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Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation

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ENERGIES 2021年第6期14卷 1773-1773页

作者： Dkhili, Nouha Salas, David Eynard, Julien Thil, Stephane Grieu, Stephane Univ Perpignan PROMES UPR 8521 CNRS Via Domitia F-66100 Perpignan France Univ OHiggins Inst Ciencias Ingn Ohiggins 2841935 Chile

In past decades, the deployment of renewable-energy-based power generators, namely solar photovoltaic (PV) power generators, has been projected to cause a number of new difficulties in planning, monitoring, and control of power distribution grids. In this paper, a control scheme for flexible asset management is proposed with the aim of closing the gap between power supply and demand in a suburban low-voltage power distribution grid with significant penetration of solar PV power generation while respecting the different systems' operational constraints, in addition to the voltage constraints prescribed by the French distribution grid operator (ENEDIS). The premise of the proposed strategy is the use of a model-based predictive control (MPC) scheme. The flexible assets used in the case study are a biogas plant and a water tower. The mixed-integer nonlinear programming (MINLP) setting due to the water tower ON/OFF controller greatly increases the computational complexity of the optimisation problem. Thus, one of the contributions of the paper is a new formulation that solves the MINLP problem as a smooth continuous one without having recourse to relaxation. To determine the most adequate size for the proposed scheme's sliding window, a sensitivity analysis is carried out. Then, results given by the scheme using the previously determined window size are analysed and compared to two reference strategies based on a relaxed problem formulation: a single optimisation yielding a weekly operation planning and a MPC scheme. The proposed problem formulation proves effective in terms of performance and maintenance of acceptable computational complexity. For the chosen sliding window, the control scheme drives the power supply/demand gap down from the initial one up to 38%.

关键词： low-voltage power distribution grids smart grid paradigm distributed generation model-based predictive control flexible asset management mixed-integer nonlinear programming relaxation computational complexity

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A Genetic Algorithm for Transmission Network Expansion Planning Considering Line Maintenance

A Genetic Algorithm for Transmission Network Expansion Plann...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Mahdavi, Meisam Kheirkhah, Ali Reza Macedo, Leonardo H. Romero, Ruben Sao Paulo State Univ Dept Elect Engn Ilha Solteira SP Brazil

ISBN: (纸本)9781728169293

This paper proposes a decimal codification genetic algorithm to solve the transmission network expansion planning (TNEP) problem considering the economic impact of line maintenance. The goal is to extend the lifespan of the time-worn lines in order to reduce the investment cost in the expansion of the transmission network and to improve the worth of the transmission system. To assess the economic impact of the maintenance on the deterioration of transmission lines and transformers, the sum of years digit method is implemented. The proposed algorithm is evaluated using the IEEE reliability test system, and the assessment of the results shows that by including the effect of line maintenance on the TNEP problem, significant savings can be made in the overall cost of the system.

关键词： Decimal codification genetic algorithm lines' degradation maintenance mixed-integer nonlinear programming transmission network expansion planning

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Location-routing problem with time-dependent demands

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 151卷 106936-106936页

作者： Yu, Vincent F. Normasari, Nur Mayke Eka Chen, Wei-Hao Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Keelung Rd Taipei 10607 Taiwan Natl Taiwan Univ Sci & Technol Ctr Cyber Phys Syst Innovat 43Sect 4Keelung Rd Taipei 10607 Taiwan Gadjah Mada Univ Dept Mech & Ind Engn Jl Graf 2 Yogyakarta 55281 Indonesia

This study, by considering the time-dependent demand (TDD) characteristic, investigates the location-routing problem with time-dependent demands (LRPTDD) as an extension of the location-routing problem (LRP). The demand in each customer site is represented by a constant demand rate over a known production period. In the LRPTDD, the locations are selected, and the routes are constructed to pick up all the demands and minimize the total distance. The picked load depends on the vehicle arrival time at the site;thus, the "time-dependency" characteristic of the LRPTDD is based on the vehicle arrival time. A mixed-integer nonlinear programming (MINLP) formulation is presented. A simulated annealing (SA) algorithm for the LRPTDD is developed. The computational study demonstrates the competitiveness of the proposed SA heuristic against other well-known algorithms for LRPs, and most importantly, its effectiveness for the LRPTDD.

关键词： Location-routing problem Time-dependent demand Simulated annealing mixed-integer nonlinear programming

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Sailing Speed and Fleet Deployment Optimization for Intercontinental Container Liner Shipping Considering Cargo Time Value

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TRANSPORTATION JOURNAL 2020年第3期59卷 254-278页

作者： Xing, Yuwei Yang, Hualong Yang, Zaili Dalian Maritime Univ Dalian Peoples R China Liverpool John Moores Univ Liverpool Merseyside England

This article collectively addresses the issues of optimizing the sailing speed of containerships and fleet deployment in intercontinental container liner shipping, taking cargo time values into consideration. A mixed-integer nonlinear programming model is developed to maximize the gross profits of a container shipping line. The sailing speed is divided into 0.1 knot intervals and a reciprocal- discretization method is proposed to transform the model into an integer-based linear programming model, which can be solved using optimization solvers such as CPLEX. A case study is conducted based on the Far East-West America liner route of a global container shipping line to assess the effectiveness of the proposed model and the reciprocal- discretization method. The results show that when the cargo time value is taken into consideration, the increased sailing speed of containerships on long legs and the decreased number of deployed containerships will generate greater operating profits for the container shipping line. The rise in bunker price will lead to a stepwise decline in sailing speed of containerships and a stepwise growth in the number of deployed containerships. Moreover, the downward trend of sailing speed due to the increase of bunker price will be somewhat delayed as the unit container cargo value increases. Several useful insights are drawn from analysis of the results.

关键词： Cargo time value intercontinental container liner shipping sailing speed fleet deployment mixed-integer nonlinear programming maritime

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Optimization Models and Algorithms for Pricing in e-Commerce

Optimization Models and Algorithms for Pricing in e-Commerce

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作者： Shams-Shoaaee, Seyed Shervin McMaster University

学位级别：博士

With the rise of online retailer giants like Amazon, and enhancements in internet and mobile technologies, online shopping is becoming increasingly popular. This has lead to new opportunities in online price optimization. The overarching motivation and theme of this thesis is to review these opportunities and provide methods and models in the context of retailers' online pricing decisions. In Chapter 2 a multi-period revenue maximization and pricing optimization problem in the presence of reference prices is formulated as a mixed integer nonlinear program. Two algorithms are developed to solve the optimization problem: a generalized Benders' decomposition algorithm and a myopic heuristic. This is followed by numerical computations to illustrate the effciency of the solution approaches as well as some managerial pricing insights. In Chapter 3 a data-driven quadratic programming optimization model for online pricing in the presence of customer ratings is proposed. A new demand function is developed for a multi-product, nite horizon, online retail environment. To solve the optimization problem, a myopic pricing heuristic as well as exact solution approaches are introduced. Using customer reviews ratings data from ***, a new customer rating forecasting model is validated. This is followed by several analytical and numerical insights. In Chapter 4 a multinomial choice model is used for customer purchase decision to find optimal personalized price discounts for an online retailer that incorporates customer locations and feedback from their reviews. Closed form solutions are derived for two special cases of this problem. To gain some analytical insights extensive numerical experiments are carried followed by several analytical and numerical insights. Thesis Doctor of Philosophy (PhD) The increase in online retail and the improvements in mobile technologies has lead to advantages and opportunities for both customers and retailers. One of these advantages is the abi

关键词： Pricing Reference pricing mixed-integer nonlinear programming Generalized Benders' decomposition Customer rating e-commerce Quadratic programming Personalized price discounts nonlinear programming

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Comprehensive approach for distribution system planning with uncertainties

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第24期13卷 5467-5477页

作者： Seta, Felipe da Silva de Oliveira, Leonardo W. de Oliveira, Edimar J. Fed Ctr Technol Educ Dept Elect Engn Angra Dos Reis RJ Brazil Fed Univ Juiz Fora UFJF Dept Elect Engn Juiz De Fora MG Brazil

This study presents a comprehensive approach for the distribution system expansion planning (DSEP) that considers investment, operation, carbon dioxide emission and reliability costs, as well as uncertainties over load demand and wind-based distributed generation. A restoration strategy is taken into account for obtaining the energy not supplied under the 'N - 1' criterion and the corresponding reliability cost over a planning horizon. The problem is modelled as mixed-integer non-linear programming by using the artificial immune system algorithm. Also, two methods to represent uncertainties are applied and compared: an interval technique through an interval power flow and a scenario-based approach. Network constraints are considered, as the limits of current, voltage and power from substations, as well as the obtaining of radial and connected topology. The novelty of the proposed interval DSEP consists of handling the uncertainties over operation in an efficient manner through a single step, instead of the several deterministic evaluations of the scenario-based approach. Numerical results are presented for well known test systems, which show the potentials of the proposed approach.

关键词： power distribution planning distributed power generation investment load flow artificial immune systems power distribution economics nonlinear programming power distribution faults integer programming power distribution reliability mixed-integer nonlinear programming artificial immune system algorithm interval technique interval power flow scenario-based approach interval DSEP known test systems distribution system expansion planning carbon dioxide emission reliability costs uncertainties overload demand wind-based distributed generation restoration strategy planning horizon

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Global optimality bounds for the placement of control valves in water supply networks

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OPTIMIZATION AND ENGINEERING 2019年第2期20卷 457-495页

作者： Pecci, Filippo Abraham, Edo Stoianov, Ivan Imperial Coll London Dept Civil & Environm Engn InfraSense Labs London England Delft Univ Technol Fac Civil Engn & Geosci Stevinweg 1 NL-2628 CN Delft Netherlands

This manuscript investigates the problem of optimal placement of control valves in water supply networks, where the objective is to minimize average zone pressure. The problem formulation results in a nonconvex mixed integer nonlinear program (MINLP). Due to its complex mathematical structure, previous literature has solved this nonconvex MINLP using heuristics or local optimization methods, which do not provide guarantees on the global optimality of the computed valve configurations. In our approach, we implement a branch and bound method to obtain certified bounds on the optimality gap of the solutions. The algorithm relies on the solution of mixed integer linear programs, whose formulations include linear relaxations of the nonconvex hydraulic constraints. We investigate the implementation and performance of different linear relaxation schemes. In addition, a tailored domain reduction procedure is implemented to tighten the relaxations. The developed methods are evaluated using two benchmark water supply networks and an operational water supply network from the UK. The proposed approaches are shown to outperform state-of-the-art global optimization solvers for the considered benchmark water supply networks. The branch and bound algorithm converges to good quality feasible solutions in most instances, with bounds on the optimality gap that are comparable to the level of parameter uncertainty usually experienced in water supply network models.

关键词： Global optimization mixed-integer nonlinear programming Valve placement Pressure management Water supply networks

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Maximizing the storage capacity of gas networks: a global MINLP approach

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OPTIMIZATION AND ENGINEERING 2019年第2期20卷 543-573页

作者： Burlacu, Robert Egger, Herbert Gross, Martin Martin, Alexander Pfetsch, Marc E. Schewe, Lars Sirvent, Mathias Skutella, Martin Friedrich Alexander Univ Erlangen Nurnberg FAU Discrete Optimizat Cauerstr 11 D-91058 Erlangen Germany Tech Univ Darmstadt Res Grp Numer & Sci Comp Dolivostr 15 D-64293 Darmstadt Germany Rhein Westfal TH Aachen Chair Management Sci Kackertstr 7 D-52072 Aachen Germany Tech Univ Darmstadt Res Grp Optimizat Dolivostr 15 D-64293 Darmstadt Germany Tech Univ Berlin Fac Math & Nat Sci 2 Str 17 Juni 136 D-10623 Berlin Germany

In this paper, we study the transient optimization of gas networks, focusing in particular on maximizing the storage capacity of the network. We include nonlinear gas physics and active elements such as valves and compressors, which due to their switching lead to discrete decisions. The former is described by a model derived from the Euler equations that is given by a coupled system of nonlinear parabolic partial differential equations (PDEs). We tackle the resulting mathematical optimization problem by a first-discretize-then-optimize approach. To this end, we introduce a new discretization of the underlying system of parabolic PDEs and prove well-posedness for the resulting nonlinear discretized system. Endowed with this discretization, we model the problem of maximizing the storage capacity as a non-convex mixed-integer nonlinear problem (MINLP). For the numerical solution of the MINLP, we algorithmically extend a well-known relaxation approach that has already been used very successfully in the field of stationary gas network optimization. This method allows us to solve the problem to global optimality by iteratively solving a series of mixed-integer problems. Finally, we present two case studies that illustrate the applicability of our approach.

关键词： mixed-integer nonlinear programming Transient gas transport optimization Storage capacity maximization Power-to-gas First-discretize-then-optimize

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The multi-floor cross-dock door assignment problem: Rising challenges for the new trend in logistics industry

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2019年 132卷 30-47页

作者： Wang, Haibo Alidaee, Bahram Texas A&M Int Univ AR Sanchez Sch Business Laredo TX 78041 USA Univ Mississippi Sch Business Adm University MS 38677 USA

This paper is concerned with the multi-floor, cross-dock door assignment problem (MCDAP) to minimize the total material handling costs. We present a novel mixed-integer nonlinear programming model and a classic linearization form for the MCDAP. We use a combination of multi-start, genetic random-key, and very-large-scale neighborhood search (VLSN) as the diversification strategies for the solution perturbation method embedded within the critical event Tabu search to solve MCDAP. We will test the proposed algorithms on a set of 60 very-large sized instances with 150-300 loading doors, and 100-300 unloading doors that raise size of cost matrix up to 90,000 by 90,000.

关键词： Cross-dock door assignment Multi-floor warehouse mixed-integer nonlinear programming Tabu Search Genetic random-key Very-large-scale neighborhood search

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CONCRETE STRUCTURE DESIGN USING mixed-integer nonlinear programming WITH COMPLEMENTARITY CONSTRAINTS

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SIAM JOURNAL ON OPTIMIZATION 2011年第3期21卷 833-863页

作者： Guerra, A. Newman, A. M. Leyffer, S. Colorado Sch Mines Div Engn Golden CO 80401 USA Colorado Sch Mines Div Econ & Business Golden CO 80401 USA Argonne Natl Lab Div Math & Comp Sci Argonne IL 60439 USA

We present a mixed-integer nonlinear programming (MINLP) formulation to achieve minimum-cost designs for reinforced concrete (RC) structures that satisfy building code requirements. The objective function includes material and labor costs for concrete, steel reinforcing bars, and formwork according to typical contractor methods. Restrictions enforce correct geometry of the cross-section dimensions for each element and relative sizes of cross-section dimensions of elements within the structure. Other restrictions define a stiffness and displacement correlation among all structural elements via finite element analysis. The design of minimum cost RC structures introduces a new class of optimization problems, namely, mixed-integer nonlinear programs with complementarity constraints. The complementarity constraints are used to model RC element strength and American Concrete Institute code-required safety factors. We reformulate the complementarity constraints as nonlinear equations and show that the resulting ill-conditioned MINLPs can be solved by using an off-the-shelf MINLP solver. Our work provides discrete-valued design solutions for an explicit representation of a process most often performed implicitly with iterative calculations. We demonstrate the capabilities of a mixed-integer nonlinear algorithm, MINLPBB, to find optimal sizing and reinforcing for cast-in-place beam and column elements in multistory RC structures. Problem instances contain up to 678 variables, of which 214 are integer, and 844 constraints, of which 582 are nonlinear. We solve problems to local optimality within a reasonable amount of computational time, and we find an average cost savings over typical-practice design solutions of 13 percent.

关键词： complementarity problems applications in optimization mixed-integer nonlinear programming

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