Solution of the Optimal Reactive Power Flow Problem Using a Discrete-Continuous CBGA Implemented in the DigSILENT Programming Language

Bernal-Romero, David Lionel; Montoya, Oscar Danilo; Arias-Londoño, Andrés

doi:10.3390/computers10110151

Cited by 8 publications

(13 citation statements)

References 39 publications

(54 reference statements)

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“…Both an AGA and an IABC [53] were developed for mission assignment and path planning in the disaster rescue task. Regarding the mission assignment, the GA-based method [54,55] has been proofed to be the best scheduling method in our former research; thus it is not necessary to consider other techniques for solving this task. Differently, as for the path planning problem, many other optimization methods can be considered, such as the particle swarm optimization (PSO) [56] method, artificial potential field algorithm (APFA) [57], or bat algorithm [58], etc.…”

Section: Discussionmentioning

confidence: 96%

Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

Liu

Wang

et al. 2021

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An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.

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Section: Discussionmentioning

confidence: 96%

Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

Liu

Wang

et al. 2021

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“…It is worth mentioning that the main complication of the optimization model ( 1)-( 11) is its MINLP nature, since binary and continuous variables are combined with nonlinear non-convex relations mainly defined by the power balance equations. To solve MINLP models, in the current literature, the use of master-slave optimization strategies is a popular method to decouple the binary problem from the continuous problem [20]. In the studied problem defined by ( 1)-( 11), the Newton metaheuristic algorithm is used in the master stage and the successive approximation power flow method is used in the slave stage.…”

Section: Model Interpretationmentioning

confidence: 99%

“…Base case PSO [14] MINLP [16] PGS [13] BFOA [10] Optimal size (bus) -300 ( 4) 600 ( 10) 781 (19) 100 ( 14) 1200 ( 19) 625 ( 10) 803 (22) 500 ( 18) 639 ( 22) 940 ( 20) 479 (20) 300 ( 22) 200 ( 20) 610 ( 25) 1000 ( 27 The information concerning branch parameters and load consumptions for the IEEE 34-node test feeder were reported in Table 1. In all the computational validations, 11 kV and 1000 kVA were considered as voltage and power bases for this system, respectively.…”

Section: Woa Based Approach For Placing Capacitors Network To Reduce ...mentioning

confidence: 99%

Efficient Reduction in the Annual Investment Costs in AC Distribution Networks via Optimal Integration of Solar PV Sources Using the Newton Metaheuristic Algorithm

et al. 2021

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This research addresses the problem of the optimal placement and sizing of (PV) sources in medium voltage distribution grids through the application of the recently developed Newton metaheuristic optimization algorithm (NMA). The studied problem is formulated through a mixed-integer nonlinear programming model where the binary variables regard the installation of a PV source in a particular node, and the continuous variables are associated with power generations as well as the voltage magnitudes and angles, among others. To improve the performance of the NMA, we propose the implementation of a discrete–continuous codification where the discrete component deals with the location problem and the continuous component works with the sizing problem of the PV sources. The main advantage of the NMA is that it works based on the first and second derivatives of the fitness function considering an evolution formula that contains its current solution (xit) and the best current solution (xbest), where the former one allows location exploitation and the latter allows the global exploration of the solution space. To evaluate the fitness function and its derivatives, the successive approximation power flow method was implemented, which became the proposed solution strategy in a master–slave optimizer, where the master stage is governed by the NMA and the slave stage corresponds to the power flow method. Numerical results in the IEEE 34- and IEEE 85-bus systems show the effectiveness of the proposed optimization approach to minimize the total annual operative costs of the network when compared to the classical Chu and Beasley genetic algorithm and the MINLP solvers available in the general algebraic modeling system with reductions of 26.89% and 27.60% for each test feeder with respect to the benchmark cases.

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“…Regarding the selection of the classical Chu and Beasley genetic algorithm (CBGA) and its discrete-continuous version of codification, which helps solve the problem of the optimal location and sizing of PV sources with a unified vector: it is important to say that this optimization algorithm was selected to solve the proposed MINLP model, as it is a widely known model and is used to solve complex optimization problems with efficient numerical performance and low computational effort. Moreover, the discrete-continuous version of the CBGA has recently yielded satisfactory results for the optimal reactive compensation problems, as reported in [19] for static compensators and in [31] for optimal reactive power flow in transmission systems.…”

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confidence: 93%

Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm

2021

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The problem of the optimal siting and sizing of photovoltaic (PV) sources in grid connected distribution networks is addressed in this study with a master–slave optimization approach. In the master optimization stage, a discrete–continuous version of the Chu and Beasley genetic algorithm (DCCBGA) is employed, which defines the optimal locations and sizes for the PV sources. In the slave stage, the successive approximation method is used to evaluate the fitness function value for each individual provided by the master stage. The objective function simultaneously minimizes the energy purchasing costs in the substation bus, and the investment and operating costs for PV sources for a planning period of 20 years. The numerical results of the IEEE 33-bus and 69-bus systems demonstrate that with the proposed optimization methodology, it is possible to eliminate about 27% of the annual operation costs in both systems with optimal locations for the three PV sources. After 100 consecutive evaluations of the DCCBGA, it was observed that 44% of the solutions found by the IEEE 33-bus system were better than those found by the BONMIN solver in the General Algebraic Modeling System (GAMS optimization package). In the case of the IEEE 69-bus system, the DCCBGA ensured, with 55% probability, that solutions with better objective function values than the mean solution value of the GAMS were found. Power generation curves for the slack source confirmed that the optimal siting and sizing of PV sources create the duck curve for the power required to the main grid; in addition, the voltage profile curves for both systems show that voltage regulation was always maintained between ±10% in all the time periods under analysis. All the numerical validations were carried out in the MATLAB programming environment with the GAMS optimization package.

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Solution of the Optimal Reactive Power Flow Problem Using a Discrete-Continuous CBGA Implemented in the DigSILENT Programming Language

Cited by 8 publications

References 39 publications

Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method

Efficient Reduction in the Annual Investment Costs in AC Distribution Networks via Optimal Integration of Solar PV Sources Using the Newton Metaheuristic Algorithm

Optimal Investments in PV Sources for Grid-Connected Distribution Networks: An Application of the Discrete–Continuous Genetic Algorithm

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