Main shaft bearings are essential transmission components in wind turbines. The failure of the main shaft bearings is inevitably accompanied by catastrophic consequences. In this regard, this paper presents a timedepe...
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Main shaft bearings are essential transmission components in wind turbines. The failure of the main shaft bearings is inevitably accompanied by catastrophic consequences. In this regard, this paper presents a timedependent reliability-based design optimization (TRBDO) approach for main shaft bearings involving mixedintegervariables. The maximization of fatigue life and the elastohydrodynamic film thickness under the load spectrum are selected as optimization objectives. The time-dependent reliability and geometric correlation are determined as nonlinear constraints. An efficient two-stage enrichment strategy is introduced to handle the timedependent probabilistic constraint with mixed-integer design variables. A mixed-integer nonlinear optimization method is developed based on a meta-heuristic algorithm to solve the formulated TRBDO problem. Eventually, the effectiveness and robustness of the proposed approach are demonstrated by a real application in a 5 MW wind turbine.
Many surrogate-assisted evolutionary algorithms (SAEAs) have been shown excellent search performance in solving expensive constrained optimization problems (ECOPs) with continuous variables, but few of them focus on E...
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Many surrogate-assisted evolutionary algorithms (SAEAs) have been shown excellent search performance in solving expensive constrained optimization problems (ECOPs) with continuous variables, but few of them focus on ECOPs with mixed-integer variables (ECOPs-MI). Hence, a population state-driven surrogate-assisted differential evolution algorithm (PSSADE) is proposed for solving ECOPs-MI, in which the adaptive population update mechanism (APUM) and the collaborative framework of global and local surrogate-assisted search (CFGLS) are combined effectively. In CFGLS, a probability-driven mixed-integer mutation (PMIU) is incorporated into the classical global DE/rand/2 and local DE/best/2 for improving the diversity and potentials of candidate solutions, respectively, and the collaborative framework further integrates both the superiority of global and local mutation for the purpose of achieving a good balance between exploration and exploitation. Moreover, the current population is adaptively reselected based on the efficient non-dominated sorting technique in APUM when the population distribution is too dense. Empirical studies on 10 benchmark problems and 2 numerical engineering cases demonstrate that the PSSADE shows a more competitive performance than the existing state-of-the-art algorithms. More importantly, PSSADE provides excellent performance in the design of infrared stealth material film.
This letter presents a distributed algorithm for aggregating a large number of households with mixed-integer variables and intricate couplings between devices. The proposed distributed gradient algorithm is applied to...
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This letter presents a distributed algorithm for aggregating a large number of households with mixed-integer variables and intricate couplings between devices. The proposed distributed gradient algorithm is applied to the double smoothed dual function of the adopted demand response model. Numerical results show that, with minimal parameter adjustments, the convergence of the dual objective exhibits a very similar behavior irrespective system size.
This paper presents a class of nonmonotone Direct Search Methods that converge to stationary points of unconstrained and boxed constrained mixed-integer optimization problems. A new concept is introduced: the quasi-de...
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This paper presents a class of nonmonotone Direct Search Methods that converge to stationary points of unconstrained and boxed constrained mixed-integer optimization problems. A new concept is introduced: the quasi-descent direction. A point x is stationary on a set of search directions if there exists no feasible qdd on that set. The method does not require the computation of derivatives nor the explicit manipulation of asymptotically dense matrices. Preliminary numerical experiments carried out on small to medium problems are encouraging.
A major challenge to implementing residential demand response is that of aligning the objectives of many households, each of which aims to minimize its payments and maximize its comfort level, while balancing this wit...
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A major challenge to implementing residential demand response is that of aligning the objectives of many households, each of which aims to minimize its payments and maximize its comfort level, while balancing this with the objectives of an aggregator that aims to minimize the cost of electricity purchased in a pooled wholesale market. This paper presents a fast distributed algorithm for aggregating a large number of households with a mixture of discrete and continuous energy levels. A distinctive feature of the method in this paper is that the non-convex demand response (DR) problem is decomposed in terms of households as opposed to devices, which allows incorporating more intricate couplings between energy storage devices, appliances, and distributed energy resources. The proposed method is a fast distributed algorithm applied to the double smoothed dual function of the adopted DR model. The method is tested on systems with up to 2560 households, each with 10 devices on average. The proposed algorithm is designed to terminate in 60 iterations irrespective of system size, which can be ideal for an on-line version of this problem. Moreover, numerical results show that with minimal parameter tuning, the algorithm exhibits a very similar convergence behavior throughout the studied systems and converges to near-optimal solutions, which corroborates its scalability.
Diminishing returns (DR)-submodular functions encompass a broad class of functions that are generally nonconvex and nonconcave. We study the problem of minimizing any DR-submodular function with continuous and general...
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Diminishing returns (DR)-submodular functions encompass a broad class of functions that are generally nonconvex and nonconcave. We study the problem of minimizing any DR-submodular function with continuous and general integervariables under box constraints and, possibly, additional monotonicity constraints. We propose valid linear inequalities for the epigraph of any DR-submodular function under the constraints. We further provide the complete convex hull of such an epigraph, which, surprisingly, turns out to be polyhedral. We propose a polynomial -time exact separation algorithm for our proposed valid inequalities with which we first establish the polynomial -time solvability of this class of mixed -integer nonlinear optimization problems.
This paper presents the application of a Sequential Evolutionary Programming (SEP) approach for solving the Profit-Based Unit Commitment problem (PBUC). The PBUC problem is a variant of the traditional Unit Commitment...
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ISBN:
(纸本)9781424402878
This paper presents the application of a Sequential Evolutionary Programming (SEP) approach for solving the Profit-Based Unit Commitment problem (PBUC). The PBUC problem is a variant of the traditional Unit Commitment (UC) that has arisen as a result of the deregulation of power system markets. Specifically, PBUC is used for Generation Companies (Genco's) in order to maximize their own profits without the responsibility of satisfying necessary the forecasted demand. The PBUC is a highly dimensional mixed-integer optimization problem, which might be very difficult to solve. The SEP approach introduced in this paper offers a good balance between accuracy and computational effort while solving the PBUC problem. For its implementation, the PBUC problem is decomposed into three sub-problems that are solved in a sequential way. The proposed method is suitable for a wide variety of power system market rules. In this paper, two case studies are considered, each with different sets of power system market rules. The obtained results were compared with those available in the literature, and they showed the effectiveness of the proposed method for reaching optimal PBUC solutions.
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