Multi-objective VAr planning for large-scale power systems using projection-based two-layer simulated annealing algorithms

2004 ◽  
Vol 151 (4) ◽  
pp. 555 ◽  
Author(s):  
Y.L. Chen ◽  
Y.L. Ke
Keyword(s):  
Simulated Annealing ◽  
Power Systems ◽  
Large Scale ◽  
Multi Objective ◽  
Annealing Algorithms

Multi-objective ant lion optimization algorithm to solve large-scale multi-objective optimal reactive power dispatch problem

2019 ◽  
Vol 38 (1) ◽  
pp. 304-324 ◽  
Author(s):  
Souhil Mouassa ◽  
Tarek Bouktir
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Reactive Power ◽  
Stability Index ◽  
Active Power ◽  
Power Losses ◽  
Content Type ◽  
Multi Objective ◽  
Ant Lion Optimization ◽  
Active Power Losses

Purpose In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems. Design/methodology/approach A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed. Findings Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions. Originality/value The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

Solving a multi-objective multi-depot stochastic location-routing problem by a hybrid simulated annealing algorithm

2009 ◽  
Vol 223 (8) ◽  
pp. 1045-1054 ◽  
Author(s):  
H A Hassan-Pour ◽  
M Mosadegh-Khah ◽  
R Tavakkoli-Moghaddam
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Genetic Operators ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Multi Objective ◽  
Location Routing ◽  
Proposed Model ◽  
Stochastic Location

This paper presents a novel mathematical model for a stochastic location-routing problem (SLRP) that minimizes the facilities establishing cost and transportation cost, and maximizes the probability of delivery to customers. In this proposed model, new aspects of a location-routing problem (LRP), such as stochastic availability of facilities and routes, are developed that are similar to real-word problems. The proposed model is solved in two stages: (i) solving the facility location problem (FLP) by a mathematical algorithm and (ii) solving the multi-objective multi-depot vehicle routing problem (MO-MDVRP) by a simulated annealing (SA) algorithm hybridized by genetic operators, namely mutation and crossover. The proposed SA can find good solutions in a reasonable time. It solves the proposed model in large-scale problems with acceptable results. Finally, a trade-off curve is used to depict and discuss a large-sized problem. The associated results are compared with the results obtained by the lower bound and Lingo 8.0 software.

scholarly journals Solving Bid-Based Dynamic Economic Dispatch in Competitive Electricity Market Using Improved Simulated Annealing Algorithm

2020 ◽  
Vol 7 (4) ◽  
pp. 621-630
Author(s):  
Riyadh Bouddou ◽  
Farid Benhamida ◽  
Ismail Ziane ◽  
Amine Zeggai ◽  
Moussa Belgacem
Keyword(s):  
Simulated Annealing ◽  
Power Systems ◽  
Electricity Markets ◽  
Electricity Market ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Economic Dispatch ◽  
Unit System ◽  
Annealing Algorithm ◽  
Dynamic Economic Dispatch

Electricity markets are open after the deregulation of power systems due to competition. An optimization problem based on dynamic economic dispatch has recently come up in the new context of deregulated power systems known as bid-based dynamic economic dispatch (BBDED). It is one of the major operations and control functions in the electricity markets used to determine the optimal operations of market participants with scheduled load demands during a specified period. BBDED involves power generation companies (GENCOs) and customers to submit energy and price bids to the independent system operator (ISO) in a day-ahead market. The ISO clears the market with the objective of social profit maximization. In this paper, a BBDED problem is solved using an improved simulated annealing algorithm (ISA), including system constraints with different periods under bidding strategies. The proposed ISA technique is implemented in MATLAB and applied on a 3-unit system, a 6-unit system, and a 40-unit large-scale system. The proposed ISA is evaluated by comparison with relevant methods available in the literature, to demonstrate and confirm its potential in terms of convergence, robustness, and effectiveness for solving the BBDED problem.

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