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Optimizing electricity peak shaving through stochastic programming and probabilistic time series forecasting

JOURNAL OF BUILDING ENGINEERING 2024年 88卷

作者： Rafayal, Syed Alnaggar, Aliaa Cevik, Mucahit Toronto Metropolitan Univ Toronto ON M5B 1G3 Canada

This paper proposes a predict-then-optimize framework to optimally schedule the charging and discharging activities of battery energy storage systems (BESS). BESS are used to eliminate peak electricity consumption loads, which pose a significant risk of microgrid system failure and increase electricity costs to end users. A major challenge in BESS lies in determining the battery usage schedule, which must be determined before actual energy consumption materializes. To address this challenge, we propose a two-phase approach. In the first phase, we develop a deep-learning-based probabilistic time-series forecasting model to predict future electricity consumption. In the second phase, the output of the prediction model is used to generate future load scenarios, which are incorporated into a two-stage stochastic programming model that determines the optimal battery usage schedule. Extensive computational experiments on real-world datasets demonstrate the effectiveness of the proposed framework in shaving peak loads and minimizing energy costs. Specifically, our proposed framework reduces daily energy peaks by up to 26%, with the potential for greater improvement as the forecast of future energy loads improves. To the best of our knowledge, this work is the first to investigate the integration of probabilistic forecasting and stochastic optimization to enhance the effectiveness of BESS in managing peak energy loads, leading to more energy-efficient buildings.

关键词： Peak shaving Energy management stochastic programming Probabilistic forecasting Forecasting Time series

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Optimal Sizing of Hybrid Renewable Energy System Using Two-Stage stochastic programming

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2024年第1期2024卷 2361858-2361858页

作者： Mohammed, Nouralden Ali, M. Montaz Univ Witwatersrand Sch Comp Sci & Appl Math Johannesburg South Africa

stochastic programming has become increasingly vital in energy applications, especially in the context of the growing need for renewable energy solutions. This paper presents a significant advancement in this field by introducing an efficient and robust algorithm for optimally sizing hybrid renewable energy systems. Utilizing a two-stage stochastic programming approach, the proposed algorithm addresses the challenges posed by the unpredictability of renewable energy sources. The proposed solution leverages the three-block alternating direction method of multipliers (ADMM), a cutting-edge technique that facilitates parallel computation and enhances computational efficiency. The distinctiveness of this method lies in its ability to solve complex stochastic optimization problems without compromising the mathematical integrity of the model. This is achieved by applying first-order optimality conditions, ensuring both robustness and efficacy. To demonstrate the practical applicability and superiority of the algorithm, a case study was conducted in a rural area of South Africa. The proposed algorithm was applied to design an optimal hybrid renewable energy system, and its performance was compared against traditional methods such as progressive hedging and Monte Carlo techniques. Results affirm the superiority of the approach, saving approximately 8.16% capital cost when compared to progressive hedging. In addition, the proposed algorithm outperforms the Monte Carlo method both in terms of CPU time and the number of cost function evaluations.

关键词： stochastic programming

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Two-stage stochastic programming for Maintenance Optimization of Multi-component Systems 68

Two-stage Stochastic Programming for Maintenance Optimizatio...

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68th Annual Reliability and Maintainability Symposium (RAMS)

作者： Zhu, Zhicheng Xiang, Yisha Liao, Ying Texas Tech Univ Dept Ind Mfg & Syst Engn 2500 Broadway Lubbock TX 79409 USA

ISBN: (数字)9781665424325

ISBN: (纸本)9781665424325

As the complexity of modern industrial systems increases faster than ever, it is imperative to a develop cost-effective maintenance plan to secure system safety while lowering maintenance cost for complex systems. However, most maintenance studies focus on single-component systems, which are not applicable to complex multi-component systems due to the various interactions among components, such as stochastic dependence, structure dependence and economic dependence. Economic dependence is the most commonly seen one among these interactions. Economic dependence means that any maintenance action incurs a system-dependence cost, regardless of the number of components maintained. Significant cost savings can be achieved by maintaining multiple components jointly instead of separately. In this paper, we study the maintenance optimization problem of multi-component systems with economic dependence among components. The objective is to determine the maintenance actions at each decision stage over a finite planning horizon so that the total maintenance cost is minimized. Such a maintenance optimization problem is challenging due to the combinatorial maintenance grouping problem with the stochastic component failure process. We present a two-stage stochastic programming model for this problem, which analytically expresses the total cost as a function of maintenance decisions. Progressive hedging algorithm is applied to solve this problem. We conduct a case study by using real-world pavement deterioration data. Experiment results provide insights on how economic dependence affects single-component maintenance decision.

关键词： Maintenance optimization Multi-component systems stochastic programming

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MOREAU ENVELOPE OF SUPREMUM FUNCTIONS WITH APPLICATIONS TO INFINITE AND stochastic programming\ast

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SIAM JOURNAL ON OPTIMIZATION 2021年第3期31卷 1635-1657页

作者： Perez-Aros, Pedro Vilches, Emilio Univ OHiggins Inst Ciencias Ingn Rancagua Chile

In this paper, we investigate the Moreau envelope of the supremum of a family of convex, proper, and lower semicontinuous functions. Under mild assumptions, we prove that the Moreau envelope of a supremum is the supremum of Moreau envelopes, which allows us to approximate possibly nonsmooth supremum functions by smooth functions that are also the suprema of functions. Consequently, we propose and study approximated optimization problems from infinite and stochastic programming for which we obtain zero-duality gap results and optimality conditions without the verification of constraint qualification conditions.

关键词： Moreau envelope subdifferential calculus supremum function infinite program-ming semi-infinite programming stochastic programming

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stochastic OD demand estimation using stochastic programming

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2024年 183卷

作者： Sun, Ran Fan, Yueyue Univ Calif Davis Dept Civil & Environm Engn Davis CA 95616 USA

Understanding the origin-destination (OD) demand of travelers can help traffic operators and mobility service providers form more efficient mobility planning and operation decisions. Large quantities of high -dimensional spatial and temporal data that are becoming increasingly available for urban transportation systems present opportunities as well as new challenges to this end. Approaching from a fresh angle of stochastic programming, we present a modeling framework for OD demand estimation based on observed traffic flow data in a transportation network. The proposed two -stage stochastic programming -based method is flexible to incorporate various design principles and risk preferences and domain knowledge regarding travel behavioral and physical rules. Additionally, a benefit comes from the scenario representation, where the point estimate can be combined with estimation of the discrete approximation to the demand distribution. As a result, we simultaneously incorporate demand parameter estimation and trip table reconstruction processes. In addition, we demonstrate that under the proposed framework, well -established theories and methods for stochastic programming, including epiconvergence and scenario -decomposition, can be exploited to advance the analytical and computational capabilities of the estimation model. The applicability and efficiency of the proposed method are illustrated through numerical examples based on highway and transit networks of various sizes.

关键词： OD demand estimation stochastic programming Decomposition methods Transportation networks

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Combining deep reinforcement learning and multi-stage stochastic programming to address the supply chain inventory management problem

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2024年 268卷

作者： Stranieri, Francesco Fadda, Edoardo Stella, Fabio Univ Milano Bicocca Dept Informat Syst & Commun DISCo Viale Sarca 336 I-20126 Milan Italy Politecn Torino Dept Control & Comp Engn DAUIN Corso Duca degli Abruzzi 24 I-10129 Turin Italy Politecn Torino Dept Math Sci DISMA Corso Duca degli Abruzzi 24 I-10129 Turin Italy

We introduce a novel heuristic designed to address the supply chain inventory management problem in the context of a two-echelon divergent supply chain. The proposed heuristic advances the current state-of-the -art by combining deep reinforcement learning with multi-stage stochastic programming. In particular, deep reinforcement learning is employed to determine the number of batches to produce, while multi-stage stochastic programming is applied to make shipping decisions. To support further research, we release a publicly available software environment that simulates a wide range of two-echelon divergent supply chain settings, allowing the manipulation of various parameter values, including those associated with seasonal demands. We then present a comprehensive set of numerical experiments considering constraints on production and warehouse capacities under fixed and variable logistic costs. The results demonstrate that the proposed heuristic significantly and consistently outperforms pure deep reinforcement learning algorithms in minimizing total costs. Moreover, it overcomes several inherent limitations of multi-stage stochastic programming models, thus underscoring its potential advantages in addressing complex supply chain scenarios.

关键词： Inventory management Deep reinforcement learning stochastic programming

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stochastic programming model for incentive-based demand response considering complex uncertainties of consumers

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第20期14卷 4488-4500页

作者： Zheng, Shunlin Sun, Yi Li, Bin Hu, Yajie Qi, Bing Shi, Kun Li, Yuanfei North China Elect Power Univ Sch Elect & Elect Engn 2 Beinong Rd Beijing Peoples R China China Elect Power Res Inst Rd 15 Qinghe Xiaoying Beijing Peoples R China State Grid Fujian Econ Res Inst Rd 221 Xiufeng Fuzhou Peoples R China

Incentive-based demand response (IBDR) has been recognized as a powerful tool to mitigate supply-demand imbalance in electricity market. However, the complex uncertainties of consumers, including participation uncertainty and responsiveness uncertainty, have been a central challenge to implement IBDR programs. In this paper, a stochastic programming model for IBDR considering the complex uncertainties of consumers is proposed. The proposed model can effectively deals with the above two uncertainties. Besides, the model of energy storage unit (ESU) has been improved to cope with properly the deviation between total actual balancing power and required balancing power. Moreover, the model enhances the applicability of IBDR to be applicable to both curtailment IBDR programs and absorbing IBDR programs by adding dynamic parameters. The model is formulated as a bi-level stochastic programming problem based on uncertain programming theory, and corresponding equivalent model is also given to solve the problem effectively. Finally, simulation results verify merits of the proposed model in cutting down total cost of DRA, decreasing risk cost of DRA and reducing balancing power deviation caused by uncertainty of consumers.

关键词： stochastic processes incentive schemes supply and demand pricing demand side management stochastic programming energy storage power markets uncertain programming theory corresponding equivalent model demand response aggregator reducing balancing power deviation two-consumer IBDR stochastic programming model incentive-based demand response complex uncertainties supply-demand imbalance participation uncertainty responsiveness uncertainty total actual balancing power required balancing power curtailment IBDR programmes bi-level stochastic programming problem

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Optimal allocation of wind-based distributed generators and STATCOMs using a hierarchical stochastic programming approach

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 237卷

作者： Dalawir, Mahzan Azzouz, Maher Azab, Ahmed Univ Windsor Elect & Comp Engn 401 Sunset Ave Windsor ON N9B 3P4 Canada Qatar Univ Elect Engn Dept POB 2713 Doha Qatar Univ Windsor Prod & Operat Management POM Res Lab 401 Sunset Ave Windsor ON N9B 3P4 Canada

Allocating static synchronous compensators (STATCOMs) to regulate given allotted wind-based distributed generators (W-DGs) during planning stages can provide high investment returns by maximizing the installed WDGs. Amidst rising global warming concerns, commitments to adopt renewable energy gain international momentum to curb greenhouse emissions and meet environmental targets, reducing reliance on fossil-based resources. Renewable energy's intermittency complicates distribution planning, stressing voltage devices and increasing network losses. This paper presents a new hierarchical stochastic planning model that addresses uncertainties related to W-DGs and generic loads. The model optimizes allocation with voltage constraints and reactive power while incorporating a two-stage mixed-integer nonlinear program (MINLP), maximizing net profit, and adding an economic dimension to promote renewable energy investments. Improved wind power modeling with historical data and collective evaluation metrics for selecting the best-fitted probability distribution function (PDF) enhances the accuracy of wind power integration assessment. The hierarchical approach considers relaxed voltage constraints in Stage I to allow maximum allotment of W-DGs while introducing STATCOMs and DGs' reactive power in Stage II to address voltage violations. Verification on the Canadian 41bus network demonstrates the advantage of the hierarchical approach in allocating more W-DGs and achieving higher profits than the simultaneous planning approach. These advances significantly enhance renewable energy integration in power systems.

关键词： Distribution network (DN) Planning Renewable energy sources (RESs) Statcoms stochastic programming Wind-based distributed generators (W-DGs)

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A SYSTEMATIC LITERATURE REVIEW ON stochastic programming MODELS IN THE RETAIL INDUSTRY

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SOUTH AFRICAN JOURNAL OF INDUSTRIAL ENGINEERING 2021年第3期32卷 97-110页

作者： Bisset, C. Terblanche, S. E. North West Univ Sch Ind Engn Potchefstroom South Africa

Optimisation under uncertainty, also referred to as stochastic optimisation, originated during the 1950s. This research field was later extended into prominent application areas, including finance, energy, and production planning. During the 1970s, stochastic programming was first applied in the marketing industry. Interest in this field grew exponentially during the early 2000s, and was identified as one of the most promising future research fields for optimisation. stochastic programming is an asset for the following world researchers due to its uncertainty calculation. This paper aims to identify and evaluate all existing stochastic programming models in the literature in the retail industry. First, a historical overview of marketing models is provided, with a specific focus on stochastic optimisation. Second, all stochastic programming model applications in the retail industry are identified by conducting a systematic literature review. Last, potential gaps for future research opportunities are identified to help retailers to improve their marketing strategies.

关键词： stochastic programming

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Climate risk management in agriculture using alternative electricity and water resources: a stochastic programming framework

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Environment Systems and Decisions 2022年第1期42卷 117-135页

作者： Jones, Erick C. Leibowicz, Benjamin D. Industrial Manufacturing and Systems Engineering The University of Texas at Arlington Woolf Hall Room 420X 500 West 1st St Arlington 76019 TX United States Operations Research and Industrial Engineering The University of Texas at Austin ETC 5.128D 204 E. Dean Keeton St. C2200 Austin 78712 TX United States

Climate change, extreme weather events, and water scarcity have severely impacted the agricultural sector. Under scarce conventional water supplies, a farm faces a decision between reducing production through deficit irrigation and leveraging alternative water and energy resources to continue producing large quantities of crops and these investments would have to be balanced against an unknown climate. Therefore, we develop a framework for farm investment decisions structured as a two-stage stochastic quadratically constrained linear program that maximizes farm profit over a 25-year period while considering an uncertain future climate and the costs of investing and operating various electricity and water technologies. We create four representative climate futures and two climate probability distributions that represent different beliefs that the decision maker might have about the likelihood of each climate scenario occurring. Then, we compare four solutions where decisions are made on information ranging from perfectly knowing the climate and weather to only the average precipitation. Our results show that expected profit and crop yield heavily depend on a decision maker’s given climate probability distributions. Aggressively preparing for an extreme climate can cause significant losses if a more moderate climate is realized. Furthermore, given a future climate, year-to-year weather variability can also corrode the potential cost savings from investing in alternative resources. The insights from this framework can help agricultural decision makers determine how to address climate uncertainty, water scarcity, and to a limited degree weather variability via investments in alternative water and electricity resources that can help improve resilience and fortify profits. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Agriculture Climate risk management Desalination Food–energy–water systems stochastic programming Water scarcity

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