An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach

This paper addresses the classical problem of optimal location and sizing of distributed generators (DGs) in radial distribution networks by presenting a mixed-integer nonlinear programming (MINLP) model. To solve such model, we employ the General Algebraic Modeling System (GAMS) in conjunction with the BONMIN solver, presenting its characteristics in a tutorial style. To operate all the DGs, we assume they are dispatched with a unity power factor. Test systems with 33 and 69 buses are employed to validate the proposed solution methodology by comparing its results with multiple approaches previously reported in the specialized literature. A 27-node test system is also used for locating photovoltaic (PV) sources considering the power capacity of the Caribbean region in Colombia during a typical sunny day. Numerical results confirm the efficiency and accuracy of the MINLP model and its solution is validated through the GAMS package. © 2019 Ain Shams UniversityUniversidad Nacional de Colombia, UN: 38945, 58838 P17211 Universidad Tecnológica de Pereira, UTP: C2019P011, C2018P020 Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), COLCIENCIAS: 727-2015This work was funded in part by the Administrative Department of Science, Technology, and Innovation of Colombia (COLCIENCIAS) through its National Scholarship Program, under Grant 727-2015 ; in part by Instituto Tecnológico Metropolitano de Medellín, under Project P17211; in part by Universidad Tecnológica de Bolívar, under Projects C2018P020 and C2019P011; and in part by Universidad Nacional de Colombia, under Proyect ”Estrategia de transformación del sector energético Colombiano en el horizonte de 2030 - Energética 2030” - ”Generación distribuida de energía eléctrica en Colombia a partir de energía solar y eólica” (Code: 58838, Hermes: 38945). Oscar D. Montoya received his BEE, M.Sc. and Ph.D degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2012 and 2014 respectively. His research interests include mathematical optimization, planning and control of power systems, renewable energies, energy storage, protective devices and smartgrids. Walter Gil-González received his BEE and M.Sc. degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2011 and 2013 respectively. He is currently studying a Ph.D in Electrical Engineering at Universidad Tecnológica de Pereira, Colombia. His research interests include mathematical optimization, planning and control of power systems, renewable energies, energy storage, protective devices and smartgrids. Luis F. Grisales received his BEE and M.Sc. degrees in Electrical Engineering from Universidad Tecnológica de Pereira, Colombia, in 2013 and 2015 respectively. He is currently studying a Ph.D in Engineering at Universidad Nacional de Colombia. Actually, is professor in the Instituto TecnolÓgico Metropolitano de Medellín, attached to the Department of Electromechanics and mechatronics, member of the research group MATyER. His research interests include mathematical modelling, optimization techniques, planning and control of power systems, renewable energies, energy storage, power electronic and smartgrids

Linear-based Newton-Raphson Approximation for Power Flow Solution in DC Power Grids

This paper presents a linear-based Newton method for load flow solution in DC power grids. This approximation is based on the classical Taylor's series expansion combined to the open-circuit voltage obtained when all the constant power load points are disconnected. This solution strategy avoids the usage of an iterative process to solve the load flow problem in DC power grids reducing its processing time. Notwithstanding its simplicity, the proposed method is very accurate when is compared to classical Gauss-Seidel or Newton-Raphson methods concerning the solution quality. Simulation results are conducted via MATLAB software by using two radial test feeders composed by 10 and 33 nodes reported in the specialized literature. © 2018 IEEE

On the mathematical modeling for optimal selecting of calibers of conductors in DC radial distribution networks: An MINLP approach

This paper addresses the problem of optimal conductor selection in direct current (DC) distribution networks with radial topology. A nonlinear mixed-integer programming model (MINLP) is developed through a branch-to-node incidence matrix. An important contribution is that the proposed MINLP model integrates a set of constraints related to the telescopic structure of the network, which allows reducing installation costs. The proposed model also includes a time-domain dependency that helps analyze the DC network under different load conditions, including renewable generation and battery energy storage systems, and different voltage regulation operative consigns. The objective function of the proposed model is made up of the total investment in conductors and the total cost of energy losses in one year of operation. These components of the objective function show multi-objective behavior. For this reason, different simulation scenarios are performed to identify their effects on the final grid configuration. An illustrative 10-nodes medium-voltage DC grid with 9 lines is used to carry out all the simulations through the General Algebraic Modeling System known as GAMS

Optimal Integration of Distributed Generators into DC Microgrids Using a Hybrid Methodology: Genetic and Vortex Search Algorithms

This paper addresses the problem of optimal location and sizing of distributed generators (DGs) in direct current (DC) grids. To solve it, we propose an optimization approach with an objective function that aims to reduce power losses due to energy transport, while considering all the constraints that represent DC grids in a distributed generation environment. For the mathematical formulation of the problem, we used a mixed-integer nonlinear programming (MINLP) model, which allowed us to evaluate the impact of all possible configurations (i.e., location and size of DGs in the DC network) on the objective function and the constraints. The solution method proposed here is a master–slave strategy that implements a hybrid solution methodology that combines a genetic algorithm (GA) and the vortex search algorithm (VSA). The GA is in charge of solving the location problem in the master stage, and the VSA is responsible for sizing the DGs in the slave stage. To evaluate the effectiveness and robustness of the proposed GA/VSA methodology, we employed two test systems (i.e., 21 and 69 buses) considering a maximum penetration of distributed generation equal to 40% of the power generated by the slack buses. Furthermore, we also implemented nine other hybrid methodologies based on metaheuristic techniques (proposed in the literature for solving the problem addressed here) to make comparisons. All the solution methods used and proposed in this paper are based on sequential programming to avoid the need for specialized software and thus reduce the complexity and cost of the solutions. The effectiveness of the proposed solution was evaluated in two scenarios: (1) peak power demand and (2) variation in power generation and demand associated with photovoltaic generation and user demand in Medellín, Colombia. The results demonstrate that the GA/VSA methodology achieved the best results in terms of solution quality and processing times in all the test scenarios proposed in this study. © 2022, King Fahd University of Petroleum & Minerals

Application of the arithmetic optimization algorithm to solve the optimal power flow problem in direct current networks

This article presents a methodology to solve to the Optimal Power Flow (OPF) problem in Direct Current (DC) networks using the Arithmetic Optimization Algorithm (AOA) and Successive Approximation (SA). This master-slave methodology solves the OPF problem in two stages: the master stage estimates the solution to the OPF problem considering its constraints and variables, and the slave stage assesses the fitness of the solution proposed by the master stage. To validate the methodology suggested in this article, three test systems cited multiple times in the literature were used: the 10, 21 and the 69 nodes test systems. In addition, three scenarios varying the allowable power limits for the Distributed Generators (DGs) are presented; thus, the methodology explores solutions under different conditions. To prove its efficiency and robustness, the solution was compared with four other methods reported in the literature: Ant Lion Optimization (ALO), Black Hole Optimization (BHO), the Continuous Genetic Algorithm (CGA), and Particle Swarm Optimization (PSO). The results show that the methodology proposed here to reduce power losses presents the best solution in terms of standard deviation. © 2022 The Author

Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm

In this study, we present a master–slave methodology to solve the problem of optimal power dispatch in a direct current (DC) microgrid. In the master stage, the Antlion Optimization (ALO) method solves the problem of power dispatch by the Distributed Generators (DGs); in the slave stage, a numerical method based on successive approximations (SA) evaluates the load flows required by the potential solutions proposed by the ALO technique. The objective functions in this paper are the minimization of energy production costs and the reduction of CO 2 emissions produced by the diesel generators in the microgrid. To favor energy efficiency and have a lower negative impact on the environment, the DC microgrids under study here include three DGs (one diesel generator and two generators based on renewable energy sources, i.e., solar energy and wind power) and a slack bus connected to a public electrical grid. The effectiveness of the proposed ALO–SA methodology was tested in the 21- and 69-bus test systems. We used three other optimization techniques to compare methods in the master stage: particle swarm optimization, continuous genetic algorithm, and black hole optimization. Additionally, we combined SA with every method to solve the load flow problem in the slave stage. The results show that, among the methods analyzed in this study, the proposed ALO–AS methodology achieves the best performance in terms of lower energy production costs, less CO 2 emissions, and shorter computational processing times. All the simulations were performed in MATLAB. © 2021, King Fahd University of Petroleum & Minerals.Ocampo-Toro, J. A., Garzon-Rivera, O. D., Grisales-Noreña, L. F., Montoya-Giraldo, O. D., & Gil-González, W. (2021). Optimal Power Dispatch in Direct Current Networks to Reduce Energy Production Costs and CO 2 Emissions Using the Antlion Optimization Algorithm. Arabian Journal for Science and Engineering, 46(10), 9995-10006

Journal of Physics: Conference Series

1201

Journal of Energy Storage

10148

Energies

en1104101