Multi-objective optimization of large wind farm parameters for harmonic instability and resonance conditions

2016 ◽  
Author(s):  
Esmaeil Ebrahimzadeh ◽  
Frede Blaabjerg ◽  
Xiongfei Wang ◽  
Claus Leth Bak
Keyword(s):  
Wind Farm ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Large Wind

scholarly journals Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4242 ◽  
Author(s):  
Van-Hai Bui ◽  
Akhtar Hussain ◽  
Woon-Gyu Lee ◽  
Hak-Man Kim
Keyword(s):  
Output Power ◽  
Wind Farm ◽  
Optimal Operation ◽  
Multi Objective Optimization ◽  
Power Fluctuation ◽  
Trade Off ◽  
Multi Objective ◽  
Optimal Output ◽  
Power Fluctuations ◽  
Optimal Set

In this paper, a multi-objective optimization method is proposed to determine trade-off between conflicting operation objectives of wind farm (WF) systems, i.e., maximizing the output power and minimizing the output power fluctuation of the WF system. A detailed analysis of the effects of different objective’s weight values and battery size on the operation of the WF system is also carried out. This helps the WF operator to decide on an optimal operation point for the whole system to increase its profit and improve output power quality. In order to find out the optimal solution, a two-stage optimization is also developed to determine the optimal output power of the entire system as well as the optimal set-points of wind turbine generators (WTGs). In stage 1, the WF operator performs multi-objective optimization to determine the optimal output power of the WF system based on the relevant information from WTGs’ and battery’s controllers. In stage 2, the WF operator performs optimization to determine the optimal set-points of WTGs for minimizing the power deviation and fulfilling the required output power from the previous stage. The minimization of the power deviation for the set-points of WTGs helps the output power of WTGs much smoother and therefore avoids unnecessary internal power fluctuations. Finally, different case studies are also analyzed to show the effectiveness of the proposed method.

Decomposition-Based Multi-Objective Optimization of Energy Noise Trade-Off in a Wind Farm

2018 ◽  
pp. 177-205
Author(s):  
Prateek Mittal ◽  
Kishalay Mitra
Keyword(s):  
Alternative Energy ◽  
Wind Farm ◽  
Pareto Frontier ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Additional Information ◽  
Gradient Based ◽  
Standards And Guidelines ◽  
Hybrid Methodology

A multi-objective optimization case study of maximization and minimization of energy generation and noise propagation is considered here. A novel hybrid methodology, as a combination of probabilistic variable decomposed multi-objective evolutionary algorithm (VdRBNSGA-II) and the newly developed deterministic gradient based Pareto frontier construction approach (nD-NNC), has been proposed to determine the optimum layout of turbines (numbers and locations) inside a wind farm. In contrast to previous case studies, the proposed approach is able to yield the alternative energy-noise solutions along with the additional information on corresponding turbine layouts (numbers and locations) on a single Pareto front. As a result, it provides a decision maker with an ample of choices to choose from different competing solutions based on the existing standards and guidelines.

scholarly journals A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

Energies ◽  
2016 ◽  
Vol 9 (3) ◽  
pp. 216 ◽  
Author(s):  
Silvio Rodrigues ◽  
Carlos Restrepo ◽  
George Katsouris ◽  
Rodrigo Teixeira Pinto ◽  
Maryam Soleimanzadeh ◽  
...  
Keyword(s):  
Wind Farm ◽  
Offshore Wind ◽  
Multi Objective Optimization ◽  
Offshore Wind Farm ◽  
Multi Objective ◽  
Optimization Framework

An Multi-Objective Optimization Model for Wind-Storage Combined Operation

2013 ◽  
Vol 860-863 ◽  
pp. 414-418
Author(s):  
Yan Qing Li ◽  
Hao Shan Li ◽  
Chi Dong ◽  
Jian Wang
Keyword(s):  
Wind Power ◽  
Optimization Model ◽  
Large Scale ◽  
Wind Farm ◽  
Economic Benefits ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Wind Power Integration ◽  
Optimization Calculation ◽  
Combined Operation

Large-scale wind power integration constituted great challenges for the power system operation and dispatching, due to the volatile and peak-reversal nature of wind power.The multi-objective optimization model of the wind farm combined with pumped-storage was studied to solve the problem.An optimization model for wind-storage combined operation was established, aiming at tracking load changes ,improving wind power economic benefits and peak shaving benefits, using improved multi-objective particle swarm optimization.The optimization calculation attempted to reduce volatility of the remaining load after removal of wind-storage joint output and increase economic benefits of wind farrms. Through the optimization calculation the wind farm and storage plant scheduling values of each time are available. The calculation example shows that the model and method are conducive to large-scale wind power integration and have a certain practicality and effectiveness.

scholarly journals A Two-Stage Placement Algorithm with Multi-Objective Optimization and Group Decision Making

2017 ◽  
Vol 17 (1) ◽  
pp. 87-103
Author(s):  
Daniela Borissova ◽  
Ivan Mustakerov
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Wind Farm ◽  
Group Decision ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Suitable Alternative ◽  
Multi Objective ◽  
Design Alternatives ◽  
Placement Algorithm

Abstract Atwo-stage placement algorithm with multi-objective optimization and group decision making is proposed. The first stage aims to determineaset of design alternatives for objects placement by multi-objective combinatorial optimization. The second stage relies on business intelligence via group decision-making based on solution of optimization task to makeachoice of the most suitable alternative. The design alternatives are determined by means of weighted sum and lexicographic methods. The group decision making is used to evaluate determined design alternatives toward the design parameters. The described algorithm is used for wind farm layout optimization problem. The results of numerical testing demonstrate the applicability of the proposed algorithm.

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