A multi-objective optimization model for fast electric vehicle charging stations with wind, PV power and energy storage

Robust operation interval of a large-scale hydro-photovoltaic power system to cope with emergencies

This article discusses the problem of minimizing power loss in unbalanced distribution systems through phase-balancing. This problem is represented by a mixed-integer nonlinear-programming mathematical model, which is solved by applying a discretely encoded Vortex Search Algorithm (DVSA). The numerical results of simulations performed in IEEE 8-, 25-, and 37-node test systems demonstrate the applicability of the proposed methodology when compared with the classical Cuh & Beasley genetic algorithm. In addition, the computation times required by the algorithm to find the optimal solution are in the order of seconds, which makes the proposed DVSA a robust, reliable, and efficient tool. All computational implementations have been developed in the MATLAB® programming environment, and all the results have been evaluated in DigSILENT© software to verify the effectiveness and the proposed three-phase unbalanced power-flow method.

https://www.mdpi.com/1996-1073/14/5/1282/pdf

Application of the Vortex Search Algorithm to the Phase-Balancing Problem in Distribution Systems

This paper discusses the minimization of the total annual operative cost for a planning period of 20 years composed by the annualized costs of the energy purchasing at the substation bus summed with the annualized investment costs in photovoltaic (PV) sources, including their maintenance costs in distribution networks based on their optimal siting and sizing. This problem is presented using a mixed-integer nonlinear programming model, which is resolved by applying a master–slave methodology. The master stage, consisting of a discrete-continuous version of the Vortex Search Algorithm (DCVSA), is responsible for providing the optimal locations and sizes for the PV sources—whereas the slave stage employs the Matricial Backward/Forward Power Flow Method, which is used to determine the fitness function value for each individual provided by the master stage. Numerical results in the IEEE 33- and 69-node systems with AC and DC topologies illustrate the efficiency of the proposed approach when compared to the discrete-continuous version of the Chu and Beasley genetic algorithm with the optimal location of three PV sources. All the numerical validations were carried out in the MATLAB programming environment.

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Optimal Design of PV Systems in Electrical Distribution Networks by Minimizing the Annual Equivalent Operative Costs through the Discrete-Continuous Vortex Search Algorithm

Survey on microgrids with flexible boundaries: Strategies, applications, and future trends

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.

https://www.mdpi.com/2079-9292/10/12/1452/pdf

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

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

This paper deals with the optimal reconfiguration problem of DC distribution networks by proposing a new mixed-integer nonlinear programming (MINLP) formulation. This MINLP model focuses on minimising the power losses in the distribution lines by reformulating the classical power balance equations through a branch-to-node incidence matrix. The general algebraic modelling system (GAMS) is chosen as a solution tool, showing in tutorial form the implementation of the proposed MINLP model in a 6-nodes test feeder with 10 candidate lines. The validation of the MINLP formulation is performed in two classical 10-nodes DC test feeders. These are typically used for power flow and optimal power flow analyses. Numerical results demonstrate that power losses are reduced by about 16% when the optimal reconfiguration plan is found. The numerical validations are made in the GAMS software licensed by Universidad Tecnologica de Bolivar.

https://www.mdpi.com/1996-1073/13/17/4440/pdf

A Mixed-Integer Nonlinear Programming Model for Optimal Reconfiguration of DC Distribution Feeders

Passivity-based nonlinear control for an isolated microgrid system is proposed in this paper. The microgrid consists of a photovoltaic array and a battery energy storage connected to a point of common converters, supplying a constant power load. The purpose of this control strategy is to maintain the output direct current voltage in its reference value under load variations, improving battery interaction. The system is represented by its state space averaged model and the proposed controller is designed using the interconnection and damping assignment strategy, which allows obtaining controller parameters while ensuring the closed-loop system stability. The unknown constant power load is estimated using an observer based on the energy function of the system. The behavior of the proposed control strategy is validated with simulation and experimental results.

Voltage Regulation of an Isolated DC Microgrid with a Constant Power Load: A Passivity-based Control Design

The problem of the optimal placement and sizing of renewable generation sources based on photovoltaic (PV) technology in electrical distribution grids operated in medium-voltage levels was studied in this research. This optimization problem is from the mixed-integer nonlinear programming (MINLP) model family. Solving this model was achieved by implementing a master–slave optimization approach, where the master–slave corresponded to the application of the modified gradient-based metaheuristic optimizer (MGbMO) and the slave stage corresponded to the application of the successive approximation power flow method. In the master stage, the problem of the optimal placement and sizing of the PV sources was solved using a discrete–continuous codification, while the slave stage was used to calculate the objective function value regarding the energy purchasing costs in terminals of the substation, as well as to verify that the voltage profiles and the power generations were within their allowed bounds. The numerical results of the proposed MGbMO in the IEEE 34-bus system demonstrated its efficiency when compared with different metaheuristic optimizers such as the Chu and Beasley genetic algorithm, the Newton metaheuristic algorithm, the original gradient-based metaheuristic optimizer, and the exact solution of the MINLP model using the general algebraic modeling system. In addition, the possibility of including meshed distribution topologies was tested with excellent numerical results.

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Diego Armando Giral-Ramírez

Papers

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

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

A Mixed-Integer Nonlinear Programming Model for Optimal Reconfiguration of DC Distribution Feeders

Voltage Regulation of an Isolated DC Microgrid with a Constant Power Load: A Passivity-based Control Design

Optimal Placement and Sizing of PV Sources in Distribution Grids Using a Modified Gradient-Based Metaheuristic Optimizer