The Elitist Non-dominated Sorting GA for Multi-objective Optimization of Standalone Hybrid Wind/PV Power Systems

2006 ◽  
Vol 6 (9) ◽  
pp. 2000-2005 ◽  
Author(s):  
Daming Xu ◽  
Longyun Kang . ◽  
Binggang Cao .
Keyword(s):  
Power Systems ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4466
Author(s):  
Maël Riou ◽  
Florian Dupriez-Robin ◽  
Dominique Grondin ◽  
Christophe Le Loup ◽  
Michel Benne ◽  
...  
Keyword(s):  
Power Systems ◽  
Rural Areas ◽  
Energy System ◽  
Design Stage ◽  
Multi Objective Optimization ◽  
Renewable Energy Resources ◽  
Nsga Ii ◽  
Renewable Integration ◽  
Multi Objective ◽  
Trade Offs

Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.

scholarly journals Multi-Objective Bee Swarm Optimization Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 133
Author(s):  
Nien-Che Yang ◽  
Danish Mehmood
Keyword(s):  
Power Systems ◽  
Optimization Problems ◽  
Filter Design ◽  
Manhattan Distance ◽  
Multi Objective Optimization ◽  
Optimal Method ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Passive Power

Harmonic distortion in power systems is a significant problem, and it is thus necessary to mitigate critical harmonics. This study proposes an optimal method for designing passive power filters (PPFs) to suppress these harmonics. The design of a PPF involves multi-objective optimization. A multi-objective bee swarm optimization (MOBSO) with Pareto optimality is implemented, and an external archive is used to store the non-dominated solutions obtained. The minimum Manhattan distance strategy was used to select the most balanced solution in the Pareto solution set. A series of case studies are presented to demonstrate the efficiency and superiority of the proposed method. Therefore, the proposed method has a very promising future not only in filter design but also in solving other multi-objective optimization problems.

Evolutionary Multi-Objective Optimization in Power Systems: State-of-the-Art

2007 ◽  
Author(s):  
F. Rivas-Davalos ◽  
E. Moreno-Goytia ◽  
G. Gutierrez-Alacaraz ◽  
J. Tovar-Hernandez
Keyword(s):  
Power Systems ◽  
State Of The Art ◽  
Multi Objective Optimization ◽  
Multi Objective

Multi-objective optimization of passive filters in industrial power systems

2016 ◽  
Vol 99 (1) ◽  
pp. 387-395 ◽  
Author(s):  
Jandecy Cabral Leite ◽  
Ignacio Pérez Abril ◽  
Maria Emilia de Lima Tostes ◽  
Roberto Celio Limão de Oliveira
Keyword(s):  
Power Systems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Passive Filters

scholarly journals A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network

2019 ◽  
Vol 11 (2) ◽  
pp. 526 ◽  
Author(s):  
Jianzhou Wang ◽  
Chunying Wu ◽  
Tong Niu
Keyword(s):  
Wind Speed ◽  
Power Systems ◽  
Data Preprocessing ◽  
Multi Objective Optimization ◽  
Forecasting Accuracy ◽  
Wind Speed Forecasting ◽  
Multi Objective ◽  
Forecasting System ◽  
The Mean ◽  
Wind Power Systems

Given the rapid development and wide application of wind energy, reliable and stable wind speed forecasting is of great significance in keeping the stability and security of wind power systems. However, accurate wind speed forecasting remains a great challenge due to its inherent randomness and intermittency. Most previous researches merely devote to improving the forecasting accuracy or stability while ignoring the equal significance of improving the two aspects in application. Therefore, this paper proposes a novel hybrid forecasting system containing the modules of a modified data preprocessing, multi-objective optimization, forecasting, and evaluation to achieve the wind speed forecasting with high precision and stability. The modified data preprocessing method can obtain a smoother input by decomposing and reconstructing the original wind speed series in the module of data preprocessing. Further, echo state network optimized by a multi-objective optimization algorithm is developed as a predictor in the forecasting module. Finally, eight datasets with different features are used to validate the performance of the proposed system using the evaluation module. The mean absolute percentage errors of the proposed system are 3.1490%, 3.0051%, 3.0618%, and 2.6180% in spring, summer, autumn, and winter, respectively. Moreover, the interval prediction is complemented to quantitatively characterize the uncertainty as developing intervals, and the mean average width is below 0.2 at the 95% confidence level. The results demonstrate the proposed forecasting system outperforms other comparative models considered from the forecasting accuracy and stability, which has great potential in the application of wind power systems.

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