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Stochastic economic dispatch of regional integrated energy system considering the pipeline dynamics using improved approximate dynamic programming

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 141卷

作者： Liang, Weikun Lin, Shunjiang Liu, Mingbo Wang, Qiong Xie, Yuquan Sheng, Xuan South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China South China Univ Technol Guangdong Key Lab Clean Energy Technol Guangzhou 511458 Peoples R China

In the regional integrated energy system (RIES), the pipeline dynamics in cooling, heating, and gas networks make their source outputs and load demands difficult to balance in real-time. Hence the economic dispatch problem of RIES requires considering the pipeline dynamics to make the results in line with the actual situation. In this paper, the method of characteristics is proposed to solve the approximate analytical solution (AAS) of partial differential equations describing the gas pipeline dynamics, and the corrected parameters in the AAS are identified by the data-driven method. The high computational accuracy of the obtained AAS is verified by an example. Considering the stochastic fluctuation of renewable energy output and the energy storage ability of cooling, heating, and gas pipelines in a RIES, a stochastic multiple energy storage model for the stochastic economic dispatch problem of RIES considering the pipeline dynamics is established. An improved approximate dynamic programming algorithm considering the coupling of power balance among several adjacent time intervals due to the pipeline dynamics is proposed to efficiently solve the stochastic multiple energy storage model. Test results on an actual RIES, including comparison with the scenario-based method, demonstrate the correctness and efficiency of the proposed model and algorithm.

关键词： RIES Stochastic economic dispatch Pipeline dynamics Method of characteristics Stochastic multiple energy storage model approximate dynamic programming

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An Integrated Decomposition and approximate dynamic programming Approach for On-Demand Ride Pooling

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2020年第9期21卷 3811-3820页

作者： Yu, Xian Shen, Siqian Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA

Through smartphone apps, drivers and passengers can dynamically enter and leave ride-hailing platforms. As a result, ride-pooling is challenging due to complex system dynamics and different objectives of multiple stakeholders. In this paper, we study ride-pooling with no more than two passenger groups who can share rides in the same vehicle. We dynamically match available drivers to randomly arriving passengers and also decide pick-up and drop-off routes. The goal is to minimize a weighted sum of passengers' waiting time and trip delay time. A spatial-and-temporal decomposition heuristic is applied and each subproblem is solved using approximate dynamic programming (ADP), for which we show properties of the approximated value function at each stage. Our model is benchmarked with the one that optimizes vehicle dispatch without ride-pooling and the one that matches current drivers and passengers without demand forecasting. Using test instances generated based on the New York City taxi data during one peak hour, we conduct computational studies and sensitivity analysis to show (i) empirical convergence of ADP, (ii) benefit of ride-pooling, and (iii) value of future supply-demand information.

关键词： Vehicles Delays Computational modeling Optimization Vehicle dynamics Stochastic processes dynamic programming Mobility on demand (MoD) supply-demand uncertainty ride-pooling spatial-temporal decomposition approximate dynamic programming

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Accelerated Continuous-Time approximate dynamic programming via Data-Assisted Hybrid Control

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IFAC-PapersOnLine 2022年第12期55卷 561-566页

作者： Daniel E. Ochoa Jorge I. Poveda

We introduce a new closed-loop architecture for the online solution of approximate optimal control problems in the context of continuous-time systems. Specifically, we introduce the first algorithm that incorporates dynamic momentum in actor-critic structures to control continuous-time dynamic plants with an affine structure in the input. By incorporating dynamic momentum in our algorithm, we are able to accelerate the convergence properties of the closed-loop system, achieving superior transient performance compared to traditional gradient-descent based techniques. In addition, by leveraging the existence of past recorded data with sufficiently rich information properties, we dispense with the persistence of excitation condition traditionally imposed on the regressors of the critic and the actor. Given that our continuous-time momentum-based dynamics also incorporate periodic discrete-time resets that emulate restarting techniques used in the machine learning literature, we leverage tools from hybrid dynamical systems theory to establish asymptotic stability properties for the closed-loop system. We illustrate our results with a numerical example.

关键词： approximate dynamic programming concurrent learning hybrid systems Lyapunov theory

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Adaptive railway traffic control using approximate dynamic programming

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2020年 113卷 91-107页

作者： Ghasempour, Taha Heydecker, Benjamin UCL Ctr Transport Studies London WC1E 6BT England

This study presents an adaptive railway traffic controller for real-time operations based on approximate dynamic programming (ADP). By assessing requirements and opportunities, the controller aims to limit consecutive delays resulting from trains that entered a control area behind schedule by sequencing them at a critical location in a timely manner, thus representing the practical requirements of railway operations. This approach depends on an approximation to the value function of dynamic programming after optimisation from a specified state, which is estimated dynamically from operational experience using reinforcement learning techniques. By using this approximation, the ADP avoids extensive explicit evaluation of performance and so reduces the computational burden substantially. In this investigation, we explore formulations of the approximation function and variants of the learning techniques used to estimate it. Evaluation of the ADP methods in a stochastic simulation environment shows considerable improvements in consecutive delays by comparison with the current industry practice of First-Come-First-Served sequencing. We also found that estimates of parameters of the approximate value function are similar across a range of test scenarios with different mean train entry delays.

关键词： approximate dynamic programming Reinforcement learning Railway traffic management Adaptive control

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Markov Decision Process-Based Resilience Enhancement for Distribution Systems: An approximate dynamic programming Approach

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IEEE TRANSACTIONS ON SMART GRID 2020年第3期11卷 2498-2510页

作者： Wang, Chong Ju, Ping Lei, Shunbo Wang, Zhaoyu Wu, Feng Hou, Yunhe Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

Because failures in distribution systems caused by extreme weather events directly result in consumers' outages, this paper proposes a state-based decision-making model with the objective of mitigating loss of load to improve the distribution system resilience throughout the unfolding events. The system topologies including on/off states of feeder lines are modeled as Markov states, and the probabilities from one Markov state to another Markov state throughout the unfolding events are determined by the component failure caused by the unfolding events. A recursive optimization model based on Markov decision processes (MDP) is developed to make state-based actions, i.e., system reconfiguration, at each decision time. To overcome the curse of dimensionality caused by enormous states and actions, an approximate dynamic programming (ADP) approach based on post-decision states and iteration is used to solve the proposed MDP-based model. IEEE 33-bus system and IEEE 123-bus system are used to validate the proposed model.

关键词： Markov processes Load modeling Meteorology Topology Power systems Resilience Fault tolerance approximate dynamic programming distribution systems Markov decision processes resilience enhancement

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ONLINE CAPACITY PLANNING FOR REHABILITATION TREATMENTS: AN approximate dynamic programming APPROACH

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PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2020年第3期34卷 381-405页

作者： Bikker, Ingeborg A. Mes, Martijn R. K. Saure, Antoine Boucherie, Richard J. Univ Twente Ctr Healthcare Operat Improvement & Res CHOIR Drienerlolaan 5 NL-7500 AE Enschede Netherlands Sint Maartensklin Dept Healthcare Logist Hengstdal 3 NL-6574 NA Nijmegen Netherlands Univ Twente Dept Appl Math Stochast Operat Res Drienerlolaan 5 NL-7500 AE Enschede Netherlands Univ Twente Dept Ind Engn & Business Informat Syst IEBIS Drienerlolaan 5 NL-7500 AE Enschede Netherlands Univ Ottawa Telfer Sch Management 55 Laurier Ave East Ottawa ON K1N 6N5 Canada

We study an online capacity planning problem in which arriving patients require a series of appointments at several departments, within a certain access time target. This research is motivated by a study of rehabilitation planning practices at the Sint Maartenskliniek hospital (the Netherlands). In practice, the prescribed treatments and activities are typically booked starting in the first available week, leaving no space for urgent patients who require a series of appointments at a short notice. This leads to the rescheduling of appointments or long access times for urgent patients, which has a negative effect on the quality of care and on patient satisfaction. We propose an approach for allocating capacity to patients at the moment of their arrival, in such a way that the total number of requests booked within their corresponding access time targets is maximized. The model considers online decision making regarding multi-priority, multi-appointment, and multi-resource capacity allocation. We formulate this problem as a Markov decision process (MDP) that takes into account the current patient schedule, and future arrivals. We develop an approximate dynamic programming (ADP) algorithm to obtain approximate optimal capacity allocation policies. We provide insights into the characteristics of the optimal policies and evaluate the performance of the resulting policies using simulation.

关键词： approximate dynamic programming healthcare logistics online capacity planning operations research Markov decision process rehabilitation treatment planning simulation

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Stochastic economic dispatch of power system with multiple wind farms and pumped-storage hydro stations using approximate dynamic programming

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IET RENEWABLE POWER GENERATION 2020年第13期14卷 2507-2516页

作者： Lin, Shunjiang Fan, Guansheng Jian, Ganyang Liu, Mingbo South China Univ Technol Sch Elect Power Engn Guangzhou 510640 Peoples R China China Southern Power Grid Elect Power Res Inst Guangzhou 510663 Peoples R China

The stochastic economic dispatch problem of power system with multiple wind farms and pumped-storage hydro stations is formulated as a specific stochastic dynamic programming (DP) model, i.e. stochastic storage model, it is impossible to obtain an accurate solution due to the curse of dimensionality. Based on the approximate DP (ADP) method, the stochastic storage model can be transformed into a series of mixed-integer linear programming (MILP) models by describing the approximate value functions (AVFs) as convex piecewise linear functions in post-decision states. The AVFs are first initialised using the results of the deterministic model under a forecast scenario of wind farm output and then trained by scanning stochastic sampling scenarios consecutively with the successive projective approximation routine algorithm. To obtain a near-optimal day-ahead dispatch scheme, the forecast scenario is substituted into the MILP models expressed by the trained AVFs and is solved forward through each time interval. The network constraints are incorporated by the while-loop detection of critical lines. Test results on an actual provincial power system and the modified IEEE 39-bus system, including the comparison among the ADP, DP, scenario-based and chance-constrained programming methods, demonstrate the feasibility and efficiency of the proposed model and algorithm.

关键词： approximation theory stochastic processes dynamic programming power generation economics wind power plants power generation dispatch pumped-storage power stations integer programming stochastic programming power system security optimisation linear programming multiple wind farms pumped-storage hydro stations approximate dynamic programming stochastic economic dispatch problem specific stochastic dynamic programming model stochastic storage model approximate DP method mixed-integer linear programming models approximate value functions AVFs convex piecewise linear functions deterministic model forecast scenario wind farm output stochastic sampling scenarios successive projective approximation routine algorithm near-optimal day-ahead dispatch scheme MILP models actual provincial power system modified IEEE 39-bus system

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Horizontal combinations of online and offline approximate dynamic programming for stochastic dynamic vehicle routing

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2020年第1期28卷 279-308页

作者： Ulmer, Marlin W. Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

Stochastic and dynamic vehicle routing problems gain increasing attention in the research community. In these problems, routing plans are dynamically updated based on realizations of stochastic information. Due to the complexity of the corresponding Markov decision processes (MDPs), the calculation of optimal policies for these problems is usually not possible and researchers draw on heuristical methods of approximate dynamic programming (ADP). These methods use simulation to approximate the value of a state and decision in the MDP. The simulations are either conducted offline or online. Offline methods such as value function approximations (VFAs) generally neglect the full detail of the state space due to aggregation. Online methods such as rollout algorithms (RAs) are often not able to capture decision and transition space sufficiently due to runtime limitations. In this paper, we alleviate this tradeoff by combining two methods of ADP, an online RA and an offline VFA in two ways. In addition to the integration of the VFA as a base policy into the online RA to strengthen the RA's simulations, we also limit the RA's simulation horizon, estimating the remaining reward-to-go again via the VFA. For two stochastic dynamic routing problems from the literature, we show how this combination outperforms state-of-the-art solutions while simultaneously reducing the required time for online calculations.

关键词： approximate dynamic programming Rollout algorithm Value function approximation dynamic vehicle routing Stochastic requests Multi-period

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Surgical scheduling under uncertainty by approximate dynamic programming

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2020年 95卷 102066-102066页

作者： Silva, Thiago A. O. de Souza, Mauricio C. Univ Fed Ouro Preto ICEA Inst Ciencias Exatas & Aplicadas Dept Engn Prod BR-35931008 Joao Monlevade MG Brazil Univ Fed Minas Gerais Dept Engn Prod EE Escola Engn Campus Pampulha BR-31270901 Belo Horizonte MG Brazil

Surgical scheduling consists of selecting surgeries to be performed within a day, while jointly assigning operating rooms, starting times and the required resources. Patients can be elective or emergency/urgent. The scheduling of surgeries in an operating theatre with common resources to emergency or urgent and elective cases is highly subject to uncertainties not only on the duration of an intervention but mainly on the arrival of emergency or urgent cases. At the beginning of the day we are given a candidate set of elective surgeries with and an expected duration and a time window the surgery must start, but the expected duration and the time window of an emergency or urgent case become known when the surgery arrives. The day is divided into decision stages. Due to the dynamic nature of the problem, at the beginning of each stage the planner can make decisions taking into account the new information available. Decisions can be to schedule arriving surgeries, and to reschedule or cancel surgeries not started yet. The objective is to minimize the total expected cost composed of terms related to refusing arriving surgeries, to canceling scheduled surgeries, and to starting surgeries out of their time window. We address the problem with an approximate dynamic programming approach embedding an integer programming formulation to support decision making. We propose a dynamic model and an approximate policy iteration algorithm making use of basis functions to capture the impact of decisions to the future stages. Computational experiments have shown with statistical significance that the proposed algorithm outperforms a lookahead reoptimization approach. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Surgical scheduling Scheduling under uncertainty approximate dynamic programming approximate policy iteration algorithm

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Static output feedback stabilization of discrete time linear time invariant systems based on approximate dynamic programming

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第16期42卷 3168-3182页

作者： Demir, Okan Ozbay, Hitay Bilkent Univ Elect & Elect Engn Ankara Cankaya Turkey

This study proposes a method for the static output feedback (SOF) stabilization of discrete time linear time invariant (LTI) systems by using a low number of sensors. The problem is investigated in two parts. First, the optimal sensor placement is formulated as a quadratic mixed integer problem that minimizes the required input energy to steer the output to a desired value. Then, the SOF stabilization, which is one of the most fundamental problems in the control research, is investigated. The SOF gain is calculated as a projected solution of the Hamilton-Jacobi-Bellman (HJB) equation for discrete time LTI system. The proposed method is compared with several examples from the literature.

关键词： Static output feedback optimal sensor placement approximate dynamic programming

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