This paper proposes a Transient Stability Constrained Optimal Power Flow (TSCOPF) formulation that models non-synchronous renewable generation equipped with synthetic inertia. The proposed optimization problem calcula...
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This paper proposes a Transient Stability Constrained Optimal Power Flow (TSCOPF) formulation that models non-synchronous renewable generation equipped with synthetic inertia. The proposed optimization problem calculates the optimal operating point of the system, accommodating high shares of non-synchronous renewable generation while ensuring transient stability in the event of critical incidents. Synthetic inertia controllers are used to improve the dynamic stability of the system in cases of very high share of renewable generation. The proposed tool is tested in the North-West Spanish system, a network with a high penetration of wind energy that causes a reduction in the total system inertia. The results of the study show that 1) synthetic inertia in renewable power plants can diminish electromechanical oscillations after a severe contingency, reducing the cost of ensuring transient stability;2) using synthetic inertia the system becomes more stable when conventional generation is decommissioned following de-carbonization and renewable promotion policies;and 3) the proposed model can be used to calculate the parameters of the synthetic inertia control.
Given the inability to foresee all possible scenarios, it is justified to desire an efficient trust-region subproblem solver capable of delivering any desired level of accuracy on demand;that is, the accuracy obtainab...
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Given the inability to foresee all possible scenarios, it is justified to desire an efficient trust-region subproblem solver capable of delivering any desired level of accuracy on demand;that is, the accuracy obtainable for a given trust-region subproblem should not be partially dependent on the problem itself. Current state-of-the-art iterative eigensolvers all fall into the class of restarted Lanczos methods;whereas, current iterative trust-region solvers at best reduce to unrestarted Lanczos methods;which in this context are well known to be numerically unstable with impractical memory requirements. In this paper, we present the first iterative trust region subproblem solver that at its core contains a robust and practical eigensolver. Our solver leverages the recently announced work of Stathopoulos and Orginos which has not been noticed by the optimization community and can be utilized because, unlike other restarted Lanczos methods, its restarts do not necessarily modify the current Lanczos sequence generated by Conjugate Gradient methods (CG). This innovated strategy can be utilized in the context of TR solvers as well. Moreover, our TR subproblem solver adds negligible computational overhead compared to existing iterative TR approaches.
Due to the absence of arc -tangent constraints, Second -Order Cone programming (SOCP) relaxation of AC Optimal Power Flow (ACOPF) does not provide a tighter solution as compared to Semi -definite programming (SDP) rel...
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ISBN:
(纸本)9781665464413
Due to the absence of arc -tangent constraints, Second -Order Cone programming (SOCP) relaxation of AC Optimal Power Flow (ACOPF) does not provide a tighter solution as compared to Semi -definite programming (SDP) relaxation for meshed networks. In order to tighten the SOCP relaxation of ACOPF, this paper presents a SOCP-based convex formulation of ACOPF with linearized arc -tangent constraints, which provides a feasible solution and a tight bound on the objective of the exact. ACOPF formulation. Also, the proposed model is scalable to large transmission networks and is computationally efficient. Numeric results on various NESTA test cases (up to 2869peagase system) demonstrate the proposed formulation's tightness, computational efficiency, and scalability to large meshed transmission networks.
The use of fast electric output regulation of combined heat and power (CHP) units can enhance the flexible operation of industrial park electric-heat coupling system (IPEHS) and promote the use of new energy sources. ...
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Optimizing a function over the efficient set of a multiojective problem plays an important role in many fields of application. This problem arises whenever the decision maker wants to select the efficient solution tha...
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Optimizing a function over the efficient set of a multiojective problem plays an important role in many fields of application. This problem arises whenever the decision maker wants to select the efficient solution that optimizes his utility function. Several methods are proposed in literature to deal with the problem of optimizing a linear function over the efficient set of a multiobjective integer linear program MOILFP. However, in many real-world problems, the objective functions or the utility function are nonlinear. In this paper, we propose an exact method to optimize a quadratic function over the efficient set of a multiobjective integer linear fractional program MOILFP. The proposed method solves a sequence of quadratic integer problems. Where, at each iteration, the search domain is reduced s6uccessively, by introducing cuts, to eliminate dominated solutions. We conducted a computational experiment, by solving randomly generated instances, to analyze the performance of the proposed method.
Efficient integrators with sensitivity propagation are an essential ingredient for the numerical solution of optimal control problems. This paper gives an overview on the acados integrators, their Python interface and...
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ISBN:
(纸本)9783907144084
Efficient integrators with sensitivity propagation are an essential ingredient for the numerical solution of optimal control problems. This paper gives an overview on the acados integrators, their Python interface and presents a workflow that allows using them with their sensitivities within a nonlinear programming (NLP) solver interfaced by CasADi. The implementation is discussed, demonstrated and provided as open-source software. The computation times of the proposed integrator and its sensitivity computation are compared to the native CasADi collocation integrator, CVODES and IDAS on different examples. A speedup of one order of magnitude for simulation and of up to three orders of magnitude for the forward sensitivity propagation is shown for an airborne wind energy system model.
nonlinear Model Predictive Control (NMPC) is an optimization-based control strategy that directly incorporates nonlinear dynamic models and has desirable stability and robustness properties. State estimation is an ess...
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nonlinear Model Predictive Control (NMPC) is an optimization-based control strategy that directly incorporates nonlinear dynamic models and has desirable stability and robustness properties. State estimation is an essential counterpart to NMPC and Moving Horizon Estimation (MHE) is also an optimization-based strategy that directly incorporates the nonlinear dynamics and constraints. However, NMPC and MHE are challenged by the computational expense of solving NLPs at each time step. For NMPC, this is avoided by advanced-step and advanced-multi-step approaches, which solve the detailed optimization off-line (possibly over multiple sampling times) and perform sensitivity-based corrections to the optimal solution on-line, with over two orders of magnitude less computation. This work complements advanced-multi-step NMPC with an advanced-multi-step MHE approach. The development solves rigorous optimization problems in background along with detailed updates to the arrival cost. On-line corrections are enabled by fast sensitivity-based NLP. The amsMHE approach is demonstrated on two large-scale distillation case studies with hundreds of state variables, and shows that nonlinear state estimation for large-scale systems can be implemented with negligible on-line computation. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
This paper presents a study on the application of Bayesian algorithm to the optimization of passive RF circuits. We evaluate the efficiency of Bayesian Optimization in improving performance parameters and compare it t...
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Aiming at the selection of refueling ports, the amount of refueling, and the speed selection faced by ships in container liner transportation, taking into account constraints such as carbon emissions, fuel consumption...
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Purpose It has always been a hot topic for online retailers to obtain consumers' product evaluations from massive online reviews. In the process of online shopping, there is no face-to-face interaction between onl...
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Purpose It has always been a hot topic for online retailers to obtain consumers' product evaluations from massive online reviews. In the process of online shopping, there is no face-to-face interaction between online retailers and customers. After collecting online reviews left by customers, online retailers are eager to acquire answers to some questions. For example, which product attributes will attract consumers? Or which step brings a better experience to consumers during the process of shopping? This paper aims to associate the latent Dirichlet allocation (LDA) model with the consumers' attitude and provides a method to calculate the numerical measure of consumers' product evaluation expressed in each word. Design/methodology/approach First, all possible pairs of reviews are organized as a document to build the corpus. After that, latent topics of the traditional LDA model noted as the standard LDA model, are separated into shared and differential topics. Then, the authors associate the model with consumers' attitudes toward each review which is distinguished as positive review and non-positive review. The product evaluation reflected in consumers' binary attitude is expanded to each word that appeared in the corpus. Finally, a variational optimization is introduced to calculate parameters mentioned in the expanded LDA model. Findings The experiment's result illustrates that the LDA model in the research noted as an expanded LDA model, can successfully assign sufficient probability with words related to products attributes or consumers' product evaluation. Compared with the standard LDA model, the expanded model intended to assign higher probability with words, which have a higher ranking within each topic. Besides, the expanded model also has higher precision on the prediction set, which shows that breaking down the topics into two categories fits better on the data set than the standard LDA model. The product evaluation of each word is calculated by the expan
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