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Multiple stage stochastic planning of integrated electricity and gas system based on distributed approximate dynamic programming

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ENERGY 2023年第1期270卷

作者： Zhang, Houwang Wu, Qiuwei Chen, Jian Lu, Lina Zhang, Jiangfeng Zhang, Shuyi Shandong Univ Sch Elect Engn Jinan 250002 Shandong Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Hangzhou Dianzi Univ Informat Engn Coll Hangzhou 311305 Zhejiang Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou 310014 Peoples R China State Grid Tianjin Chengnan Elect Power Supply Bra Tianjin 300201 Peoples R China

With the increasing coupling of integrated electricity and gas systems, it's essential to implement a proper planning scheme. The planning of the integrated electricity and gas systems usually consists of multiple planning stages and various uncertainties, making the problem difficult to solve. In this paper, combining the approximate dynamic programming method and the alternating direction multiplier method, a distributed approximate dynamic programming based multiple stage stochastic planning scheme is proposed for the integrated electricity and gas systems. The multiple stage stochastic planning scheme realizes the coordination of new installation and expansion of power grid lines, natural gas pipelines, substations, city gates, photovoltaics, wind turbines, gas turbines and energy storage devices, while considering both the long-term and the short-term uncertainties. The multiple stage stochastic planning model is reformulated as a Markov decision process with a sequential decision strategy, in which the investment variables are decided stage by stage with long-term uncertainties gradually revealed in the planning period. The distributed approximate dynamic programming algorithm is used to solve the multiple stage stochastic planning model, which is in the form of the Markov decision process, by decoupling in both temporal and spatial dimensions. Numerical tests on the test system with a 24-node power grid and a 30node natural gas network verify the effectiveness of the multiple stage stochastic planning model.

关键词： Integrated electricity and gas systems Multiple stage stochastic planning Markov decision process approximate dynamic programming Alternating direction multiplier method

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Real-Time Optimization for Microgrid Energy Scheduling Based on approximate dynamic programming 4

Real-Time Optimization for Microgrid Energy Scheduling Based...

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4th International Conference on Smart Power and Internet Energy Systems (SPIES)

作者： Hao, Haowei Luo, Yanhong Yang, Dongsheng Northeastern Univ Dept Elect Engn Shenyang Peoples R China

ISBN: (纸本)9781665489577

This paper proposes an approximate dynamic programming (ADP)-based energy scheduling (ADPES) approach for the real-time optimization of microgrid. The uncertainties from renewable energy sources (RES), load demand and electricity price are considered in the system operation. Firstly, the real-time scheduling problem of microgrid is modeled as a Markov decision processes (MDP) within the optimization horizon. Then, the approximated value functions are introduced to the solving processes to prevent the dimensionality curse. On this basis, the near-optimal solution can be obtained by recursively solving the Bellman's equation, and the real-time prediction information can be updated to enhance the optimality in the stochastic environments. At last, by comparing with several existing optimization approaches, the simulation results show the effectiveness and superiority of proposed method in both deterministic and stochastic cases.

关键词： microgrid approximate dynamic programming energy scheduling real-time optimization uncertainty

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approximate dynamic programming for network recovery problems with stochastic demand

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2021年 151卷 102358-102358页

作者： Ulusan, Aybike Ergun, Ozlem NE Univ Dept Mech & Ind Engn Boston MA 02115 USA Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA

Immediately after a disruption, in order to minimize the negative impact inflicted on the society, its imperative to re-establish the interdicted critical services enabled by the infrastructure networks. In this paper, we study the stochastic network recovery problem that tackles the planning of restoration activities (considering limited resources) on interdicted infrastructure network links so that the pre-disruption critical service flows can be re-established as quickly as possible. As an illustrative case study, we consider a disaster scenario on a road infrastructure network that obstructs the flow of relief-aid commodities and search-and-rescue teams between critical service providing facilities and locations in need of these critical services. As in the case of many realistic applications, we consider the amount of demand for critical services as stochastic. First, we present a Markov decision process (MDP) formulation for the stochastic road network recovery problem (SRNRP), then we propose an approximate dynamic programming (ADP) approach to heuristically solve SRNRP. We develop basis functions to capture the important complex network interactions that can be used to approximate cost-to-go values for the MDP states. We conduct computational experiments on a set of small-scale randomly generated instances and demonstrate that the ADP approach provides near-optimal results regardless of the demand distribution and network topology. In order to develop a practical approach suitable for solving real world sized instances, we propose a framework where we first develop an ADP model and derive a policy on a spatially aggregated network of large scale instance. Next, we show the performance of this policy through computational testing on the large scale disaggregated network. Moreover, we provide managerial insights by assessing the importance of each basis function in the ADP model contributing to the recovery policies. We test this approach on a case study based

关键词： Network recovery problem Stochastic networks Demand uncertainty Post-disaster response approximate dynamic programming

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Integrated approximate dynamic programming and Equivalent Consumption Minimization Strategy for Eco-Driving in a Connected and Automated Vehicle

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第11期70卷 11204-11215页

作者： Deshpande, Shreshta Rajakumar Jung, Daniel Bauer, Leo Canova, Marcello Ohio State Univ Ctr Automot Res Dept Mech & Aerosp Engn Columbus OH 43212 USA Linkoping Univ Dept Elect Engn Linkoping Ostergotland Sweden Fiat Chrysler Automobiles FCA US Auburn Hills MI 48326 USA

Recent improvements in vehicle-to-everything (V2X) communication and onboard computing power have enabled the development of control algorithms that jointly optimize the vehicle velocity and powertrain control in Connected and Automated Vehicles (CAVs), commonly referred to as the Eco-Driving problem. This paper presents a novel and computationally efficient algorithm to optimize the velocity planning and energy management in a CAV with a hybrid electric powertrain. The Eco-Driving problem is formulated as a dynamic, constrained optimization problem in the spatial domain, where information about the upcoming speed limits and road topography is assumed known. This problem is solved by embedding an Equivalent Consumption Minimization Strategy (ECMS) into a dynamic programming (DP) optimization to obtain a sub-optimal solution that provides results close to the global optimum at a fraction of the computational cost. Further, a multi-layer hierarchical control architecture is proposed as a path to a causal, real-time implementation. The DP-ECMS algorithm is converted into a Model Predictive Control (MPC) framework by using principles of approximate dynamic programming (ADP). This causal implementation is finally benchmarked to a global optimal solution obtained with DP for different scenarios.

关键词： Computational modeling Mathematical model Engines Batteries Torque Optimization Energy management approximate dynamic programming optimization equivalent consumption minimization strategy model predictive control eco-driving CAV

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Solving the joint military medical evacuation problem via a random forest approximate dynamic programming approach

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 221卷

作者： Rodriguez, Channel A. Jenkins, Phillip R. Robbins, Matthew J. Air Force Inst Technol Dept Operat Sci 2950 Hobson Way Wright Patterson AFB OH 45433 USA

Members of the armed forces rely on having an effective and efficient medical evacuation (MEDEVAC) process for evacuating casualties from the battlefield to medical treatment facilities during combat operations. Determining which MEDEVAC units to task to respond to incoming requests for service, known as the MEDEVAC dispatching problem, is a natural sequential decision making problem and is the focus of this paper. Unlike previous research in this area, this paper incorporates triage classification errors and the possibility of having blood transfusion kits on board select MEDEVAC units. A discounted, infinite-horizon continuous-time Markov decision process model is formulated to examine the MEDEVAC dispatching problem and to compare generated dispatching policies to the currently practiced United States military policy. We utilize an approximate dynamic programming (ADP) technique that leverages a random forest value function approximation within an approximate policy iteration algorithmic framework to develop high-quality policies for both small-scale and large-scale problem instances. A representative planning scenario involving joint combat operations in South Korea is developed and utilized to investigate the differences between the various policies. Results from the analysis indicate that applying ADP techniques can improve current practices by as much as 29% with regard to a life-saving performance metric. This research is of particular interest to the military medical community and can inform the procedures of future military MEDEVAC operations.

关键词： Markov decision processes Medical evacuation approximate dynamic programming Policy iteration Random forest

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Maximizing Net Present Value for Resource Constraint Project Scheduling Problems with Payments at Event Occurrences Using approximate dynamic programming

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ALGORITHMS 2024年第5期17卷 180页

作者： Phuntsho, Tshewang Gonsalves, Tad Sophia Univ Dept Informat & Comp Sci Chiyoda Ku Tokyo 1028554 Japan Royal Univ Bhutan Gedu Coll Business Studies POB 21006 Bongo Bhutan

Resource Constraint Project Scheduling Problems with Discounted Cash Flows (RCPSPDC) focuses on maximizing the net present value by summing the discounted cash flows of project activities. An extension of this problem is the Payment at Event Occurrences (PEO) scheme, where the client makes multiple payments to the contractor upon completion of predefined activities, with additional final settlement at project completion. Numerous approximation methods such as metaheuristics have been proposed to solve this NP-hard problem. However, these methods suffer from parameter control and/or the computational cost of correcting infeasible solutions. Alternatively, approximate dynamic programming (ADP) sequentially generates a schedule based on strategies computed via Monte Carlo (MC) simulations. This saves the computations required for solution corrections, but its performance is highly dependent on its strategy. In this study, we propose the hybridization of ADP with three different metaheuristics to take advantage of their combined strengths, resulting in six different models. The Estimation of Distribution Algorithm (EDA) and Ant Colony Optimization (ACO) were used to recommend policies for ADP. A Discrete cCuckoo Search (DCS) further improved the schedules generated by ADP. Our experimental analysis performed on the j30, j60, and j90 datasets of PSPLIB has shown that ADP-DCS is better than ADP alone. Implementing the EDA and ACO as prioritization strategies for Monte Carlo simulations greatly improved the solutions with high statistical significance. In addition, models with the EDA showed better performance than those with ACO and random priority, especially when the number of events increased.

关键词： approximate dynamic programming net present value discounted cash flows Discrete Cuckoo Search Estimation of Distribution Algorithm Ant Colony Optimization Payment at Event Occurrences

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approximate dynamic programming with Gaussian Processes for Optimal Control of Continuous-Time Nonlinear Systems 21st

Approximate Dynamic Programming with Gaussian Processes for ...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Beppu, Hirofumi Maruta, Ichiro Fujimoto, Kenji Kyoto Univ Grad Sch Engn Dept Aeronaut & Astronaut Kyoto 6158540 Japan

In this paper, a new algorithm for realization of approximate dynamic programming (ADP) with Gaussian processes (GPs) for continuous-time (CT) nonlinear input-affine systems is proposed to infinite horizon optimal control problems. The convergence for the ADP algorithm is proven based on the assumption of an exact approximation, where both the cost function and the control input converge to their optimal values, that is, the solution to the Hamilton-Jacobi-Bellman (HJB) equation. The approximation errors, however, are unavoidable in almost every case of applications. In order to tackle the problem, the proposed algorithm is derived with the proof of convergence, where the cost function and the control input, which are both approximated, converge to those of the ADP as the number of data points for GPs approaches infinity. A numerical simulation demonstrates the effectiveness of the proposed algorithm. Copyright (C) 2020 The Authors.

关键词： approximate dynamic programming heuristic dynamic programming value iteration optimal control Gaussian processes nonparametric models

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Off-line approximate dynamic programming for the vehicle routing problem with a highly variable customer basis and stochastic demands

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COMPUTERS & OPERATIONS RESEARCH 2023年第1期159卷

作者： Dastpak, Mohsen Errico, Fausto Jabali, Ola Ecole Technol Super Dept Genie Construct Montreal PQ H3C 1K3 Canada GERAD Montreal PQ H3T 1J4 Canada CIRRELT Montreal PQ H3C 3J7 Canada Politecn Milan Dipartimento Elettron Informaz & Bioingn Piazza Leonardo da Vinci 32 I-20133 Milan Italy

We study a stochastic variant of the vehicle routing problem (VRP) arising in the context of domestic donor collection services. The problem we consider combines the following attributes. Customers requesting services are variable, in the sense that they are stochastic, but are not restricted to a predefined set. Furthermore, demand volumes are also stochastic and are observed upon visiting customers. The objective is to maximize the expected served demands while meeting vehicle capacity and time restrictions. We call this problem the VRP with a highly Variable Customer basis and Stochastic Demands (VRP-VCSD). We first propose a classical Markov Decision Process (MDP) formulation for the VRP-VCSD. The resulting model is, however, unusable due to the explosion in the dimension of the state and action *** solve the VRP-VCSD, we propose a number of methodological contributions aimed at reducing the state and action spaces. We first reformulate the MDP as an MDP with a consecutive action selection procedure. In this formulation, we enforce the treatment of a single vehicle (as opposed to multiple vehicles) at each decision epoch. We then introduce an observation function that selects a subset of the available information, which is deemed relevant for the considered vehicle in each epoch. We develop a Q-learning algorithm called QN-CO. In particular, we use a continuous state representation and incorporate a two-layer artificial neural network to approximate the Q values. Furthermore, we propose an aggregation strategy yielding a fixed-size output. Finally, we enhance our algorithm with Replay Memory and a Double Q *** conduct a thorough computational analysis. Results show that QN-CO considerably outperforms five benchmark policies. Moreover, we show that QN-CO can compete with specialized methods developed for the particular case of the VRP-VCSD where customer locations and expected demands are known in advance.

关键词： Stochastic vehicle routing problem approximate dynamic programming Q-learning

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Production Planning of Cascaded Hydropower Stations using approximate dynamic programming

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IFAC-PapersOnLine 2023年第2期56卷 10069-10076页

作者： Alexander Svensson Marcial Magnus Perninge Department of Physics and Electrical Engineering at Linnaeus University Växjö Sweden

We formulate the day-ahead bidding problem for a hydropower producer having several hydropower plants residing in a river basin. We present a novel approach inspired by dynamic programming with approximations in value and policy space by neural networks. This allows for more accurate modeling of the problem by avoiding linear approximations of the production function and bidding. Stochastic programming is a method frequently used in literature to solve the hydropower production planning problem. Stochastic programming is used on linearized systems and under assumptions of known distributions of the involved stochastic processes. We test the proposed algorithm on a simplified system, suitable for Stochastic programming and compare the obtained policy with the results from Stochastic programming. The results show that the algorithm obtains a policy similar to that of Stochastic programming.

关键词： Analysis and control in deregulated power systems approximate dynamic programming hydropower production planning neural networks

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Introducing a User-friendly approximate dynamic programming through Region-based Approximation

Introducing a User-friendly Approximate Dynamic Programming ...

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American Control Conference (ACC)

作者： Sardarmehni, Tohid Song, Xingyong Univ Texas Rio Grande Valley Mech Engn Edinburg TX 78539 USA Texas A&M Univ Dept Engn Technol & Ind Distribut College Stn TX 77843 USA Texas A&M Univ Dept Mech Engn Coll Engn College Stn TX 77843 USA

ISBN: (纸本)9781665441971

One of the main challenges in applying approximate dynamic programming (ADP) to solve real-world optimal control problems is finding a proper Value Function Approximator (VFA). Generally, one selects the proper VFA by trial & error, which might be challenging and time-consuming. This paper tries to alleviate the need for trial & error in selecting a proper VFA in ADP solutions. Also, the paper provides a fast and computationally light training method. The backbone of the proposed solution is partitioning the domain of training to smaller regions in which the value function varies slowly. Afterward, a separate Linear-in-Parameter Neural Network (LIPNN) is trained to capture the value function's behavior in each region. The paper shows that the method improves the precision in value function approximation, which leads to improvement in the performance of the closed-loop system. Meanwhile, the paper discusses the possibility of expanding the training domain in ADP solutions by region-based approximation. At last, the paper provides two real-world simulation examples to evaluate the effectiveness of the solution.

关键词： region-based approximation approximate dynamic programming optimal control

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