scholarly journals A Model Predictive Control Design for Power Generation Heavy-Duty Gas Turbines

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2182 ◽  
Author(s):  
Alessandro Rosini ◽  
Alessandro Palmieri ◽  
Damiano Lanzarotto ◽  
Renato Procopio ◽  
Andrea Bonfiglio
Keyword(s):  
Power Generation ◽  
Model Predictive Control ◽  
Electric Power ◽  
Predictive Control ◽  
Gas Turbines ◽  
Feedback Linearization ◽  
Renewable Energy Sources ◽  
Current Standard ◽  
Power Generators ◽  
Highly Nonlinear

The new electric power generation scenario, characterized by growing variability due to the greater presence of renewable energy sources (RES), requires more restrictive dynamic requirements for conventional power generators. Among traditional power generators, gas turbines (GTs) can regulate the output electric power faster than any other type of plant; therefore, they are of considerable interest in this context. In particular, the dynamic performance of a GT, being a highly nonlinear and complex system, strongly depends on the applied control system. Proportional–integral–derivative (PID) controllers are the current standard for GT control. However, since such controllers have limitations for various reasons, a model predictive control (MPC) was designed in this study to enhance GT performance in terms of dynamic behavior and robustness to model uncertainties. A comparison with traditional PID-based controllers and alternative model-based control approaches (feedback linearization control) found in the literature demonstrated the effectiveness of the proposed approach.

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.

scholarly journals Dynamic Economic Dispatch On Micro Grid Electrical Systems Using Quadratic Programming

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Heri Suryoatmojo
Keyword(s):  
Power Generation ◽  
Quadratic Programming ◽  
Electric Power ◽  
Renewable Energy Sources ◽  
Economic Dispatch ◽  
Small Scale ◽  
Distributed Power Generation ◽  
Optimal Capacity ◽  
Micro Grid ◽  
Dynamic Economic Dispatch

Currently the needs of electric power increased rapidly along with the development of technology. The increase in power requirements is contrary to the availability of sources of energy depletion of oil and coal. This problem affects the national electrical resistance. To meet the needs of large electric power with wide area coverage is required small scale distributed power generation. This distributed generation (DG) of renewable energy sources sought to minimize the use of energy resources such as oil and coal and connected to the micro grid and use the battery as a power balance. Because of there are many DGs and the use of batteries, therefore it is important to determine the optimal power generation of each plant as well as the use of battery based on the optimal capacity so that requirement of electric power can be met with minimal cost each time. This optimization is known as Dynamic Economic Dispatch. In this study, the methods of Quadratic Programming is required to solve the optimization problem.

Nonlinear Model Predictive Control via Feedback Linearization of a Greenhouse

2000 ◽  
Vol 33 (25) ◽  
pp. 191-196 ◽  
Author(s):  
Sandra Piñón ◽  
Miguel Peña ◽  
Carlos Soria ◽  
Benjamín Kuchen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Feedback Linearization ◽  
Nonlinear Model Predictive Control
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