This paper focuses on economical operation of a grid-connected microgrid in real-time. An online optimization model is developed to achieve the microgrid operation in the process of dynamic optimal control, and throug...
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ISBN:
(纸本)9789881563958
This paper focuses on economical operation of a grid-connected microgrid in real-time. An online optimization model is developed to achieve the microgrid operation in the process of dynamic optimal control, and through information feedback and online optimization to reduce the impact of uncertainty in a MG. The online optimization model has the characteristics of nonlinear and discontinuous, so an approximate dynamic programming (ADP) algorithm is used to solve this online model. Finally, the proposed online optimization model is tested in the European benchmark microgrid system. The simulation results show that the online optimization model overcomes the impacts of random fluctuations of renewable energy and loads and reduces the operating cost of the microgrid.
dynamicprogramming(DP) is not a useful tool for solving many control problems because of its complexity in computation. In this paper,we propose approximate dynamic programming(ADP) optimal control strategy for ship ...
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ISBN:
(纸本)9781479900305
dynamicprogramming(DP) is not a useful tool for solving many control problems because of its complexity in computation. In this paper,we propose approximate dynamic programming(ADP) optimal control strategy for ship course trajectory tracking control *** system transformation,we convert the optimal tracking problem into designing a infinite-horizon optimal regulator for the tracking error ***-dependent Heuristic dynamicprogramming(ADHDP) technique,as one form of ADR is presented to obtain the infinite-horizon optimal tracking *** the ship course optimal tracking control simulation results,we can see that the ADHDP controller makes the performance index and the control sequence for the error dynamics converge to the optimal *** BP neural networks are used as parametric structures to implement ADHDP *** two neural networks aim at approximating the cost function and the control law,respectively.
This paper investigates the mean standard deviation shortest path problem in large scale stochastic transportation networks and proposes a graph embedding and approximate dynamic programming (GE-ADP) method. GE-ADP em...
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ISBN:
(纸本)9781728191423
This paper investigates the mean standard deviation shortest path problem in large scale stochastic transportation networks and proposes a graph embedding and approximate dynamic programming (GE-ADP) method. GE-ADP employs the graph embedding technique to represent the nodes of a graph with fixed d-dimensional vectors, and in the meanwhile, establishes extended Bellman equations to describe the recursive relationships of both the first-order and second-order moments of travel time for all the nodes, when given a deterministic routing policy. approximate dynamic programming (ADP) is used to evaluate the performance of the given routing policy, and an improved policy is derived, thereafter. The process of policy evaluation and policy improvement loops until the optimal policy is reached. Both theoretical analysis and simulation results show the efficiency of the proposed algorithm when compared with state of the arts, and GE-ADP offers a flexible trade-off parameter (d) between the algorithm's accuracy and efficiency.
dynamicprogramming (DP) provides a systematic, closed-loop solution for optimal control problems. However, it suffers from the curse of dimensionality in higher orders. approximate dynamic programming (ADP) methods c...
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ISBN:
(纸本)9780791886670
dynamicprogramming (DP) provides a systematic, closed-loop solution for optimal control problems. However, it suffers from the curse of dimensionality in higher orders. approximate dynamic programming (ADP) methods can remedy this by finding near-optimal rather than exact optimal solutions. In summary, ADP uses function approximators, such as neural networks, to approximate optimal control solutions. ADP can then converge to the near-optimal solution using techniques such as reinforcement learning (RL). The two main challenges in using this approach are finding a proper training domain and selecting a suitable neural network architecture for precisely approximating the solutions with RL. Users select the training domain and the neural networks mostly by trial and error, which is tedious and time-consuming. This paper proposes trading the closed-loop solution provided by ADP methods for more effectively selecting the domain of training. To do so, we train a neural network using a small and moving domain around the reference signal. We asses the method's effectiveness by applying it to a widely used benchmark problem, the Van der Pol oscillator;and a real-world problem, controlling a quadrotor to track a reference trajectory. Simulation results demonstrate comparable performance to traditional methods while reducing computational requirements.
Multi-stage decision problems under uncertainty are abundant in process industries. Markov decision process (MDP) is a general mathematical formulation of such problems. Whereas stochastic programming and dynamic prog...
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ISBN:
(纸本)9780769537528
Multi-stage decision problems under uncertainty are abundant in process industries. Markov decision process (MDP) is a general mathematical formulation of such problems. Whereas stochastic programming and dynamicprogramming are the standard methods to solve MDPs, their unwieldy computational requirements limit their usefulness in real applications. approximate dynamic programming (ADP) combines simulation and function approximation to alleviate the "curse-of-dimensionality" associated with the traditional dynamicprogramming approach. In this paper, the method of ADP, which abates the curse-of-dimensionality by solving the DP within a carefully chosen, small subset of the state space, was introduced;a survey of recent research directions within the field of ADP had been made.
Based on the hypothesis that pumped-hydro power storage (PHPS) station is available for multi-day optimization and adjustment, the paper has proposed a long-term operation optimization model of PHPS station based on a...
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ISBN:
(纸本)9781479950324
Based on the hypothesis that pumped-hydro power storage (PHPS) station is available for multi-day optimization and adjustment, the paper has proposed a long-term operation optimization model of PHPS station based on approximate dynamic programming (ADP). In this multistage decision model, across the stages, value function approximation (VFA) of the reservoir energy storage was used to keep the overall optimization characteristics;During the stages, generated energy & generating periods, and electricity consumption for pumping & pumping periods are used as decision variables to conduct daily optimization operation. The paper got the approximate optimal solution through iteration solution decision variable and value function so as to avoid "curse of dimensionality" in conventional multistage decision model. According to the experiment, the ADP-based model can accurately describe the long-term operation modes of PHPS station, and its calculation methods are more appropriate for this kind of large-scale optimized decision problem than dynamicprogramming (DP) and conventional mathematic planning methods.
Intermittent electricity generation from renewable sources is characterized by a wide range of fluctuations in frequency spectrum. The medium-frequency component of 0.01 Hz - 1 Hz cannot be filtered out by system iner...
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ISBN:
(纸本)9781479964161
Intermittent electricity generation from renewable sources is characterized by a wide range of fluctuations in frequency spectrum. The medium-frequency component of 0.01 Hz - 1 Hz cannot be filtered out by system inertia and automatic generation control (AGC) and thus it results in deterioration of frequency quality. In this paper, an approximate dynamic programming (ADP) based supplementary frequency controller for thermal generators is developed to attenuate renewable generation fluctuation in medium-frequency range. A policy iteration based training algorithm is employed for online and model-free learning. Our simulation results demonstrate that the proposed supplementary frequency controller can effectively adapt to changes in the system and provide improved frequency control. Further sensitivity analysis validates that the supplementary frequency controller significantly attenuates the dependence of frequency deviation on the medium-frequency component of renewable generation fluctuation.
As more renewable, yet volatile, forms of energy like solar and wind are being incorporated into the grid, the problem of finding optimal control policies for energy storage is becoming increasingly important. These s...
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ISBN:
(纸本)9781479945528
As more renewable, yet volatile, forms of energy like solar and wind are being incorporated into the grid, the problem of finding optimal control policies for energy storage is becoming increasingly important. These sequential decision problems are often modeled as stochastic dynamic programs, but when the state space becomes large, traditional (exact) techniques such as backward induction, policy iteration, or value iteration quickly become computationally intractable. approximate dynamic programming (ADP) thus becomes a natural solution technique for solving these problems to near-optimality using significantly fewer computational resources. In this paper, we compare the performance of the following: various approximation architectures with approximate policy iteration (API), approximate value iteration (AVI) with structured lookup table, and direct policy search on a benchmarked energy storage problem (i.e., the optimal solution is computable).
A lot of work has recently been published regarding metrics that could identify high quality paths in Wireless Mesh Networks (WMN). While results are encouraging, no optimal strategy has yet been identified that could...
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ISBN:
(纸本)9781424488650
A lot of work has recently been published regarding metrics that could identify high quality paths in Wireless Mesh Networks (WMN). While results are encouraging, no optimal strategy has yet been identified that could estimate link quality and incorporate both the link reliability measurements as well as the bandwidth capacity. Furthermore, link estimation remains an open problem. Considering multi-user environment, any optimal solution would also need to consider multiple communication flows. These arguments have led us to study an approximate dynamic programming (DP) solution capable of utilizing limited network knowledge and stochastic process. Instead of proposing yet another link metric, we analyze a stochastic DP solution to the routing problem using a well established routing metric. Unlike deterministic DP where communication demands are fixed a priori and an optimal path is calculated before any real demands are known, we consider a more realistic scenario with stochastic metrics and formulate the optimal strategy for routing in WMNs. Performance results are given using simulation results.
Batch process is often subject to a high degree of uncertainty in raw material quality and other initial feedstock conditions. One of the key objectives in operating a batch process is achieving consistent performance...
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Batch process is often subject to a high degree of uncertainty in raw material quality and other initial feedstock conditions. One of the key objectives in operating a batch process is achieving consistent performance and constraint satisfaction in the presence of these uncertainties This study presents a method for optimal control of a fed-batch process, which can actively and robustly cope with system uncertainty. As in dual control, the method aims to achieve an optimal balance between control actions (exploitation) and probing actions (exploration), leading to improved process performance by actively reducing system uncertainty. An optimal solution of the dual control problem can be found by stochastic dynamicprogramming but it is computationally intractable in most practical cases. In this study, an approximate dynamic programming (ADP) method for solving the dual control problem is tailored to a batch process which involves non-stationary and nonlinear dynamics Rewards are formulated to maximize a given end objective while satisfying path constraints. Performance of the ADP-based dual controller is tested on a fed batch bioreactor with two uncertain parameters. Copyright (C) 2020 The Authors.
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