This paper aims to promote the lifespan benefit of multiple battery energy storage (BES) in real-time scheduling. An effective real-time scheduling model is formulated with the proposed concept of multiple BES (MBES) ...
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This paper aims to promote the lifespan benefit of multiple battery energy storage (BES) in real-time scheduling. An effective real-time scheduling model is formulated with the proposed concept of multiple BES (MBES) comprehensive lifespan benefit, which makes a tradeoff between MBES short-term operation and long-term profits. Then, a novel piece-wise linear function (PLF) based continuous ADP (PLFC-ADP) algorithm is proposed to optimize the scheduling model under uncertainties. A new decomposed value function approximation method employing both BES state of charge and BES cumulative life loss is proposed to achieve high optimality and wide applicability. Combined with the difference-based decomposed slope update method to train the PLF slopes with empirical knowledge, the proposed PLFC-ADP algorithm can handle the increasing computation complexity of MBES scheduling and obtain the approximate optimality of stochastic real-time scheduling. Numerical analysis demonstrates the validity of the proposed scheduling model, and superior computation tractability and solution optimality of the proposed PLFC-ADP algorithm.
Many researches on efficient depth maps coding issues have been carried out giving particular attention to sharp edge preservation. Platelet-based coding method is an edge-aware coding scheme that uses a segmentation ...
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ISBN:
(纸本)9780819489371
Many researches on efficient depth maps coding issues have been carried out giving particular attention to sharp edge preservation. Platelet-based coding method is an edge-aware coding scheme that uses a segmentation procedure based on recursive quadtree decomposition. Then, the depth map is modeled using piecewiselinear platelet and wedgelet functions. However, the estimation of these functions is a computationally expensive task making the platelet-based techniques not adapted to online applications. In this paper, we propose to exploit edge detection in order to reduce the encoding delay of the platelet/wedgelet estimation process. The proposed approach shows significant gain in terms of encoding delay, while providing competitive R-D performances w.r.t. the original platelet-based codec. The subjective evaluation shows significant less degradation along sharp edges.
This paper proposes a real-time joint regulating reserve deployment (RRD) model of electric vehicles (EVs) and coal-fired generators (CGs) considering EV battery degradation. The superiority of the proposed model lies...
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This paper proposes a real-time joint regulating reserve deployment (RRD) model of electric vehicles (EVs) and coal-fired generators (CGs) considering EV battery degradation. The superiority of the proposed model lies in the integration of the EVs' fast regulation speed and CGs' large regulation capacity. The whole optimization model is formulated under a two-stage Markov Decision Process (MDP) to correspond to the real-time RRD process. The user behavior characteristic and EV battery degradation are taken into account to stimulate the users to participate in RRD. Based on the formulated MDP, a piece-wiselinear (PWL) function based scalable approximate dynamic programming (ADP) algorithm for different EV clusters is constructed to solve the realtime RRD model under uncertainties. A separable slope update method is proposed to update the slopes of PLF for different EV clusters with different user behaviors. The proposed ADP based real-time RRD algorithm (ADP-RTRRD) provides the approximate optimal real-time RRD policy with the empirical knowledge embedded through off-line training. Numerical simulation on a modified IEEE-39 bus system and Henan power grid in China verify the superiority of the proposed joint RRD model and the advantages of the proposed ADP-RTRRD algorithm.
A practical transportation problem for finding the “departure” time at “all source nodes” in order to arrive at “some destination nodes” at specified time for both FIFO (i.e., First In First Out) and Non-FIFO “...
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A practical transportation problem for finding the “departure” time at “all source nodes” in order to arrive at “some destination nodes” at specified time for both FIFO (i.e., First In First Out) and Non-FIFO “Dynamic ” Networks is considered in this study. Although shortest path (SP) for dynamic networks have been studied/documented by various researchers, contributions from this present work consists of a sparse matrix storage scheme for efficiently storing large scale sparse network’s connectivity, a concept of Time Delay Factor (TDF) combining with a “general piece- wiselinearfunction” to describe the link cost as a function of time for Non-FIFO links’ costs, and Backward Dijkstra SP Algorithm with simple heuristic rules for rejecting unwanted solutions during the backward search algorithm. Both small-scale (academic) networks as well as large- scale (real-life) networks are investigated in this work to explain and validate the proposed dynamic algorithms. Numerical results obtained from this research work have indicated that the newly proposed dynamic algorithm is reliable, and efficient. Based on the numerical results, the calculated departure time at the source node(s), for a given/specified arrival time at the destination node(s), can be non-unique, for some Non-FIFO networks’ connectivity.
A practical transportation problem for finding the "departure" time at "all source nodes" in order to arrive at "some destination nodes" at specified time for both FIFO(i.e., First In Fir...
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A practical transportation problem for finding the "departure" time at "all source nodes" in order to arrive at "some destination nodes" at specified time for both FIFO(i.e., First In First Out) and Non-FIFO "Dynamic " Networks is considered in this study. Although shortest path(SP) for dynamic networks have been studied/documented by various researchers, contributions from this present work consists of a sparse matrix storage scheme for efficiently storing large scale sparse network's connectivity, a concept of Time Delay Factor(TDF) combining with a "general piecewiselinearfunction" to describe the link cost as a function of time for Non-FIFO links' costs, and Backward Dijkstra SP Algorithm with simple heuristic rules for rejecting unwanted solutions during the backward search algorithm. Both small-scale(academic) networks as well as largescale(real-life) networks are investigated in this work to explain and validate the proposed dynamic algorithms. Numerical results obtained from this research work have indicated that the newly proposed dynamic algorithm is reliable, and efficient. Based on the numerical results, the calculated departure time at the source node(s), for a given/specified arrival time at the destination node(s), can be non-unique, for some Non-FIFO networks' connectivity.
After introducing fusion functions, the directional monotonicity of fusion functions is introduced and studied. Moreover, in special cases the sets of all vectors with respect to which the studied fusion functions are...
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ISBN:
(纸本)9783319088525;9783319088518
After introducing fusion functions, the directional monotonicity of fusion functions is introduced and studied. Moreover, in special cases the sets of all vectors with respect to which the studied fusion functions are increasing (decreasing) are completely characterized.
This short article analyzes an interesting property of the Bregman iterative procedure, which is equivalent to the augmented Lagrangian method, for minimizing a convex piece-wise linear function J(x) subject to linear...
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This short article analyzes an interesting property of the Bregman iterative procedure, which is equivalent to the augmented Lagrangian method, for minimizing a convex piece-wise linear function J(x) subject to linear constraints Ax=b. The procedure obtains its solution by solving a sequence of unconstrained subproblems of minimizing , where b (k) is iteratively updated. In practice, the subproblem at each iteration is solved at a relatively low accuracy. Let w (k) denote the error introduced by early stopping a subproblem solver at iteration k. We show that if all w (k) are sufficiently small so that Bregman iteration enters the optimal face, then while on the optimal face, Bregman iteration enjoys an interesting error-forgetting property: the distance between the current point and the optimal solution set X (au) is bounded by ayenw (k+1)-w (k) ayen, independent of the previous errors w (k-1),w (k-2),aEuro broken vertical bar,w (1). This property partially explains why the Bregman iterative procedure works well for sparse optimization and, in particular, for a"" (1)-minimization. The error-forgetting property is unique to J(x) that is a piece-wise linear function (also known as a polyhedral function), and the results of this article appear to be new to the literature of the augmented Lagrangian method.
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