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The 7th Asia-Pacific Power and Energy Engineering Conference (APPEEC 2015)

作者： Hang Dai Ni Zhang Wencong Su Department of Electrical and Computer Engineering University of Michigan

The study of unit commitment(UC) aims to find reasonable schedules for generators to optimize power systems' operation. Many papers have been published that solve UC through different me-thods. Articles that systematically summarize UC problems' progress in order to update research-ers interested in this field are needed. Because of its promising performance, stochastic pro-gramming(SP) has become increasingly researched. Most papers, however, present SP's UC solv-ing approaches differently, which masks their relationships and makes it hard for new research-ers to quickly obtain a general idea. Therefore, this paper tries to give a structured bibliographic survey of SP's applications in UC problems.

关键词： Unit Commitment stochastic programming Review

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Medium-Term Hydrothermal Scheduling of the Infiernillo Reservoir Using stochastic Dual Dynamic programming (SDDP): A Case Study in Mexico

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ENERGIES 2023年第17期16卷 6288页

作者： Marin Cruz, Ignacio Badaoui, Mohamed Mota Palomino, Ricardo Inst Politecn Nacl Escuela Super Ingn Mecan & Elect Ave Luis Enrique Erro S-N Mexico City 07738 DF Mexico

This article aims to obtain and evaluate medium-term operating policies for the hydrothermal scheduling problem by using the stochastic dual dynamic programming (SDDP) approach. To this end, to feed the mathematical model and build the probability distribution functions that best fit each month of the actual inflow volume, monthly inflow data recorded from 1938 to 2018 for the Infiernillo reservoir located in Mexico were employed. Moreover, we simulated inflow volume scenarios using the Monte Carlo method for each month of a one-year planning period. The SDDP approach to solving the optimization problem consisted of the simulation of one forward scenario per iteration and the stabilization of the total operating cost as a convergence criterion, which results in an operating policy. We then assessed its quality by estimating the one-sided optimality gap. It is worth mentioning that the best operation policy required scenario trees of up to 17,000 inflow realizations per stage. Additionally, to study the evolution of the expected value along the planning horizon of the main variables involved in the medium-term hydrothermal scheduling problem, we simulated the best operation policy over 10,000 inflow scenarios. Finally, to show the practical value of the proposed approach, we report its computational complexity.

关键词： stochastic programming medium-term hydrothermal scheduling stochastic dual dynamic programming Infiernillo reservoir

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A mathematical programming based decision support system for waste to resource market trading

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JOURNAL OF CLEANER PRODUCTION 2024年 437卷

作者： Mah, Angel Xin Yee Ng, Tsan Sheng Wang, Bo Tiong, Yong Wei Lee, Jonathan T. E. Wang, Chi-Hwa Natl Univ Singapore NUS Environm Res Inst Singapore City 138602 Singapore Campus Res Excellence & Technol Enterprise CREATE Energy & Environm Sustainabil Solut Megac E2S2 Singapore City 138602 Singapore Natl Univ Singapore Dept Ind Syst Engn & Management Singapore City 117576 Singapore Natl Univ Singapore Dept Chem & Biomol Engn Singapore City 117585 Singapore Natl Univ Singapore Energy Studies Inst Singapore City 119620 Singapore

In a linear economy, consumers typically dispose end-of-life products which are eventually incinerated or landfilled. Digital platforms could unlock rich waste reutilization opportunities and economic benefits by connecting the waste producers with potential waste buyers in online marketplaces. While previous studies mostly focus on input-output matching, this study proposes a decision support system that can be incorporated to an online marketplace to optimize the waste trading based on economic considerations. Moreover, the decision support system could facilitate higher level decisions such as capacity planning and incentive design. Based on a case study on Singapore's organic waste streams, the waste trading platform presents economic benefits for agents that sell their waste. Additionally, enforcing waste reutilization targets would require relevant policy support in terms of incentives or levies, where the levies can be implemented as co-payment rate for the agents. For instance, results showed that when the food waste reutilization target is set as 6 t/d, increasing the copayment rate from 0 to 20% allows the waste trading network to be self-sustaining without external funding. Finally, sensitivity analyses show that collecting more waste records in the database helps in optimizing the investment decisions, preferably when the number of records exceeds 64.

关键词： Circular economy Industrial symbiosis Mathematical optimization Waste to resource Digital platform stochastic programming

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Dynamic capacity planning of hospital resources under COVID-19 uncertainty using approximate dynamic programming

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第1期75卷 13-25页

作者： Gokalp, Elvan Cakir, M. Selim Satis, Hasan Univ Bath Bath England Univ Bristol Bristol England Gazi Univ Fac Med Ankara Turkiye

COVID-19 pandemic has resulted in an inflow of patients into the hospitals and overcrowding of healthcare resources. Healthcare managers increased the capacities reactively by utilizing expensive but quick methods. Instead of this reactive capacity expansion approach, we propose a proactive approach considering different realizations of demand uncertainties in the future due to COVID-19. For this purpose, a stochastic and dynamic model is developed to find the right amount of capacity increase in the most critical hospital resources. Due to the problem size, the model is solved with Approximate Dynamic programming. Based on the data collected in a large tertiary hospital in Turkey, the experiments show that ADP performs better than a benchmark myopic heuristic. Finally, sensitivity analysis is performed to explore the impact of different epidemic dynamics and cost parameters on the results.

关键词： stochastic programming dynamic programming health services simulation

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Dispersion Sensitive Optimal Control: A Conditional Value-at-Risk-Based Tail Flattening Approach via Sequential Convex programming

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2024年第6期32卷 2468-2475页

作者： Echigo, Kazuya Sheridan, Oliver Buckner, Samuel Acikmese, Behcet Univ Washington William E Boeing Dept Aeronaut & Astronaut Seattle WA 98195 USA

In this brief, we propose a sequential convex programming (SCP) framework for minimizing the terminal state dispersion of a stochastic dynamical system about a prescribed destination-an important property in high-risk contexts such as spacecraft landing. Our proposed approach seeks to minimize the conditional value-at-risk (CVaR) of the dispersion, thereby shifting the probability distribution away from the tails. This approach provides an optimization framework that is not overly conservative and can accurately capture more information about true distribution, compared with methods which consider only the expected value, or robust optimization methods. The main contribution of this brief is to present an approach that: 1) establishes an optimization problem with CVaR dispersion cost 2) approximated with one of the two novel surrogates which is then 3) solved using an efficient SCP algorithm. In 2), two approximation methods, a sampling approximation (SA) and a symmetric polytopic approximation (SPA), are introduced for transforming the stochastic objective function into a deterministic form. The accuracy of the SA increases with sample size at the cost of problem size and computation time. To overcome this, we introduce the SPA, which avoids sampling by using an alternative approximation and thus offers significant computational benefits. Monte Carlo simulations indicate that our proposed approaches minimize the CVaR of the dispersion successfully.

关键词： Dispersion Cost function Optimization Optimal control Gaussian distribution Tail Uncertainty Convex programming nonlinear programming stochastic programming

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Joint optimal congestion control and power allocation for MCMR mesh networks based on stochastic programming

Joint optimal congestion control and power allocation for MC...

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11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015)

作者： Kewei Li Xiaoqian Li Yunfei Peng Bo Jiang Wuhan Second Ship Design and Research Institute Hubei China

ISBN: (纸本)9781510821279

A stochastic model for joint optimal congestion control and power allocation in multi-channel multi-radio wireless mesh networks is proposed. The design problem characterizes the stochastic traffic of the network data flows and the capacity of radio channels in multi-channel multi-radio wireless mesh networks as some random variables. The corresponding design problem is formulated as the stochastic network utility maximization problem subject to some stochastic constrains, which is a chance constrained programming. The genetic algorithm is used to solve this stochastic maximization problem. The simulation is conducted in the Hyacinth network, and the result shows the quantitative relationship between the network performance and the confidence level.

关键词： Congestion Control Cross-Layer Genetic Algorithm stochastic programming Wireless Mesh Network Wireless mesh networks stochastic models Genetic Algorithm (GA) power splitting stochastic programming design problems Mesh networks wireless channels

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A bi-objective stochastic programming model for optimising automated material handling systems with reliability considerations

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第19期52卷 5597-5610页

作者： Tavana, Madjid Fazlollahtabar, Hamed Hassanzadeh, Reza La Salle Univ Business Syst & Analyt Dept Lindback Distinguished Chair Informat Syst & Deci Philadelphia PA 19141 USA Univ Paderborn Business Informat Syst Dept Fac Business Adm & Econ D-33098 Paderborn Germany Iran Univ Sci & Technol Fac Ind Engn Tehran Iran Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran

The optimisation of material handling systems (MHSs) can lead to substantial cost reductions in manufacturing systems. Choosing adequate and relevant performance measures is critical in accurately evaluating MHSs. The majority of performance measures used in MHSs are time-based. However, moving materials within a manufacturing system utilise time and cost. In this study, we consider both time and cost measures in an optimisation model used to evaluate an MHS with automated guided vehicles. We take into account the reliability of the MHSs because of the need for steadiness and stability in the automated manufacturing systems. Reliability is included in the model as a cost function. Furthermore, we consider bi-objective stochastic programming to optimise the time and cost objectives because of the uncertainties inherent in the optimisation parameters in real-world problems. We use perceptron neural networks to transform the bi-objective optimisation model into a single objective model. We use numerical experiments to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.

关键词： material handling system stochastic programming automated guided vehicle reliability perceptron neural network

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Two-stage stochastic programming problems involving multi-choice parameters

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APPLIED MATHEMATICS AND COMPUTATION 2014年 240卷 109-114页

作者： Barik, S. K. Biswal, M. P. Chakravarty, D. Indian Inst Technol Dept Math Kharagpur 721302 W Bengal India Indian Inst Technol Dept Min Engn Kharagpur 721302 W Bengal India

In this paper, we propose a two-stage stochastic linear programming model considering some of the right hand side parameters of the first stage constraints as multi-choice parameters and rest of the right hand side parameters of the constraints as exponential random variables with known means. Both the randomness and multi-choiceness are simultaneously considered for the model parameters. Randomness is characterized by some random variables with its distribution and multi-choiceness is handled by using interpolating polynomials. To solve the proposed problem, first we remove the fuzziness and then for multi-choice parameters interpolating polynomials are established. After establishing the deterministic equivalent of the model, standard mathematical programming technique is applied to solve the problem. A numerical example is presented to demonstrate the usefulness of the proposed methodology. (C) 2014 Elsevier Inc. All rights reserved.

关键词： stochastic programming Two-stage stochastic programming Exponential random variables Multi-choice parameter Lagrange interpolating polynomials

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Optimal Charging of Electric Vehicles using a stochastic Dynamic programming Model and Price Prediction

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SAE INTERNATIONAL JOURNAL OF PASSENGER CARS-ELECTRONIC AND ELECTRICAL SYSTEMS 2015年第2期8卷 379-393页

作者： Mody, Sagar Steffen, Thomas Loughborough Univ Technol Loughborough Leics England

The idea of grid friendly charging is to use electricity from the grid to charge batteries when electricity is available in surplus and cheap. The goal is twofold: to avoid putting additional load on the electricity grid and to reduce the cost to the consumer. To achieve this, a smart meter and a tariff with variable electricity prices has to be in place. In Day Ahead tariff (DA), prices are announced in advance for the next day, and this information can be used to select the cheapest times to charge the battery by the required amount. The optimization method is very simple, and it only has to be run once per day. However, the balance of supply and demand is not fully known in advance. Therefore Real Time Pricing (RTP) tariff supplies electricity at spot market rate depending on the current balance. This makes the charging process less predictable because it adds a stochastic element, but it does offer the potential of higher savings if future prices can be predicted with a reasonable degree of accuracy. This paper proposes an optimal controller based on a stochastic dynamic program (SDP), which predicts future price changes from available data. The controller takes into account price variability via a simple grid model that allows of unexpected price rises and a gradual return to a normal grid price. The DP algorithm has two variables, the state of charge (SoC) and the current electricity cost. It traces the expected total cost based on the stochastic model and makes a decision 'to charge or not' to minimize the expected (average) total cost. The results show that in case of a positive probability of price rises, the time to charge is chosen slightly before the lowest expected cost during the night. This is a rational solution, because waiting longer does increase the risk of an unexpected price spike. In the trivial case of a zero probability of unexpected price rises, the solution converges to the one found by the previous deterministic optimization algorithm.

关键词： ELECTRIC vehicles CHARGING effects stochastic programming MATHEMATICAL models AUTOMOBILE sales & prices

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stochastic programming MODEL FOR FUEL TREATMENT MANAGEMENT

STOCHASTIC PROGRAMMING MODEL FOR FUEL TREATMENT MANAGEMENT

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作者： MOHANNAD REDA A KABLI Texas A&M University

学位级别：硕士

Due to the increased number and intensity of wildfires, the need for solutions that minimize the impact of fire are needed. Fuel treatment is one of the methods used to mitigate the effects of fire at a certain area. In this thesis, a two-stage stochastic programming model for fuel treatment management is constructed. The model optimizes the selection of areas for fuel treatment under budget and man- hour constraints. The process makes use of simulation tools like PHYGROW, which mimics the growth of vegetation after treatment, and FARSITE, which simulates the behavior of fire. The model minimizes the costs of fuel treatment as well as the potential losses when fire occurs. Texas Wildfire Risk Assessment Model (TWRA) used by Texas Forest Service (TFS) is used to quantify risk at each area. The model is applied at TX 12, which is a fire planning unit under the administration of TFS. Results show that the total of the expenditures on fuel treatment and the expected impact justify the efforts of fuel treatment.

关键词： fuel treatment optimization stochastic programming Thesis

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