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Material and energy coupling systems optimization for large-scale industrial refinery with sustainable energy penetration under multiple uncertainties using two-stage stochastic programming

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APPLIED ENERGY 2024年 371卷

作者： Xu, Tiantian Long, Jian Zhao, Liang Du, Wenli East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Qingyuan Innovat Lab Quanzhou 362801 Peoples R China

The coupling of multimedia materials and energy with sustainable energy penetration in large-scale industrial refineries significantly lowers energy consumption and greenhouse gas (GHG) emissions. This paper presents a sustainable retrofitting framework for coupled production materials and steam systems (CPMSS) utilising twostage stochastic programming (TSSP). A novel sustainable energy-integrated CPMSS (SEICPMSS) model that includes wind, solar, and hydrogen energy and considers multiple operating modes and hydrogen consumption of the process units was established. First-principles models of wind turbines, solar thermal collectors, and proton exchange membrane electrolysers were adopted to sustainably retrofit the CPMSS. Latin hypercube sampling and Gaussian mixture model methods were applied to classify high volumes of uncertain wind speed and solar radiation data. A SEICPMSS optimisation model formulated as a mixed-integer nonlinear programming problem was developed by considering the investment costs, carbon taxes, and operational costs. Finally, a series of case studies from the industrial refinery's CPMSS were conducted to illustrate the effectiveness of the proposed method. The optimisation results indicate that the TSSP method can reduce operating costs by 5.3355 x 108 CNY/year and decrease GHG emissions by 7.4104 x 106 t/year compared to the traditional CPMSS without the integration of sustainable energy systems.

关键词： Refinery Integrated sustainable energy system mixed-integer nonlinear programming Two-stage stochastic programming Uncertainty Greenhouse gas emission

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Equipment Selection for Coupling a Microgrid with a Power-to-Gas System in the Context of Optimal Design and Operation

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COMPUTERS & CHEMICAL ENGINEERING 2024年 181卷

作者： Akulker, Handan Aydin, Erdal Bogazici Univ Dept Chem Engn TR-34342 Istanbul Turkiye Ondokuz Mayis Univ Dept Chem Engn TR-55139 Samsun Turkiye Koc Univ Dept Chem & Biol Engn TR-34457 Istanbul Turkiye Koc Univ TUPRAS Energy Ctr KUTEM TR-34450 Istanbul Turkiye

This study proposes a one-layer deterministic mixed-integer nonlinear programming to design and schedule a PTG-integrated microgrid. The key contribution is that optimal equipment selection, design, and scheduling, considering the PTG system at the core of the problem, are determined just in a single formulation. Scenarios based on different carbon dioxide taxes and natural gas prices are investigated. Only one wind turbine farm is chosen when the carbon dioxide tax is increased from 50 $/ton to 100 $/ton. On the other hand, when the natural gas price is increased from 1.548 $/m3 to 1.72 $/m3, two wind turbine farms are selected. Solar panel arrays are not chosen in all the scenarios. Generated power by solar panels is not enough for installation despite their much lower carbon dioxide emissions and negligible operational costs. Consequently, the optimal equipment selections may change linked to the natural gas price and carbon dioxide tax.

关键词： mixed-integer nonlinear programming Carbon dioxide emission taxing Multi-energy microgrid Optimal design Power-to-gas system Optimal scheduling

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OUTER APPROXIMATION FOR PSEUDO-CONVEX mixed-integer nonlinear PROGRAM PROBLEMS

JOURNAL OF NONLINEAR AND VARIATIONAL ANALYSIS

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JOURNAL OF nonlinear AND VARIATIONAL ANALYSIS 2024年第2期8卷 181-197页

作者： Wei, Zhou Chen, Liang Yao, Jen-Chih Hebei Univ Hebei Key Lab Machine Learning & Computat Intellig Baoding 071002 Peoples R China Hebei Univ Coll Math & Informat Sci Baoding 071002 Peoples R China Chinese Acad Sci LSEC Acad Math & Syst Sci Beijing 100190 Peoples R China China Med Univ China Med Univ Hosp Res Ctr Interneural Comp Taichung 40402 Taiwan

Outer approximation (OA) for solving convex mixed-integer nonlinear programming (MINLP) problems is heavily dependent on the convexity of functions and a natural issue is to relax the convexity assumption. This paper is devoted to OA for dealing with a pseudo-convex MINLP problem. By solving a sequence of nonlinear subproblems, we use Lagrange multiplier rules via Clarke subdifferentials of subproblems to introduce a parameter and then equivalently reformulate such MINLP as the mixed-integer linear program (MILP) master problem. Then, an OA algorithm is constructed to find the optimal solution to the MNILP by solving a sequence of MILP relaxations. The OA algorithm is proved to terminate after a finite number of steps. Numerical examples are illustrated to test the constructed OA algorithm.

关键词： . Clarke Subdifferential mixed-integer nonlinear programming MILP master program Outer Approximation Pseudo-convexity

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MIP RELAXATIONS IN FACTORABLE programming

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SIAM JOURNAL ON OPTIMIZATION 2024年第3期34卷 2856-2882页

作者： He, Taotao Tawarmalani, Mohit Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200030 Peoples R China Purdue Univ Mitchell E Daniels Jr Sch Business W Lafayette IN 47907 USA

In this paper, we develop new discrete relaxations for nonlinear expressions in factorable programming. We utilize specialized convexification results as well as composite relaxations to develop mixed-integer programming relaxations. Our relaxations rely on ideal formulations of convex hulls of outer-functions over a combinatorial structure that captures local inner-function structure. The resulting relaxations often require fewer variables and are tighter than currently prevalent ones. Finally, we provide computational evidence to demonstrate that our relaxations close approximately 60\%--70\% of the gap relative to McCormick relaxations and significantly improve the relaxations used in a state-of-the-art solver on various instances involving polynomial functions.

关键词： mixed-integer nonlinear programming mixed-integer programming relaxation convexification incremental formulation relaxation propagation

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Thermodynamic modeling and optimal short-term scheduling of a green H2-fueled combined heat and power generation system for industrial zones

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 84卷 235-245页

作者： Jabari, Hamid Ghazani, Ardalan Shafiei Jabari, Farkhondeh Sahand Univ Technol Fac Mech Engn Tabriz Iran Niroo Res Inst NRI Power Syst Operat & Planning Res Dept Tehran Iran

This paper presents a novel green combined heat and power generation system for industrial applications. It composes of a hydrogen-fueled combined cycle power plant and a battery energy storage. Firstly, the hydrogenair rich mixture enters the combustion chamber of the gas turbine cycle for generating electricity, while recovering the heating energy from the exhausted flue gases for driving a steam turbine cycle. The heating energy extracted from the condenser heat exchanger is also used to supply the heating demand of the end-user. Moreover, a comprehensive thermodynamic model and analysis of H2 combustion process is provided to estimate the adiabatic flame temperature based on enthalpies of reactants and products. The optimum operating point of the cogeneration system is found by solving a mixed-integer nonlinear problem using MATLAB and GAMS over a 24-h study horizon aiming to minimize its daily operation cost considering all technical constraints of system components and load-generation balance criterion. It is found that the energy efficiency of the hydrogen-fueled combined cycle changes from 46 to 58 % while generating 11.331-12.982 MW electricity and 4.436-4.984 MW heat. Two cases are studied without and with participation of battery in energy procurement strategy. It is demonstrated that the objective function of the optimization problem improves from 18 $ cost in case 1 to 29 $ revenue in case 2.

关键词： Hydrogen-steam combined cycle Combined heat and power (CHP) Battery Adiabatic flame temperature mixed-integer nonlinear programming (MINLP) problem

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Benefit Maximizing Network Design in the public sector

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COMPUTERS & OPERATIONS RESEARCH 2025年 178卷

作者： Aboolian, Robert Karimi, Majid Calif State Univ San Marcos Coll Business Adm Dept Operat & Supply Chain Management 333 S Twin Oaks Valley Rd San Marcos CA 92096 USA

This paper introduces the Benefit Maximization Network Design Problem (BNDP), an innovative framework aimed at optimizing public service systems. By integrating consumer choice models with a focus on minimizing consumer disutility, the BNDP strategically maximizes societal welfare by optimizing the net benefits derived from service utilization, carefully weighing the costs of service capacity against the value delivered to the public. An essential aspect of this study is the development of an epsilon-optimal solution approach, which effectively addresses the intricate optimization challenges inherent in public service system design, including nonlinear equilibrium conditions. This approach efficiently identifies the optimal number, locations, and capacities for service facilities, ensuring practical and effective solutions despite the model's complexity. A practical application of the BNDP is demonstrated through a detailed case study on preventive healthcare in San Diego, showcasing the model's ability to refine operational solutions and significantly enhance societal benefits. The results underscore the importance of prioritizing benefit maximization over simplistic capacity thresholds and participation maximization, leading to either higher levels of consumer participation while using optimal levels of resources or comparable levels of customer participation with significantly fewer resources and reasonable utilization rates. Furthermore, the paper explores the model's applicability in the private sector to maximize corporate profits, highlighting its versatility. The study concludes by advocating for policymakers to prioritize the use of BNDP in establishing optimal budgets for public agencies rather than allocating resources based on exogenous capacities.

关键词： Location models Service system design Elastic demand Congestion Stochastic demand User-choice mixed-integer nonlinear programming Public sector

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Energy-efficient optimization of multi-echelon inventory systems

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2025年 285卷

作者： Nguyen, Hong-Nguyen Godichaud, Matthieu Amodeo, Lionel Univ Danang Univ Sci & Technol Danang Vietnam Univ Technol Troyes Comp Sci & Digital Soc Lab LIST3N F-10004 Troyes France

Efficient energy management in the cold supply chain is crucial for reducing costs and environmental impact. This study presents an integrated distribution inventory system that focuses on energy considerations throughout the supply chain. The model incorporates energy usage from production, warehousing, and transportation processes into the average total cost of the system, providing a comprehensive analysis of energy cost components. By considering various factors such as production rate, ordering policy, warehouse filling level, and truck types, the model offers insights into the energy efficiency of the system. The model is formulated as a mixed-integer nonlinear programming (MINLP) problem. To solve this problem, a heuristic algorithm is proposed, aiming to optimize the total cost, including energy costs, while providing near-optimal decision variables. A study based on a real-world company serves as a practical illustration of the model's effectiveness. A comparison between the integrated inventory system and a non-inventory system reveals significant reductions in energy consumption for warehousing (23.85%) and the overall system costs (2.74%). After testing four groups of datasets, the proposed heuristic algorithm outperforms the LINGO solver in terms of cost minimization (for the first two groups) and computational time, validating its efficiency. Sensitivity analyses are performed to assess the impact of key parameters such as energy unit costs, distance, transportation speed, and demand on energy costs and system performance. These analyses provide valuable insights for decision-makers, supporting informed decision-making and the identification of practical strategies for optimizing energy usage.

关键词： Multi-echelon inventory system Energy efficiency mixed-integer nonlinear programming Supply chain management Joint Economic Lot-Sizing

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Joint maintenance planning and production scheduling optimization model for green environment

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JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING 2024年第1期30卷 153-174页

作者： Attia, Ahmed M. Alatwi, Ahmad O. Al Hanbali, Ahmad Alsawafy, Omar G. King Fahd Univ Petr & Minerals Dept Ind & Syst Engn Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent Mfg & Robot Dhahran Saudi Arabia

PurposeThis research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon ***/methodology/approachA mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy *** was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.Research limitations/implicationsThe proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop *** implicationsMaintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 ***/valueThis work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.

关键词： Maintenance planning Production scheduling Green environment mixed-integer nonlinear programming

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Solving AC Optimal Power Flow with Discrete Decisions to Global Optimality

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INFORMS JOURNAL ON COMPUTING 2023年第2期35卷 458-474页

作者： Aigner, Kevin-Martin Burlacu, Robert Liers, Frauke Martin, Alexander Friedrich Alexander Univ Erlangen Nurnberg Discrete Optimizat D-91058 Erlangen Germany Energie Campus Nurnberg D-90429 Nurnberg Germany Fraunhofer Inst Integrated Circuits D-90411 Nurnberg Germany

We present a solution framework for general alternating current optimal power flow (AC OPF) problems that include discrete decisions. The latter occur, for instance, in the context of the curtailment of renewables or the switching of power-generation units and transmission lines. Our approach delivers globally optimal solutions and is provably convergent. We model AC OPF problems with discrete decisions as mixed-integer non-linear programs (MINLPs). The solution method starts from a known framework that uses piecewise linear relaxations. These relaxations are modeled as mixed-integer linear pro-grams and adaptively refined until some termination criterion is fulfilled. In this work, we extend and complement this approach by problem-specific as well as very general algorith-mic enhancements. In particular, these are mixed-integer second order cone programs as well as primal and dual cutting planes. For example, objective and no-good cuts help to compute good feasible solutions in which outer approximation constraints tighten the relaxations. We present extensive numerical results for various AC OPF problems in which discrete decisions play a major role. Even for hard instances with a large proportion of dis-crete decisions, the method is able to generate high-quality solutions efficiently. Further-more, we compare our approach with state-of-the-art MINLP solvers. Our method outperforms all other algorithms.

关键词： AC optimal power flow mixed-integer nonlinear programming discrete decisions second order cone programming piecewise linear relaxation generator switching transmission line switching curtailment of renewables

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Sparse convex optimization toolkit: a mixed-integer framework

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OPTIMIZATION METHODS & SOFTWARE 2023年第6期38卷 1269-1295页

作者： Olama, Alireza Camponogara, Eduardo Kronqvist, Jan Univ Fed Santa Catarina Automation & Syst Engn Dept Florianopolis Brazil KTH Royal Inst Technol Dept Math Stockholm Sweden Fed Univ Santa Catarina UFSC Dept Automation & Syst Engn Florianopolis Brazil

This paper proposes an open-source distributed solver for solving Sparse Convex Optimization (SCO) problems over computational networks. Motivated by past algorithmic advances in mixed-integer optimization, the Sparse Convex Optimization Toolkit (SCOT) adopts a mixed-integer approach to find exact solutions to SCO problems. In particular, SCOT combines various techniques to transform the original SCO problem into an equivalent convex mixed-integer nonlinear programming (MINLP) problem that can benefit from high-performance and parallel computing platforms. To solve the equivalent mixed-integer problem, we present the Distributed Hybrid Outer Approximation (DiHOA) algorithm that builds upon the LP/NLP-based branch-and-bound and is tailored for this specific problem structure. The DiHOA algorithm combines the so-called single- and multi-tree outer approximation, naturally integrates a decentralized algorithm for distributed convex nonlinear subproblems, and employs enhancement techniques such as quadratic cuts. Finally, we present detailed computational experiments that show the benefit of our solver through numerical benchmarks on 140 SCO problems with distributed datasets. To show the overall efficiency of SCOT we also provide solution profiles comparing SCOT to other state-of-the-art MINLP solvers.

关键词： Sparse optimization mixed-integer nonlinear programming distributed computing outer approximation >

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