

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“…Note that this approach is different to the one commonly taken in reservoir operation optimization studies, where releases in discrete time periods from the reservoirs are considered as decision variables. In fact, when using stage differences as control variables they are treated as parameters to be optimized, an approach that is sometimes called optimization of myopic policies [25].…”

Section: Initial Model-based Optimization: 1d-sa With Nsga-iimentioning

confidence: 99%

Analysis of Flood Storage Area Operations in Huai River Using 1D and 2D River Simulation Models Coupled with Global Optimization Algorithms

et al. 2019

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This article addresses the issue of flood management using four flood storage areas in the middle section of Huai River in China which protect the important downstream city of Bengbu. The same areas are also used by the local population as residential and agricultural zones. An optimization problem is therefore posed, with two objectives of simultaneously minimizing the downstream flood risk in Bengbu city and the storage areas’ economic damages. The methodology involved development of river flood models using HEC-RAS, with varying complexity, such as 1-dimensional (1D) model with storage areas represented as lumped conceptual reservoirs, and 2-dimensional (2D) models with detailed representation of the terrain, land-use and hydrodynamics in the storage areas. Experiments of coupling these models with global optimization algorithms (NSGA-II, PESA-II and SPEA-II) were performed (using the HEC-RAS Controller), in which the two objective functions were minimized, while using stage differences between the river and the storage areas as decision variables for controlling the opening/closing of the gates at the lateral structures that link the river with the storage areas. The comparative analysis of the results indicate that more refined optimal operational strategies that spread the damages across all storage areas can be obtained only with the detailed flood simulation models, regardless of the optimization algorithm used.

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Section: Initial Model-based Optimization: 1d-sa With Nsga-iimentioning

confidence: 99%

Analysis of Flood Storage Area Operations in Huai River Using 1D and 2D River Simulation Models Coupled with Global Optimization Algorithms

et al. 2019

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“…a Poisson process. Since we no longer have deterministic information about future arrivals and only the past is fully observable for us, we need to solve a stochastic shortest path problem: A state variable is the minimal history that is necessary and sufficient to compute the decision function [24]. For our case, this includes all the decisions made in the past that can affect the current load of the system and the ZIC power profile in (21).…”

Section: B Scenario IImentioning

confidence: 99%

From Packet to Power Switching: Digital Direct Load Scheduling

Alizadeh

Scaglione

Thomas

2012

IEEE J. Select. Areas Commun.

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Abstract-At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making priceaware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.

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“…ADP is a modeling and algorithmic framework for stochastic optimization problems [14]. In this framework, the multitime period optimization is viewed as Markov decision process (MDP).…”

Section: Introductionmentioning

confidence: 99%

Stochastic Optimization of Economic Dispatch for Microgrid Based on Approximate Dynamic Programming

Shuai

Fang

et al. 2019

IEEE Trans. Smart Grid

237

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This paper proposes an approximate dynamic programming (ADP) based approach for the economic dispatch (ED) of microgrid with distributed generations (DGs). The timevariant renewable generation, electricity price and the power demand are considered as stochastic variables in this work. An ADP based ED (ADPED) algorithm is proposed to optimally operate the microgrid under these uncertainties. To deal with the uncertainties, Monte Carlo (MC) method is adopted to sample the training scenarios to give empirical knowledge to ADPED. The piecewise linear function (PLF) approximation with improved slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intraday optimization process. The algorithm can make full use of historical prediction error distribution to reduce the influence of inaccurate forecast on the system operation. Numerical simulations demonstrate the effectiveness of the proposed approach. The near-optimal decision obtained by ADPED is very close to the global optimality. And it can be adaptive to both day-ahead and intra-day operation under uncertainty.

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Approximate Dynamic Programming I: Modeling

Cited by 17 publications

References 32 publications

Analysis of Flood Storage Area Operations in Huai River Using 1D and 2D River Simulation Models Coupled with Global Optimization Algorithms

Analysis of Flood Storage Area Operations in Huai River Using 1D and 2D River Simulation Models Coupled with Global Optimization Algorithms

From Packet to Power Switching: Digital Direct Load Scheduling

Stochastic Optimization of Economic Dispatch for Microgrid Based on Approximate Dynamic Programming

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