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

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.

Synthesis of LC-Oscillators Using Rival Multi-Objective Multi-Constraint Optimization Kernels

2015 ◽  
pp. 1-27 ◽  
Author(s):  
Ricardo Póvoa ◽  
Ricardo Lourenço ◽  
Nuno Lourenço ◽  
António Canelas ◽  
Ricardo Martins ◽  
...  
Keyword(s):  
Design Automation ◽  
State Of The Art ◽  
Voltage Controlled Oscillator ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Lc Oscillator ◽  
Lc Oscillators ◽  
Statistical Results ◽  
Multiple Synthesis

This chapter presents a state-of-the-art multi-objective/multi-constraint design automation approach applied to the design of an LC-Voltage Controlled Oscillator and an LC-Oscillator for a 130 nm technology node and leading to sets of design solutions showing figures-of-merit around -192 dBc/Hz and -186 dBc/Hz, respectively. The proposed approach, implemented in AIDA-C, guarantees accuracy by using commercial circuit simulators (HSPICE® and ELDO®) to evaluate the performance of the tentative circuit solutions, where the number of time-consuming circuit simulations is efficiently pruned by the optimization kernel. Three multi-objective optimization algorithms, the NSGA-II, the MOPSO, and the MOSA, are experimented with in the synthesis of the quoted oscillators and compared in terms of performance using statistical results obtained from multiple synthesis runs for each one of the oscillators. The performance of the optimized oscillators is then compared to other state-of-the-art results, showing the benefits of the presented multi-objective design approach.

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

A Memetic Optimization Strategy Based on Dimension Reduction in Decision Space

2015 ◽  
Vol 23 (1) ◽  
pp. 69-100 ◽  
Author(s):  
Handing Wang ◽  
Licheng Jiao ◽  
Ronghua Shang ◽  
Shan He ◽  
Fang Liu
Keyword(s):  
Dimension Reduction ◽  
Optimization Problems ◽  
State Of The Art ◽  
Search Space ◽  
Optimization Strategy ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Multi Objective ◽  
Decision Variables

There can be a complicated mapping relation between decision variables and objective functions in multi-objective optimization problems (MOPs). It is uncommon that decision variables influence objective functions equally. Decision variables act differently in different objective functions. Hence, often, the mapping relation is unbalanced, which causes some redundancy during the search in a decision space. In response to this scenario, we propose a novel memetic (multi-objective) optimization strategy based on dimension reduction in decision space (DRMOS). DRMOS firstly analyzes the mapping relation between decision variables and objective functions. Then, it reduces the dimension of the search space by dividing the decision space into several subspaces according to the obtained relation. Finally, it improves the population by the memetic local search strategies in these decision subspaces separately. Further, DRMOS has good portability to other multi-objective evolutionary algorithms (MOEAs); that is, it is easily compatible with existing MOEAs. In order to evaluate its performance, we embed DRMOS in several state of the art MOEAs to facilitate our experiments. The results show that DRMOS has the advantage in terms of convergence speed, diversity maintenance, and portability when solving MOPs with an unbalanced mapping relation between decision variables and objective functions.

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