secondorderconeprogramming (SOCP) method is introduced to derive the source location by relaxing the acoustic energy measurement model into convex optimization. The corresponding LS-SOCP and WLS-SOCP algorithm are ...
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This article proposes a new primal-dual path-following interior point algorithm for second-order cone programming. Each iterate always follows the usual wide neighborhood ..., it does not necessarily stay within it, b...
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This article proposes a new primal-dual path-following interior point algorithm for second-order cone programming. Each iterate always follows the usual wide neighborhood ..., it does not necessarily stay within it, but must stay within the wider neighbourhood ...(t, β). This paper shows that the algorithm has ... iteration complexity bound which is better than that of usual wide neighbourhood algorithm O(n log ...), where n is the dimension of the problem, ... with ... the required precision, α ... [0, 1] the given constant number and (x ..., s ...) the initial interior solution. It is the best result in regard to the iteration complexity bound in the context of path-following method for second-order cone programming. (ProQuest: ... denotes formulae/symbols omitted.)
An improved adaptive equalizer based on the principle of minimum mean square error (MMSE) is proposed. This optimization problem which is shown to be convex, is transformed to second-ordercone (SOC) and solved using ...
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We propose a new smoothing function in this paper by smoothing the symmetric perturbed Fischer-Burmeister function. Based on this new function, we present a new non-interior continuation method for solving the second-...
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We propose a new smoothing function in this paper by smoothing the symmetric perturbed Fischer-Burmeister function. Based on this new function, we present a new non-interior continuation method for solving the second-order cone programming. We adopt a variant merit function. Our algorithm needs to solve only one system of linear equations and to perform only one line search at each iteration. It can start from an arbitrary point and does not require the iteration points to be in the set of strictly feasible solutions. We prove the global and local quadratic convergence of the algorithm under suitable assumptions. Numerical results indicate that our algorithm performs well.
This paper is concerned with nonlinear, semidefinite, and second-ordercone programs. A general algorithm, which includes sequential quadratic programming and sequential quadratically constrained quadratic programming...
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This paper is concerned with nonlinear, semidefinite, and second-ordercone programs. A general algorithm, which includes sequential quadratic programming and sequential quadratically constrained quadratic programming methods, is presented for solving these problems. In the particular case of standard nonlinear programs, the algorithm can be interpreted as a prox-regularization of the Solodov sequential quadratically constrained quadratic programming method presented in Mathematics of Operations Research (2004). For such type of methods, the main cost of computation amounts to solve a linear cone program for which efficient solvers are available. Usually, "global convergence results" for these methods require, as for the Solodov method, the boundedness of the primal sequence generated by the algorithm. The other purpose of this paper is to establish global convergence results without boundedness assumptions on any of the iterative sequences built by the algorithm.
Volumetric locking may occur in plastic analysis of incompressible materials using low-order finite elements due to incompressibility constraints. This study presents a locking-free smoothed five-node quadrilateral el...
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Volumetric locking may occur in plastic analysis of incompressible materials using low-order finite elements due to incompressibility constraints. This study presents a locking-free smoothed five-node quadrilateral element-based approach for plastic analysis in structural engineering. The proposed Q5-element employing four cell-based smoothing domains effectively alleviates the volumetric locking issues, herein the problems under plane strain conditions. The resulting large-scale optimization problem is formulated in a conic programming form, enabling efficient use of the interior-point optimizer. Numerical investigations demonstrate the method's effectiveness in alleviating volumetric locking, accurately predicting collapse and shakedown limits, and generating interaction diagrams for load-carrying capacity and structural collapse mechanisms.
With the increasing penetration of distributed generation (DG), the supply-demand imbalance and voltage overruns in the distribution network have intensified, and there is an urgent need to introduce flexibility resou...
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With the increasing penetration of distributed generation (DG), the supply-demand imbalance and voltage overruns in the distribution network have intensified, and there is an urgent need to introduce flexibility resources for regulation. This paper proposes co-planning of electric vehicles (EVs) and soft opening points (SOPs) to improve the flexibility of the active distribution network, thereby improving the economics and flexibility of the distribution network. Firstly, this paper establishes a charging pile day-ahead dispatchable prediction model and a real-time dispatchable potential assessment model through Monte Carlo sampling simulation. It replaces the traditional energy storage model with this model and then solves the EV and SOP collaborative planning model using a second-order conical planning algorithm with the objective function of minimizing the annual integrated cost. At the same time, the flexibility of the distribution network is analyzed by two indicators: power supply and demand balance and branch load margin. Finally, the optimization method proposed in this paper is analyzed and validated on an improved IEEE 33-node distribution system. Example results show that the planning method proposed in this paper can effectively reduce the annual comprehensive operating cost of distribution networks, meet the flexibility index, and be conducive to improving the economy and flexibility of distribution network operation.
This paper investigates the optimal scheduling of battery energy storage system operations considering energy load uncertainty. We develop a novel two-stage distributionally robust optimization model to determine an o...
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This paper investigates the optimal scheduling of battery energy storage system operations considering energy load uncertainty. We develop a novel two-stage distributionally robust optimization model to determine an optimal battery usage schedule that minimizes the worst-case energy costs considering peak load costs. The model leverages deep-learning-based probabilistic forecasting in the construction of the ambiguity set. Specifically, we develop a Deep Autoregressive Recurrent Networks model to generate a probabilistic forecast of energy loads over a time horizon. The output of the forecasting model is then used to construct a marginal-moment ambiguity set for the distributionally robust optimization model. To solve the proposed model, we establish a closed-form characterization of the optimal second-stage objective function value. Leveraging this closed-form expression and using second-order conic duality, we derive an exact single-level mixed integer second-order conic reformulation of the problem. Extensive computational experiments, conducted on a real dataset, demonstrate the value of our proposed model and the effectiveness of the resulting battery schedule. The results show that the proposed model outperforms several benchmarks, including two-stage stochastic programming. Furthermore, the accuracy of the load forecast significantly impacts the effectiveness of the optimal battery schedule in eliminating peak loads by achieving up to 18% reduction in the maximum energy load.
To predict strain localization behaviors of granular soils, the modified Cam-Clay (MCC) model is incorporated into the second-order cone programming optimized micropolar continuum finite-element method (mpcFEM-SOCP). ...
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To predict strain localization behaviors of granular soils, the modified Cam-Clay (MCC) model is incorporated into the second-order cone programming optimized micropolar continuum finite-element method (mpcFEM-SOCP). Based on a cylindrical cavity expansion problem, a biaxial compression problem, and a rigid strip footing problem, the numerical analyses reveal that the nonphysical strain localization behaviors including mesh-dependency of shear band, rumpling, or bifurcation can be alleviated or even removed if mpcFEM-SOCP is implemented appropriately. Furthermore, the internal characteristic length in mpcFEM-SOCP is a macroscopic physical parameter that characterizes the microscopic response of soil particles and is utilized to model the shear band width. A comparison between mpcFEM-SOCP and discrete element method (DEM) is performed, and the analysis results disclose that the internal characteristic length is closely related to the median particle size, and the evolution trend of the local void ratio in the specimen predicted by mpcFEM-SOCP agrees well with that by DEM. A larger shear dilatancy, however, is generally simulated by the latter. Finally, in the undrained analysis of the rigid footing problem, the evolution curves of excess pore-water pressure predicted by standard finite-element method and mpcFEM-SOCP may differ to some extent, as they enable the observations on the interesting evolution behaviors of excess pore-water pressure.
Line-commutated converter (LCC)-based high-voltage DC (HVDC) systems have been integrated with bulk AC power grids for interregional transmission of renewable power. The nonlinear LCC model brings additional nonconvex...
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Line-commutated converter (LCC)-based high-voltage DC (HVDC) systems have been integrated with bulk AC power grids for interregional transmission of renewable power. The nonlinear LCC model brings additional nonconvexity to optimal power flow (OPF) of hybrid AC-DC power grids. A convexification method for the LCC station model could address such nonconvexity but has rarely been discussed. We devise an equivalent reformulation for classical LCC station models that facilitates second-ordercone convex relaxation for the OPF of LCC-based AC-DC power grids. We also propose sufficient conditions for exactness of convex relaxation with its proof. Equivalence of the proposed LCC station models and properties, exactness, and effectiveness of convex relaxation are verified using four numerical simulations. Simulation results demonstrate a globally optimal solution of the original OPF can be efficiently obtained from relaxed model.
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