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A two-stage stochastic optimization model for port infrastructure planning

MARITIME ECONOMICS & LOGISTICS 2024年第2期26卷 185-211页

作者： Bhurtyal, Sanjeev Hernandez, Sarah Eksioglu, Sandra Yves, Manzi Univ Arkansas 4190 Bell Engn Ctr Fayetteville AR 72701 USA Univ Arkansas Dept Ind Engn 4190 Bell Engn Ctr Fayetteville AR 72701 USA

This paper investigates inland port infrastructure investment planning under uncertain commodity (such as coal, petroleum, manufactured products, nonmetallic minerals) demand conditions. A two-stage stochastic optimization is developed to model the impact of demand uncertainty on infrastructure planning and transportation decisions. The model minimizes expected total costs, including capacity expansion costs, associated with handling equipment and storage infrastructure, and the expected transportation costs. To solve the problem, an accelerated Benders decomposition algorithm is implemented. The use of a stochastic approach is justified by comparing the value of stochastic solution with its corresponding deterministic solution. For demonstration, the model is applied to the Arkansas section of the McClellan-Kerr Arkansas River Navigation System (MKARNS). Given data availability, the model is generalizable to other regions. Results show that as investment in port capacities (handling equipment and storage infrastructure) increases by $8 million, the percent of commodity volumes that moves via waterways (in ton-miles) increases by 1%. For the Arkansas application, the model determines nonmetallic minerals as the most affected commodity by investment, and it identifies a cluster of ports at Little Rock where the investment would have the most significant impact. The contribution of the paper is in introducing a stochastic modeling framework to quantify mode shift dependencies on inland waterways port infrastructure (handling equipment and storage). Comparison of a stochastic approach to the state-of-the-literature deterministic approaches, shows that a failure to use a stochastic modeling to capture uncertainty in commodity demand could cost up to $21 M per year. The model serves as a decision-making tool for optimal, distributed allocation of monetary investments, that encourages mode shift to inland waterways.

关键词： Inland waterway port Benders decomposition algorithm stochastic programming Port infrastructure investment

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Optimal power bidding of overseas PV plants in Singapore wholesale electricity market

ENERGY CONVERSION AND ECONOMICS

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ENERGY CONVERSION AND ECONOMICS 2024年第2期5卷 73-77页

作者： Xu, Yan Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Singapore's power sector has set targets of net-zero emissions by 2050. Given the limited land space of the country, a key strategy to decarbonize the power grid is to import clean power from renewable energy resources such as photovoltaic (PV) plants installed at overseas locations. The present electricity market rules require such overseas PV plants to maintain constant power generation during each bidding period. To meet such requirements, energy storage systems (ESSs) are to be deployed in the PV plants to compensate for the PV power fluctuation. This paper proposes an optimal power bidding approach for maximizing the profit of the PV plant participating in the Singapore wholesale electricity market. The problem is formulated as a stochastic programming model, which takes the short-term PV power forecasting as the input, maximizes the expected profit considering the PV power selling revenue and the penalty cost for power shortfall during each bidding cycle (30 min), and satisfies constraints of the ESS. The proposed method can also be used for determining the optimal size of the ESS. Simulation results have verified the effectiveness of the proposed method.

关键词： electricity market energy storage system optimal power bidding photovoltaic stochastic programming

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A chance-constrained programming approach to optimal management of car-rental fleets of electric vehicles

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2025年 41卷

作者： Zanvettor, Giovanni Gino Casini, Marco Giannitrapani, Antonio Paoletti, Simone Vicino, Antonio Univ Siena Dipartimento Ingn Informaz & Sci Matematiche Via Roma 56 I-53100 Siena Italy

In the current context of growing electrification of the transport sector, offering rental and sharing programs for electric vehicles is considered one of the strategies to achieve decarbonization targets. Such programs should be supported by suitable optimization tools to manage the vehicle fleet, and make rental provision profitable for its operator. In this paper, we consider a rental system having a single station for electric vehicle pickup and delivery. For this system, we address the operational problem of simultaneously assigning rental requests to vehicles and determining the charging policies during inactivity intervals. The objective is to maximize the profit for the operator by minimizing the costs for electricity. The considered problem is complicated by uncertainty regarding the battery energy level when a vehicle returns to the station. This leads to a chance-constrained programming formulation, where the request-to-vehicle assignment and charging policies are determined by minimizing electricity costs while ensuring that the energy demand of the served requests is met with a prescribed high probability. Since the formulated mixed-integer problem with probabilistic constraints is hard to solve, a suboptimal approach is proposed, consisting of two sequential steps. In the first step, request-to-vehicle assignment is accomplished via a suitably designed heuristic procedure. Then, for a given assignment, the charging policy of each vehicle is determined by solving a relaxed chance-constrained problem. Numerical results are presented to assess the performance of both the assignment procedure and the optimization problem which determines the electric vehicle charging policies.

关键词： Electric vehicles Car-rental system Resource allocation stochastic programming Chance-constrained optimization

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A benchmark dataset for the retail multiskilled personnel planning under uncertain demand

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Data Science 2024年第1期7卷 13-27页

作者： Henao, César Augusto Porto, Andrés Felipe González, Virginia I. Department of Industrial Engineering Universidad Del Norte Barranquilla Colombia Department of Industrial Engineering Institución Universitaria Americana Barranquilla Colombia Departament d'Organització d'Empreses Universitat Politècnica de Catalunya Barcelona Spain

In this data article, we present and describe datasets designed to address multiskilled personnel assignment problems (MPAP) under uncertain demand. The data article introduces simulated datasets and a real dataset obtained from a retail store in Chile. The real dataset provides details on the structure of the store, including the number of departments and workers, the type of labor contract, the cost parameter values, and the average demand across all store departments. The simulated datasets, consisting of 18 categorized text files, were generated through Monte Carlo simulation to encapsulate information about the stochastic demand for store departments. These text files are classified based on: (i) type of sample (in-sample or out-of-sample), (ii) type of truncation method (zero-truncated or percentile-truncated), and (iii) demand coefficient of variation (5%, 10%, 20%, 30%, 40%, 50%). This categorization allows academics and practitioners to select the scenarios that meet with their specific research or application needs, increasing the flexibility and applicability of the datasets. In addition, researchers and practitioners can use these comprehensive real and simulated datasets to benchmark the performance of diverse optimization methods under uncertain demand, thereby ensuring robust multiskilling levels for similar MPAPs. Furthermore, we offer an Excel workbook with the capability to generate up to 10,000 demand scenarios for varying coefficients of variation in demand. © 2024 - The authors. Published by IOS Press.

关键词： Multiskilling personnel scheduling retail stochastic programming workforce flexibility

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Battery Bidding Strategy Under Uncertainty Considering Market Practical Situations

Battery Bidding Strategy Under Uncertainty Considering Marke...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Ren, Weihang Guan, Yongpei Univ Florida Dept Ind & Syst Engn Gainesville FL 32611 USA

ISBN: (纸本)9798350381849;9798350381832

Storing off-peak electricity and supplying it during peak demand offers advantages in energy production, social impact, and environmental preservation. At the same time, private battery owners can profit from this energy arbitrage, driving diverse storage methods. Yet, varying market conditions and nonconvex nature of the electricity pricing lead to unpredictable market schemes. This paper employs stochastic programming to develop robust bidding strategies for uncertain market situations. We create efficient algorithms for battery owners, generate bidding strategies in each market, and analyze their structural performance. Numerical experiments demonstrate our algorithms' empirical performance, offering insights for battery owners.

关键词： Battery bidding energy storage stochastic programming

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stochastic Microgrid Sizing Considering Electric Vehicle Charging Demand

Stochastic Microgrid Sizing Considering Electric Vehicle Cha...

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International Conference on Smart Energy Systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Magalhaes, Marcelo Montandon Terada, Lucas Zenichi Cortez, Juan Carlos Soares, Joao Rider, Marcos J. Vale, Zita Univ Estadual Campinas Dept Energy Syst DSE Campinas Brazil Polytech Porto GECAD Sch Engn ISEP Porto Portugal

ISBN: (纸本)9798350386509;9798350386493

Microgrids represent a pivotal shift in energy technology, offering a suite of advantages over conventional power grids, such as improved reliability, cost efficiency, and environmental benefits due to their ability to integrate renewable sources and provide flexible energy solutions, underscoring the urgent need for grid modernization and strategic planning. This paper presents a stochastic economic model of linear programming (LP) for microgrid sizing, considering the charging scheduling profiles of electric vehicles (EVs) within a grid. The model is based on linear approximation techniques for the operation of EV and battery energy storage system (BESS). The model also incorporates the injection of energy from EV into energy distributions system (EDS) using vehicle-to-grid (V2G) technology, which considers compensation for the owners.

关键词： Distributed energy resources Electric vehicles Microgrids sizing stochastic programming Vehicle-to-grid

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Multi-stage Dynamic Scheduling Optimization for Autonomous Mobility-on-Demand Systems 21

Multi-stage Dynamic Scheduling Optimization for Autonomous M...

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21st IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Tian, Hangqi Wang, Ning Guo, Yuntao Tongji Univ Sch Automot Studies 4800 Caoan Rd Shanghai Peoples R China Tongji Univ Minist Educ Key Lab Rd & Traff Engn 4800 Caoan Rd Shanghai Peoples R China

ISBN: (纸本)9798350388084;9798350388077

Autonomous Mobility-on-Demand (AMoD) system is booming in urban transportation, however, the uncertain and complex fleet dynamic scheduling remains one of the main challenges for operators. Therefore, this study proposes a multistage dynamic scheduling approach for shared autonomous electric vehicles (SAEVs). Specifically, considering the matching characteristics of the different tasks, the vehicle order-taking and charging tasks are modelled as bipartite graph maximum weight matching problems with the objectives of maximizing order revenue and minimizing charging mileage. The vehicle repositioning task is modelled as integer programming with the objective of minimizing the vehicle dispatch mileage, and stochastic chance-constrained programming is applied to cope with uncertain user demands. A solution strategy combining the KM algorithm with Gurobi solver and greedy strategy is designed to obtain the real-time scheduling instructions. Finally, numerical experiments based on real travel data validate the advantages of the proposed approach in reducing fleet scheduling costs and improving system operational revenues.

关键词： Shared autonomous electric vehicles Dynamic scheduling stochastic programming Kuhn-Munkres algorithm

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Two-stage chance-constrained programming based on Gaussian mixture model and piecewise linear decision rule for refinery optimization

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COMPUTERS & CHEMICAL ENGINEERING 2024年 184卷

作者： Yang, Yu Calif State Univ Long Beach Dept Chem Engn Long Beach CA 90840 USA

The two -stage chance -constrained program (CCP) is studied for a refinery optimization problem. In stage -I, the refinery decision -makers determine the type and quantity of crude oil procurement under operational uncertainties to maximize the expected profit under all possibilities. In stage -II, process unit flowrates are adjusted based on the realized uncertainties and available crude oil, while introducing probabilistic constraints to manage the off -spec risk. To solve such a two -stage optimization problem, we propose a novel approach using Gaussian mixture model (GMM) to characterize uncertainties, and piecewise linear decision rule for stage -II operations. Comparing to the conventional scenario -based mixed -integer linear program (MILP), our new approach offers three advantages. First, it leverages a well -developed global optimization scheme for joint CCP to avoid scenario generation and potential bias. Second, the data -driven GMM enables CCP to handle uncertainties with general distributions. Third, the stage -II variables are parameterized via Gaussian component induced piecewise linear decision rule to strike an excellent trade-off between optimality and computational time. A simplified refinery plant, consisting of distillation, cracker, reformer, isomerization, and desulfurization units, is used as a test bed to demonstrate the superiority of the proposed optimization method in solution time, probabilistic feasibility, and optimality over the large-scale scenario -based MILP.

关键词： stochastic programming Chance-constrained optimization Gaussian mixture model Piecewise linear decision rule

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Risk Quantification with Management of Aggregated Energy Resources

Risk Quantification with Management of Aggregated Energy Res...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Roy, Trishant Hedman, Kory W. Arizona State Univ Sch Elect Comp & Energy Engn ECEE Tempe AZ 85281 USA

ISBN: (纸本)9798350381849;9798350381832

Unpredictabilities and uncertainties are inherent in the management of distributed energy resources (DERs). This poses operational challenges for the utility/aggregator managing the resources and brings the need for an efficient decision-making framework under uncertainty for participation in electricity markets. This paper presents mixed-integer linear programming formulations considering the aggregator's risk preferences/tolerance in the day-ahead and real-time markets. Since DERs also introduce risks for the system operator responsible for the reliability of the electric grid, effective and easy-to-interpret risk quantification methodologies are expected to be beneficial from a practical outlook. A near-term risk metric is developed from the operator's perspective to assess the future performance of aggregators to help them make appropriate decisions. Various simulations using the NREL PERFORM dataset are conducted to analyze the effect of different risk preferences of the aggregator on both the profitability and the quantified risk metric.

关键词： Distributed energy resources aggregation uncertainty assessment electricity markets stochastic programming

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Data-driven stochastic optimization for power grids scheduling under high wind penetration

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2023年第1期14卷 41-65页

作者： Xie, Wei Yi, Yuan Zhou, Zhi Wang, Keqi Northeastern Univ Boston MA 02115 USA Rensselaer Polytech Inst Troy NY 12180 USA Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA

To address the environmental concern and improve the economic efficiency, the wind power is rapidly integrated into smart grids. However, the inherent uncertainty of wind energy raises operational challenges. To ensure the cost-efficient, reliable and robust operation, it is critically important to find the optimal decision that can correctly and rigorously hedge against all sources of uncertainty. In this paper, we propose data-driven stochastic unit commitment (SUC) to guide the power grids scheduling. Specifically, given the finite historical data, the posterior predictive distribution is developed to quantify the wind power prediction uncertainty accounting for both inherent stochastic uncertainty of wind power generation and input model estimation error. For complex power grid systems, a finite number of scenarios is used to estimate the expected cost in the planning horizon. To further control the impact of finite sampling error induced by using the sample average approximation (SAA), we propose a parallel computing based optimization solution methodology, which can quickly find the reliable optimal unit commitment decision hedging against various sources of uncertainty. The empirical study over six-bus and 118-bus systems demonstrates that our approach can provide more cost-efficient and robust performance than the existing deterministic and stochastic unit commitment approaches.

关键词： stochastic programming Unit commitment Parallel computing Wind power Power grids scheduling Renewable energy

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