Optimal Placement of Laying Hen House Temperature Sensors Based on Genetic Algorithm

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

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Optimal Sizing and Placement of Capacitor Banks in Distribution Networks Using a Genetic Algorithm

Electricity ◽

10.3390/electricity2020012 ◽

2021 ◽

Vol 2 (2) ◽

pp. 187-204

Author(s):

Gian Giuseppe Soma

Keyword(s):

Genetic Algorithm ◽

Electricity Market ◽

Distribution Systems ◽

Reactive Power ◽

Electricity Consumption ◽

Distribution Networks ◽

Optimal Placement ◽

Capacitor Banks ◽

Control Pattern ◽

Definition Of

Nowadays, response to electricity consumption growth is mainly supported by efficiency; therefore, this is the new main goal in the development of electric distribution networks, which must fully comply with the system’s constraints. In recent decades, the issue of independent reactive power services, including the optimal placement of capacitors in the grid due to the restructuring of the electricity industry and the creation of a competitive electricity market, has received attention from related companies. In this context, a genetic algorithm is proposed for optimal planning of capacitor banks. A case study derived from a real network, considering the application of suitable daily profiles for loads and generators, to obtain a better representation of the electrical conditions, is discussed in the present paper. The results confirmed that some placement solutions can be obtained with a good compromise between costs and benefits; the adopted benefits are energy losses and power factor infringements, taking into account the network technical limits. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of capacitor banks in distribution systems, with the definition of a suitable control pattern, have been proved.

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Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Electronics ◽

10.3390/electronics10020172 ◽

2021 ◽

Vol 10 (2) ◽

pp. 172

Author(s):

Sunny Katyara ◽

Muhammad Fawad Shaikh ◽

Shoaib Shaikh ◽

Zahid Hussain Khand ◽

Lukasz Staszewski ◽

...

Keyword(s):

Genetic Algorithm ◽

Energy Management ◽

Fuzzy Inference System ◽

Distribution System ◽

Fuzzy Inference ◽

Management Strategies ◽

Test System ◽

Optimal Placement ◽

Inference System ◽

Simulated Environment

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

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Optimal placement of loudspeakers and microphones in an enclosure using genetic algorithm

Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003. ◽

10.1109/cca.2003.1223278 ◽

2004 ◽

Cited By ~ 13

Author(s):

A. Montazeri ◽

J. Poshtan ◽

M.H. Kahaei

Keyword(s):

Genetic Algorithm ◽

Optimal Placement

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Optimal Placement of Horizontal- and Vertical-Axis Wind Turbines in a Wind Farm for Maximum Power Generation Using a Genetic Algorithm

ASME 2011 5th International Conference on Energy Sustainability, Parts A, B, and C ◽

10.1115/es2011-54080 ◽

2011 ◽

Author(s):

Xiaomin Chen ◽

Ramesh Agarwal

Keyword(s):

Genetic Algorithm ◽

Wind Turbines ◽

Wind Farm ◽

Optimization Problem ◽

Vertical Axis ◽

Optimal Placement ◽

Vertical Axis Wind Turbines ◽

Wake Effects ◽

Horizontal Axis Wind Turbines ◽

Wake Model

In this paper, we consider the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal–Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally place the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed.

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