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Model predictive multirate control for mixed-integer optimisation of redundant refrigeration circuits

JOURNAL OF PROCESS CONTROL 2019年 76卷 112-121页

作者： Luchini, Elisabeth Schirrer, Alexander Jakubek, Stefan Kozek, Martin Vienna Univ Technol Inst Mech & Mechatron Div Control & Proc Automat Getreidemarkt 9 A-1060 Vienna Austria

The temperature control of an insulated cool box (ICB) can be efficiently provided by model predictive control. However, if the cooling capacity is supplied by redundant refrigeration circuits (RCs) with both continuous and switched control variables, the overall control design becomes complex: (1) Time constants of the ICB and RCs differ by a factor of approximately 100. (2) Switched and continuous control variables require mixed-integer optimisation. (3) Power consumption, wear, control performance and output tracking call for multi-objective optimisation. In this work a global linear Model Predictive Controller (MPC) is designed to compute a desired cooling capacity at a low sampling rate over a long prediction horizon, while a mixed-integer optimisation (MIO) provides the actual, optimally modulated cooling capacity utilising a fast sampling rate and a short prediction horizon. The control concept is presented, the interaction between the two MPC instances is described, stability and feasibility are discussed, and simulation results demonstrate the performance of the concept. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Hierarchical MPC mixed-integer optimisation Multiple sampling rates Refrigeration system

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A hierarchical MPC for multi-objective mixed-integer optimisation applied to redundant refrigeration circuits

A hierarchical MPC for multi-objective mixed-integer optimis...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Luchini, Elisabeth Shirrer, Alexander Kozek, Martin Inst Mech & Mechatron Div Control & Proc Automat Getreidemarkt 9-325-A5 A-1060 Vienna Austria

Temperature control of an insulated cool box (ICB) can be efficiently provided by model predictive control. However, if the cooling capacity is supplied by redundant refrigeration circuits (RCs) with both continuous and switched control variables, the overall control design becomes complex: 1) Time constants of the ICB and RCs differ by a factor of approximately 100. 2) Switched and continuous control variables require mixed-integer optimisation. 3) Power consumption, wear, control performance and output tracking call for multi-objective optimisation. In this work a global linear Model Predictive Controller (MPC) is designed to compute a desired cooling capacity at a low sampling frequency for a long prediction horizon, while a mixed-integer MPC (MI-MPC) provides the actual cooling capacity utilising a fast sampling frequency and a short prediction horizon. The control concept is presented, a stability prove is given and simulation results demonstrate the performance of the concept. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： hierarchical MPC mixed-integer optimisation multiple sampling rates refrigeration system

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A hierarchical MPC for multi-objective mixed-integer optimisation applied to redundant refrigeration circuits

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IFAC-PapersOnLine 2017年第1期50卷 9058-9064页

作者： Luchini E. Schirrer A. Kozek M. Institute of Mechanics and Mechatronics Division of Control and Process Automation Getreidemarkt 9/325-A5 Vienna A-1060 Austria

关键词： hierarchical MPC mixed-integer optimisation multiple sampling rates refrigeration system

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Process and machine selection in sampling-based tolerance-cost optimisation for dimensional tolerancing

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第17期60卷 5201-5216页

作者： Hallmann, Martin Schleich, Benjamin Wartzack, Sandro Friedrich Alexander Univ Erlangen Nurnberg FAU Engn Design Martensstr 9 Erlangen Germany

Tolerance-cost optimisation, i.e. using optimisation techniques for tolerance allocation, is frequently used to determine a cost-efficient tolerance design that can meet the stringent requirements on high-quality products. Besides various manufacturing aspects, the selection of available alternative machines and processes hold great potential for an early optimal process planning by identifying their best combination. Although machine/process selection by minimum cost and mixed-integer optimisation is often applied in theory and practice, their proper implementation in tolerance-cost optimisation based on sampling techniques for tolerance analysis, which can statistically consider various individual part tolerance distributions, has not been studied so far. With the aim to overcome this drawback, this article focuses on machine/process selection in sampling-based tolerance-cost optimisation for dimensional tolerances considering the respective machine characteristics of several machine options, e.g. process capabilities and manufacturing distributions. A comparative study proves that machine/process selection by mixed-integer optimisation leads to minimum total manufacturing costs since it covers the whole search space, including all technically feasible machine combinations and thus identifies the global cost minimum.

关键词： Tolerance-cost optimisation optimal tolerance allocation alternative machine process selection mixed-integer optimisation statistical tolerance analysis process planning

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An optimal control approach to scheduling maintenance and production in parallel lines of reactors using decaying catalysts

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COMPUTERS & CHEMICAL ENGINEERING 2020年 142卷 107025-107025页

作者： Adloor, S. D. Vassiliadis, V. S. Univ Cambridge Dept Chem Engn & Biotechnol Philippa Fawcett Dr Cambridge CB3 0AS England Cambridge Simulat Solut Ltd 8 Cody Rd Cambridge CB25 9LS England

In this work, an optimal control approach for scheduling maintenance and production in a process using decaying catalysts, originally developed in a previous study for a single reactor, is extended to parallel lines of reactors. Unlike traditional mixed-integer optimisation methods, this approach involves formulating this problem as a multistage mixed-integer optimal control problem (MSMIOCP) and using a methodology that enables solution as a standard nonlinear optimisation problem. The methodology's features of a feasible path approach and scheduling catalyst changeovers without combinatorial optimisation techniques can be advantageous in providing reliable, robust and efficient solutions in comparison to mixed-integer methods. The MSMIOCP formulation is applied to the case study of an industrial process that operates a single feed over a set of 4 parallel reactors and produces a single product. The solutions obtained using the proposed methodology are of high quality and highlight the potential advantages of this approach over mixed-integer techniques. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Optimal control problem mixed-integer optimisation Catalyst replacement Parallel lines Scheduling Production planning

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Output-only identification of self-excited systems using discrete-time Lur'e models with application to a gas-turbine combustor

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INTERNATIONAL JOURNAL OF CONTROL 2024年第2期97卷 187-212页

作者： Paredes, Juan A. Yang, Yulong Bernstein, Dennis S. Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA

A self-excited system is a nonlinear system with the property that a constant input yields a bounded, nonconvergent response. Nonlinear identification of self-excited systems is considered using a Lur'e model structure, where a linear model is connected in feedback with a nonlinear feedback function. To facilitate identification, the nonlinear feedback function is assumed to be continuous and piecewise affine (CPA). The present paper uses least-squares optimisation to estimate the coefficients of the linear dynamics and the slope vector of the CPA nonlinearity, as well as mixed-integer optimisation to estimate the order of the linear dynamics and the breakpoints of the CPA function. The proposed identification technique requires only output data, and thus no measurement of the constant input is required. This technique is illustrated on a diverse collection of low-dimensional numerical examples as well as data from a gas-turbine combustor.

关键词： Nonlinear system identification nonlinear feedback self-excited oscillations least squares mixed-integer optimisation

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Model-driven real-time control coordination for distribution grids with medium-scale photovoltaic generation

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IET RENEWABLE POWER GENERATION 2017年第12期11卷 1603-1612页

作者： Krata, Jaroslaw Saha, Tapan Kumar Yan, Ruifeng Univ Queensland Sch Informat Technol & Elect Engn Level 4Gen Purpose South Bldg Brisbane Qld 4072 Australia Univ Queensland Global Change Inst BldgTop Staff House Rd Brisbane Qld 4072 Australia

Increasing penetration of photovoltaic (PV) generation may significantly impact existing voltage regulation schemes. In particular, the lack of coordination between control devices and fast change in PV generation can cause voltage magnitude to go beyond limits. This study addresses the issue by providing a novel real-time centralised voltage control algorithm to tackle voltage fluctuations caused by variability of PV power generation. The new method considers autonomous response of upstream voltage regulation devices and provides optimised set-points for the chosen downstream controllers. Furthermore, the investigated method was tested in the real-time digital simulator on a model of 11kV grid located in a semi-rural area of Queensland, Australia. The control algorithm was designed in MATLAB with a multidimensional, non-linear, mixed-integer optimisation. The parallel computing implementation of the KNITRO optimiser helped to provide quicker and more accurate response than the existing uncoordinated control schemes. As a result, optimised control coordination leads to a substantial reduction in voltage fluctuations and decrease in regulation time. Moreover, the proposed method detects and eliminates unnecessary control operations. Thus, it can also prevent voltage hunting in distribution networks.

关键词： power distribution control power grids photovoltaic power systems power generation control voltage control digital simulation control engineering computing power engineering computing integer programming nonlinear programming distribution networks voltage hunting prevention regulation time optimised control coordination KNITRO optimiser parallel computing mixed-integer optimisation nonlinear optimisation multidimensional optimisation MATLAB Australia Queensland real-time digital simulator downstream controllers upstream voltage regulation devices PV power generation variability voltage fluctuations control devices real-time centralised voltage control algorithm voltage magnitude voltage regulation schemes medium-scale photovoltaic generation distribution grids model-driven real-time control coordination

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Binary classification with covariate selection through l0-penalised empirical risk minimisation

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ECONOMETRICS JOURNAL 2021年第1期24卷 103-120页

作者： Chen, Le-Yu Lee, Sokbae Acad Sinica Inst Econ 128Sect 2Acad Rd Taipei 115 Taiwan Columbia Univ Dept Econ 420 West 118th St New York NY 10027 USA Inst Fiscal Studies Ctr Microdata Methods & Practice 7 Ridgmt St London WC1E 7AE England

We consider the problem of binary classification with covariate selection. We construct a classification procedure by minimising the empirical misclassification risk with a penalty on the number of selected covariates. This optimisation problem is equivalent to obtaining an l(0)-penalised maximum score estimator. We derive probability bounds on the estimated sparsity as well as on the excess misclassification risk. These theoretical results are nonasymptotic and established in a high-dimensional setting. In particular, we show that our method yields a sparse solution whose l(0)-norm can be arbitrarily close to true sparsity with high probability and obtain the rates of convergence for the excess misclassification risk. We implement the proposed procedure via the method of mixed-integer linear programming. Its numerical performance is illustrated in Monte Carlo experiments and a real data application of the work-trip transportation mode choice.

关键词： Classification covariate selection finite-sample property maximum score estimation mixed-integer optimisation penalised estimation

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Scheduling of gas turbine compressor washing

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INTELLIGENT AUTOMATION AND SOFT COMPUTING 2006年第1期12卷 63-73页

作者： Hovland, G Antoine, M Univ Queensland Sch Informat Technol & Elect Engn St Lucia Qld 4067 Australia ABB Power Technol Syst Plant Optimisat Baden Switzerland

This paper describes an estimation method for gas turbine compressor degradation and an economical optimisation model for determining the optimal compressor washing cycles. The optimisation model aims at minimising fuel consumption and emissions in combined-cycle power plants. The results presented are of significant importance for power plants operators that have the possibility of frequently connecting and disconnecting to the power grid. By optimising power generation periods and levels, downtimes and maintenance scheduling, the operators ensure that the plant operates at a high efficiency level in periods when fuel prices are high. High efficiency levels ensure low fuel consumption and emission levels. The work presented in this paper was implemented as a product prototype at ABB Utility Automation in 2003 and patented in 2005, [1].

关键词： compressor washing mixed-integer optimisation

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*** - An open-source Julia tool for holistic transmission and distribution grid planning 2

*** - An open-source Julia tool for holistic transmission an...

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2nd International Workshop on Open Source Modelling and Simulation of Energy Systems (OSMSES)

作者： Rossini, Matteo Ergun, Hakan Rossi, Marco Ric Sistema Energet RSE Milan Italy Katholieke Univ Leuven Dept Elect Engn ESAT Leuven Belgium EnergyVille Genk Belgium

ISBN: (纸本)9798350311228

In the ever complex world of the power systems, robust decision making in the context of investment planning becomes a difficult problem. In the decision making process, the costs and benefits of implementing complementary or competing technologies (AC versus DC or storage versus classical transmission expansion) need to be assessed. Additionally, the electrification of industrial demand and the integration of smart loads, such as heat pumps or electric vehicles, pave the way for demand flexibility provision on large scale. Considering that the majority of the demand flexibility is located in the distribution grid, there is a need for planning models and tools able to perform combined transmission and distribution grid planning. This paper introduces ***, a Julia/JuMP-based opensource tool for holistic planning of transmission and distribution grids which includes a complete set of planning candidates for transmission and distribution networks and fully internalizes demand flexibility and storage usage. We use stochastic optimisation, in order to find robust decisions with respect to different climate conditions and operating hours within given climate years. To keep the optimisation problem tractable, we introduce a novel decomposition between the transmission and distribution grid planning problems. We demonstrate that using the proposed approach a speed improvement of up to 100 times can be achieved for cases with a large number of distribution grids, with negligible increase of the objective function value and a good solution quality in terms of power flow.

关键词： Holistic planning mixed-integer optimisation transmission grid planning distribution grid planning demand flexibility storage hvdc

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