LMI and YALMIP: Modeling and Optimization Toolbox in MATLAB

Ravat, Akhilesh Kumar; Dhawan, Amit; Tiwari, Manish

doi:10.1007/978-981-15-6840-4_41

Cited by 16 publications

(9 citation statements)

References 5 publications

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“…(3) Based on the calculated optimal operating conditions of each device in the heat and gas subsystems, the power consumption within the system is calculated based on Equations ( 30) and (31). Then, based on the algorithmic flowchart, the optimal capacity combinations and optimal operating conditions of the PV component, the grid, and the BT in the electronic system are calculated and then combined with the capacity and operating conditions of the RSOC (SOFC mode of operation), calculated based on Equation (1), to derive the minimum electric energy cost C COE (3) Based on the calculated optimal operating conditions of each device in the heat and gas subsystems, the power consumption within the system is calculated based on Equations ( 30) and (31). Then, based on the algorithmic flowchart, the optimal capacity combinations and optimal operating conditions of the PV component, the grid, and the BT in the electronic system are calculated and then combined with the capacity and operating conditions of the RSOC (SOFC mode of operation), calculated based on Equation…”

Section: Model Solving Methodsmentioning

confidence: 99%

Multi-Energy Flow Integrated Energy System Considering Economic Efficiency Targets: Capacity Allocation and Scheduling Study

Zhang,

He,

Han

et al. 2024

Processes

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An integrated energy system (IES) breaks down barriers between different energy subsystems, enhancing energy reliability and efficiency. However, issues such as uneven equipment capacity allocation and suboptimal scheduling persist in multi-energy flow IES. To maximize economic benefits while ensuring energy balance and the operational characteristics of the equipment, a capacity matching optimization and scheduling strategy model for IES was developed. Firstly, mathematical models for the electricity, gas, and thermal networks within the IES were established. Secondly, considering the efficiency of energy conversion between different forms and constraints of energy storage in the electricity–thermal–gas interconnected energy system, optimization solutions were obtained using regional contraction algorithms and sequential quadratic programming methods. Finally, case studies conducted in a real park demonstrated that, through optimized capacity matching, unit prices for electricity, heat, and gas decreased by 39.9%, 90.5%, and 74.2%, respectively, effectively improving the economic viability of the system.

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Section: Model Solving Methodsmentioning

confidence: 99%

Multi-Energy Flow Integrated Energy System Considering Economic Efficiency Targets: Capacity Allocation and Scheduling Study

Zhang,

He,

Han

et al. 2024

Processes

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“…In this section the Chua circuit is simulated to illustrate the above control method for chaos synchronization. The simulation is performed within the Matlab 2021a environment while the underlying optimisations are carried out using the Yalmip toolbox [33]. Chua's circuit in Fig.…”

Section: Numerical Examplementioning

confidence: 99%

Invariant Ellipsoids Method for Chaos Synchronization in a Class of Chaotic Systems

Fedele

2022

IJRCS

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This paper presents an invariant sets approach for chaos synchronization in a class of master-slave chaotic systems affected by bounded perturbations. The method provides the optimal state-feedback gain in terms of the minimal ellipsoid that guarantees minimum synchronization error bound. The problem of finding the optimal invariant ellipsoid is formulated in terms of a semi-definite programming problem that can be easily solved using various simulation and calculus tools. The effectiveness of the proposed criterion is illustrated by numerical simulations on the synchronization of Chua's systems.

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“…Remark Problem 1 can be solved efficiently by means of state‐of‐the‐art (and readily available) LMI solvers, such as those detailed in [22, 23]. …”

Section: Lmi‐based Passivationmentioning

confidence: 99%

LMI‐based passivation of LTI systems with application to marine structures

Faedo

Peña-Sanchez

Carapellese

et al. 2021

IET Renewable Power Gen

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Due to the inherent relevance of passive (physically representative) models for control, state‐estimation, and motion simulation in the field of marine systems, in this paper, an optimisation‐based approach to passivation of linear time‐invariant (LTI) systems is proposed with application to physically consistent dynamical modelling of marine structures. In particular, the presented strategy is based upon the introduction of a suitably designed perturbation, computed via minimisation of a linear objective subject to a specific set of linear matrix inequalities (LMIs). The performance of the passivation technique is showcased in terms of two case studies: An offshore platform, with a frequency‐domain response computed by means of hydrodynamic codes, and a 1:20 scale wave energy converter (WEC), is characterised in terms of real experimental data.

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LMI and YALMIP: Modeling and Optimization Toolbox in MATLAB

Cited by 16 publications

References 5 publications

Multi-Energy Flow Integrated Energy System Considering Economic Efficiency Targets: Capacity Allocation and Scheduling Study

Multi-Energy Flow Integrated Energy System Considering Economic Efficiency Targets: Capacity Allocation and Scheduling Study

Invariant Ellipsoids Method for Chaos Synchronization in a Class of Chaotic Systems

LMI‐based passivation of LTI systems with application to marine structures

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