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Multi-objective optimisation for safe multi-floor process plant layout using the Dow's Fire & Explosion Index

JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES 2022年 76卷 104722-104722页

作者： Ejeh, Jude O. Liu, Songsong Papageorgiou, Lazaros G. Univ Sheffield Dept Chem & Biol Engn Mappin St Sheffield S1 3JD S Yorkshire England Harbin Inst Technol Sch Management Harbin 150001 Peoples R China UCL Ctr Proc Syst Engn Dept Chem Engn Torrington Pl London WC1E 7JE England

In this work, we propose a multi-objective mixed-integer linear programming (MILP) model for evaluation of safe multi-floor process plant layout using the Dow's Fire & Explosion Index (F&EI). Two objectives are considered, namely the total monetary cost and the financial risk, and solved via the -constraint method. The total monetary cost consisted of the piping costs, horizontal and vertical pumping costs, land purchase cost, fixed and area-dependent floor construction cost, and the cost of protection devices. The financial risk was evaluated as the maximum probable property damage cost obtained using the F&EI evaluation procedure. The proposed model was applied to an ethylene oxide plant consisting of 7 equipment items with Pareto-optimal solutions showing that the financial risk can be greatly reduced by layout reconfiguration without the need for protection devices and their associated monetary costs. Further increase in the safety levels of the plant can then be achieved through protection device installation at a cost. These, and more information obtained, which are non-existent in previous single objective considerations, are helpful for a more informed decision making process in the planning stages of the design of chemical process plants.

关键词： Multi-objective optimisation Dow's Fire and Explosion Index mixed-integer linear programming Multi-floor process plant layout

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Nuclear fleet flexibility: Modeling and impacts on power systems with renewable energy

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APPLIED ENERGY 2022年 314卷

作者： Lynch, Arthur Perez, Yannick Gabriel, Sophie Mathonniere, Gilles Univ Paris Saclay Inst Techno Econ Energy Syst I Tese French Alternat Energies & Atom Energy Commiss CE F-91191 Gif Sur Yvette France Univ Paris Saclay Lab Genie Industriel CentraleSupelec Gif Sur Yvette France

This paper identifies the underlying physical mechanisms that frame the flexibility and operation of nuclear technologies, with analysis focused on the effects of two structural characteristics of nuclear fleets, i.e. nuclear schedule optimization and minimal power variations, on simplified French power system simulations. We develop a method to simulate nuclear schedule optimization to reflect how plant managers maximize plant availability during peak-demand periods. Using this schedule optimization, we compute each plant's minimum power level and its variation over time to evaluate the flexibility potential. Three nuclear flexibility hypotheses are considered: one where fleet schedule is considered constant, which is standard practice in the energy systems modeling literature, one with an optimized fleet schedule with constant minimum power, and one with an optimized fleet schedule with physics-induced minimum power variations. Sensitivity analysis highlights the links between the fleet's schedule optimization, minimal power variations, relative share of nuclear and re-newables in the capacity mix, and results of simulation models. We find that the nuclear fleet's optimization and associated realistic minimum power variations gain in importance with increasing relative share of nuclear in the capacity mix. The schedule's importance holds as renewables' installed capacities heighten as the residual de-mand level decreases. This paper highlights the potential benefits of modeling nuclear schedule optimizations and the resulting minimum power evolutions. Both aspects are crucial for evaluating nuclear flexibility char-acteristics in simplified low-carbon electric systems that use a significant share of renewable energy.

关键词： Nuclear energy Flexible power operation Nuclear schedule Renewable energy integration Unit commitment Economic dispatch mixed-integer linear programming

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Supplier selection and order allocation planning using predictive analytics and multi-objective programming

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 174卷

作者： Islam, Samiul Amin, Saman Hassanzadeh Wardley, Leslie J. Toronto Metropolitan Univ Dept Mech & Ind Engn Toronto ON Canada Cape Breton Univ Shannon Sch Business Sydney NS Canada

Supplier Selection and Order Allocation (SSOA) are two critical strategic decisions in supply chain management. It is challenging to make these decisions when the demand is unknown. Prediction of demand is a complex problem as it highly depends on some parameters such as product cost. In this research, a three-stage framework is proposed to tackle the hurdles of SSOA planning problem. In the first stage, a hybrid deep learning technique based on multistep Long-Short Term Memory (LSTM) network is developed to determine the future product demands. The efficiency of the developed model is evaluated using two standard error measuring techniques. Then, the results are compared with two other forecasting models to have accurate forecast. One of them is Seasonal Auto-Regressive Integrated Moving Average (SARIMA), and the other one is a deep learning model named Multilayer Perceptron (MLP). In the second stage, a fuzzy supplier evaluation model based on Strengths, Weaknesses, Opportunities, and Threats (SWOT) model is developed to consider qualitative criteria. The third stage fetches the results from the forecasting model in Stage 1, and the results of the fuzzy model in the second stage. A unique multi-objective programming model is developed to select the best suppliers and to determine the allocated orders to them. To derive the efficient solutions, the weighted-sum method is used. The application of the proposed framework is discussed using a real dataset from the Canadian Juice industries. The results of the performance comparison among the considered forecasting models show that the developed LSTM model can lead to less forecasting errors compared to the SARIMA and MLP models.

关键词： Deep Learning Multilayer Perceptron SWOT Analysis mixed-integer linear programming Supplier Selection and Order Allocation

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Technology pathways for industrial cogeneration systems: Optimal investment planning considering long-term trends

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APPLIED ENERGY 2022年 324卷

作者： Hoettecke, Lukas Schuetz, Thomas Thiem, Sebastian Niessen, Stefan Siemens AG Technol Gunther Scharowsky Str 1 Erlangen Germany Tech Univ Darmstadt Technol & Econ Multimodal Energy Syst Fraunhoferstr 4 Darmstadt Germany

Cogeneration plants appear highly attractive for industrial companies as production sites typically combine demands for electricity, heating, and cooling. However, gas-fired cogeneration might become economically less attractive with further increasing carbon prices and companies' commitment to decarbonization targets. This paper proposes a methodology to determine technology pathways from an optimization model. Technology pathways describe the evolution of an energy supply system over a multi-decade planning horizon. Therefore, projected long-term trends are integrated into the decision-making process for energy supply infrastructure. Three investment strategies are compared regarding their accuracy and computational complexity. The methodology is illustrated for industrial sites from dairy, pharmaceutical and automotive industry in Southern Germany. Results highlight the advantages of technology pathways compared to optimization approaches based on a representative year. A strategic planning approach identifies optimal decarbonization pathways with economic saving potentials of up to 20 %. For most of the considered case studies, a sequential decision approach with a rolling horizon heuristic yields similar results in 89 % less solving time.

关键词： Energy systems Industrial cogeneration Multi-year planning mixed-integer linear programming

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Partial hyperplane activation for generalized intersection cuts

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MATHEMATICAL programming COMPUTATION 2020年第1期12卷 69-107页

作者： Kazachkov, Aleksandr M. Nadarajah, Selvaprabu Balas, Egon Margot, Francois Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA 15213 USA Univ Illinois Coll Business Adm Chicago IL 60608 USA

The generalized intersection cut paradigm is a recent framework for generating cutting planes in mixed integer programming with attractive theoretical properties. We investigate this computationally unexplored paradigm and observe that a key hyperplane activation procedure embedded in it is not computationally viable. To overcome this issue, we develop a novel replacement to this procedure called partial hyperplane activation (PHA), introduce a variant of PHA based on a notion of hyperplane tilting, and prove the validity of both algorithms. We propose several implementation strategies and parameter choices for our PHA algorithms and provide supporting theoretical results. We computationally evaluate these ideas in the COIN-OR framework on MIPLIB instances. Our findings shed light on the the strengths of the PHA approach as well as suggest properties related to strong cuts that can be targeted in the future.

关键词： mixed-integer linear programming Cutting planes Intersection cuts

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Application of energy efficiency obligation scheme for electricity distribution companies in Turkey

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ENERGY POLICY 2022年 163卷 112851-112851页

作者： Unal, Berat Berkan Onaygil, Sermin Acuner, Ebru Cin, Rabia Istanbul Tech Univ Dept Energy Sci & Technol Ayazaga Campus TR-34469 Istanbul Turkey Turkish German Univ Dept Energy Sci & Technol TR-34820 Istanbul Turkey

To realize the existing energy-saving potential in a short time according to 2012/27/EU Energy Efficiency Directive, in 2017, Turkey published National Energy Efficiency Action Plan (NEEAP), containing Energy Efficiency Obligation Schemes EEOS establishment action. Since research studies are needed to guide the NEEAP, this study was conducted to establish an exemplary model for Turkish EEOS. Based on the literature, the main hypothesis of the study is "EEOS could contribute significantly to NEEAP targets". Electricity Distribution Companies (EDCs) are assumed as obligated parties, a guideline containing standard energy efficiency (EE) actions for residential, commercial, and industrial sectors was prepared. Concerning EDCs' market shares, mixedinteger linear programming models, minimizing EE actions' costs, were developed to examine four annual saving targets (0.8%, 1.5%, 2%, and least) under three scenarios (yearly-based obligation, yearly-based with using 5% of each action and no constraints). Saving target 2% for 7 years with no constraint gives the highest energysaving (above 200 TWh) and the least is from the least annual target for 10 years with yearly based obligation (below 100 TWh). In conclusion, if EDCs fulfill their obligations under specified targets and scenarios, Turkish NEEAP's saving target can be fulfilled between 10% and 44%, supporting the hypothesis of the study.

关键词： EEOS Energy efficiency mixed-integer linear programming Electricity distribution companies Energy efficiency actions manual

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Medium-term planning of active distribution systems considering voltage-dependent loads, network reconfiguration, and CO2 emissions

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 135卷 107541-107541页

作者： Mejia, Mario A. Macedo, Leonardo H. Munoz-Delgado, Gregorio Contreras, Javier Padilha-Feltrin, Antonio Sao Paulo State Univ Dept Elect Engn Ave Brasil 56 BR-15385000 Ilha Solteira SP Brazil Univ Castilla La Mancha Escuela Tecn Super Ingn Ind Ciudad Real 13071 Spain

This work proposes a novel mixed-integer linear programming model to address the medium-term reinforcement planning for active distribution networks, taking into account multiple investment options and CO2 emission limits. The investment plan jointly includes (i) the replacement of overloaded conductors, (ii) the installation of voltage control equipment such as voltage regulators and capacitor banks, and (iii) the installation of distributed energy resources, such as dispatchable and non-dispatchable renewable generators, and energy storage units. Uncertainties associated with the demand for electricity, energy prices at the substation, and non-dispatchable distributed generation are addressed through scenario-based stochastic optimization. In contrast to conventional planning methods, the proposed approach models the load as voltage-dependent in order to achieve substantial reductions in energy consumption. As another outstanding feature, network reconfiguration, which is an operational planning alternative that is normally addressed independently, is incorporated within the planning options. The objective function of the model is aimed at establishing an investment strategy with minimal total costs, but that satisfies the operational restrictions of the network and CO2 emissions cap. A 69-node system was used to test the proposed model and, the results show that modeling the load as voltage-dependent and integrating network reconfiguration into the medium-term planning actions helps to achieve an effective network that, in addition to being environmentally friendly, has low total planning costs. Finally, the scalability of the proposed method was evaluated using a real 2313-node system.

关键词： Active distribution networks mixed-integer linear programming Network reconfiguration Renewable distributed generation Voltage-dependent loads

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A mathematical formulation and an NSGA-II algorithm for minimizing the makespan and energy cost under time-of-use electricity price in an unrelated parallel machine scheduling

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PEERJ COMPUTER SCIENCE 2022年 8卷 e844页

作者： Rego, Marcelo F. Pinto, Julio Cesar E. M. Cota, Luciano P. Souza, Marcone J. F. Univ Fed Vales Jequitinhonha & Mucuri Dept Comp Diamantina MG Brazil Univ Fed Ouro Preto Dept Comp Ouro Preto MG Brazil Inst Tecnol Vale Ouro Preto MG Brazil

In many countries, there is an energy pricing policy that varies according to the time-of-use. In this context, it is financially advantageous for the industries to plan their production considering this policy. This article introduces a new bi-objective unrelated parallel machine scheduling problem with sequence-dependent setup times, in which the objectives are to minimize the makespan and the total energy cost. We propose a mixed-integer linear programming formulation based on the weighted sum method to obtain the Pareto front. We also developed an NSGA-II method to address large instances of the problem since the formulation cannot solve it in an acceptable computational time for decision-making. The results showed that the proposed NSGA-II is able to find a good approximation for the Pareto front when compared with the weighted sum method in small instances. Besides, in large instances, NSGA-II outperforms, with 95% confidence level, the MOGA and NSGA-I multi-objective techniques concerning the hypervolume and hierarchical cluster counting metrics. Thus, the proposed algorithm finds non-dominated solutions with good convergence, diversity, uniformity, and amplitude.

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A smart discrete charging method for optimum electric vehicles integration in the distribution system in presence of demand response program

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JOURNAL OF ENERGY STORAGE 2022年 47卷

作者： Saeedirad, Meysam Rokrok, Esmaeel Joorabian, Mahmood Islamic Azad Univ Dept Elect Engn Khorramabad Branch Khorramabad Iran Lorestan Univ Engn Dept Lorestan Iran Shahid Chamran Univ Ahvaz Engn Dept Ahvaz Iran

Electric vehicle charging is one of the common techniques for techno-economic energy management in the distribution system, which, if implemented properly, brings several benefits such as the peak load shaving, total costs reduction, losses minimization, etc. Unlike traditional charging methods, it is not necessary to completely charge electric vehicles. Instead, each vehicle can be charged smartly just based on its demand for its next trips. In this way, charging takes less time and the total consumption is balanced without any discomfort for the drivers. To implement this smart method, electric vehicle owners must fill in an information sheet that contains the number and lengths of the their next. Then, its required energy is calculated this info, and the initial battery state of charge. Finally, the charging management system starts the planning according to its upstream distribution system operator. Several parameters are needed for the planning such as time of use tariff of the electricity, the distribution transformer or substation physical limits, the rate of the charge (normal or fast), etc. The aim of the planning is to minimize the charging cost considering the technical and economic constraints. Here, the YALMIP and MOSEK software are used as the solver of the proposed mixed-integer linear programming model.

关键词： Electric vehicle charging Demand response program mixed-integer linear programming Distribution system

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Predicting Lagrangian Multipliers for mixed integer linear Programs

arXiv

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arXiv 2023年

作者： Demelas, Francesco Le Roux, Joseph Lacroix, Mathieu Parmentier, Axel Laboratoire d’Informatique de Paris-Nord Université Sorbonne Paris Nord CNRS France CERMICS École des Ponts France

Lagrangian Relaxation stands among the most efficient approaches for solving mixed integer linear Programs (MILPs) with difficult constraints. Given any duals for these constraints, called Lagrangian Multipliers (LMs), it returns a bound on the optimal value of the MILP, and Lagrangian methods seek the LMs giving the best such bound. But these methods generally rely on iterative algorithms resembling gradient descent to maximize the concave piecewise linear dual function: the computational burden grows quickly with the number of relaxed constraints. We introduce a deep learning approach that bypasses the descent, effectively amortizing per instance optimization. A probabilistic encoder based on a graph neural network computes, given a MILP instance and its Continuous Relaxation (CR) solution, high-dimensional representations of relaxed constraints, which are turned into LMs by a decoder. We train the encoder and the decoder jointly by directly optimizing the bound obtained from the predicted multipliers. Our method is applicable to any problem with a compact MILP formulation, and to any Lagrangian Relaxation providing a tighter bound than CR. Experiments on two widely known problems, Multi-Commodity Network Design and Generalized Assignment, show that our approach closes up to 85 % of the gap between the continuous relaxation and the best Lagrangian bound, and provides a high-quality warm-start for descent-based Lagrangian methods. © 2023, CC BY.

关键词： mixed-integer linear programming

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