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

2021 ◽  
Vol 13 (24) ◽  
pp. 13633
Author(s):  
Oscar Danilo Montoya ◽  
Luis Fernando Grisales-Noreña ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Fitness Function ◽  
Distribution Networks ◽  
Voltage Regulation ◽  
Successive Approximation Method ◽  
Optimization Approach ◽  
Voltage Profile ◽  
Matlab Programming ◽  
Bus System

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.

scholarly journals Operating Cost Reduction in Distribution Networks Based on the Optimal Phase-Swapping Including the Costs of the Working Groups and Energy Losses

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4535
Author(s):  
Oscar Montoya ◽  
Jorge Alarcon-Villamil ◽  
Jesus Hernández
Keyword(s):  
Distribution Networks ◽  
Voltage Regulation ◽  
Energy Losses ◽  
Asymmetric Distribution ◽  
Three Phase ◽  
Working Groups ◽  
Sine Cosine Algorithm ◽  
Matlab Programming ◽  
Power Curves ◽  
Bus System

The problem of optimal phase-balancing in three-phase asymmetric distribution networks is addressed in this research from the point of view of combinatorial optimization using a master–slave optimization approach. The master stage employs an improved sine cosine algorithm (ISCA), which is entrusted with determining the load reconfiguration at each node. The slave stage evaluates the energy losses for each set of load connections provided by the master stage by implementing the triangular-based power flow method. The mathematical model that was solved using the ISCA is designed to minimize the annual operating costs of the three-phase network. These costs include the annual costs of the energy losses, considering daily active and reactive power curves, as well as the costs of the working groups tasked with the implementation of the phase-balancing plan at each node. The peak load scenario was evaluated for a 15-bus test system to demonstrate the effectiveness of the proposed ISCA in reducing the power loss (18.66%) compared with optimization methods such as genetic algorithm (18.64%), the classical sine cosine algorithm (18.42%), black-hole optimizer (18.38%), and vortex search algorithm (18.59%). The IEEE 37-bus system was employed to determine the annual total costs of the network before and after implementing the phase-balancing plan provided by the proposed ISCA. The annual operative costs were reduced by about 13% with respect to the benchmark case, with investments between USD 2100 and USD 2200 in phase-balancing activities developed by the working groups. In addition, the positive effects of implementing the phase-balancing plan were evidenced in the voltage performance of the IEEE 37-bus system by improving the voltage regulation with a maximum of 4% in the whole network from an initial regulation of 6.30%. All numerical validations were performed in the MATLAB programming environment.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

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

2021 ◽  
Vol 11 (23) ◽  
pp. 11525
Author(s):  
Oscar Danilo Montoya ◽  
Luis Fernando Grisales-Noreña ◽  
Lázaro Alvarado-Barrios ◽  
Andres Arias-Londoño ◽  
Cesar Álvarez-Arroyo
Keyword(s):  
Power Flow ◽  
Fitness Function ◽  
Solution Space ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Solar Pv ◽  
Flow Method ◽  
Power Flow Method

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.

Multi-point optimization of transonic airfoils using an enhanced genetic algorithm

2017 ◽  
Vol 232 (7) ◽  
pp. 1347-1360 ◽  
Author(s):  
Narjes Timnak ◽  
Alireza Jahangirian
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Fitness Function ◽  
Single Point ◽  
Navier Stokes ◽  
Computational Time ◽  
Adaptive Mutation ◽  
Function Evaluation ◽  
New Techniques ◽  
New Methods

In this study, two new techniques are proposed for accelerating the multi-point optimization of an airfoil shape by genetic algorithms. In such multi-point evolutionary optimization, the objective function has to be evaluated several times more than a single-point optimization. Thus, excessive computational time is crucial in these problems particularly, when computational fluid dynamics is used for fitness function evaluation. Two new techniques of preadaptive range operator and adaptive mutation rate are proposed. An unstructured grid Navier–Stokes flow solver with a two-equation [Formula: see text] turbulence model is used to evaluate the objective function. The new methods are applied for optimum design of a transonic airfoil at two speed conditions. The results show that using the new methods can increase the aerodynamic efficiency of optimum airfoil at each operating condition with about 30% less computational time in comparison with the conventional genetic algorithm approach.

scholarly journals Annual Cost and Loss Minimization in a Radial Distribution Network by Capacitor Allocation Using PSO

2021 ◽  
Vol 11 (24) ◽  
pp. 11840
Author(s):  
Muhammad Bilal ◽  
Mohsin Shahzad ◽  
Muhammad Arif ◽  
Barkat Ullah ◽  
Suhaila Badarol Hisham ◽  
...  
Keyword(s):  
Objective Function ◽  
Radial Distribution ◽  
Annual Cost ◽  
Reactive Power ◽  
Optimal Solution ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Use Case ◽  
Voltage Profile ◽  
Sensitivity Indices

Increasing power demand from passive distribution networks has led to deteriorated voltage profiles and increased line flows. This has increased the annual operations and installation costs due to unavoidable reinforcement equipment. This work proposes the reduction in annual costs by optimal placement of capacitors used to alleviate power loss in radial distribution networks (RDNs). The optimization objective function is formulated for the reduction in operation costs by (i) reducing the active and reactive power losses, and (ii) the cost and installation of capacitors, necessary to provide the reactive power support and maintain the voltage profile. Initially, the network buses are ranked according to two loss sensitivity indices (LSIs), i.e., active loss sensitivity with respect to node voltage (LSI1) and reactive power injection (LSI2). The sorted bus list is then fed to the particle swarm optimization (PSO) for solving the objective function. The efficacy of the proposed work is tested on different IEEE standard networks (34 and 85 nodes) for different use cases and load conditions. In use case 1, the values finalized by the algorithm are selected without considering their market availability, whereas in use case 2, market-available capacitor sizes close to the optimal solution are selected. Furthermore, the static and seasonal load profiles are considered. The results are compared with recent methods and have shown significant improvement in terms of annual cost, losses and line flows reduction, and voltage profile.

Genetic Algorithm (GA) and Internal Energy Transfer Calculations Using Neural Network (NN) Methods

2012 ◽  
Author(s):  
Lionel Raff ◽  
Ranga Komanduri ◽  
Martin Hagan ◽  
Satish Bukkapatnam
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Objective Function ◽  
Fitness Function ◽  
Solution Space ◽  
Large Ensemble ◽  
Nonlinear Functional ◽  
Squared Error ◽  
Time Required

Genetic algorithms (GA), like NNs, can be used to fit highly nonlinear functional forms, such as empirical interatomic potentials from a large ensemble of data. Briefly, a genetic algorithm uses a stochastic global search method that mimics the process of natural biological evolution. GAs operate on a population of potential solutions applying the principle of survival of the fittest to generate progressively better approximations to a solution. A new set of approximations is generated in each iteration (also known as generation) of a GA through the process of selecting individuals from the solution space according to their fitness levels, and breeding them together using operators borrowed from natural genetics. This process leads to the evolution of populations of individuals that have a higher probability of being “fitter,” i.e., better approximations of the specified potential values, than the individuals they were created from, just as in natural adaptation. The most time-consuming part in implementing a GA is often the evaluation of the objective or the fitness function. The objective function O[P] is expressed as sum squared error computed over a given large ensemble of data. Consequently, the time required for evaluating the objective function becomes an important factor. Since a GA is well suited for implementing on parallel computers, the time required for evaluating the objective function can be reduced significantly by parallel processing. A better approach would be to map out the objective function using several possible solutions concurrently or beforehand to improve computational efficiency of the GA prior to its execution, and using this information to implement the GA. This will obviate the need for cumbersome direct evaluation of the objective function. Neural networks may be best suited to map the functional relationship between the objective function and the various parameters of the specific functional form. This study presents an approach that combines the universal function approximation capability of multilayer neural networks to accelerate a GA for fitting atomic system potentials. The approach involves evaluating the objective function, which for the present application is the mean squared error (MSE) between the computed and model-estimated potential, and training a multilayer neural network with decision variables as input and the objective function as output.

scholarly journals A Hybrid Optimization Approach for Power Loss Reduction and DG Penetration Level Increment in Electrical Distribution Network

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6008
Author(s):  
Teketay Mulu Beza ◽  
Yen-Chih Huang ◽  
Cheng-Chien Kuo
Keyword(s):  
Power Loss ◽  
Distribution Network ◽  
Distribution Networks ◽  
Test System ◽  
Base Case ◽  
Optimization Approach ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Electrical Distribution ◽  
Power Loss Reduction

The electrical distribution system has experienced a number of important changes due to the integration of distributed and renewable energy resources. Optimal integration of distributed generators (DGs) and distribution network reconfiguration (DNR) of the radial network have significant impacts on the power system. The main aim of this study is to optimize the power loss reduction and DG penetration level increment while keeping the voltage profile improvements with in the permissible limit. To do so, a hybrid of analytical approach and particle swarm optimization (PSO) are proposed. The proposed approach was tested on 33-bus and 69-bus distribution networks, and significant improvements in power loss reduction, DG penetration increment, and voltage profile were achieved. Compared with the base case scenario, power loss was reduced by 89.76% and the DG penetration level was increased by 81.59% in the 69-bus test system. Similarly, a power loss reduction of 82.13% and DG penetration level increment of 80.55% was attained for the 33-bus test system. The simulation results obtained are compared with other methods published in the literature.

scholarly journals An Effective Coordination Setting for Directional Overcurrent Relays Using Modified Harris Hawk Optimization

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3007
Author(s):  
Muhammad Irfan ◽  
Seung-Ryle Oh ◽  
Sang-Bong Rhee
Keyword(s):  
Distribution Networks ◽  
Optimal Operation ◽  
Mixed Integer ◽  
Coordination Problem ◽  
Roulette Wheel Selection ◽  
Medium Level ◽  
Overcurrent Relays ◽  
Comparison Of The Results ◽  
Matlab Programming ◽  
Bus System

The relay optimization expresses quite a challenge for smooth and optimal operation of power system networks. The relay optimization is formulated as a mixed integer non-linear problem and is highly constrained. Furthermore, a reliable relaying system must be able to detect and isolate the faulted portion in a timely manner. Therefore, it is necessary to find optimal parameters for relay settings to be able to respond in a timely way to the encountered fault and at the same time keep in consideration the operational and coordination constraints. This paper proposes modified Harris hawk optimization (MHHO), which is based on the intelligent preying tactics of Harris hawks and the improvement of intended modifications, crowding distance and roulette wheel selection. The proposed algorithm has been tested on IEEE 8 and 15-bus systems, using MATLAB programming. The test systems are the distribution networks covering the medium level voltage for consideration. The simulation results verified the success of MHHO to find optimal settings for the relays. For IEEE 8-bus system, MHHO was able to give 35.45% improvement in the results in comparison to other algorithms. Furthermore, for the IEEE 15-bus system, MHHO showed 24.09% improvement on average. The comparison of the results obtained by MHHO with the other state-of-the-art algorithms proved that it is the strong candidate for optimization of the relay coordination problem.

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