Optimal Integration of Photovoltaic Sources in Distribution Networks for Daily Energy Losses Minimization Using the Vortex Search Algorithm

This paper deals with the optimal siting and sizing problem of photovoltaic (PV) generators in electrical distribution networks considering daily load and generation profiles. It proposes the discrete-continuous version of the vortex search algorithm (DCVSA) to locate and size the PV sources where the discrete part of the codification defines the nodes. Renewable generators are installed in these nodes, and the continuous section determines their optimal sizes. In addition, through the successive approximation power flow method, the objective function of the optimization model is obtained. This objective function is related to the minimization of the daily energy losses. This method allows determining the power losses in each period for each renewable generation input provided by the DCVSA (i.e., location and sizing of the PV sources). Numerical validations in the IEEE 33- and IEEE 69-bus systems demonstrate that: (i) the proposed DCVSA finds the optimal global solution for both test feeders when the location and size of the PV generators are explored, considering the peak load scenario. (ii) In the case of the daily operative scenario, the total reduction of energy losses for both test feeders are 23.3643% and 24.3863%, respectively; and (iii) the DCVSA presents a better numerical performance regarding the objective function value when compared with the BONMIN solver in the GAMS software, which demonstrates the effectiveness and robustness of the proposed master-slave optimization algorithm.

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

Electronics ◽

10.3390/electronics10121452 ◽

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.

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Optimal Selection of Conductors in Three-Phase Distribution Networks Using a Discrete Version of the Vortex Search Algorithm

Computation ◽

10.3390/computation9070080 ◽

2021 ◽

Vol 9 (7) ◽

pp. 80

Author(s):

John Fernando Martínez-Gil ◽

Nicolas Alejandro Moyano-García ◽

Oscar Danilo Montoya ◽

Jorge Alexander Alarcon-Villamil

Keyword(s):

Objective Function ◽

Power Flow ◽

Phase Distribution ◽

Search Algorithm ◽

Distribution Networks ◽

Discrete Version ◽

Optimal Selection ◽

Flow Method ◽

Three Phase ◽

Selection Of

In this study, a new methodology is proposed to perform optimal selection of conductors in three-phase distribution networks through a discrete version of the metaheuristic method of vortex search. To represent the problem, a single-objective mathematical model with a mixed-integer nonlinear programming (MINLP) structure is used. As an objective function, minimization of the investment costs in conductors together with the technical losses of the network for a study period of one year is considered. Additionally, the model will be implemented in balanced and unbalanced test systems and with variations in the connection of their loads, i.e., Δ− and Y−connections. To evaluate the costs of the energy losses, a classical backward/forward three-phase power-flow method is implemented. Two test systems used in the specialized literature were employed, which comprise 8 and 27 nodes with radial structures in medium voltage levels. All computational implementations were developed in the MATLAB programming environment, and all results were evaluated in DigSILENT software to verify the effectiveness and the proposed three-phase unbalanced power-flow method. Comparative analyses with classical and Chu & Beasley genetic algorithms, tabu search algorithm, and exact MINLP approaches demonstrate the efficiency of the proposed optimization approach regarding the final value of the objective function.

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Efficient Operative Cost Reduction in Distribution Grids Considering the Optimal Placement and Sizing of D-STATCOMs Using a Discrete-Continuous VSA

Applied Sciences ◽

10.3390/app11052175 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2175

Author(s):

Oscar Danilo Montoya ◽

Walter Gil-González ◽

Jesus C. Hernández

Keyword(s):

Objective Function ◽

Reactive Power ◽

Programming Model ◽

Search Algorithm ◽

Solution Space ◽

Distribution Networks ◽

Optimal Placement ◽

Mixed Integer ◽

Solution Vector ◽

The Impact

The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.

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Improved Genetic Algorithm for Phase-Balancing in Three-Phase Distribution Networks: A Master-Slave Optimization Approach

Computation ◽

10.3390/computation9060067 ◽

2021 ◽

Vol 9 (6) ◽

pp. 67

Author(s):

Oscar Danilo Montoya ◽

Alexander Molina-Cabrera ◽

Luis Fernando Grisales-Noreña ◽

Ricardo Alberto Hincapié ◽

Mauricio Granada

Keyword(s):

Genetic Algorithm ◽

Reactive Power ◽

Search Algorithm ◽

Peak Load ◽

Distribution Networks ◽

Numerical Results ◽

Optimization Approach ◽

Three Phase ◽

Operative Costs ◽

Power Curves

This paper addresses the phase-balancing problem in three-phase power grids with the radial configuration from the perspective of master–slave optimization. The master stage corresponds to an improved version of the Chu and Beasley genetic algorithm, which is based on the multi-point mutation operator and the generation of solutions using a Gaussian normal distribution based on the exploration and exploitation schemes of the vortex search algorithm. The master stage is entrusted with determining the configuration of the phases by using an integer codification. In the slave stage, a power flow for imbalanced distribution grids based on the three-phase version of the successive approximation method was used to determine the costs of daily energy losses. The objective of the optimization model is to minimize the annual operative costs of the network by considering the daily active and reactive power curves. Numerical results from a modified version of the IEEE 37-node test feeder demonstrate that it is possible to reduce the annual operative costs of the network by approximately 20% by using optimal load balancing. In addition, numerical results demonstrated that the improved version of the CBGA is at least three times faster than the classical CBGA, this was obtained in the peak load case for a test feeder composed of 15 nodes; also, the improved version of the CBGA was nineteen times faster than the vortex search algorithm. Other comparisons with the sine–cosine algorithm and the black hole optimizer confirmed the efficiency of the proposed optimization method regarding running time and objective function values.

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Optimal power flow for distribution networks using gravitational search algorithm

Electrical Engineering ◽

10.1007/s00202-014-0302-5 ◽

2014 ◽

Vol 96 (4) ◽

pp. 335-345 ◽

Cited By ~ 14

Author(s):

Jordan Radosavljević ◽

Miroljub Jevtić ◽

Nebojša Arsić ◽

Dardan Klimenta

Keyword(s):

Power Flow ◽

Optimal Power Flow ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Distribution Networks ◽

Optimal Power ◽

Gravitational Search

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Future search algorithm for optimal integration of distributed generation and electric vehicle fleets in radial distribution networks considering techno-environmental aspects

SN Applied Sciences ◽

10.1007/s42452-021-04466-y ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Varaprasad Janamala ◽

U. Kamal Kumar ◽

Thandava Krishna Sai Pandraju

Keyword(s):

Objective Function ◽

Electric Vehicle ◽

Radial Distribution ◽

Greenhouse Gas Emission ◽

Search Algorithm ◽

Distribution Networks ◽

Environmental Aspects ◽

Future Search ◽

Radial Distribution Networks ◽

The Impact

AbstractIn this paper, a new nature-inspire meta-heuristic algorithm called future search algorithm (FSA) is proposed for the first time to solve the simultaneous optimal allocation of distribution generation (DG) and electric vehicle (EV) fleets considering techno-environmental aspects in the operation and control of radial distribution networks (RDN). By imitating the human behavior in getting fruitful life, the FSA starts arbitrary search, discovers neighborhood best people in different nations and looks at worldwide best individuals to arrive at an ideal solution. A techno-environmental multi-objective function is formulated using real power loss, voltage stability index. The active and reactive power compensation limits and different operational constraints of RDN are considered while minimizing the proposed objective function. Post optimization, the impact of DGs on conventional energy sources is analyzed by evaluating their greenhouse gas emission. The effectiveness of the proposed methodology is presented using different case studies on Indian practical 106-bus agriculture feeder for DGs and 36-bus rural residential feeder for simultaneous allocation of DGs and EV fleets. Also, the superiority of FSA in terms of global optima, convergence characteristics is compared with various other recent heuristic algorithms.

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Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs

Applied Sciences ◽

10.3390/app11083353 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3353

Author(s):

Oscar Danilo Montoya ◽

Harold R. Chamorro ◽

Lazaro Alvarado-Barrios ◽

Walter Gil-González ◽

César Orozco-Henao

Keyword(s):

Objective Function ◽

Optimal Power Flow ◽

Reactive Power ◽

Programming Model ◽

Distribution Networks ◽

Optimal Placement ◽

Mixed Integer ◽

Energy Losses ◽

Optimization Approach ◽

Operative Costs

This paper proposes a new hybrid master–slave optimization approach to address the problem of the optimal placement and sizing of distribution static compensators (D-STATCOMs) in electrical distribution grids. The optimal location of the D-STATCOMs is identified by implementing the classical and well-known Chu and Beasley genetic algorithm, which employs an integer codification to select the nodes where these will be installed. To determine the optimal sizes of the D-STATCOMs, a second-order cone programming reformulation of the optimal power flow problem is employed with the aim of minimizing the total costs of the daily energy losses. The objective function considered in this study is the minimization of the annual operative costs associated with energy losses and installation investments in D-STATCOMs. This objective function is subject to classical power balance constraints and device capabilities, which generates a mixed-integer nonlinear programming model that is solved with the proposed genetic-convex strategy. Numerical validations in the 33-node test feeder with radial configuration show the proposed genetic-convex model’s effectiveness to minimize the annual operative costs of the grid when compared with the optimization solvers available in GAMS software.

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Exact minimization of the energy losses and the CO2 emissions in isolated DC distribution networks using PV sources

DYNA ◽

10.15446/dyna.v88n217.93099 ◽

2021 ◽

Vol 88 (217) ◽

pp. 178-184

Author(s):

Alexander Molina ◽

Oscar Danilo Montoya ◽

Walter Gil-González

Keyword(s):

Mathematical Formulation ◽

Main Idea ◽

Distribution Networks ◽

Global Optimum ◽

Mixed Integer ◽

Energy Losses ◽

Electrical Networks ◽

Convex Model ◽

Daily Energy ◽

Minlp Model

This paper addresses the optimal location and sizing of photovoltaic (PV) sources in isolated direct current (DC) electrical networks, considering time-varying load and renewable generation curves. The mathematical formulation of this problem corresponds to mixed-integer nonlinear programming (MINLP), which is reformulated via mixed-integer convex optimization: This ensures the global optimum solving the resulting optimization model via branch & bound and interior-point methods. The main idea of including PV sources in the DC grid is to minimize the daily energy losses and greenhouse emissions produced by diesel generators in isolated areas. The GAMS package is employed to solve the MINLP model, using mixed and integer variables; also, the CVX and MOSEK solvers are used to obtain solutions from the proposed mixed-integer convex model in the MATLAB. Numerical results demonstrate important reductions in the daily energy losses and the harmful gas emissions when PV sources are optimally integrated into DC grid.

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Distributed Generation Capacity Planning for Distribution Networks to Minimize Energy Loss

Advances in Computer and Electrical Engineering - Handbook of Research on Emerging Technologies for Electrical Power Planning, Analysis, and Optimization ◽

10.4018/978-1-4666-9911-3.ch005 ◽

2016 ◽

pp. 76-95

Author(s):

Adnan Anwar ◽

Md. Apel Mahmud ◽

Md. Jahangir Hossain ◽

Himanshu Roy Pota

Keyword(s):

Distributed Generation ◽

Distribution System ◽

Capacity Planning ◽

Power Flow ◽

Optimal Power Flow ◽

Cost Optimization ◽

Distribution Network ◽

Peak Load ◽

Distribution Networks ◽

Multi Phase

This chapter presents an unbalanced multi-phase optimal power flow (UMOPF) based planning approach to determine the optimum capacities of multiple distributed generation units in a distribution network. An adaptive weight particle swarm optimization algorithm is used to find the global optimum solution. To increase the efficiency of the proposed scheme, a co-simulation platform is developed. Since the proposed method is mainly based on the cost optimization, variations in loads and uncertainties within DG units are also taken into account to perform the analysis. An IEEE 123 node distribution system is used as a test distribution network which is unbalanced and multi-phase in nature, for the validation of the proposed scheme. The superiority of the proposed method is investigated through the comparisons of the results obtained that of a Genetic Algorithm based OPF method. This analysis also shows that the DG capacity planning considering annual load and generation uncertainties outperform the traditional well practised peak-load planning.

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