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62nd IEEE Conference on Decision and Control (CDC)

作者： Jeon, Minjae Tong, Lang Zhao, Qing Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

ISBN: (纸本)9798350301243

We address the problem of behind-the-meter EV charging by a prosumer, co-optimized with rooftop solar, electric battery, and flexible consumption devices such as water heaters and HVAC systems. We consider a scenario involving a time-of-use net energy metering tariff, alongside stochastic solar production and random EV charging demand, a finite-horizon surplus-maximization problem is formulated. We show that a procrastination threshold policy that delays EV charging to the last possible moment is optimal when EV charging is co-optimized with flexible demand, and the policy thresholds can be easily computed offline. When battery storage is part of the co-optimization, we show that the prosumer's net consumption is a two-threshold piecewise linear function of the behind-the-meter renewable generation under the optimal policy, and the procrastination threshold policy remains optimal, although the thresholds cannot be computed easily. We propose a straightforward myopic solution which becomes optimal strategy when the storage state of charge doesn't reach boundary, and demonstrate in a numerical simulation that involves real-world data.

关键词： EV charging distributed energy resources stochastic dynamic programming net energy metering

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Reinforcement Learning Applied to the dynamic Capacitated Profitable Tour Problem with stochastic Requests 24th

Reinforcement Learning Applied to the Dynamic Capacitated Pr...

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24th International Conference on Computational Science and Its Applications (ICCSA)

作者： Caspar, Marvin Wendt, Oliver Univ Kaiserslautern Landau Business Informat Syst & Operat Res BISOR Postfach 3049 D-67653 Kaiserslautern Germany

ISBN: (纸本)9783031646041;9783031646058

This paper investigates the dynamic Capacitated Profitable Tour Problem with stochastic Requests (DCPTPSR), a variant of the Traveling Salesman Problem (TSP) with profits. In the DCPTPSR, online decisions must be made for accepting and scheduling requests over a finite number of periods. Requests follow a discrete-time stochastic process, and each request is characterized by a location, demand, and prize. Accepted requests must be served on a TSP tour such that the collected prize minus the transportation costs becomes maximal. The DCPTPSR has practical applications in food delivery and less-thantruckload transportation, where requests arrive in an online fashion and immediate decisions about acceptance and scheduling must be made. We model the DCPTPSR by a Markov Decision Process (MDP) and propose a stochastic dynamic programming (SDP) algorithm for solving the problem to optimality. Addressing the computational challenges involved in SDP, we present a framework that integrates Reinforcement Learning (RL) as an alternative solution method. We perform an extensive numerical study where instances with up to incoming 25 requests can be solved by SDP while our RL approach can be used to adequately solve instances with even up to 100 incoming requests. Particularly, the performance of the RL approach is very close to the optimal policy by SDP and outperforms both the first come first serve heuristic and the first accept traveling salesman algorithm. The latter algorithm accepts requests if the available capacity enables it and fulfills these demands in an optimal TSP tour afterward. Especially instances with scarce capacity show considerable potential for savings in request acceptance and transportation scheduling decisions if both decisions are made simultaneously.

关键词： Online Optimization Profitable Tour Problem Temporal Difference Learning stochastic dynamic programming

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Refactoring an Electric-Market Simulation Software for Massively Parallel Computations 1

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9th Latin American High Performance Computing Conference (CARLA)

作者： Seveso, Franco Marichal, Raul Dufrechou, Ernesto Ezzatti, Pablo Univ Republica Inst Comp INCO Montevideo Uruguay

ISBN: (数字)9783031238215

ISBN: (纸本)9783031238208;9783031238215

In the last two decades, Uruguay has been immersed in the process of significantly changing its energy generation matrix, especially by the introduction of wind and solar sources. In this context, SimSEE, a simulation and optimization software designed to help decision-making in generating and distributing electrical energy, is extensively used. The design of this tool is conceived for conventional CPUs and follows a sequential execution paradigm. This paper focuses on a refactoring of SimSEE that enables leveraging massively-parallel hardware platforms, seeking to adapt the tool for the increasing size and complexity of Uruguay's electric market. We extend our previous ideas about reorganizing the software architecture to exploit the parallelism in each time-step of SimSEE's simulation. In more detail, we present two variants following this parallelism pattern, a straightforward parallel version that requires replicating the used memory and a variant that implies limited performance restrictions but requires a minimal memory overhead.

关键词： Coarse-grained parallelism Electric energy generation stochastic dynamic programming Memory usage

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The value of bidirectional option contracts on the multi-period ordering under inflation 16

The value of bidirectional option contracts on the multi-per...

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16th International Conference on Service Systems and Service Management (ICSSSM)

作者： Wan, Nana Wu, Xiaozhi Southwest Univ Sci & Technol Sch Econ & Management Mianyang Sichuan Peoples R China Univ Elect Sci & Technol China Zhongshan Inst Zhongshan Peoples R China

ISBN: (纸本)9781728119410

This paper considers the rising market price and the time varying demand caused by the inflation. To manage these above risks, the retailer has an opportunity to place two different types of orders, viz., the firm order and the bidirectional options order, from the supplier in each period. This paper formulates a periodic-review inventory model for the retailer with bidirectional option contracts under inflation. Based on stochastic dynamic programming, this paper studies the structure of the optimal ordering policy in each period. Then this paper provides an approximation to evaluate the corresponding policy parameters. By taking the case without bidirectional option contracts as a benchmark, this paper demonstrates that the utilization of bidirectional option contracts might prompt the retailer to enhance the service level and improve the performance.

关键词： periodic-review inventory stochastic dynamic programming optimal ordering policies bidirectional option contracts inflation

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Influence of the maximum flow ramping rates on the water value 5

Influence of the maximum flow ramping rates on the water val...

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5th International Workshop on Hydro Scheduling in Competitive Electricity Markets

作者： Guisandez, I. Perez-Diaz, J. I. Wilhelmi, J. R. Tech Univ Madrid UPM EI Caminos Canales & Puertos Dept Hydraul Energy & Environm Engn C Prof Aranguren S-N Madrid 28040 Spain

This paper presents a study on the influence of maximum ramping rates (rho) on the water values of a real hydropower plant. For this purpose, a master-slave algorithm, based on stochastic dynamic programming (SDP) and mixed integer linear programming (MILP), is used. The master module, based on SDP, has a 1-year planning period with weekly time steps and considers 3 state variables: stored volume of water in the reservoir at the beginning of each week;weekly water inflow;and average weekly energy price. The slave module, based on MILP, has a 1-week planning period with hourly time steps and considers: maximum legal and minimum technical storage capacities;maximum and minimum technical stored volumes for power generation;maximum and minimum flows released through the hydro units, the bottom outlets and the spillways according to the stored volume;one different power-discharge piecewise linear non-concave curve, as a function of the initial and estimated final stored volumes;start-up and shut-down costs of the hydro units;wear and tear costs of the hydro units caused by power variations;hourly evaporation losses according to the stored volume;hourly water inflows and energy prices;and up and down rho. The results indicate that the water values of the hydropower plant are very sensitive to the presence of rho;especially during the months with low water inflows. Water values decrease quadratically when increasing rho. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Maximum ramping rates Mixed integer linear programming stochastic dynamic programming Water value

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Assessment of Parameter Uncertainty in Autoregressive Streamflow Models for stochastic Long-term Hydrothermal Scheduling

Assessment of Parameter Uncertainty in Autoregressive Stream...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Bezerra, Bernardo Veiga, Alvaro Barroso, Luiz Augusto Pereira, Mario Pontificia Univ Catolica Rio de Janeiro Rio de Janeiro Brazil PSR Rio De Janeiro Brazil

ISBN: (纸本)9781467327299

Hydrothermal systems optimal scheduling requires the representation of uncertainties in future inflows in order to hedge against adverse future low inflows by committing thermal plants, and also to store water in reservoirs while avoiding spillage when high future inflows occur. stochastic optimization technics has been widely used as a tool for long-term hydrothermal scheduling. These models rely on Monte Carlo simulation in order to capture the inflow uncertainty during the planning horizon. Since the parameters of these models are typically estimated from historical data, it is not surprising that the actual performance of a chosen reservoirs strategy often significantly differs from the designer's initial expectations due to unavoidable modeling ambiguity. The objective of this work is to assess the impact of inflow parameter uncertainty on the stochastic hydrothermal scheduling. The results presented in this work may be useful for the improvement of stochastic optimization techniques. The results presented show that the uncertainty on the parameters of the stochastic model consists on a supplementary source of risk that should be taken into account in the scheduling model.

关键词： stochastic dynamic programming parameter uncertainty hydro scheduling bootstrap

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An organizational model for transitional negotiations: concepts, design and applications

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GROUP DECISION AND NEGOTIATION 2008年第2期17卷 157-173页

作者： Sankaran, Siva Bui, Tung Univ Hawaii Honolulu HI 96822 USA Calif State Univ Northridge Northridge CA 91330 USA

Negotiations generally tend to focus efforts on attaining optimality in single-problem contexts that are ad hoc, disparate and temporary in nature. Once negotiators reach agreement, the process usually attains closure and the long-term impact of the outcome is rarely considered. In organizational settings, decisions involving quid pro quos are, however, made on a continuous basis. Since organizational environments are constantly in flux, negotiated solutions that appeared successful on a given problem at first might no longer work out to be effective in the long run. We postulate that organizations evolve from one state to another and negotiations play an important part in these transitions. From this perspective, decision-making in organizations or between them can be modeled using sequential Markov chains that converge on homeostasis. This leads to a prescriptive approach for transitional negotiations that allow for assessment of the long-term impact of decisions and suggest acceptance of possible short-term losses in favor of the better payoffs that are to come. We provide a hydraulic dam example to illustrate the transitional aspect of decision-making over time. Based on earlier successful GDSS, we also suggest a software architecture that would allow the proposed theoretical model to be implemented as an organization negotiation support system with practical benefits.

关键词： group decision and negotiation computational organizational modeling Markov decision processes stochastic dynamic programming cybernetics organizational engineering negotiation support

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Optimization under uncertainty: state-of-the-art and opportunities

Optimization under uncertainty: state-of-the-art and opportu...

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International Conference on Foundations of Computer-Aided Process Operations

作者： Sahinidis, NV Univ Illinois Dept Chem & Biomol Engn Urbana IL 61801 USA

A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Uncertainty, for instance, governs the prices of fuels, the availability of electricity, and the demand for chemicals. A key difficulty in optimization under uncertainty is in dealing with an uncertainty space that is huge and frequently leads to very large-scale optimization models. Decision-making under uncertainty is often further complicated by the presence of integer decision variables to model logical and other discrete decisions in a multi-period or multi-stage setting. This paper reviews theory and methodology that have been developed to cope with the complexity of optimization problems under uncertainty. We discuss and contrast the classical recourse-based stochastic programming, robust stochastic programming, probabilistic (chance-constraint) programming, fuzzy programming, and stochastic dynamic programming. The advantages and shortcomings of these models are reviewed and illustrated through examples. Applications and the state-of-the-art in computations are also reviewed. Finally, we discuss several main areas for future development in this field. These include development of polynomial-time approximation schemes for multi-stage stochastic programs and the application of global optimization algorithms to two-stage and chance-constraint formulations. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： stochastic programming fuzzy programming stochastic dynamic programming global optimization approximation algorithms

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Evaluating Approaches for Estimating the Water Value of a Hydropower Plant in the Day-Ahead Electricity Market 6th

Evaluating Approaches for Estimating the Water Value of a Hy...

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6th International Workshop on Hydro Scheduling in Competitive Electricity Markets

作者： Guisandez, Ignacio Ignacio Perez-Diaz, Juan Univ Politecn Madrid Calle Prof Aranguren 3 Madrid 28040 Spain

ISBN: (纸本)9783030033118;9783030033101

This paper addresses the question of whether the use of complex algorithms, based on mixed integer linear programming, to solve the intrastage decision problems of a stochastic dynamic programming (SDP) based annual scheduling model aimed to calculate the water value of a hydropower plant is a fruitful effort. To this purpose, four 1000-year long simulations using the water value obtained from four different optimisation SDP-based scheduling models (three using mixed integer linear programming to solve the intrastage decision problems and other using linear programming) are compared. The results suggest that the small increase in profit does not make up for the necessary increase in computational time. Nonetheless, the study should be replicated using other hydropower plants and more complicated topologies in order to get more sound conclusions.

关键词： Linear programming Mixed integer linear programming stochastic dynamic programming Water value

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Optimal Control of Atlantic Population Canada Geese

Optimal Control of Atlantic Population Canada Geese

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International Congress on Modelling and Simulation (MODSIM05)

作者： Hauser, C. E. Runge, M. C. Cooch, E. G. Univ Queensland Brisbane Qld 4072 Australia

ISBN: (纸本)9780975840023

The hunting of geese and other waterfowl is an activity that circulates millions of dollars each year in North America. The Atlantic population of Canada Geese (Branta Canadensis) has historically been a major target for hunters throughout the eastern parts of the United States and Canada, although numbers declined significantly in the 1990s. Resident (non-migratory) populations of geese and migratory populations during the non-breeding season can damage crops and cause public nuisance complaints. Thus, management of goose populations can be a balance between providing high harvest opportunity while not allowing populations to get so large as to cause damage. We investigate optimal control of the Atlantic population of Canada Geese. We seek to maximize harvest, while maintaining the population within acceptable upper and lower bounds. Control is obtained by setting the harvest rate on breeding adult birds each year. The optimal harvest strategy is informed by the population state, and must incorporate a range of uncertainties regarding population dynamics and the ability to control the population. The first uncertainty is environmental variation. This is unpredictable and uncontrollable, and represented by stochasticity in breeding productivity from year to year. The second uncertainty considered in this paper is a limited ability to control the population. Given a finite number of hunters, there may be an upper limit on the total number of birds that can be harvested each year. We explore a range of limits to total annual harvest. Structural uncertainty is the third uncertainty considered. Models constructed to represent population dynamics are not a perfect description of the true dynamics. In particular, there is disagreement about the strength of density dependence underlying Canada Goose dynamics. We pose two reasonable but contrasting models of density dependence in this study. Both models of population dynamics include age structure. Canada Geese, like other

关键词： stochastic dynamic programming harvest control Canada Geese Branta canadensis uncertainty age structure

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