Ant lion optimizer for solving optimal reactive power dispatch problem in power systems

Mouassa, Souhil; Bouktir, Tarek; Salhi, A.

doi:10.1016/j.jestch.2017.03.006

Cited by 136 publications

(125 citation statements)

References 30 publications

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“…38 This method captured the interest of many researchers, and it has been successfully used for finding the optimal solution of power system problems. [39][40][41][42][43][44][45][46][47][48] The ability of ALO in solving the electrical economic dispatch problem was investigated in Tung and Chakravorty. 39 The performance of the ALO was test on a power system with both three and six units.…”

Section: Discussionmentioning

confidence: 99%

“…41 The optimal reactive power dispatch in electric power system is investigated as complex optimization problem using ALO in Mouassa et a. 42 Several power systems are used to study the applicability of the proposed ALO algorithm. Lambda iteration, PSO, and GA methods are compared with ALO for ELD in Kamboj et al 43 However, single power system is used in Kamboj et al 43 for testing the proposed optimization schemes.…”

Section: Discussionmentioning

confidence: 99%

“…The literature [38][39][40][41][42][43][44][45][46][47][48] reveals that ALO is fertile optimization algorithm. It enjoys the advantages of rapid response and reduced computation requirements, as it requires only few parameters to carry out the search.…”

Section: Discussionmentioning

confidence: 99%

“…The ALO algorithm consists of five steps: random walk of ants, building ant lion traps, entrapment of ants in the traps, catch the preys, and re‐building the traps . This method captured the interest of many researchers, and it has been successfully used for finding the optimal solution of power system problems …”

Section: Introductionmentioning

confidence: 99%

“…The ALO technique gave better results than the dynamic programming method . The optimal reactive power dispatch in electric power system is investigated as complex optimization problem using ALO in Mouassa et a . Several power systems are used to study the applicability of the proposed ALO algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Hatata

Hafez

2018

Int Trans Electr Energ Syst

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Summary Economic load dispatch (ELD) became recently a mandatory tool for reliable and economic power system operation. ELD improves the security of the power supply while minimizing the generation costs. Daily load curve imposes conflicting requirements on ELD scheme, such as fulfilling demand during peak periods while limiting the cost. Demand response (DR) is an intelligent approach that ensures load demand fulfillment without introducing additional costs. DR allows shifting the electricity consumption to the non‐peak periods. Therefore, DR could be considered as a virtual power plant. ELD is usually solved as nonlinear constraint optimization problem. Three simple, fertile, and robust meta‐heuristic optimizations are competing in this article for solving ELD incorporating DR as virtual power plant. These are ant lion optimizer (ALO), particle swarm optimization (PSO), and artificial immune system (AIS). The objectives are minimizing the overall cost, system losses, and emission levels. Variety of distinctive study systems are considered to test the functionality and feasibility of the proposed algorithms; they are 6‐bus, 30‐bus, and 118‐bus IEEE systems. MATLAB environment is used for coding ALO, PSO, AIS, and the systems under concern. The comprehensive results show that incorporating DR in ELD reduces the costs/losses/emissions without affecting the power system security and reliability. The comparative results verify the robustness and reliability of ALO in minimizing the overall operating cost and emission levels of the power systems under concern while confining with the constraints. Moreover, they show that ALO has the highest accuracy and convergence efficiency while enjoying the reduced computation storage and execution time.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Hatata

Hafez

2018

Int Trans Electr Energ Syst

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Optimal reactive power dispatch and distributed generation placement based on a hybrid co‐evolution algorithm and bi‐level programming

Chen

Wang

2021

Int Trans Electr Energ Syst

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To improve the economy and stability of distribution networks with a high penetration of distributed generation (DG), active management strategies should be considered during the planning stages and operating stage. This article presents a bi-level model of DG placement and an optimal reactive power dispatch, where the total investment and operation cost are minimized through an optimal allocation of the DG at the upper planning level, and the active power loss and voltage deviation are optimized based on the optimal reactive power output of the DG, static var compensator (SVC), and shunt capacitor bank (SCB). In this study, a period division strategy based on the Grey relation analysis was determined to reduce the switching time of the SCB. The co-evolution algorithm based on the List of Symbols and Abbreviations: α t c and β t c , shape parameters of the distribution functions; δ s , demand curves of load type s; ε, smallest constant used to avoid a zero-division error; ε t,t+1 , Grey absolute correlation degree; Γ, gamma function; λ 1 , penalty function of the node voltage crossing line; ρ, air density; ρ t,t+1 , Grey comprehensive correlation degree; θ nq , voltage phase angle difference of the branch between nodes n and q; θ si , ratio of load type s in the total load of node i; Β, step size control parameter; B nq , susceptance difference of the branch between nodes n and q; C c , carbon dioxide emission cost; C CO2 , carbon emission penalty cost; C q , load cost; C inv , unit investment cost of DG; C inv+ope , investment and operation cost of DG; C ope , unit operating cost of DG; cosθ, power factor; C p , active power loss cost; C p , wind energy utilization coefficient; c t , the Weibull scale parameter; C uep , unit electricity price; D 0 , starting point of zero operator; D 1 , initial valued operator; E f , a fixed-point; E m , greenhouse gas emission intensity; E max , maximum value of solar radiation; f g , current global best fitness values; f i , fitness values of the present sparrow; f w , current global worst fitness values; G nq , conductance difference of the branch between nodes n and q; iter max , maximum number of iterations; J, total number of branches; k c

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An enhanced arithmetic optimization algorithm for optimal control of reactive power

Sahay,

Upputuri,

Kumari

et al. 2023

Optim Control Appl Methods

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In this article, an enhanced arithmetic optimization algorithm (EAOA) is utilized to resolve the optimal reactive power dispatch problem (ORPD) of power plants, which is a non‐linear, non‐smooth, complex optimization problem. Typically, it is formulated as a constrained optimal power flow problem. This paper utilizes the gamma distribution to generate initial random solutions, which improves the accuracy of the arithmetic optimization algorithm (AOA). The original mode of deviation of math optimizer accelerated (MOA) is replaced with the improved pattern of change in the prey's energy in Harris Hawk Optimization (HHO), which creates a superior balance between the exploration and exploitation of AOA. This paper implements logarithmic and exponential operators in place of division and multiplication operators, which improves the exploration ability of AOA and also utilizes the gamma distribution in the generation of random numbers, which are essential for the selection of operators in both search phase which enlarges the search area and improves the global optimum solution. A parametric analysis is performed on 10 benchmark functions for nine possible values before selecting the algorithm's initial parameters. A comparative statistical analysis along with Friedman's test is carried out on 23 benchmark functions to test the overall performance as compared to Sine‐Cosine Algorithm (SCA), Swarm‐Salp Algorithm, Grey Wolf Optimization (GWO), and basic Arithmetic Optimization Algorithm (AOA). EAOA is applied to the IEEE 57 bus system for optimal reactive power control. EAOA reports a significant reduction in a power loss of 23.2 MW, which is 18.596% smaller than the system's base case power loss of 28.5 MW as compared to Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat‐inspired Algorithm (BAT), Cuckoo Search Algorithm (CSA), and Modified Ant Lion Algorithm (MALO) which reports a reduction in a power loss of 14.73%, 15.43%, 15.43%, 16.14%, and 16.14% respectively. EAOA reduces the TVD to 0.76 pu, compared to ABC, FA, BAT, CSA, and MALO, which minimizes the TVD to 0.84, 0.82, 0.88, 0.82, and 0.79, respectively. This proves that EAOA performs better in resolving ORCP problems regarding the quality of solutions and convergence. A detailed comparative statistical analysis of EAOA with CSA, BAT, and WOA (Whale Optimization Algorithm) is conducted in terms of Probability Distribution Functions (PDF) and Cumulative Distribution Functions (CDF), along with the Wilcoxson sign rank test, which proves that EAOA gives the solution with a higher degree of precision.

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Ant lion optimizer for solving optimal reactive power dispatch problem in power systems

Cited by 136 publications

References 30 publications

Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Optimal reactive power dispatch and distributed generation placement based on a hybrid co‐evolution algorithm and bi‐level programming

An enhanced arithmetic optimization algorithm for optimal control of reactive power

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