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Real-time Risk-averse Dispatch of an Integrated Electricity and Natural Gas System via Condi-tional Value-at-risk-based Lookup-table Ap-proximate dynamic programming

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Protection and Control of Modern Power Systems 2024年第2期9卷 47-60页

作者： Jianquan Zhu Guanhai Li Ye Guo Jiajun Chen Haixin Liu Yuhao Luo Wenhao Liu School of Electric Power Engineering South China University of Tech-nologyGuangzhou 510640China South China University of Technology GuangzhouChina

The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this *** is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process *** of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable *** address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable *** line pack is used as the state variable to describe the impact of one period’s decision on the *** facilitates the reduction of load shedding and wind power *** the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk *** simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme ***,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an *** Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.

关键词： Integrated electricity and natural gas system approximate dynamic programming real-time dispatch risk-averse conditional value-at-risk

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approximate dynamic programming based control of hyperbolic PDE systems using reduced-order models from method of characteristics

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COMPUTERS & CHEMICAL ENGINEERING 2013年 57卷 122-132页

作者： Munusamy, Sudhakar Narasimhan, Sridharakumar Kaisare, Niket S. Indian Inst Technol Dept Chem Engn Madras 600036 Tamil Nadu India

approximate dynamic programming (ADP) is a model based control technique suitable for nonlinear systems. Application of ADP to distributed parameter systems (DPS) which are described by partial differential equations is a computationally intensive task. This problem is addressed in literature by the use of reduced order models which capture the essential dynamics of the system. Order reduction of DPS described by hyperbolic PDEs is a difficult task as such systems exhibit modes of nearly equal energy. The focus of this contribution is ADP based control of systems described by hyperbolic PDEs using reduced order models. Method of characteristics (MOC) is used to obtain reduced order models. This reduced order model is then used in ADP based control for solving the set-point tracking problem. Two case studies involving single and double characteristics are studied. Open loop simulations demonstrate the effectiveness of MOC in reducing the order and the closed loop simulations with ADP based controller indicate the advantage of using these reduced order models. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Method of characteristics approximate dynamic programming Distributed parameter system Hyperbolic PDE Model based control

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Revisiting approximate dynamic programming and its Convergence

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第12期44卷 2733-2743页

作者： Heydari, Ali South Dakota Sch Mines & Technol Dept Mech Engn Rapid City SD 57701 USA

Value iteration-based approximate/adaptive dynamic programming (ADP) as an approximate solution to infinite-horizon optimal control problems with deterministic dynamics and continuous state and action spaces is investigated. The learning iterations are decomposed into an outer loop and an inner loop. A relatively simple proof for the convergence of the outer-loop iterations to the optimal solution is provided using a novel idea with some new features. It presents an analogy between the value function during the iterations and the value function of a fixed-final-time optimal control problem. The inner loop is utilized to avoid the need for solving a set of nonlinear equations or a nonlinear optimization problem numerically, at each iteration of ADP for the policy update. Sufficient conditions for the uniqueness of the solution to the policy update equation and for the convergence of the inner-loop iterations to the solution are obtained. Afterwards, the results are formed as a learning algorithm for training a neurocontroller or creating a look-up table to be used for optimal control of nonlinear systems with different initial conditions. Finally, some of the features of the investigated method are numerically analyzed.

关键词： approximate dynamic programming nonlinear control systems optimal control

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approximate dynamic programming-based control of a building cooling system with thermal storage

Approximate dynamic programming-based control of a building ...

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4th IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)

作者： Borghesan, F. Vignali, R. Piroddi, L. Prandirti, M. Strelec, M. Politecn Milan DEIB Milan Italy Honeywell Prague Lab Prague Czech Republic

ISBN: (纸本)9781479929849

The paper addresses the energy management of a building cooling system comprising a chiller plant with two chillers, a thermal storage unit, and a cooling load representing a building. Uncertainty affects the system since the cooling load depends on the building occupancy. The goal is to minimize the energy consumption of the cooling system, while preserving comfort in the building. This is achieved by optimally distributing the cooling load demand among the chillers and the thermal storage unit, and modulating the building temperature set-point to some (limited) extent. The problem can be decomposed into a static optimization problem, and a dynamic programming problem, which is solved based on the abstraction to a Markov chain of the stochastic hybrid system modeling the cooling system.

关键词： approximate dynamic programming cooling systems energy management stochastic hybrid systems Markov chain abstraction

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approximate dynamic programming Based Controller Design Using an Improved Learning Algorithm with Application to Tracking Control of Aircraft

Approximate Dynamic Programming Based Controller Design Usin...

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Chinese Intelligent Automation Conference (CIAC)

作者： Luo, Xiong Zhou, Yuchao Sun, Zengqi USTB Sch Comp & Commun Engn 30 Xueyuan Rd Beijing 100083 Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9783642384608;9783642384592

The strategy using approximate/adaptive dynamic programming (ADP) has been widely used to design a learning controller for complex systems of higher dimension in recent years. This paper aims at handling an important problem in the design of ADP learning controllers, which is the improvement of learning algorithm for its convergence performance. We analyze ADP controller implementation framework according to the requirement of tracking control task, with emphasis on providing an improved weight-updating gradient descent approach in optimizing connection weights in network structures. A comparison of the proposed method and classic ADP design for tracking and controlling pitch angle of aircraft is presented. It verifies the feasibility in the design of the proposed ADP based controller.

关键词： approximate dynamic programming Controller Learning algorithm Aircraft control

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Online Optimization of Collaborative Web Service QoS Prediction Based on approximate dynamic programming

Online Optimization of Collaborative Web Service QoS Predict...

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2014International Conference on Identification,Information and Knowledge in The Internet of Things(2014信息与知识物联网国际会议)

作者： Xiong Luo Hao Luo Xiaohui Chang School of Computer and Communication Engineering University of Science and Technology Beijing (USTB)Beijing 100083China

ISBN: (纸本)9781479980048

More recently,with the increasing demand of web services on the World Wide Web used in the Internet of Things (IoTs),there has been a growing interest in the study of efficient web service quality evaluation approaches through the use of prediction strategies to obtain accurate quality-of-service (QoS) *** unpredictable network environment imposes very challenging obstacles to web service QoS *** of the traditional web service QoS prediction approaches are implemented only using a set of static model parameters with the help of designer’s priori *** the traditional QoS prediction approaches,our algorithm in this paper is realized by incorporating approximate dynamic programming (ADP)-based online parameter tuning strategy into the QoS prediction *** online learning and optimization,the proposed approach implements the QoS prediction with automatic parameter tuning capability,and the prior knowledge or identification of the prediction model is not *** near-optimal performance of QoS prediction can therefore be *** studies are carried out to demonstrate the effectiveness of the proposed ADP-based prediction approach.

关键词： approximate dynamic programming QoS prediction online learning parameter tuner

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On-Policy and Off-Policy Value Iteration Algorithms for Stochastic Zero-Sum dynamic Games

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Journal of Systems Science & Complexity 2025年第1期38卷 421-435页

作者： GUO Liangyuan WANG Bing-Chang ZHANG Ji-Feng School of Control Science and Engineering Shandong UniversityJinan 250063China School of Automation and Electrical Engineering Zhongyuan University of TechnologyZhengzhou 450007China Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

This paper considers the value iteration algorithms of stochastic zero-sum linear quadratic games with unkown ***-policy and off-policy learning algorithms are developed to solve the stochastic zero-sum games,where the system dynamics is not *** analyzing the value function iterations,the convergence of the model-based algorithm is *** equivalence of several types of value iteration algorithms is *** effectiveness of model-free algorithms is demonstrated by a numerical example.

关键词： approximate dynamic programming on-policy off-policy stochastic zero-sum games valueiteration

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Real-Time Energy Management of Hydrogen Energy Hub-Based Industrial Integrated Energy Distribution System

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 1779-1789页

作者： Zhu, Mengshu Fang, Jiakun Ai, Xiaomeng Cui, Shichang Zhong, Zhiyao Wen, Jinyu Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Technol Wuhan 430074 Peoples R China

This paper firstly proposes a hydrogen energy hub to satisfy the diverse hydrogen demands of industrial users consuming renewable energy for low-carbon emissions. In the hydrogen energy hub, two techniques, i.e., water electrolysis and biomass gasification, are coupled through complementary oxygen utilization. Then, an industrial integrated energy distribution system is formally constructed by incorporating multiple hydrogen energy hubs, battery energy storage, renewable energy sources, and a gas generator to coordinate local renewable resources. Accordingly, a real-time energy management system for a hydrogen energy hub-based industrial integrated energy distribution system is further proposed to efficiently coordinate multiple resources in the distribution network. To minimize the real-time operational cost of an industrial integrated energy distribution system under stochastic environments of time-variant renewable generation, biomass moisture content, electricity prices, and electrical loads, multidimensional piecewise linear function-based approximate dynamic programming is adapted. Finally, case studies validate the techno-economic benefits of the proposed system and the advantages of the proposed algorithm.

关键词： Hydrogen Production Renewable energy sources Real-time systems Biomass Electricity Costs Hydrogen energy hub real-time energy management integrated energy distribution system approximate dynamic programming industrial hydrogen demands

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dynamic assignment of delivery order bundles to in-store customers

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2025年 133卷

作者： Mancini, Simona Ulmer, Marlin W. Gansterer, Margaretha Univ Palermo Dept Engn Palermo Italy Univ Klagenfurt Dept Operat Energy & Environm Management Klagenfurt Austria Otto von Guericke Univ Dept Management Sci Magdeburg Germany

Many larger grocery stores offer home delivery services. However, the delivery cost is usually high and such services are rarely profitable. One way of reducing cost is by outsourcing some orders to in-store customers fora compensation. While initially single orders were dynamically assigned to customers, companies started exploring the assignment of order bundles instead to reduce per-order compensation and exploit consolidation potential. We investigate the value of dynamic assignment of bundles in this work. To this end, we consider a setting where all orders are known and, over time, unknown in-store customers enter the system fora short time and offer transportation of bundles of orders for compensation. The store decides dynamically which bundle to assign to which in-store customer (if any). At the end of the time horizon, the remaining orders are delivered by a dedicated fleet of store employees. The goal of the store is to minimize the compensation prices together with the delivery cost. We propose a threshold-based policy with scenario-based tuning. Popularity and compensation price thresholds are determined a priori by solving a set of perfect information scenarios. In every state, bundles are only assigned if they are popular enough and the compensation is comparably low. The thresholds (i.e., popularity threshold and compensation threshold) are adapted over time to account for the decrease in assignment opportunities. We show the effectiveness of our policy in a comprehensive computational study and highlight the value of bundle assignments compared to assigning individual orders. We further show that our strategy not only reduces the compensation paid to in-store customers but also the final routing cost.

关键词： Crowdsourcing Order bundling Sequential decision making approximate dynamic programming

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Integrating simulation, optimization and reinforcement learning for a general class of stochastic scheduling problems

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 279卷

作者： Xie, Fang Li, Yutong Li, Haitao Yantai Univ Sch Econ & Management Yantai 264005 Shandong Peoples R China Univ Missouri St Louis Supply Chain & Analyt Dept St Louis MO 63121 USA

We study a general class of stochastic scheduling problems that include various machine scheduling and resource-constrained project scheduling problems under uncertainty as special cases. A Markov decision process (MDP) model built upon discrete-time Markov chain is developed for the addressed class of problems. To tackle the curse-of-dimensionalities of obtaining the exact closed-loop policy to the MDP model, we present a general approximate dynamic programming framework that integrates simulation, optimization, and reinforcement learning, called Sim-Opt-RL, to provide quality and computationally tractable closed-loop policy. We implement the Sim-Opt-RL framework for a well-known and well-studied stochastic resource-constrained project scheduling (SRCPSP), with a custom designed genetic algorithm, which outperforms the existing closed-loop algorithm, and is competitive with the state-of-the-art open-loop algorithms for the SRCPSP. It is able to provide quality closedloop solutions for instances with size of up to 120 activities in less than 30 min on average.

关键词： Stochastic scheduling Uncertain activity duration approximate dynamic programming Stochastic resource-constrained project sched- uling Simulation

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