scholarly journals Special Issue “Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov”: Foreword by Guest Editors

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 282
Author(s):  
Napsu Karmitsa ◽  
Sona Taheri
Keyword(s):  
Nonsmooth Optimization ◽  
Unit Commitment ◽  
Multiple Instance Learning ◽  
Special Issue ◽  
Unit Commitment Problem ◽  
Nonsmooth Problems ◽  
Commitment Problem ◽  
Difference Of Convex ◽  
The Difference ◽  
Decomposable Structures

Nonsmooth optimization refers to the general problem of minimizing (or maximizing) functions that have discontinuous gradients. This Special Issue contains six research articles that collect together the most recent techniques and applications in the area of nonsmooth optimization. These include novel techniques utilizing some decomposable structures in nonsmooth problems—for instance, the difference-of-convex (DC) structure—and interesting important practical problems, like multiple instance learning, hydrothermal unit-commitment problem, and scheduling the disposal of nuclear waste.

scholarly journals Feasible Modified Subgradient Method for Solving the Thermal Unit Commitment Problem as a New Approach

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Ummuhan Basaran Filik ◽  
Mehmet Kurban
Keyword(s):  
Unit Commitment ◽  
Test System ◽  
Thermal Unit ◽  
Unit Commitment Problem ◽  
New Approach ◽  
Thermal Plant ◽  
Commitment Problem ◽  
Power System Engineering ◽  
The Difference ◽  
Feasible Solutions

The Lagrangian relaxation- (LR-) based methods are commonly used to solve the thermal unit commitment (UC) problem which is an important subject in power system engineering. The main drawback of this group of methods is the difference between the dual and the primal solutions which gives some significant problems on the quality of the feasible solutions. In this paper, a new approach, feasible modified subgradient (F-MSG) method which does not require finding an unconstrained global minimum of the Lagrangian function and knowing an optimal value of the problem under consideration in order to update dual variables at the each iteration, is firstly used for solving the thermal UC problem. The major advantage of the proposed approach is that it guarantees the zero duality gap and convergence independently from the size of the problem. In order to discuss the advantages of this method, the four-unit Tuncbilek thermal plant, which is located in Kutahya region in Turkey, is chosen as a small test system. The numerical results show that F-MSG gives better solutions as compared to the standard LR method.

Unit Commitment Problem Solution for Renewable Integrated Generation

2017 ◽  
Vol 10 (4) ◽  
pp. 13
Author(s):  
TIWARI SHUBHAM ◽  
DWIVEDI BHARTI ◽  
DAVE M.P. ◽  
◽  
◽  
...  
Keyword(s):  
Unit Commitment ◽  
Problem Solution ◽  
Unit Commitment Problem ◽  
Commitment Problem

Application of Hopfield neural network in unit commitment problem

2009 ◽  
Vol 29 (4) ◽  
pp. 1028-1031
Author(s):  
Wei-xin GAO ◽  
Xiang-yang MU ◽  
Nan TANG ◽  
Hong-liang YAN
Keyword(s):  
Neural Network ◽  
Unit Commitment ◽  
Hopfield Neural Network ◽  
Unit Commitment Problem ◽  
Commitment Problem

scholarly journals A meliorated Harris Hawks optimizer for combinatorial unit commitment problem with photovoltaic applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ayani Nandi ◽  
Vikram Kumar Kamboj
Keyword(s):  
Power Generation ◽  
Power System ◽  
Electric Power ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Electric Power System ◽  
Unit Commitment Problem ◽  
Spinning Reserve ◽  
Commitment Problem ◽  
Sine Cosine Algorithm

AbstractConventional unit commitment problem (UCP) consists of thermal generating units and its participation schedule, which is a stimulating and significant responsibility of assigning produced electricity among the committed generating units matter to frequent limitations over a scheduled period view to achieve the least price of power generation. However, modern power system consists of various integrated power generating units including nuclear, thermal, hydro, solar and wind. The scheduling of these generating units in optimal condition is a tedious task and involves lot of uncertainty constraints due to time carrying weather conditions. This difficulties come to be too difficult by growing the scope of electrical power sector day by day, so that UCP has connection with problem in the field of optimization, it has both continuous and binary variables which is the furthermost exciting problem that needs to be solved. In the proposed research, a newly created optimizer, i.e., Harris Hawks optimizer (HHO), has been hybridized with sine–cosine algorithm (SCA) using memetic algorithm approach and named as meliorated Harris Hawks optimizer and it is applied to solve the photovoltaic constrained UCP of electric power system. In this research paper, sine–cosine Algorithm is used for provision of power generation (generating units which contribute in electric power generation for upload) and economic load dispatch (ELD) is completed by Harris Hawks optimizer. The feasibility and efficacy of operation of the hybrid algorithm are verified for small, medium power systems and large system considering renewable energy sources in summer and winter, and the percentage of cost saving for power generation is found. The results for 4 generating units, 5 generating units, 6 generating units, 7 generating units, 10 generating units, 19 generating units, 20 generating units, 40 generating units and 60 generating units are evaluated. The 10 generating units are evaluated with 5% and 10% spinning reserve. The efficacy of the offered optimizer has been verified for several standard benchmark problem including unit commitment problem, and it has been observed that the suggested optimizer is too effective to solve continuous, discrete and nonlinear optimization problems.

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