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This study addresses the problem of optimal Battery Energy Storage System (BESS) operation in power distribution networks in a realistic scenario that reflects the variability in photovoltaic generation and energy demand on a typical day of operation in two region of Colombia. To solve this problem, it was developed a Nonlinear Programming (NLP) model that includes three objective functions, which were approached from a single-objective optimization perspective. These objective functions focus on power distribution systems’ operating costs, energy losses due to energy transport, and carbon dioxide emissions at the substation node. The proposed mathematical model was solved using the SNOPT solver in the General Algebraic Modeling System (GAMS). To validate the numerical results, the 33-node test system to match the energy generation and demand characteristics of the city of Medellín, Colombia was used as a grid-connected scenario. Similarly, an adapted version of the 27-node test system that considers the energy generation and demand characteristics specific to the municipality of Capurganá, Colombia was used, to analyze the effect of the BESS in a standalone network. The proposed methodology was compared with four metaheuristic optimization methods documented in the literature, including the Chu and Beasley genetic algorithm, the particle swarm optimization algorithm, the vortex search algorithm, and the ant lion optimizer. According to the findings, the proposed GAMS-based methodology outperformed the four metaheuristic optimization methods in terms of solution quality and average processing times for the three objective functions under analysis. These results demonstrate the effectiveness and robustness of GAMS in achieving a solution for the optimal operation of all BESSs installed in the grid-connected and standalone systems.

This work addressed the problem of integration and operation of Ion-lithium batteries of different technical characteristics in grid-connected networks (GCN) for reducing the energy losses and CO2emissions. To formulate the mathematical problem, nonlinear mathematical modeling was used, taking into account the previously mentioned objective functions and all the constraints that represent the operation of the grid in a scenario of variable photovoltaic generation and power demand. This paper proposed a master–slave methodology, which uses a parallel discrete version of the vortex search algorithm (PDVSA) for solving the problem of location and selection of the kind of battery in the GNC. The slave stage is entrusted to find the operation scheme of the batteries proposed for the master stage. This task is carried out by a particle swarm optimization algorithm (PSO) that defines the power to be supplied or stored for each battery. Using a matrix hourly power flow for calculating the effect in the objective function and constraints that represent the problem. As a test scenario, it was used a GCN of 33 buses and 32 lines that considered the photovoltaic generation and demand of Medellín-Colombia, and all technical and environmental indexes of the local grid operator. Six comparison methodologies were used from the literature and developed in this work to evaluate the effectiveness of the proposed methodology. Being all programming in MATLAB software, executing each one 1000 times with the aim to evaluate the performance in terms of solution, repeatability, and processing times. The results obtained demonstrating that the proposed PDVSA/PSO methodology achieved the best results in terms of solution, with reductions of 154.9094 kWh (6.23%) in energy losses and reductions of 0.0252 tons of CO2(0.2548%) in pollutant gas emissions compared to the base case. Regarding repeatability, the PDVSA/PSO exhibits standard deviation values of 0.1383% for energy losses and 0.0034% for CO2emissions.

This research paper addresses the problem related to the location, technology selection, and operation of battery energy storage systems (BESS) in microgrids under a variable scenario of distributed generation (DG) and power demand for an average year of operation. For this purpose, the problem was mathematically formulated through a mixed-integer nonlinear programming (MINLP) model that considers the minimization of total annual operating costs. Three master–slave methodologies based on metaheuristic algorithms were used to solve the MINLP model, with three different algorithms employed in the master stage: Parallel Monte Carlo method (PMC), Parallel Chu & Beasley Genetic Algorithm (PCBGA), and Parallel Particle Swarm Optimizer (PPSO). The Vortex Search Algorithm (VSA) was used in the slave stage to solve the problem of BESS operation. A matrix-based successive approximation power flow was also used in this stage to determine the value of the objective functions and evaluate the technical and operational constraints defined in the mathematical model. To validate the results, the 33-node test system was adapted to the generation and demand characteristics of the city of Medellin was used. Simulation results obtained using MATLAB software for the test scenario showed that the CBGA-VSA methodology had the best performance in terms of solution quality, repeatability, and processing times for the minimization of total annual operating costs.

This article addresses the challenge of battery energy management in a Direct Current (DC) Microgrid (MG) with distributed photovoltaic generators working at their highest power point. This study adopted a multi-objective approach whose main goals is to reduce energy losses and operating costs. To mathematically model this problem, we used Multi-Objective Mixed-Integer Nonlinear Programming (MOMINLP) that incorporated two conflicting objective functions that minimize—at the same time—the energy losses and operating costs of DC MGs. To solve this problem, we followed a master–slave approach. Two multi-objective algorithms were used in the master stage: the Multi-Objective Particle Swarm Optimizer (MOPSO) and the Multi-Objective Lion Ant Optimizer (MOALO). The slave stage applied an hourly load flow analysis employing the method of Successive Approximations (SAs). To validate our methodology, the 33-bus test feeder was adapted by incorporating specific data on generated power and electricity demand from a specific region in Colombia. These data (obtained from the local network operator) are projections of electricity demand for an average day in the region under analysis. The methodology was implemented in the MATLAB environment, and each algorithm was run 100 times to assess standard deviations, maximum reductions, average reductions, and processing times. The two algorithms were compared to identify which one exhibited the best performance when they solved the problem examined here. The results proved that the MOPSO was the most effective approach in terms of minimizing operating costs and significantly reducing energy losses compared to the benchmark scenario.

In this paper, we address the problem of optimal battery operation in power distribution networks in a scenario of high photovoltaic penetration and variable power demand during a typical day of operation. We mathematically modeled this problem using a Nonlinear Programming (NLP) model, which incorporates three different objective functions, each approached from a single-objective optimization perspective. The three objective functions we considered were (i) the reduction in operating costs, (ii) the minimization of energy losses, and (iii) the reduction in CO2emissions. To solve the proposed NLP model, we implemented a master–slave methodology. In this methodology, the master stage uses the Parallel version of the Ant Lion Optimizer (PALO) to determine the batteries’ charging and discharging operations at each time interval. The master stage, for its part, employs an hourly power flow analysis based on the successive approximation method to calculate the values of the objective functions and the constraints associated with the mathematical model. To validate the performance of the proposed methodology, the 33-node test system was adapted to the specific characteristics of power generation and demand in the city of Medellín in Colombia. The simulations conducted using MATLAB demonstrated that the PALO excelled in terms of solution quality, repeatability, and processing times when it came to minimizing the economic, technical, and environmental indicators under analysis. According to the results, the proposed methodology outperformed three other optimization approaches reported in the specialized literature, and used here for comparison purposes: the Chu and Beasley genetic algorithm, the particle swarm optimization algorithm, and the vortex search algorithm.

This research paper addresses the issue of placement, technology selection and operation of BESS energy storage systems (BESS) in microgrids under a variable distributed generation (DG) and energy demand scenario for an average year of operation. To address this issue, a mixed-integer nonlinear programming (MINLP) model was formulated. The model considers two different objective functions, which are optimized using a mono-objective approach: (i) total energy losses and (ii) total CO2emissions. The MINLP model was solved using a master–slave methodology based on metaheuristic algorithms. In the master stage, the Chu & Beasley parallel genetic algorithm (PCBGA) was used to solve the BESS placement and technology selection problem, while in the slave stage, the vortex search algorithm (VSA) was used to solve the BESS operation problem. A matrix-based successive approximation power flow was also used in this stage to determine the objective functions and evaluate the technical and operational constraints defined in the mathematical model. To verify the results, the 33-node test system was adjusted to the generation and demand characteristics of the city of Medellín. In addition, the proposed methodology was compared with two optimization techniques used in the master stage, namely the Parallel Monte Carlo (PMC) method and the Parallel Particle Swarm Optimizer (PPSO). Simulation results obtained using MATLAB software for the test scenario showed that the CBGA-VSA methodology outperformed the other techniques in terms of solution quality, repeatability and processing times to minimize total annual operating costs.

This paper addresses the problem of managing battery energy in urban and rural alternating current networks, aiming at improving their financial, technical, and environmental indicators. To this end, a mathematical model was formulated that proposes as objective functions the optimization of energy operational costs of the grid, the minimization of power losses associated with energy transport, and the reduction in CO2emissions related to the production of energy. This model also considers the set of constraints involved in the operation of an alternating current network in an environment with distributed energy resources (Photovoltaic generators + Batteries storage systems). A master–slave strategy that combines a parallel version of the Vortex Search Algorithm (VSA) and an Hourly Power Flow method based on Successive Approximations (HPFSA) was proposed as the solution methodology. To create the test scenarios, urban and rural electrical networks documented in the specialized literature were adapted using generation and demand data of an average day of operation in Medellín-Antioquia (Urban) and Capurganá-Chocó (Rural). These data represent the energy behavior of an urban and a rural network within the Colombian territory. In relation to the distributed energy devices, the integration of three Photovoltaic Distributed Generators (PV DGs) and three lithium-ion batteries of different types into the electrical networks was considered. In addition, the energy production costs and CO2emissions of the local network and the diesel fuel in Colombia were determined, as well as the maintenance costs associated with the batteries and the PV DGs. With the purpose of validating the effectiveness of the proposed method in terms of solution, repeatability, and processing times, two comparison methods reported in the specialized literature were employed to solve the problem addressed in this paper. In the urban and rural networks under study, the proposed solution methodology achieved the best results in terms of solution quality, repeatability, and processing time.