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Market-Based Optimal Control of Plug-In Hybrid Electric Vehicle Fleets and Economic Analysis

JOURNAL OF ENERGY ENGINEERING 2016年第3期142卷 1-12页

作者： Li, Zhihao Jiang, Yong Zhang, Xu Tian, Wei GE Global Res Ctr 1 Res Circle Niskayuna NY 12309 USA IIT Galvin Ctr Elect Innovat 10 W 35th StSTE 16D81 Chicago IL 60616 USA IIT Elect & Comp Engn Dept 3301 S Dearborn St Chicago IL 60616 USA

This paper proposes an optimization algorithm for coordinating charging/discharging activities of plug-in hybrid electric vehicles (PHEVs) within the electricity market environment. PHEV Fleets are considered as a promising solution to provide vehicle-to-grid (V2G) services. The interactions between PHEVs and the power grid will bring many challenges to the current power market. In this paper, V2G is taken into account in the ancillary service market for providing frequency regulation service. Large-scale PHEV fleet integration will cause load volatility and destabilize the grid if it is implemented without proper control. This paper proposes an optimal strategy to maximize the V2G profits while minimizing the charging costs. The optimal strategy is based on the price forecast for both residential electricity and market regulation. Because of the stochastic nature of electricity price, the proposed optimal problem is solved using stochastic dynamic programming to obtain an optimal solution with the price uncertainties taken into account. Constraints related to vehicle use as well as technical limitations are also considered. Numerical results show the effectiveness of the proposed optimal control strategy and the additional costs arising from discharging batteries for ancillary service can be partially or completely compensated by V2G profits.

关键词： stochastic dynamic programming Economical optimal solutions Frequency regulation Plug-in hybrid electric vehicle (PHEV) Vehicle-to-grid (V2G)

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No Longer in the Dark: Utilizing Imperfect Advance Load Information for Single-Truck Operators

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TRANSPORTATION SCIENCE 2022年第6期56卷 1573-1597页

作者： Najafi, Mehdi Zolfagharinia, Hossein Ryerson Univ Ted Rogers Sch Management Toronto ON M5G 2C3 Canada Sharif Univ Technol Dept Ind Engn Tehran 14588 Iran

This study investigates how imperfect advance load information (IALI) can improve profits and other operational indicators, such as empty movements, for a single -truck company. To analyze the value of IALI, we first develop a deterministic mathemati-cal model. Then, we propose a stochastic dynamic programming approach that can utilize IALI. After designing a comprehensive set of experiments, we employ both models using a dynamic implementation mechanism to assess the benefits of using IALI. Our statistical analysis reveals that (1) utilizing IALI can significantly improve a single-truck company's profits, by as much as almost 30% on average, and (2) the impact of using IALI can be affected by other factors (e.g., network size). In another set of experiments, we examine the benefits of IALI in a new environment where there are two classes of shippers, high risk and low risk. The results suggest that the potential benefits can be even larger with two classes of shippers. Last, we collect data over two three-week periods for a single-truck company that operates in Ontario, Canada, and we apply our methods for evaluating the benefits of IALI.

关键词： long-haul transportation imperfect information advance load information stochastic dynamic programming single-truck operators

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A stochastic operational planning model for a zero emission building with emission compensation

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APPLIED ENERGY 2021年 302卷 117415-117415页

作者： Thorvaldsen, Kasper Emil Korpas, Magnus Lindberg, Karen Byskov Farahmand, Hossein Norwegian Univ Sci & Technol Dept Elect Power Engn Trondheim Norway SINTEF Community Oslo Norway

The primary objective of Zero Emission Buildings (ZEBs) is to achieve net zero emission over the buildings' lifetime. To achieve this goal, accurate cost-effective emission compensation is needed during the operational phase. This paper presents a stochastic planning model comprising an emission inventory for the operation of ZEBs. The operational planning methodology uses stochastic dynamic programming (SDP) to analyze and represent the expected future cost curve (EFCC) for operation based on the electricity price and accumulated CO2eq-inventory during the year. Failing to compensate for net zero emission makes the leftover amount subject to a penalty cost at the end of the year. This renders the overall problem multi-objective optimization including emission compensation and cost of operation. The model is applied to a case study of a Norwegian building, tested for a range of penalty costs for leftover CO2eq-inventory. The results show that, for a ZEB, including emission compensation demonstrates a significant impact on the operation of the building. The penalty cost puts a limit on how much the operational cost increase for additional compensation should be, influencing the end CO2eq-inventory. Increasing penalty costs decreases the end inventory, and a penalty cost of 10 EUR/kgCO(2equ) resulted in zero emission. The case achieving zero emission had an operational cost increase of 4.8% compared to operating without a penalty cost. This shows the importance of accounting for emissions during the operation of a ZEB, and the value of having an operational strategy that presents the future impact of operation.

关键词： Operational planning stochastic dynamic programming Grid interaction Demand-side management Hourly CO2eq-intensity

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Úlohy stochastického dynamického programování: teorie a aplikace

Úlohy stochastického dynamického programování: teorie a...

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作者： Lendel, Gabriel Charles University of Prague

Title: stochastic dynamic programming Problems: Theory and Applications Author: Gabriel Lendel Department: Department of Probability and Mathematical Statistics Supervisor: Ing. Karel Sladký CSc. Supervisors e-mail address: sladky@*** Abstract: In the present work we study Markov decision processes which provide a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. We study iterative procedures for finding policy that is optimal or nearly optimal with respect to the selec- ted criteria. Specifically, we mainly examine the task of finding a policy that is optimal with respect to the total expected discounted reward or the average expected reward for discrete or continuous systems. In the work we study policy iteration algorithms and aproximative value iteration algorithms. We give numerical analysis of specific problems. Keywords: stochastic dynamic programming, Markov decision process, policy ite- ration, value iteration

关键词： stochastic dynamic programming Markov decision processes policy iteration value iteration

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Temporal concatenation for Markov decision processes

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PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2022年第4期36卷 999-1026页

作者： Song, Ruiyang Xu, Kuang Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Grad Sch Business Stanford CA USA

We propose and analyze a temporal concatenation heuristic for solving large-scale finite-horizon Markov decision processes (MDP), which divides the MDP into smaller sub-problems along the time horizon and generates an overall solution by simply concatenating the optimal solutions from these sub-problems. As a "black box" architecture, temporal concatenation works with a wide range of existing MDP algorithms. Our main results characterize the regret of temporal concatenation compared to the optimal solution. We provide upper bounds for general MDP instances, as well as a family of MDP instances in which the upper bounds are shown to be tight. Together, our results demonstrate temporal concatenation's potential of substantial speed-up at the expense of some performance degradation.

关键词： Markov decision process stochastic dynamic programming

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Markov switching autoregressive modeling of wind power forecast errors

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ELECTRIC POWER SYSTEMS RESEARCH 2020年 189卷 106641-106641页

作者： Latimier, R. Le Goff Le Bouedec, E. Monbet, V ENS Rennes SATIE Lab Rennes France Univ Grenoble LEGI Grenoble France Univ Rennes 1 IRMAR Rennes France

Forecast errors constitute the main hurdle to integrating variable renewable energies into electrical power systems. Errors are inherent to forecasting, although their magnitude varies significantly with respect to both the method adopted and the time horizon. Their dynamic and stochastic modeling is mandatory for power systems to efficiently balance out these errors. A Markov Switching Autoregressive - MS-AR - approach is proposed herein for wind power forecast errors. This particular model is able to identify weather regimes according to the forecast reliability. Such regimes are controlled by a Markov chain whose state - not directly observable - determines the AR model parameters. The statistical features of the data artificially generated by this model are very similar to those of the actual forecast error. This model is used to solve the optimal management of a storage associated with a wind farm. The resolution is performed by means of stochastic dynamic programming while comparing the proposed MS-AR approach with several other models. In this illustrative problem, a 15% reduction in operating costs is derived from a fine model of forecast errors.

关键词： Forecast errors Markov switching autoregressive stochastic dynamic programming Wind power forecast

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Online learning for distributed optimal control of an electric vehicle fleet

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ELECTRIC POWER SYSTEMS RESEARCH 2022年 212卷

作者： Latimier, R. Le Goff Cherot, G. Ben Ahmed, H. ENS Rennes CNRS SATIE Bruz France

The management of electrical power systems requires the resolution of large-scale problems whose agents are linked by coupling constraints. Nevertheless, decomposition methods cannot provide an exact solution while dealing with temporal dynamics in a stochastic environment. Indeed, each agent would have to solve a local minimisation in which future quantities intervene. However, these quantities depend on other agents' future decisions which are still unknown. In order to enhance the existing approximate approaches to this challenge, the proposed method involves Alternating Direction Method of Multipliers to overcome the large dimension by an iterative resolution of local coordinated minimisations. Uncertain temporal dynamics are handled by a stochastic dynamic programming approach. In order to make local problems solvable, the online learning of a Markov process is added. The agents can then anticipate future global variations in a local probabilistic way. The optimal charging of an electric vehicle fleet paired with a wind power plant is considered as a case study. The expected benefits are highlighted, both at the outset and after training the anticipative models. The discussion addresses the learning parameters allowing the fastest convergence.

关键词： Alternating direction method of multipliers Decomposition method stochastic dynamic programming Virtual power plant Online learning Markov process

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Modeling the impacts of a changing and disturbed environment on an endangered beluga whale population

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ECOLOGICAL MODELLING 2023年第1期483卷

作者： McHuron, Elizabeth A. Castellote, Manuel Boor, Himes Shelden, Kim E. W. Warlick, Amanda J. McGuire, Tamara L. Wade, Paul R. Goetz, Kimberly T. Univ Washington Cooperat Inst Climate Ocean & Ecosyst Studies Seattle WA 98105 USA Alaska Fisheries Sci Ctr Natl Marine Fisheries Serv Marine Mammal Lab Seattle WA 98115 USA Montana State Univ Ecol Dept Bozeman MT 59717 USA Univ Washington Sch Aquat & Fishery Sci Seattle WA 98115 USA Cook Inlet Beluga Whale Photoid Project Anchorage AK 99515 USA

Climate change and disturbance from human activities are key threats facing many wildlife populations worldwide. The ability to quantify the effects of such threats on individual health and population dynamics is critical for effective management and conservation. We used stochastic dynamic programming (SDP), a method for implementing state-dependent life history theory, to explore the impacts of changes in prey availability and anthropogenic disturbance on survival and reproductive success of Cook Inlet belugas (Delphinapterus leucas, CIB), an endangered and isolated population in decline. We predicted behavioral decisions (whether to forage, travel to a new location, or rest) of pregnant CIBs within a spatially and seasonally dynamic prey landscape. We used those decisions to explore time-activity budgets and spatial use under a variety of hypothetical environ-mental scenarios and estimate the resulting impacts on body condition and vital rates. In all scenarios, foraging activity was highest during summer to capitalize on abundant prey, which in the model was assumed to be eulachon (Thaleichthys pacificus) and salmonids (Oncorhynchus spp.). This resulted in large increases in blubber reserves that pregnant CIBs relied on during October to April when prey was assumed to be less abundant. Prey availability outside of summer months was still critical, as it either exacerbated or buffered against reductions in prey availability during summer months. Spatial predictions of habitat use indicated that pregnant CIBs should forage in areas used historically that now appear to be abandoned, suggesting that prey availability alone is unlikely to explain the recent range contraction of CIBs to upper Cook Inlet. Reductions in prey availability from late spring to early fall adversely affected vital rates, but intermittent disturbances that resulted in lost foraging opportunities, such as those caused by anthropogenic activities during the ice-free season, had little impact on

关键词： Bioenergetics Cook Inlet Delphinapterus leucas Population consequence of disturbance Prey availability stochastic dynamic programming

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Penalty-Based Algorithms for the stochastic Obstacle Scene Problem

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INFORMS JOURNAL ON COMPUTING 2014年第2期26卷 370-384页

作者： Aksakalli, Vural Ari, Ibrahim Istanbul Sehir Univ TR-34662 Istanbul Turkey

We consider the stochastic obstacle scene problem wherein an agent needs to traverse a spatial arrangement of possible obstacles, and the status of the obstacles may be disambiguated en route at a cost. The goal is to find an algorithm that decides what and where to disambiguate en route so that the expected length of the traversal is minimized. We present a polynomial-time method for a graph-theoretical version of the problem when the associated graph is restricted to parallel avenues with fixed policies within the avenues. We show how previously proposed algorithms for the continuous space version can be adapted to a discrete setting. We propose a generalized framework encompassing these algorithms that uses penalty functions to guide the navigation in real time. Within this framework, we introduce a new algorithm that provides near-optimal results within very short execution times. Our algorithms are illustrated via computational experiments involving synthetic data as well as an actual naval minefield data set.

关键词： probabilistic path planning stochastic dynamic programming Markov decision process Canadian traveler's problem

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Online Linear programming: Dual Convergence, New Algorithms, and Regret Bounds

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OPERATIONS RESEARCH 2021年第5期70卷 2948页

作者： Li, Xiaocheng Ye, Yinyu Imperial Coll London Imperial Coll Business Sch London SW7 2AZ England Stanford Univ Dept Management Sci & Engn Stanford CA 94305 USA

We study an online linear programming (OLP) problem under a random input model in which the columns of the constraint matrix along with the corresponding coefficients in the objective function are independently and identically drawn from an unknown distribution and revealed sequentially over time. Virtually all existing online algorithms were based on learning the dual optimal solutions/prices of the linear programs (LPs), and their analyses were focused on the aggregate objective value and solving the packing LP, where all coefficients in the constraint matrix and objective are nonnegative. However, two major open questions were as follows. (i) Does the set of LP optimal dual prices learned in the existing algorithms converge to those of the "offline" LP? (ii) Could the results be extended to general LP problems where the coefficients can be either positive or negative? We resolve these two questions by establishing convergence results for the dual prices under moderate regularity conditions for general LP problems. Specifically, we identify an equivalent form of the dual problem that relates the dual LP with a sample average approximation to a stochastic program. Furthermore, we propose a new type of OLP algorithm, action-history-dependent learning algorithm, which improves the previous algorithm performances by taking into account the past input data and the past decisions/actions. We derive an O(log n log log n) regret bound (under a locally strong convexity and smoothness condition) for the proposed algorithm, against the O(root n) bound for typical dual-price learning algorithms, where n is the number of decision variables. Numerical experiments demonstrate the effectiveness of the proposed algorithm and the action-history-dependent design.

关键词： online linear programming stochastic programming sequential decision making stochastic dynamic programming

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