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Optimal configuration of energy storage for alleviating transmission congestion in renewable energy enrichment region

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JOURNAL OF ENERGY STORAGE 2024年 82卷

作者： Zhao, Yingjie Zhang, Yao Li, Yongfei Chen, Yuxuan Huo, Wei Zhao, Hanting Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China

High -penetration renewable energy development causes transmission congestion in power system operation. Such transmission congestion in short period can be alleviated by energy storage configuration, instead of investing and expanding new transmission lines. This paper presents an optimal configuration method of energy storage for alleviating transmission congestion in renewable energy enrichment region. In order to obtain the characteristics of renewable energy outputs, a scenario generation technique for renewable energy outputs considering spatio-temporal correlation is first proposed. A Monte Carlo -based approach for evaluating the transmission congestion is proposed for identifying the potential locations of energy storage installation. Finally, an optimal configuration method of energy storage based on stochastic programming is proposed for alleviating transmission congestion. Numerical experiments are carried out on a modified IEEE-RTS 24 -bus system and a practical 129 -bus system. Numerical results show that energy storage can improve the flexibility of power system operation and the utilization of renewable energy generation. Especially, in the practical 129 -bus system, the proposed method reduces renewable energy curtailment by 625.26 MWh and 444.43 MWh under fall and winter typical scenarios, respectively. And, investing some energy storage devices is more economical when the duration of transmission congestion is relatively short.

关键词： Spatio-temporal correlation Transmission congestion evaluation Energy storage configuration Renewable energy utilization stochastic programming Monte Carlo method

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SimPO: Simultaneous Prediction and Optimization

SimPO: Simultaneous Prediction and Optimization

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IEEE International Conference on Services Computing (IEEE SCC)

作者： Zhang, Bing Ong, Yuya Jeremy Nakamura, Taiga IBM Res Almaden San Jose CA 95120 USA

ISBN: (数字)9781665481465

ISBN: (纸本)9781665481465

Many machine learning (ML) models are integrated within the context of a larger system as part of a key component for decision-making processes. Concretely, predictive ML models are often employed in estimating the parameters for the input values that are utilized for optimization models as isolated processes. Traditionally, the predictive ML models are built first, then the model outputs are used to generate decision values separately. However, it is often the case that the prediction values that are trained independently of the optimization process produce sub-optimal solutions. In this paper, we propose a formulation for the Simultaneous Prediction and Optimization (SimPO) framework. This framework introduces the use of a joint weighted loss of a decision-driven predictive ML model and an optimization objective function, which is optimized end-to-end directly through gradient-based methods.

关键词： Decision Support Machine Learning Optimization Recommendation Systems stochastic programming

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Modeling Adoption of Carinata Production and Designing the Supply Chain of Carinata-Based Sustainable Aviation Fuel Production in the Southern United States

Modeling Adoption of Carinata Production and Designing the S...

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作者： Ullah, Kazi Masel University of Georgia

学位级别：Ph.D., Doctor of Philosophy

Carinata (Brassica Carinata) is a promising crop for producing sustainable aviation fuel (SAF) in the Southern United States. However, a lot of information on the adoption of carinata as a feedstock for SAF production is missing. Similarly, information about the cost-effectiveness and environmental feasibility of carinata-based SAF across the supply chain is still unknown. In this context, this study explored the adoption challenges of carinata as a newly introduced bioenergy crop using a spatially explicit agent-based model (ABM), and then estimated the production cost and intensity of greenhouse gas emission of carinata-based SAF production with deterministic and 12 multiperiod stochastic supply chain models. The ABM assumed the principles of profit maximization, neighborhood influences, risk aversion behavior of farmers, followed by three estimation procedures - a) the profitability difference between traditional crop rotations (with and without carinata at different contract prices), b) the adoption rate of neighboring farmers, and c) their land allocation decisions from managing a risky portfolio of enterprises. The deterministic supply chain model was developed with a Mixed-Integer Linear programming approach integrated with the Geographical Information System and Life Cycle Analysis for a 20 years simulation period along with four annual seasons for continuous seed supply purposes. The stochastic models were the extension of the supply chain configuration of the deterministic model, where models were run for four different simulation periods (5, 10, 15, 20-years) with three probabilistic scenarios (99%, 95%, and 90% confidence interval). The supply chain models determined the locations of farms and facilities (e.g., storage units, crushing mills, biorefineries) by minimizing the overall cost of the systems. The ABM results showed that the proportion of land allocated to carinata to the total farmland under field crops were 38% and 85% after 33 years under the

关键词： Agent-based modeling Bioenergy Greenhouse gas Life cycle assessment Mixed-integer programming stochastic programming

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Uncertain random geometric programming problems

arXiv

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arXiv 2023年

作者： Mondal, Tapas Ojha, Akshay Kumar Pani, Sabyasachi School of Basic Sciences Indian Institute of Technology Bhubaneswar India

In this paper, we introduce a deterministic formulation for the geometric programming problem, wherein the coefficients are represented as independent linear-normal uncertain random variables. To address the challenges posed by this combination of uncertainty and randomness, we introduce the concept of an uncertain random variable and present a novel framework known as the linear-normal uncertain random variable. Our main focus in this work is the development of three distinct transformation techniques: the optimistic value criteria, pessimistic value criteria, and expected value criteria. These approaches allow us to convert a linear-normal uncertain random variable into a more manageable random variable. This transition facilitates the transformation from an uncertain random geometric programming problem to a stochastic geometric programming problem. Furthermore, we provide insights into an equivalent deterministic representation of the transformed geometric programming problem, enhancing the clarity and practicality of the optimization process. To demonstrate the effectiveness of our proposed approach, we present a numerical *** Codes 90C15 © 2023, CC BY.

关键词： stochastic programming

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MULTI-OBJECTIVE TRANSPORTATION PROBLEM UNDER TYPE-2 TRAPEZOIDAL FUZZY NUMBERS WITH PARAMETERS ESTIMATION AND GOODNESS OF FIT

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TRANSPORT 2021年第4期36卷 317-338页

作者： Kamal, Murshid Alarjani, Ali Haq, Ahteshamul Yusufi, Faiz Noor Khan Ali, Irfan Aligarh Muslim Univ Dept Stat & Operat Res Aligarh Uttar Pradesh India Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Saudi Arabia

The problem of transportation in real-life is an uncertain multi-objective decision-making problem. In particular, by taking into account the conflicting objectives, Decision-Makers (DMs) are looking for the best transport set up to determine the optimum shipping quantity subject to certain capacity constraints on each route. This paper presented a Multi-Objective Transportation Problem (MOTP) where the objective functions are considered as Type-2 trapezoidal fuzzy numbers (T2TpFN), respectively. Demand and supply in constraints are in multi-choice and probabilistic random variables, respectively. Also considered the "rate of increment in Transportation Cost (TC) and rate of decrement in profit on transporting the products from ith sources to jth destinations due to" (or additional cost) of each product due to the damage, late deliveries, weather conditions, and any other issues. Due to the presence of all these uncertainties, it is not possible to obtain the optimum solution directly, so first, we need to convert all these uncertainties from the model into a crisp equivalent form. The two-phase defuzzification technique is used to transform T2TpFN into a crisp equivalent form. Multi-choice and probabilistic random variables are transformed into an equivalent value using stochastic programming (SP) approach and the binary variable, respectively. It is assumed that the supply and demand parameter follows various types of probabilistic distributions like Weibull, Extreme value, Cauchy and Pareto, Normal distribution, respectively. The unknown parameters of probabilistic distributions estimated using the maximum likelihood estimation method at the defined probability level. The best fit of the probability distributions is determined using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), respectively. Using the Fuzzy Goal programming (FGP) method, the final problem is solved for the optimal decision. A case study is intended to provid

关键词： multi-objective optimization transportation problem fuzzy goal programming multi-choice maximum likelihood estimation Akaike information criterion Bayesian information criterion stochastic programming

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Inexact stochastic mirror descent for two-stage nonlinear stochastic programs

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MATHEMATICAL programming 2021年第1-2期187卷 533-577页

作者： Guigues, Vincent FGV Sch Appl Math Rio De Janeiro Brazil

We introduce an inexact variant of stochastic mirror descent (SMD), called inexact stochastic mirror descent (ISMD), to solve nonlinear two-stage stochastic programs where the second stage problem has linear and nonlinear coupling constraints and a nonlinear objective function which depends on both first and second stage decisions. Given a candidate first stage solution and a realization of the second stage random vector, each iteration of ISMD combines a stochastic subgradient descent using a prox-mapping with the computation of approximate (instead of exact for SMD) primal and dual second stage solutions. We provide two convergence analysis of ISMD, under two sets of assumptions. The first convergence analysis is based on the formulas for inexact cuts of value functions of convex optimization problems shown recently in Guigues (SIAM J. Optim. 30(1), 407-438, 2020). The second convergence analysis provides a convergence rate (the same as SMD) and relies on new formulas that we derive for inexact cuts of value functions of convex optimization problems assuming that the dual function of the second stage problem for all fixed first stage solution and realization of the second stage random vector, is strongly concave. We show that this assumption of strong concavity is satisfied for some classes of problems and present the results of numerical experiments on two simple two-stage problems which show that solving approximately the second stage problem for the first iterations of ISMD can help us obtain a good approximate first stage solution quicker than with SMD.

关键词： Inexact cuts for value functions Inexact stochastic mirror descent Strong concavity of the dual function stochastic programming

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Resource allocation for contingency planning: An inexact proximal bundle method for stochastic optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期291卷 1008-1023页

作者： Moazeni, Somayeh Collado, Ricardo A. Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

Resource contingency planning aims to mitigate the effects of unexpected disruptions in supply chains. While these failures occur infrequently, they often have disastrous consequences. This paper formulates the resource allocation problem in contingency planning as a two-stage stochastic optimization problem with a risk-averse recourse function. The solution method proposed relies on an inexact proximal bundle method with subgradient approximations through a scenario reduction mechanism. The paper extends the inexact oracle to a more general risk-averse setting, and proves that it meets the requirements of the oracle in the inexact bundle method, ensuring convergence to an optimal solution. The practical performance of the developed inexact bundle method under risk aversion is investigated for our resource allocation problem. We create a library of test problems and obtain their optimal values by applying the exact bundle method. The computed solutions from the developed inexact bundle method are compared against these optimal values, under different coherent risk measures. Our analyses indicate that our inexact bundle method significantly reduces the computational time of solving the resource allocation problem in comparison to the exact bundle method, and is capable of achieving a high percentage of optimality within a much shorter time. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Logistics Risk-averse optimization stochastic programming Proximal bundle method

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EMPIRE: An open-source model based on multi-horizon programming for energy transition analyses

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SOFTWAREX 2022年 17卷

作者： Backe, Stian Skar, Christian del Granado, Pedro Crespo Turgut, Ozgu Tomasgard, Asgeir Norwegian Univ Sci & Technol Dept Ind Econ & Technol Management Trondheim Norway

Energy and power system models represent important insights on the technical operations of energy technologies that supply the energy consumption in time steps with hourly resolution. This paper presents the European Model for Power system Investments with Renewable Energy (EMPIRE) that combines short-term operations with the representation of long-term planning decisions including infrastructure expansion. The EMPIRE model has an unique mathematical modelling structure based on multi-horizon stochastic programming, which means investment decisions are subject to short-term uncertainty represented by different realizations of operational scenarios. The model is open source and ready to use to analyse energy transition scenarios towards 2050 and beyond. This paper outlines the building blocks of the model and its software structure. We also present an illustrative example of results from using the software. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Power system planning software stochastic programming Multi-horizon representation Energy systems modelling

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Tri-level scheduling of integrated electric vehicle parking lots and solar distributed generations in local and wholesale electricity markets

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 158卷

作者： Zeynali, Saeed Nasiri, Nima Ravadanegh, Sajad Najafi Kubler, Sylvain Le Traon, Yves Univ Luxembourg SnT 6 Rue Richard Coudenhove Kalergi L-1359 Luxembourg Luxembourg Azarbaijan Shahid Madani Univ Elect Engn Dept Resilient Smart Grids Res Lab Tabriz Iran

This study investigates a tri-level optimal market strategy for the integrated electric vehicle fleets and solar distributed generations (IEVSDG) to engage in the local electricity market (LEM) as a strategic price -maker. The distribution company (Disco) which operates the LEM, participates in the wholesale electricity market (WEM) to provide its consumers, is also a strategic price -maker. For this purpose, the IEVSDGs are integrated at the first level of the optimization problem, while the Disco operator and WEM operator form the second and third levels, respectively. In other words, Disco acts as an intermediary retailer that links the LEM (modelled by IEEE 69 -bus distribution system) to WEM (modelled by an IEEE 24 -bus transmission network). The study puts forward a novel solution strategy, where the second and third level problems are conjoined through the Karush-Kuhn-Tucker (KKT) conditions. Moreover, the equilibrium point between the first level and this conjoined problem is achieved through the alternating direction method of multipliers (ADMM). In a hybrid robust optimization (RO) and stochastic programming (SP) approach, the uncertain specifications, such as the arrival/departure times and daily travelled miles are modelled through the SP scenarios. On other hand, the RO was deployed to handle solar power forecasting uncertainties. Different case studies of dumb and smart charging were devised to evaluate the method. The outcomes show that the proposed three -level approach leads to 57.21% reduction in the LEM price, and 0.86% reduction in the WEM price. Furthermore, the smart charging strategy eliminated 105 MWh of load interruptions.

关键词： Alternating direction method of multipliers Electric vehicles Local electricity market Robust optimization stochastic programming Wholesale electricity market

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Risk-averse multistage stochastic programs with expected conditional risk measures

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COMPUTERS & OPERATIONS RESEARCH 2024年 172卷

作者： Khatami, Maryam Silva, Thuener Pagnoncelli, Bernardo K. Ntaimo, Lewis Univ North Texas G Brint Ryan Coll Business Dept Informat Technol & Decis Sci Denton TX 76205 USA Pontifical Catholic Univ Rio de Janeiro Dept Ind Engn Rio De Janeiro Brazil Univ Cote dAzur SKEMA Business Sch Lille France Texas A&M Univ Dept Ind & Syst Engn College Stn TX USA

We study risk-averse multistage stochastic programs with expected conditional risk measures (ECRMs). ECRMs are attractive because they are time-consistent, which means that a plan made today will not be changed in the future if the problem is re-solved given a realization of the random variables. We show that the computational burden of solving the risk-averse problems based on ECRMs is the same as the risk-neutral ones. We consider ECRMs for both quantile and deviation mean-risk measures, deriving the Bellman equations in each case. Finally, we illustrate our results with extensive numerical computations for problems from two applications: hydrothermal scheduling and portfolio selection. The results show that the ECRM approach provides higher expected costs in the early stages to hedge against cost spikes in later stages for the hydrothermal scheduling problem. For the portfolio selection problem, the new approach gives well-diversified portfolios over time. Overall, the ECRM approach provides superior performance over the risk-neutral model under extreme scenario conditions.

关键词： stochastic programming Decomposition algorithms Expected conditional risk measures

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