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Self-Sustainability Assessment for a High Building Based on linear programming and Computational Fluid Dynamics

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ALGORITHMS 2023年第2期16卷 107-107页

作者： Oliveira, Carlos Baptista, Jose Cerveira, Adelaide Univ Tras os Montes & INESC TEC UTAD Pole Dept Engn Escola Ciencias & Tecnol P-5000801 Vila Real Portugal Univ Tras os Montes & INESC TEC UTAD Pole Dept Matemat Escola Ciencias & Tecnol P-5000801 Vila Real Portugal

With excess energy use from non-renewable sources, new energy generation solutions must be adopted to make up for this excess. In this sense, the integration of renewable energy sources in high-rise buildings reduces the need for energy from the national power grid to maximize the self-sustainability of common services. Moreover, self-consumption in low-voltage and medium-voltage networks strongly facilitates a reduction in external energy dependence. For consumers, the benefits of installing small wind turbines and energy storage systems include tax benefits and reduced electricity bills as well as a profitable system after the payback period. This paper focuses on assessing the wind potential in a high-rise building through computational fluid dynamics (CFD) simulations, quantifying the potential for wind energy production by small wind turbines (WT) at the installation site. Furthermore, a mathematical model is proposed to optimize wind energy production for a self-consumption system to minimize the total cost of energy purchased from the grid, maximizing the return on investment. The potential of a CFD-based project practice that has wide application in developing the most varied processes and equipment results in a huge reduction in the time and costs spent compared to conventional practices. Furthermore, the optimization model guarantees a significant decrease in the energy purchased at peak hours through the energy stored in energy storage systems (ESS). The results show that the efficiency of the proposed model leads to an investment amortization period of 7 years for a lifetime of 20 years.

关键词： distributed energy resources small wind turbine optimization mixed-integer linear programming computational fluid dynamics self-consumption

Minimizing the operation costs of a smart home using a HEMS with a MILP-based model predictive control approach

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IFAC-PapersOnLine 2023年第2期56卷 8720-8725页

作者： I.L.R. Gomes M.G. Ruano A. Ruano Faculty of Science & Technology University of Algarve 8005 Faro Portugal IDMEC Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal ICT Universidade de Évora Portugal CISUC University of Coimbra Coimbra Portugal

The energy management of a home nowadays is a challenging task, since it is necessary to take into account not only technical aspects, but also information regarding the purchase and sale prices of energy, in order to obtain an operation with minimum cost. Thus, it is necessary to develop advanced energy management systems, the so-called home energy management systems (HEMS). This paper presents a new HEMS with a mixed-integer linear programming (MILP)-based model predictive control (MPC) approach. This approach allows to obtain better results than an approach purely using MILP, by having access to updated information at every moment. The results of a real case study in Algarve, Portugal, show the superiority of MILP-based MPC over MILP and over experimental results.

关键词： Home energy management system model predictive control mixed-integer linear programming energy storage renewable energy demand response

Risk-Averse Antibiotics Time Machine Problem

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arXiv 2025年

作者： Tuncer, Deniz Kocuk, Burak Industrial Engineering Program Sabancı University Istanbul34956 Turkey

Antibiotic resistance, which is a serious healthcare issue, emerges due to uncontrolled and repeated antibiotic use that causes bacteria to mutate and develop resistance to antibiotics. The Antibiotics Time Machine Problem aims to come up with treatment plans that maximize the probability of reversing these mutations. Motivated by the severity of the problem, we develop a risk-averse approach and formulate a scenario-based mixed-integer linear program with a conditional value-at-risk objective function. We propose a risk-averse scenario batch decomposition algorithm that partitions the scenarios into manageable risk-averse subproblems, enabling the construction of lower and upper bounds. We develop several algorithmic enhancements in the form of stronger no-good cuts and symmetry breaking constraints in addition to scenario regrouping and warm starting. We conduct extensive computational experiments for static and dynamic versions of the problem on a real dataset and demonstrate the effectiveness of our approach. Our results suggest that risk-averse solutions can achieve significantly better worst-case performance compared to risk-neutral solutions with a slight decrease in terms of the average performance, especially for the dynamic version. Although our methodology is presented in the context of the Antibiotics Time Machine Problem, it can be adapted to other risk-averse problem settings in which the decision variables come from special ordered sets of type one. Copyright © 2025, The Authors. All rights reserved.

关键词： mixed-integer linear programming

Global and local approaches for the minimization of a sum of pointwise minima of convex functions

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arXiv 2025年

作者： Van Dessel, Guillaume Glineur, François 4 Avenue Georges Lemaître Louvain-la-Neuve Belgium UCLouvain CORE 34 Voie du Roman Pays Louvain-la-Neuve Belgium

Numerous machine learning and industrial problems can be modeled as the minimization of a sum of N so-called clipped (or truncated) convex functions (SCC), i.e. each term of the sum stems as the pointwise minimum between a constant and a convex function. In this work, we extend this framework to capture more problems of interest. Specifically, we allow each term of the sum to be a pointwise minimum of an arbitrary number of convex functions, called components, turning the objective into a sum of pointwise minima of convex functions (SMC). Local. As emphasized in dedicated works, problem (SCC) is already NP-hard, highlighting an appeal for scalable local heuristics. In this spirit, one can express (SMC) objectives as the difference between two convex functions to leverage the possibility to apply (DC) algorithms to compute critical points of the problem. Our approach does not rely on the above (DC) decomposition but rather on a bi-convex reformulation of the problem. From there, we derive a family of local methods, dubbed as relaxed alternating minimization (r-AM) methods, that include classical alternating minimization (AM) as a special case. We prove that every accumulation point of r-AM is critical. In addition, we show the empirical superiority of r-AM, compared to traditional AM and (DC) approaches, on piecewise-linear regression and restricted facility location problems. Global. Under mild assumptions, (SCC) can be cast as a mixed-integer convex program (MICP) using perspective functions. This approach can be generalized to (SMC) but introduces many copies of the primal variable. In contrast, we suggest a compact big-M based (MICP) equivalent formulation of (SMC), free of these extra variables. Finally, we showcase practical examples where solving our (MICP), restricted to a neighbourhood of a given candidate (i.e. output iterate of a local method), will either certify the candidate’s optimality on that neighbourhood or providing a new point, strictly better, t

关键词： mixed-integer linear programming

Energy-Efficient Operation of Train Platoons in Urban Rail Transit Systems:An MIP Approach

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Energy-Efficient Operation of Train Platoons in Urban Rail T...

第43届中国控制会议

作者： Haodong Li Jiateng Yin Tao Tang Andrea D'Ariano Mengxing You State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University School of Systems Science Beijing Jiaotong University Department of Civi Computer Science and Aeronautical Technologies Engineering Nanchang Rail Transit Group Co. Ltd.

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Train platooning,which allows multiple train units to be virtually coupled into a platoon with very short following distances,has become an emerging technology in railway *** study investigates the energy-efficient operation of train platoons to reduce the energy consumption of trains while satisfying time-varying passenger demands in *** develop a mixed integer programming(MIP) model to optimize the timetable,running speed and coupling strategies of *** that the small-size train platoons consume much less energy but can serve fewer passengers,our model involves bi-objectives,i.e.,to minimize the passenger waiting time and to minimize the overall energy consumption of *** also construct a series of model constraints to limit the movement of trains as well as the time-dependent flows of passengers,since a part of passengers during the peak period may have to wait for a longer time due to the capacity limitation of different sizes of ***,we conduct real-world case studies on the Beijing subway Yizhuang line to verify the effectiveness of our *** results indicate that the energy-efficient operation of train platoons can achieve a better trade-off between service quality and system energy consumption.

关键词： Flexible coupling mixed-integer linear programming Energy savings Passenger flow

ReLU Surrogates in mixed-integer MPC for Irrigation Scheduling

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arXiv 2024年

作者： Agyeman, Bernard T. Liu, Jinfeng Shah, Sirish L. Department of Chemical & Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada

Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. mixed-integer Model Predictive Control (MPC) has emerged as an effective approach for addressing the complex scheduling problems in agricultural irrigation. However, the computational complexity of mixed-integer MPC still poses a significant challenge, particularly in large-scale applications. This study proposes an approach to enhance the computational efficiency of mixed-integer MPC-based irrigation schedulers by employing ReLU surrogate models to describe the soil moisture dynamics of the agricultural field. By leveraging the mixed-integer linear representation of the ReLU operator, the proposed approach transforms the mixed-integer MPC-based scheduler with a quadratic cost function into a mixed-integer quadratic program, which is the simplest class of mixed-integer nonlinear programming problems that can be efficiently solved using global optimization solvers. The effectiveness of this approach is demonstrated through comparative studies conducted on a large-scale agricultural field across two growing seasons, involving other machine learning surrogate models, specifically Long Short-Term Memory (LSTM) networks, and the widely used triggered irrigation scheduling method. The ReLU-based approach significantly reduces solution times—by up to 99.5%—while achieving comparable performance to the LSTM approach in terms of water savings and Irrigation Water Use Efficiency (IWUE). Moreover, the ReLU-based approach maintains enhanced performance in terms of total prescribed irrigation and IWUE compared to the widely-used triggered irrigation scheduling method. © 2024, CC BY.

关键词： mixed-integer linear programming

A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids

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arXiv 2024年

作者： Maya, Neethu Poolla, Bala Kameshwar Srinivasan, Seshadhri Sundararajan, Narasimhan Sundaram, Suresh Department of Aerospace Engineering Indian Institute of Science Bangalore India National Renewable Energy Laboratory GoldenCO United States TVS Sensing Solutions Madurai India School of Electrical and Electronics Engineering Nanyang Technological University Singapore

This paper presents a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for optimizing power management in microgrids, particularly focusing on external power exchanges among diverse prosumers. DIPPS utilizes a dynamic objective function with a time-varying binary parameter to control the timing of power transfers to the external grid, facilitated by efficient usage of energy storage for surplus renewable power. The microgrid power scheduling problem is modeled as a mixed-integer nonlinear programming (MINLP-PS) and subsequently transformed into a mixed-integer linear programming (MILP-PS) optimization through McCormick's relaxation to reduce computational complexity. A predictive window with 6 data points is solved at an average of 0.92s, a 97.6% improvement over the 38.27s required for the MINLP-PS formulation, implying the numerical feasibility of the DIPPS approach for real-time implementation. Finally, the approach is validated against a static objective using real-world load data across three case studies with different time-varying parameters, demonstrating the ability of DIPPS to optimize power exchanges and efficiently utilize distributed resources while shifting the external power transfers to a specified time duration. © 2024, CC BY.

关键词： mixed-integer linear programming

A diving heuristic for mixed-integer problems with unbounded semi-continuous variables

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arXiv 2024年

作者： Halbig, Katrin Hoen, Alexander Gleixner, Ambros Witzig, Jakob Weninger, Dieter Friedrich-Alexander-Universität Erlangen-Nürnberg Department of Data Science Cauerstr. 11 Erlangen91058 Germany Zuse Institute Berlin Takustr. 7 Berlin14195 Germany Hochschule für Technik und Wirtschaft Berlin Berlin10313 Germany SAP SE Dietmar-Hopp-Allee 17 Walldorf69190 Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Department of Mathematics Cauerstr. 11 Erlangen91058 Germany

Semi-continuous decision variables arise naturally in many real-world applications. They are defined to take either value zero or any value within a specified range, and occur mainly to prevent small nonzero values in the solution. One particular challenge that can come with semi-continuous variables in practical models is that their upper bound may be large or even infinite. In this article, we briefly discuss these challenges, and present a new diving heuristic tailored for mixed-integer optimization problems with general semi-continuous variables. The heuristic is designed to work independently of whether the semi-continuous variables are bounded from above, and thus circumvents the specific difficulties that come with unbounded semi-continuous variables. We conduct extensive computational experiments on three different test sets, integrating the heuristic in an open-source MIP solver. The results indicate that this heuristic is a successful tool for finding high-quality solutions in negligible time. At the root node the primal gap is reduced by an average of 5 % up to 21 %, and considering the overall performance improvement, the primal integral is reduced by 2 % to 17 % on *** Codes 90C10, 90C59, 90C90 © 2024, CC BY.

关键词： mixed-integer linear programming

Factor Complexity of the Most Significant Digits of and

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arXiv 2024年

作者： Golafshan, Mehdi Mitrofanov, Ivan Department of Mathematics University of Liège Liège Belgium Department of Mathematics Saarland University Saarbrücken Germany

We investigate unipotent dynamics on a torus and apply these techniques to the following problem. Let d be a positive integer, and let a > 0 be a real number. For an integer b ≥ 5, such that a and b are multiplicatively independent, consider the sequence (wn), where wn is the most significant digit of (formula presented) when expressed in base b. We prove that the complexity function of the sequence (wn) is, up to finitely many exceptions, a polynomial *** Codes Primary:11K31, 37B10, 11A63 Secondary: 68R15, 11K06, 11K16 © 2024, CC BY.

关键词： mixed-integer linear programming