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Multi-Agent Learning-Based Nearly Non-Iterative stochastic dynamic Transactive Energy Control of Networked Microgrids

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IEEE TRANSACTIONS ON SMART GRID 2022年第1期13卷 688-701页

作者： Pan, Zhenning Yu, Tao Li, Jie Wu, Yufeng Chen, Junbin Lu, Jidong Zhang, Xiaoshun South China Univ Technol Coll Elect Power Guangzhou 510640 Peoples R China Guangdong Prov Key Lab Intelligent Measurement & Guangzhou Peoples R China Northeastern Univ Foshan Grad Sch Foshan 528311 Peoples R China

Coordination of networked microgrids (MGs) offers a promising solution to utilize distributed resources flexibilities and accommodate renewable energy. This paper studies real-time coordination of distribution system operation (DSO) and MGs considering multivariate uncertainty. Current researches suffer from inadaptability to dynamic system uncertainty, extensive iterations, and dependence on prediction. To fill these gaps, A novel multi-agent learning based stochastic dynamic programming (MASDP) is proposed to obtain the optimal policy for entities. Specifically, transactive energy control (TEC) mechanism, which requires only market-based information interactions, is employed to facilitate coordination between MG and DSO. A data-driven offline self-learning is proposed for entities to learn how to manage resources in response to system uncertainty. After sufficient offline learning, online operation of MASDP can be run in both non-iterative and iterative manners, by which near-optimal/optimal real-time solutions of DSO and MGs can be given sequentially and distributedly. Numerical comparisons with state-of-art policies and TEC algorithms verify the optimality, efficiency, adaptability, and scalability of MASDP.

关键词： Uncertainty Real-time systems dynamic programming Distribution networks stochastic processes Power demand Microgrids Microgrids distribution networks transactive energy control stochastic optimization real-time optimization stochastic dynamic programming multi-agent learning

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Optimal stopping analysis of a radiation detection system to protect cities from a nuclear terrorist attack

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RISK ANALYSIS 2008年第2期28卷 353-371页

作者： Atkinson, Michael P. Cao, Zheng Wein, Lawrence M. Stanford Univ Grad Sch Business Stanford CA 94305 USA Inst Computat & Math Engn Stanford CA 94305 USA Stanford Univ Dept Phys Stanford CA 94305 USA

We formulate and analyze an optimal stopping problem concerning a terrorist who is attempting to drive a nuclear or radiological weapon toward a target in a city center. In our model, the terrorist needs to travel through a two-dimensional lattice containing imperfect radiation sensors at some of the nodes, and decides at each node whether to detonate the bomb or proceed. We consider five different scenarios containing various informational structures and two different sensor array topologies: the sensors are placed randomly or they form an outer wall around the periphery of the city. We find that sensors can act as a deterrent in some cases, and that the government prefers the outer wall topology unless the sensors have a very low detection probability and the budget is tight (so that they are sparsely deployed).

关键词： homeland security Stackelberg game stochastic dynamic programming

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Optimal operation of hydrothermal systems with Hydrological Scenario Generation through Bootstrap and Periodic Autoregressive Models

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2012年第3期222卷 606-615页

作者： Souza, Reinaldo Castro Marques Marcato, Andre Luis Dias, Bruno Henriques Cyrino Oliveira, Fernando Luiz Univ Fed Juiz de Fora Juiz De Fora Brazil Pontifical Catholic Univ Rio de Janeiro Rio De Janeiro Brazil Univ Fed Fluminense Niteroi RJ Brazil

In electrical power systems with strong hydro generation, the use of adequate techniques to generate synthetic hydrological scenarios is extremely important for the evaluation of the ways the system behaves in order to meet the forecast energy demand. This paper proposes a new model to generate natural inflow energy scenarios in the long-term operation planning of large-sized hydrothermal systems. This model is based on the Periodic Autoregressive Model, PAR (p), where the identification of the p orders is based on the significance of the Partial Autocorrelation Function (PACF) estimated via Bootstrap, an intensive computational technique. The scenarios generated through this new technique were applied to the operation planning of the Brazilian Electrical System (BES), using the previously developed methodology of stochastic dynamic programming based on Convex Hull algorithm (SDP-CHull). The results show that identification via Bootstrap is considerably more parsimonious, leading to the identification of lower orders models in most cases which retains the statistical characteristics of the original series. Additionally it presents a closer total mean operation cost when compared to the cost obtained via historic series. (C) 2012 Elsevier B.V. All rights reserved.

关键词： OR in energy Long-term operation planning Periodic model Bootstrap stochastic dynamic programming

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Optimizing competitive uses of water for irrigation and fisheries

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AGRICULTURAL WATER MANAGEMENT 2011年第1期101卷 42-51页

作者： Lap Doc Tran Schilizzi, Steven Chalak, Morteza Kingwell, Ross Univ Western Australia Sch Agr & Resource Econ Ctr Environm Econ & Policy Crawley WA 6009 Australia Nong Lam Univ Dept Econ Ho Chi Minh City Vietnam Dept Agr & Food S Perth WA 6151 Australia

Choosing the appropriate reservoir water management strategy can be difficult when the water has multiple uses. This study examines this problem for reservoir managers where water use involves irrigation and fisheries. A stochastic dynamic programming (SDP) model is developed to facilitate reservoir management, using a case study illustration for southern Vietnam. The model includes the response of rice and fish yields to key factors including reservoir water levels, the timing and quantity of water release, and climatic conditions. The model also accounts for variation in rainfall patterns, irrigation requirements, and the demand for low water levels during the fish harvest season. Three production scenarios are examined where the reservoir's water is used for: only producing rice (scenario 1), only producing fish (scenario 2), and producing rice and fish (scenario 3). Key findings are: (1) for scenario 1, adequate water should be released to meet rice growing water requirements and residual water should be stored as a source of water in case of low rainfall, (2) for scenario 2, sufficient water needs to be released prior to the fish harvest to maximize this harvest;and (3) for scenario 3, water should be released prior to fish harvest, but sufficient water should remain to satisfy the water requirements of rice. When the reservoir is managed for joint production of rice and fish, net benefits are 6% greater than when the reservoir is managed solely for rice production. The SDP model developed in this paper could be adapted and applied to other multiple-use resources such as forests, river basins, and land. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Reservoir water management Irrigation Fisheries Optimization Multiple-use resources stochastic dynamic programming

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An effective approach for job-shop scheduling with uncertain processing requirements

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IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION 1999年第2期15卷 328-339页

作者： Luh, PB Chen, D Thakur, LS Univ Connecticut Storrs CT 06268 USA

Production systems often involve various uncertainties such as unpredictable customer orders or inaccurate estimate of processing times. Managing such uncertainties is becoming critical in the era of "time-based competition." For example, if a schedule is generated without considering possible orders in the future, new orders of significant urgency may interrupt those already scheduled, causing serious violation of their promised delivery dates. The consideration of uncertainties, however, has been proven to be very difficult because of the combinatorial nature of discrete optimization compounded further by the presence of uncertain factors. This paper presents an effective approach for job-shop scheduling considering uncertain arrival times, processing times, due dates, and part priorities, A separable problem formulation that balances modeling accuracy and solution method complexity is presented with the goal to minimize expected part tardiness and earliness cost. This optimization is subject to arrival time and operation precedence constraints (to be satisfied for each possible realization), and machine capacity constraints (to be satisfied in the expected value sense). A solution methodology based on a combined Lagrangian relaxation and stochastic dynamic programming is developed to obtain dual solutions. A good dual solution is then selected by using "ordinal optimization," and the actual schedule is dynamically constructed based on the dual solution and the realization of random events. The computational complexity of the overall algorithm is only slightly higher than the one without considering uncertainties. To evaluate the quality of the schedule, a dual cost is proved to be a lower bound to the optimal expected cost for the stochastic formulation considered here. Numerical testing supported by simulation demonstrates that near optimal solutions are obtained, and uncertainties are effectively handled for problems of practical sizes.

关键词： job-shop scheduling Lagrangian relaxation stochastic dynamic programming uncertainties

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Accounting for parametric uncertainty in Markov decision processes

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ECOLOGICAL MODELLING 2013年 254卷 15-21页

作者： Schapaugh, Adam W. Tyre, Andrew J. Univ Nebraska Sch Nat Resources Lincoln NE 68510 USA Univ Nebraska Lincoln NE 68510 USA

Markov decision processes have become the standard tool for modeling sequential decision-making problems in conservation. In many real-world applications, however, it is practically infeasible to accurately parameterize the state transition function. In this study, we introduce a new way of dealing with ambiguity in the state transition function. In contrast to existing methods, we explore the effects of uncertainty at the level of the policy, rather than at the level of decisions within states. We use information-gap decision theory to ask the question of how much uncertainty in the state transition function can be tolerated while still delivering a specified expected value given by the objective function. Accordingly, the goal of the optimization problem is no longer to maximize expected value, but to maximize local robustness to uncertainty (while still meeting the desired level of performance). We analyze a simple land acquisition problem, using info-gap decision theory to propagate uncertainties and rank alternative policies. Rather than requiring information about the extent of parameter uncertainty at the outset, info-gap addresses the question of how much uncertainty is permissible in the state transition function before the optimal policy would change. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Information-gap Markov decision process Reserve selection stochastic dynamic programming Uncertainty

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Hydrothermal systems operation planning using a discretization of energy interchange between subsystems

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ELECTRIC POWER SYSTEMS RESEARCH 2016年 132卷 67-77页

作者： Conceicao, Wellington C. Marcato, Andre L. M. Ramos, Tales Pulinho Passos Filho, Joao Alberto Brandi, Rafael B. S. David, Pedro Americo M. S. da Silva Junior, Ivo Chaves Fed Inst Southeastern Minas Gerais Juiz De Fora MG Brazil Univ Fed Juiz de Fora Dept Elect Energy Juiz De Fora MG Brazil Energy Res Co Rio De Janeiro RJ Brazil

This article presents an alternative approach to solve hydrothermal systems operation planning based on stochastic dynamic programming. Under the presented approach, the hydroelectric power plants are grouped into equivalent subsystems of energy and the expected cost functions are modeled by a piecewise linear approximation, by means of the Convex Hull algorithm. Also, under this methodology, the problem is solved independently for each subsystem such that the state variables to be considered are the energy storage and energy net interchange of the subsystem. The presented results have shown that this subsystems separation technique reduces significantly the computation time when compared with the traditional techniques of stochastic dynamic programming, demonstrating the effectiveness of the proposed methodology. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Operation planning stochastic dynamic programming Convex Hull algorithm Equivalent systems of energy Energy interchange Hydrothermal dispatch

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Trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus

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ENERGY 2016年第Part1期115卷 1259-1271页

作者： Du, Yongchang Zhao, Yue Wang, Qinpu Zhang, Yuanbo Xia, Huaicheng Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Zhongtong Bus Hold Co Ltd Liaocheng 252000 Shandong Peoples R China YanShan Univ Qinhuangdao 066000 Hebei Peoples R China

A trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus is presented in this paper, which includes the offline stochastic dynamic programming part and the online implementation part performed by equivalent consumption minimization strategy. In the offline part, historical driving cycles of the fixed route are divided into segments according to the position of bus stops, and then a segment-based stochastic driving condition model based on Markov chain is built. With the segment-based stochastic model obtained, the control set for real-time implemented equivalent consumption minimization strategy can be achieved by solving the offline stochastic dynamic programming problem. Results of stochastic dynamic programming are converted into a 3-dimensional lookup table of parameters for online implemented equivalent consumption minimization strategy. The proposed strategy is verified by both simulation and hardware-in-loop test of real-world driving cycle on an urban bus route. Simulation results show that the proposed method outperforms both the well-tuned equivalent consumption minimization strategy and the rule-based strategy in terms of fuel economy, and even proved to be close to the optimal result obtained by dynamic programming. Furthermore, the practical application potential of the proposed control method was proved by hardware-in-loop test. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Plug-in hybrid electric bus Energy management Markov chain stochastic dynamic programming Equivalent consumption minimization strategy

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Optimal nonmyopic gambling strategy for the generalized Kelly criterion

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NAVAL RESEARCH LOGISTICS 1997年第7期44卷 639-654页

作者： Cetinkaya, S Parlar, M MCMASTER UNIV DEGROOTE SCH BUSINESS HAMILTON ON L8S 4M4 CANADA UNIV WATERLOO DEPT MANAGEMENT SCI WATERLOO ON N2L 3G1 CANADA

We consider the optimal wagers to be made by a gambler who starts with a given initial wealth. The gambler faces a sequence of two-outcome games, i.e., ''win'' vs. ''lose,'' and wishes to maximize the expected value of his terminal utility. It has been shown by Kelly, Bellman, and others that if the terminal utility is of the form log x, where x is the terminal wealth, then the optimal policy is myopic, i.e., the optimal wager is always to bet a constant fraction of the wealth provided that the probability of winning exceeds the probability of losing. In this paper we provide a critique of the simple logarithmic assumption for the utility of terminal wealth and solve the problem with a more general utility function. We show that in the general case, the optimal policy is not myopic, and we provide analytic expressions for optimal wager decisions in tens of the problem parameters. We also provide conditions under which the optimal policy reduces to the simple myopic case. (C) 1997 John Wiley & Sons, Inc.

关键词： stochastic dynamic programming gambling nonmyopic optimal policy

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Optimization of Perimeter Patrol Operations Using Unmanned Aerial Vehicles

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JOURNAL OF GUIDANCE CONTROL AND dynamicS 2012年第2期35卷 434-441页

作者： Kalyanam, Krishnamoorthy Chandler, Phil Pachter, Meir Darbha, Swaroop Infoscitex Corp Dayton OH 45431 USA USAF Res Lab Control Design & Anal Branch Wright Patterson AFB OH 45433 USA USAF Inst Technol Dept Elect Engn Wright Patterson AFB OH 45433 USA Texas A&M Univ Dept Mech Engn College Stn TX 77843 USA

This paper addresseses the following base perimeter patrol problem: a team of unmanned aerial vehicles (UAVs) equipped with cameras and a remotely located operator cooperatively perform the task of perimeter surveillance. There are m alert stations/sites on the perimeter where a nearby breaching of the perimeter by an intruder is flagged by an unattended ground sensor (UGS). To determine whether an incursion flagged by a UGS is a false alarm or a real threat, a patrolling UAV flies to the alert site to investigate the alert. The longer a UAV dwells (loiters) at an alert site, the more information it gathers;however, this also increases the delay in responding to other alerts. The decision problem for a UAV is to determine the optimal dwell time so as to maximize the expected payoff. In this paper, patrols consisting of one and two UAVs are considered. A stochastic dynamic programming approach is employed to obtain optimal policies for the patrolling UAVs. Theoretical performance bounds from queueing systems literature have been used to benchmark the optimal controller. Also, simulation results for the optimal patrols showing the expected information gained and response time for different alert arrival rates are presented.

关键词： UAV stochastic dynamic programming Sensors Markov Decision Process Monte Carlo Simulation Probability Density Functions MATLAB

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