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Algorithms for joint sensor and control nodes selection in dynamic networks

AUTOMATICA 2019年 106卷 124-133页

作者： Nugroho, Sebastian A. Taha, Ahmad F. Gatsis, Nikolaos Summers, Tyler H. Krishnan, Ram Univ Texas San Antonio Dept Elect & Comp Engn 1 UTSA Circle San Antonio TX 78249 USA Univ Texas Dallas Dept Mech Engn 800 W Campbell Rd Richardson TX 75080 USA

The problem of placing or selecting sensors and control nodes plays a pivotal role in the operation of dynamic networks. This paper proposes optimal algorithms and heuristics to solve the Simultaneous Sensor and Actuator Selection Problem (SSASP) in linear dynamic networks. In particular, a sufficiency condition of static output feedback stabilizability is used to obtain the minimal set of sensors and control nodes needed to stabilize an unstable network. We then show that SSASP can be written as a mixed-integer nonconvex problem. To solve this nonconvex combinatorial problem, three methods based on (i) mixed-integer nonlinear programming, (ii) binary search algorithms, and (iii) simple heuristics are proposed. The first method yields optimal solutions to SSASP given that some constants are appropriately selected. The second method requires a database of binary sensor/actuator combinations, returns optimal solutions, and necessitates no tuning parameters. The third approach is a heuristic that yields suboptimal solutions but is computationally attractive. The theoretical properties of these methods are discussed and numerical tests on dynamic networks showcase the trade-off between optimality and computational time. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Sensor and control nodes selection Static output feedback control mixed-integer nonlinear programming Combinatorial heuristics

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Multiperiod optimization model for oilfield production planning: bicriterion optimization and two-stage stochastic programming model

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OPTIMIZATION AND ENGINEERING 2019年第4期20卷 1227-1248页

作者： Awasthi, Utsav Marmier, Remy Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA TOTAL SA CSTJF Pau France

In this work, we present different tools of mathematical modeling that can be used in oil and gas industry to help improve the decision-making for field development, production optimization and planning. Firstly, we formulate models to compare simultaneous multiperiod optimization and sequential single period optimization for the maximization of net present value and the maximization of total oil production over long term time horizons. This study helps to identify the importance of multiperiod optimization in oil and gas production planning. Further, we formulate a bicriterion optimization model to determine the ideal compromise solution between maximization of the two objective functions, the net present value (NPV) and the total oil production. To account for the importance of hedging against uncertainty in the oil production, we formulate a two-stage stochastic programming model to compute an improved expected value of NPV and total oil production for uncertainties in oil prices and productivity indices.

关键词： Bicriterion optimization mixed-integer nonlinear programming Multiperiod optimization nonlinear programming Oilfield production planning Stochastic programming

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Integrated optimization model for distribution network design: a case study of the clothing industry

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2019年第4期26卷 1269-1292页

作者： Liu, Ming Xu, Xifen Zhang, Ding Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Jiangsu Peoples R China SUNY Coll Oswego Sch Business Oswego NY 13126 USA

Distribution network design affects a firm's operating costs and its customer service level. In this paper, we consider distribution network design for improving the closed-loop logistics in a clothing company. We formulate the problem as a mixed-integer nonlinear programming model with an objective of minimizing the annual operating costs. Our model simultaneously determines the optimal number of regional distribution centers (RDCs), identifies location and relative size for each RDC, allocates each city distribution center to a specific RDC, decides on supply ratio for each contracted plant, and specifies the annual operating costs and service level for the best scenario, as well as other scenarios. Test results show that this marketing initiative at the studied company can effectively reduce its annual operating costs. Although this paper is a specific case study, it provides several managerial insights and modeling references for similar facility location and allocation problems.

关键词： distribution network design facility location-allocation mixed-integer nonlinear programming optimization clothing industry

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Optimal power flow problem considering multiple-fuel options and disjoint operating zones: A solver-friendly MINLP model

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年第0期113卷 45-55页

作者： Pourakbari-Kasmaei, Mahdi Lehtonen, Matti Fotuhi-Firuzabad, Mahmud Marzband, Mousa Sanches Mantovani, Jose Roberto Aalto Univ Dept Elect Engn & Automat Maarintie 8 Espoo 02150 Finland Sharif Univ Technol Dept Elect Engn Tehran Iran Northumbria Univ Newcastle Dept Maths Phys & Elect Engn Fac Engn & Environm Newcastle Upon Tyne Tyne & Wear England Islamic Azad Univ Lahijan Branch Dept Elect Engn Lahijan Iran State Univ Sao Paulo UNESP Dept Elect Engn Ilha Solteira Brazil

This paper proposes a solver-friendly model for disjoint, non-smooth, and nonconvex optimal power flow (OPF) problems. The conventional OPF problem is considered as a nonconvex and highly nonlinear problem for which finding a high-quality solution is a big challenge. However, considering practical logic-based constraints, namely multiple-fuel options (MFOs) and prohibited operating zones (POZs), jointly with the non-smooth terms such as valve point effect (VPE) results in even more difficulties in finding a near-optimal solution. In complex problems, the nonlinearity itself is not a big issue in finding the optimal solution, but the nonconvexity does matter and considering MFO, POZ, and VPE increase the degree of nonconvexity exponentially. Another primary concern in practice is related to the limitations of the existing commercial solvers in handling the original logic-based models. These solvers either fail or show intractability in solving the equivalent mixed integer nonlinear programming (MINLP) models. This paper aims at addressing the existing gaps in the literature, mainly handling the MFOs and POZs simultaneously in OPF problems by proposing a solver-friendly MINLP (SF-MINLP) model. In this regard, due to the actions that are done in the pre-solve step of the existing commercial MINLP solvers, the most adaptable model is obtained by melting the primary integer decision variables, associated with the feasible region, into the objective function. For the verification and didactical purposes, the proposed SF-MINLP model is applied to the IEEE 30-bus system under two different loading conditions, namely normal and increased, and details are provided. The model is also tested on the IEEE 118-bus system to reveal its effectiveness and applicability in larger-scale systems. Results show the effectiveness and tractability of the model in finding a high-quality solution with high computational efficiency.

关键词： mixed-integer nonlinear programming Multiple-fuel option Non-smooth terms Optimal power flow Prohibited operating zones

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Design, implementation and simulation of an MPC algorithm for switched nonlinear systems under combinatorial constraints

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JOURNAL OF PROCESS CONTROL 2019年 81卷 15-30页

作者： Buerger, Adrian Zeile, Clemens Altmann-Dieses, Angelika Sager, Sebastian Diehl, Moritz Karlsruhe Univ Appl Sci Fac Management Sci & Engn Moltkestr 30 D-76133 Karlsruhe Germany Univ Freiburg Dept Microsyst Engn IMTEK Syst Control & Optimizat Lab Georges Koehler Allee 102 D-79110 Freiburg Germany Otto von Guericke Univ Fac Math Inst Math Optimizat MathOpt Grp Univ Pl 2 D-39106 Magdeburg Germany Univ Freiburg Dept Math Ernst Zermelo Str 1 D-79104 Freiburg Germany

Within this work, we present a warm-started algorithm for Model Predictive Control (MPC) of switched nonlinear systems under combinatorial constraints based on Combinatorial Integral Approximation (CIA). To facilitate high-speed solutions, we introduce a preprocessing step for complexity reduction of CIA problems, and include this approach within a new toolbox for solution of CIA problems with special focus on MPC. The proposed algorithm is implemented and utilized within an MPC simulation study for a solar thermal climate system with nonlinear system behavior and uncertain operation conditions. The results are analyzed in terms of solution quality, constraint satisfaction and runtime of the solution steps, showing the applicability of the proposed algorithm and implementations. (C) 2019 The Author(s). Published by Elsevier Ltd.

关键词： Model predictive control Switched dynamic systems mixed-integer nonlinear programming Optimal control Approximation methods and heuristics

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A joint decomposition method for global optimization of multiscenario nonconvex mixed-integer nonlinear programs

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JOURNAL OF GLOBAL OPTIMIZATION 2019年第3期75卷 595-629页

作者： Ogbe, Emmanuel Li, Xiang Queens Univ Dept Chem Engn Kingston ON K7L 3N6 Canada

This paper proposes a joint decomposition method that combines Lagrangian decomposition and generalized Benders decomposition, to efficiently solve multiscenario nonconvex mixed-integer nonlinear programming (MINLP) problems to global optimality, without the need for explicit branch and bound search. In this approach, we view the variables coupling the scenario dependent variables and those causing nonconvexity as complicating variables. We systematically solve the Lagrangian decomposition subproblems and the generalized Benders decomposition subproblems in a unified framework. The method requires the solution of a difficult relaxed master problem, but the problem is only solved when necessary. Enhancements to the method are made to reduce the number of the relaxed master problems to be solved and ease the solution of each relaxed master problem. We consider two scenario-based, two-stage stochastic nonconvex MINLP problems that arise from integrated design and operation of process networks in the case study, and we show that the proposed method can solve the two problems significantly faster than state-of-the-art global optimization solvers.

关键词： Generalized Benders decomposition Dantzig-Wolfe decomposition Lagrangian decomposition Joint decomposition mixed-integer nonlinear programming Global optimization Stochastic programming

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Flexible, reliable, and renewable power system resource expansion planning considering energy storage systems and demand response programs

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IET RENEWABLE POWER GENERATION 2019年第11期13卷 1862-1872页

作者： Hamidpour, Hamidreza Aghaei, Jamshid Pirouzi, Sasan Dehghan, Shahab Niknam, Taher Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Semirom Branch Power Syst Grp Semirom Iran Islamic Azad Univ Qazvin Branch Fac Elect Biomed & Mechatron Engn Qazvin Iran

This study presents a flexible, reliable, and renewable power system resource planning approach to coordinate generation, transmission, and energy storage (ES) expansion planning in the presence of demand response (DR). The flexibility and reliability of the optimal resource expansion planning are ensured by means of appropriate constraints incorporated into the proposed planning tool where thermal generation units, ES systems, and DR programs are considered as flexibility resources. The proposed planning tool is a mixed-integer non-linear programming (MINLP) problem due to the non-linear and non-convex constraints of AC power flow equations. Accordingly, to linearise the proposed MINLP problem, the AC nodal power balance constraints are linearised by means of the first-order expansion of Taylor's series and the line flow equations are linearised by means of a polygon. Additionally, the stochastic programming is used to characterise the uncertainty of loads, a maximum available power of wind farms, forecasted energy price, and availability/unavailability of generation units and transmission lines by means of a sufficient number of scenarios. The proposed planning tool is implemented on the IEEE 6-bus and the IEEE 30-bus test systems under different conditions. Case studies illustrate the effectiveness of the proposed approach based on both flexibility and reliability criteria.

关键词： integer programming load flow wind power plants power generation planning stochastic programming nonlinear programming power transmission planning demand side management thermal power stations power transmission lines power generation economics power transmission economics concave programming energy storage AC power flow equations AC nodal power balance constraints first-order expansion stochastic programming maximum available power planning tool IEEE 30-bus test systems reliability criteria flexible power system resource expansion planning renewable power system resource expansion planning energy storage systems energy storage expansion planning demand response optimal resource expansion planning thermal generation units ES systems DR programs flexibility resources mixed-integer nonlinear programming nonconvex constraints demand response programs reliable power system resource expansion planning

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Approximating electrical distribution networks via mixed-integer nonlinear programming

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第2期33卷 245-257页

作者： Lakhera, Sanyogita Shanbhag, Uday V. McInerney, Michael K. Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA Citibank NA New York NY USA

Given urban data derived from a geographical information system (GIS), we consider the problem of constructing an estimate of the electrical distribution system of an urban area. We employ the image data to obtain an approximate electrical load distribution over a network of a prespecificed discretization. Together with partial information about existing substations, we determine the optimal placement of electrical substations to sustain such a load that minimizes the cost of capital and losses. This requires solving large-scale quadratic programs with discrete variables for which we present a novel penalization-smoothing scheme. The choice of locations allows one to determine the optimal flows in this network, as required by physical requirements which provide us with an approximation of the distribution network. Furthermore, the scheme allows for approximating systems in the presence of no-go areas, such as lakes and fields. We examine the performance of our algorithm on the solution of a set of location problems and observe that the scheme is capable of solving large-scale instances, well beyond the realm of existing mixed-integer nonlinear programming solvers. We conclude with a case study in which a stage-wise extension of this scheme is developed to reflect the temporal evolution of load. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Optimization nonlinear programming mixed-integer nonlinear programming Distribution system design GIS

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Model-based bidding strategies on the primary balancing market for energy-intense processes

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COMPUTERS & CHEMICAL ENGINEERING 2019年 120卷 4-14页

作者： Schaefer, Pascal Westerholt, Hermann Graf Schweidtmann, Artur M. Ilieva, Svetlina Mitsos, Alexander Rhein Westfal TH Aachen AVT Aachener Verfahrenstech Proc Syst Engn Forckenbeckstr 51 D-52072 Aachen Germany TRIMET Aluminium SE Aluminiumallee 1 D-45356 Essen Germany

Energy-intense enterprises that flexibilize their electricity consumption can market this either at electricity spot markets or by offering ancillary services on demand, such as balancing power. We formulate optimization of the balancing power bidding strategy as a mixed-integer nonlinear program considering both price forecasts for the ancillary service market and hourly varying spot market prices. We solve this two-stage approach by decomposition into a nonlinear bidding problem and a mixed-integer linear scheduling problem. We consider aluminum electrolysis participating in the German primary balancing market. We show savings in weekly production costs of 5-20% compared to stationary operation. The savings due to the optimal bidding strategy are up to twice the savings from pure exploitation of electricity spot market price spreads. We thus demonstrate that energy-intense processes can systematically take advantage of highly profitable demand-side management measures beyond a spot market price adjusted production. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Demand side management Aluminum electrolysis Ancillary service markets mixed-integer nonlinear programming Model-based bidding strategies

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Strategic placements of PMUs for power network observability considering redundancy measurement

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MEASUREMENT 2019年 134卷 606-623页

作者： Babu, Rohit Bhattacharyya, Biplab Indian Sch Mines Indian Inst Technol Dept Elect Engn Dhanbad 826004 Jharkhand India

This paper proposes a mixed integer nonlinear programming (MINLP) formulation with binary-valued variables for the optimal installation of the phasor measurement units (PMUs) in a power network. The MINLP framework has been solved by a two-phase branch-and-bound algorithm (BBA) for the optimal PMU placement (OPP) problem. The present work deals with the solution of the OPP problem without considering the radial buses including in the optimal solution. PMU is pre-assigned to each bus connected to a radial bus such that the radial buses are excluded from the list of potential PMU locations. The programming technique is used to determine the optimum number of PMUs and their locations to make the interconnected power network completely observable. The proposed BBA may provide multiple solutions at the lowest objective value, each time the optimizer routine restarts. Therefore, the obtained optimal solutions are being ranked regarding the measurement redundancy index. The two-phase branch-and-bound algorithm is applied to IEEE standard test systems as well as to New England 39-bus and a northern regional power grid 246-bus system. The proposed algorithm has been compared with the existing programming techniques in the recent literature. The simulation results obtained by the proposed BBA indicate the effectiveness and the efficiency to detect the desired target. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Binary integer linear programming Branch-and-bound Interconnected power network mixed-integer nonlinear programming Measurement redundancy Optimal PMU placement Observability analysis Phasor measurement unit Two-phase branch-and-bound algorithm

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