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This research addresses the problem regarding the efficient operation of photovoltaic (PV) plants in monopolar direct current (DC) distribution networks from a perspective of convex optimization. PV plant operation is formulated as a nonlinear programming (NLP) problem while considering two single-objective functions: the minimization of the expected daily energy losses and the reduction in the expected CO2 emissions at the terminals of conventional generation systems. The NLP model that represents the energy management system (EMS) design is transformed into a convex optimization problem via the second-order cone equivalent of the product between two positive variables. The main contribution of this research is that it considers the uncertain nature of solar generation and expected demand curves through robust convex optimization. Numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness and robustness of the proposed second-order cone programming model in defining an EMS for a monopolar DC distribution network. A comparative analysis with four different combinatorial optimizers is carried out, i.e., multiverse optimization (MVO), the salp swarm algorithm (SSA), the particle swarm optimizer (PSO), and the crow search algorithm (CSA). All this is achieved while including an iterative convex method (ICM). This analysis shows that the proposed robust model can find the global optimum for two single-objective functions. The daily energy losses are reduced by 44.0082% with respect to the benchmark case, while the CO2 emissions (kg) are reduced by 27.3771%. As for the inclusion of uncertainties, during daily operation, the energy losses increase by 22.8157%, 0.2023%, and 23.7893% with respect to the benchmark case when considering demand uncertainty, PV generation uncertainty, and both. Similarly, CO2 emissions increase by 11.1854%, 0.9102%, and 12.1198% with regard to the benchmark case. All simulations were carried out using the Mosek solver in the Yalmip tool of the MATLAB software. [ABSTRACT FROM AUTHOR]

This research addresses the efficient integration and sizing of flexible alternating current transmission systems (FACTS) in electrical distribution networks via a convex optimization approach. The exact mixed-integer nonlinear programming (MINLP) model associated with FACTS siting and sizing aims for the minimization of the expected annual operating costs of the network (i.e., energy losses and FACTS purchasing costs). The constraints of this problem include power equilibrium equalities, voltage regulation bounds, and device capacities, among others. Due to the power equilibrium constraints per node and period, the MINLP model is a non-convex optimization problem. To transform the exact MINLP model into a mixed-integer convex one, the approximation of the product between two variables in the complex domain is relaxed through its hyperbolic equivalent, which generates a set of convex cones. The main advantage of the proposed mixed-integer convex model is that it ensures the global optimum of the problem, even when considering objective multiplexes. Numerical simulations in the IEEE 33-, 69-, and 85-bus grids demonstrate the effectiveness and robustness of FACTS integration via the proposed convex approach in comparison with the exact solution of the MINLP model in the GAMS software as well as with combinatorial optimization algorithms (i.e., the black widow optimizer and the vortex search algorithm). All simulations were carried out in MATLAB with Yalmip optimization and the Gurobi and Mosek solvers. The simulation results show that, for a fixed operation of the FACTS devices (i.e., a VAR compensator) during the day, the annual operating costs are reduced by 12.63%, 13.97%, and 26.53% for the IEEE 33-, 69-, and 85-bus test systems, respectively, while for the operation variable, the reductions are by 14.24%, 15.79%, and 30.31%, respectively. [ABSTRACT FROM AUTHOR]

This research addresses the power flow analysis in bipolar asymmetric direct current (DC) networks by applying Broyden's numerical method. This general successive approximations method allows for a simple Newton-based recursive formula to reach the roots of multiple nonlinear equations. The main advantage of Broyden's approach is its simple but efficient structure which can be applied to real complex nonlinear equations.The power flow problem in bipolar DC networks is still challenging, as multiple operating options must be considered, e.g., the possibility of having a solidly grounded or floating neutral wire. The main goal of this research is to contribute with a generalization of Broyden's method for the power flow solution in bipolar DC networks, with the main advantage that, under well-defined conditions, this is a numerical method equivalent to the matricial backward/forward power flow, which is equivalent to the successive approximations power flow method. Numerical results in the 21-, 33-, and 85-bus grids while considering two connections for the neutral wire (i.e., solidly grounded at any node or floating) show the effectiveness of Broyden's method in the power flow solution for bipolar asymmetric DC networks. All numerical simulations were carried out in the MATLAB programming environment. [ABSTRACT FROM AUTHOR]

Thermoelectric generators (TEGs) have the potential to convert waste heat into electrical energy, making them attractive for energy harvesting applications. However, accurately estimating TEG parameters from industrial systems is a complex problem due to the mathematical complex non-linearities and numerous variables involved in the TEG modeling. This paper addresses this research gap by presenting a comparative evaluation of three optimization methods, Particle Swarm Optimization (PSO), Salps Search Algorithm (SSA), and Vortex Search Algorithm (VSA), for TEG parameter estimation. The proposed integrated approach is significant as it overcomes the limitations of existing methods and provides a more accurate and rapid estimation of TEG parameters. The performance of each optimization method is evaluated in terms of root mean square error (RMSE), standard deviation, and processing time. The results indicate that all three methods perform similarly, with average RMSE errors ranging from 0.0019 W to 0.0021 W, and minimum RMSE errors ranging from 0.0017 W to 0.0018 W. However, PSO has a higher standard deviation of the RMSE errors compared to the other two methods. In addition, we present the optimized parameters achieved through the proposed optimization methods, which serve as a reference for future research and enable the comparison of various optimization strategies. The disparities observed in the optimized outcomes underscore the intricacy of the issue and underscore the importance of the integrated approach suggested for precise TEG parameter estimation. [ABSTRACT FROM AUTHOR]

The problem regarding the optimal placement and sizing of different FACTS (flexible alternating current transmission systems) in electrical distribution networks is addressed in this research by applying a master–slave optimization approach. The FACTS analyzed correspond to the unified power flow controller (UPFC), the thyristor-controlled shunt compensator (TCSC, also known as the thyristor switched capacitor, or TSC), and the static var compensator (SVC). The master stage is entrusted with defining the location and size of each FACTS device using hybrid discrete-continuous codification through the application of the black widow optimization (BWO) approach. The slave stage corresponds to the successive approximations power flow method based on the admittance grid formulation, which allows determining the expected costs of the energy losses for a one-year operation period. The numerical results in the IEEE 33-, 69-, and 85-bus grids demonstrate that the best FACTS device for locating in distribution networks is the SVC, given that, when compared to the UPFC and the TCSC, it allows for the best possible reduction in the equivalent annual investment and operating cost. A comparative analysis with the General Algebraic Modeling System software, with the aim to solve the exact mixed-integer nonlinear programming model, demonstrated the proposed BWO approach's effectiveness in determining the best location and size for the FACTS in radial distribution networks. Reductions of about 12.63 % and 13.97 % concerning the benchmark cases confirmed that the SVC is the best option for reactive power compensation in distribution grids. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to tackle the problem of optimal neutral wire grounding in bipolar DC networks including asymmetric loading, which naturally involves mixed-integer nonlinear programming (MINLP) and is challenging to solve. This MINLP model is transformed into a recursive mixed-integer quadratic (MIQ) model by linearizing the hyperbolic relation between voltage and powers in constant power terminals. A recursive algorithm is implemented to eliminate the possible errors generated by linearization. The proposed recursive MIQ model is assessed in two bipolar DC systems and compared against three solvers of the GAMS software. The results obtained validate the performance of the proposed MIQ model, which finds the global optimum of the model while reducing power losses for bipolar DC systems with 21, 33, and 85 buses by 4.08%, 2.75%, and 7.40%, respectively, when three nodes connected to the ground are considered. Furthermore, the model exhibits a superior performance when compared to the GAMS solvers. The impact of grounding the neutral wire in bipolar DC networks is also studied by varying the number of available nodes to be grounded. The results show that the reduction in power losses is imperceptible after grounding the third node for the three bipolar DC systems under study. [ABSTRACT FROM AUTHOR]

The problem regarding the optimal integration of efficient reactive power compensation in radial and meshed distribution networks using fixed-step capacitor banks and distribution static compensators (D-STATCOMs) is addressed in this research paper by proposing a master–slave optimization methodology. Radial and meshed distribution topologies are considered for the grid structure while including variable active and reactive demand curves. An economic analysis is performed, considering the net present value of the optimization plan, as well as the costs of energy losses and the capacitor banks' acquisition, installation, and operation. In the case of the D-STATCOMs, an annualized costs analysis is presented. In the master stage, the discrete version of the generalized normal distribution optimization (GNDO) algorithm selects the nodes and the sizes of the capacitor banks. In the slave stage, the successive approximations power flow approach is implemented. Numerical results in the IEEE 33-bus grid (with both radial and meshed topologies) and the IEEE 85-bus grid (with a radial configuration) demonstrated the proposed master–slave optimization's effectiveness in minimizing the project's expected net present value for a planning period of five years. Moreover, a simulation in the IEEE 69-bus grid under peak operation conditions showed that the GNDO approach is an excellent optimization technique to solve the studied problem when compared to combinatorial and exact optimization methods. In addition, numerical validations considering D-STATCOMs in the IEEE 85-bus grid confirmed the effectiveness and robustness of the GNDO approach in addressing problems associated with optimal reactive power compensation in medium-voltage distribution systems. [ABSTRACT FROM AUTHOR]

This work addressed the problem regarding the optimal integration of battery systems (BS) in grid-connected networks (GCNs) with the purpose of reducing energy losses and CO2 emissions, for which it formulates a mathematical model that considers the constraints associated with the operation of GCNs in a distributed generation environment that includes BS and variable power generation related to photovoltaic (PV) distributed generation (DG) and demand. As solution strategies, three different master–slave methodologies are employed that are based on sequential programming methods, with the aim to avoid the implementation of commercial software. In the master stage, to solve the problem regarding the location and the type of batteries to be used, parallel-discrete versions of the Montecarlo method (PMC), a genetic algorithm (PDGA), and the search crow algorithm (PDSCA) are employed. In the slave stage, the particle swarm optimization algortihm (PSO) is employed to solve the problem pertaining to the operation of the batteries, using a matrix hourly power flow to assess the impact of each possible solution proposed by the master–slave methodologies on the objective functions and constraints. As a test scenario, a GCN based on the 33-bus test systems is used, which considers the generation, power demand, and CO2 emissions behavior of the city of Medellín (Colombia). Each algorithm is executed 1000 times, with the aim to evaluate the effectiveness of each solution in terms of its quality, standard deviation, and processing times. The simulation results obtained in this work demostrate that PMC/PSO is the master–slave methodology with the best performance in terms of solution quality, repeatability, and processing time. [ABSTRACT FROM AUTHOR]

Thermoelectric generators (TEGs) have gained significant interest as a sustainable energy source, due to their ability to convert thermal energy into electrical energy through the Seebeck effect. However, the power output of TEGs is highly dependent on the thermoelectric material properties and operational conditions. Accurate modeling and parameter estimation are essential for optimizing and designing TEGs, as well as for integrating them into smart grids to meet fluctuating energy demands. This work examines the challenges of accurate modeling and parameter estimation of TEGs and explores various optimization metaheuristics techniques to find TEGs parameters in real applications from experimental conditions. The paper stresses the importance of determining the properties of TEGs with precision and using parameter estimation as a technique for determining the optimal values for parameters in a TEG mathematical model that represent the actual behavior of a thermoelectric module. This methodological approach can improve TEG performance and aid in efficient energy supply and demand management, thus reducing the reliance on traditional fossil fuel-based power generation. [ABSTRACT FROM AUTHOR]

Humans have always been searching for new and efficient ways to convert fuels into usable energy. Solid oxide fuel cells, energy conversion devices capable of generating electrical energy, are widely used due to their high thermal energy production. In this research, fuzzy control was used to manage the voltage and current of solid oxide fuel cells. Simulations were conducted in two evaluation modes: checking the voltage, current, and power of the fuel cell, with and without the use of fuzzy control, and analyzing maximum power point tracking (MPPT) using fuzzy control. In the second mode, when connected to the load, the performance of the fuel cell was evaluated in the transient state, and the role of the controller was clearly visible According to the current–power characteristic of the fuel cell, which is a nonlinear curve and has a maximum point, and by using the fuzzy controller and the appropriate selection of input and output functions, this research aimed to make the system work at the maximum power point at all times. To this effect, a printer was used between the fuel cell and the load, and a fuzzy controller was used to set the cycle of activities, whose input was the slope of the current–power curve. The results show that this controller performs well and is faster when compared to the P&O control method. MATLAB software was used to design and analyze the system and, in order to validate the model, the transient behavior of the fuel cell was studied. The results were compared, and an acceptable match was observed. [ABSTRACT FROM AUTHOR]

This paper proposes a recursive conic approximation methodology to deal with the optimal power flow (OPF) problem in unbalanced bipolar DC networks. The OPF problem is formulated through a nonlinear programming (NLP) representation, where the objective function corresponds to the minimization of the expected grid power losses for a particular load scenario. The NLP formulation has a non-convex structure due to the hyperbolic equality constraints that define the current injection/absorption in the constant power terminals as a function of the powers and voltages. To obtain an approximate convex model that represents the OPF problem in bipolar asymmetric distribution networks, the conic relation associated with the product of two positive variables is applied to all nodes with constant power loads. In the case of nodes with dispersed generation, a direct replacement of the voltage variables for their expected operating point is used. An iterative solution procedure is implemented in order to minimize the error introduced by the voltage linearization in the dispersed generation sources. The 21-bus grid is employed for all numerical validations. To validate the effectiveness of the proposed conic model, the power flow problem is solved, considering that the neutral wire is floating and grounded, and obtaining the same numerical results as the traditional power flow methods (successive approximations, triangular-based, and Taylor-based approaches): expected power losses of 95.4237 and 91.2701 kW, respectively. To validate the effectiveness of the proposed convex model for solving the OPF problem, three combinatorial optimization methods are implemented: the sine-cosine algorithm (SCA), the black-hole optimizer (BHO), and the vortex search algorithm (VSA). Numerical results show that the proposed convex model finds the global optimal solution with a value of 22.985 kW, followed by the VSA with a value of 22.986 kW. At the same time, the BHO and SCA are stuck in locally optimal solutions (23.066 and 23.054 kW, respectively). All simulations were carried out in a MATLAB programming environment. [ABSTRACT FROM AUTHOR]

The problem of the output voltage regulation in a DC-DC boost converter feeding a DC microgrid is addressed in this research via the passivity-based control theory with a proportional–integral action (PI-PBC). Two external input estimators were implemented in conjunction with the proposed controller to make it sensorless and adaptive. The first estimator corresponds to the immersion & invariance (I&I) approach applied to calculate the expected value of the DC load, which is modeled as an unknown DC current. The second estimator is based on the disturbance–observer (DO) approach, which reaches the value of the voltage input. The main advantage of both estimators is that these ensure exponential convergence under steady-state operating conditions, and their parametrization only requires the definition of an integral gain. A comparative analysis with simulations demonstrates that the proposed PI-PBC approach is effective in regulating/controlling the voltage profile in unknown DC loads as compared to the adaptive sliding mode controller. Experimental validations have demonstrated that the proposed PI-PBC approach, in conjunction with the I&I and the DO estimators, allowed regulation of the voltage output profile in the terminals of the DC load with asymptotic stability properties and fast convergence times (1.87 ms) and acceptably overshoots (6.1%) when the voltage input varies its magnitude (from 10 to 12 V and from 10 to 8 V) considering that the DC load changed with a square waveform between 1 and 2 A with 100 Hz. [ABSTRACT FROM AUTHOR]

The objective of this research is to propose an efficient energy management system for photovoltaic (PV) generation units connected to monopolar DC distribution networks via convex optimization while considering a day-ahead dispatch operation scenario. A convex approximation is used which is based on linearization via Taylor's series expansion to the hyperbolic relations between voltages and powers in the demand nodes. A recursive solution methodology is introduced via sequential convex programming to minimize the errors introduced by the linear approximation in the power balance constraints. Numerical results in the DC version of the IEEE 33-bus grid demonstrate the effectiveness of the proposed convex model when compared to different combinatorial optimization methods, with the main advantage that the optimal global solution is found thanks to the convexity of the solution space and the reduction of the error via an iterative solution approach. Different objective functions are analyzed to validate the effectiveness of the proposed iterative convex methodology (ICM), which corresponds to technical (energy losses reduction), economic (energy purchasing and maintenance costs), and environmental (equivalent emissions of CO 2 to the atmosphere in conventional sources) factors. The proposed ICM finds reductions of about 43.9754 % in daily energy losses, 26.9957 % in energy purchasing and operating costs, and 27.3771 % in CO 2 emissions when compared to the benchmark case in the DC version of the IEEE 33-bus grid. All numerical validations were carried out in the MATLAB programming environment using the SEDUMI and SDPT3 tools for convex programming and our own scripts for metaheuristic methods. [ABSTRACT FROM AUTHOR]

This paper deals with the problem regarding the optimal operation of photovoltaic (PV) generation sources in AC distribution networks with a single-phase structure, taking into consideration different objective functions. The problem is formulated as a multi-period optimal power flow applied to AC distribution grids, which generates a nonlinear programming (NLP) model with a non-convex structure. Three different objective functions are considered in the optimization model, each optimized using a single-objective function approach. These objective functions are (i) an operating costs function composed of the energy purchasing costs at the substation bus, added with the PV maintenance costs; (ii) the costs of energy losses; and (iii) the total CO2 emissions at the substation bus. All these functions are minimized while considering a frame of operation of 24 h, i.e., in a day-ahead operation environment. To solve the NLP model representing the studied problem, the General Algebraic Modeling System (GAMS) and its SNOPT solver are used. Two different test feeders are used for all the numerical validations, one of them adapted to the urban operation characteristics in the Metropolitan Area of Medellín, which is composed of 33 nodes, and the other one adapted to isolated rural operating conditions, which has 27 nodes and is located in the department of Chocó, Colombia (municipality of Capurganá). Numerical comparisons with multiple combinatorial optimization methods (particle swarm optimization, the continuous genetic algorithm, the Vortex Search algorithm, and the Ant Lion Optimizer) demonstrate the effectiveness of the GAMS software to reach the optimal day-ahead dispatch of all the PV sources in both distribution grids. [ABSTRACT FROM AUTHOR]

The design of an efficient energy management system (EMS) for monopolar DC networks with high penetration of photovoltaic generation plants is addressed in this research through a convex optimization point of view. The EMS is formulated as a multi-objective optimization problem that involves economic, technical, and environmental objective functions subject to typical constraints regarding power balance equilibrium, thermal conductor capabilities, generation source capacities, and voltage regulation constraints, among others, using a nonlinear programming (NLP) model. The main characteristic of this NLP formulation of the EMS for PV plants is that it is a nonconvex optimization problem owing to the product of variables in the power balance constraint. To ensure an effective solution to this NLP problem, a linear approximation of the power balance constraints using the McCormick equivalent for the product of two variables is proposed. In addition, to eliminate the error introduced by the linearization method, an iterative solution methodology (ISM) is proposed. To solve the multi-objective optimization problem, the weighted optimization method is implemented for each pair of objective functions in conflict, with the main advantage that in this extreme the Pareto front has the optimal global solution for the single-objective function optimization approach. Numerical results in the monopolar version of the IEEE 33-bus grid demonstrated that the proposed ISM reaches the optimal global solution for each one of the objective functions under analysis. It demonstrated that the convex optimization theory is more effective in the EMS design when compared with multiple combinatorial optimization methods. [ABSTRACT FROM AUTHOR]

The problem regarding of optimal power flow in bipolar DC networks is addressed in this paper from the recursive programming stand of view. A hyperbolic relationship between constant power terminals and voltage profiles is used to resolve the optimal power flow in bipolar DC networks. The proposed approximation is based on the Taylors' Taylor series expansion. In addition, nonlinear relationships between dispersed generators and voltage profiles are relaxed based on the small voltage voltage-magnitude variations in contrast with power output. The resulting optimization model transforms the exact nonlinear non-convex formulation into a quadratic convex approximation. The main advantage of the quadratic convex reformulation lies in finding the optimum global via recursive programming, which adjusts the point until the desired convergence is reached. Two test feeders composed of 21 and 33 buses are employed for all the numerical validations. The effectiveness of the proposed recursive convex model is verified through the implementation of different metaheuristic algorithms. All the simulations are carried out in the MATLAB programming environment using the convex disciplined tool known as CVX with the SEDUMI and SDPT3 solvers. [ABSTRACT FROM AUTHOR]

This research deals with the problem regarding the optimal siting and sizing of distribution static compensators (D-STATCOMs) via the application of a master–slave optimization technique. The master stage determines the nodes where the D-STATCOMs must be located and their nominal rates by applying the generalized normal distribution optimizer (GNDO) with a discrete–continuous codification. In the slave stage, the successive approximations power flow method is implemented in order to establish the technical feasibility of the solution provided by the master stage, i.e., voltage regulation and device capabilities, among other features. The main goal of the proposed master–slave optimizer is to minimize the expected annual operating costs of the distribution grid, which includes the energy loss and investment costs of the D-STATCOMs. With the purpose of improving the effectiveness of reactive power compensation during the daily operation of the distribution grid, an optimal reactive power flow (ORPF) approach is used that considers the nodes where D-STATCOMs are located as inputs in order to obtain their daily expected dynamical behavior with regard to reactive power injection to obtain additional net profits. The GNDO approach and the power flow method are implemented in the MATLAB programming environment, and the ORPF approach is implemented in the GAMS software using a test feeder comprising 33 nodes with both radial and meshed configurations. A complete comparative analysis with the Salp Swarm Algorithm is presented in order to demonstrate the effectiveness of the proposed two-stage optimization approach in the fixed operation scenario regarding the final objective function values. In addition, different tests considering the possibility of hourly power injection using D-STATCOMs through the ORPF solution demonstrate that additional gains can be obtained in the expected annual operative costs of the grid. [ABSTRACT FROM AUTHOR]

Due to the need to include renewable energy resources in electrical grids as well as the development and high implementation of PV generation and DC grids worldwide, it is necessary to propose effective optimization methodologies that guarantee that PV generators are located and sized on the DC electrical network. This will reduce the operation costs and cover the investment and maintenance cost related to the new technologies (PV distributed generators), thus satisfying all technical and operative constraints of the distribution grid. It is important to propose solution methodologies that require short processing times, with the aim of exploring a large number of scenarios while planning energy projects that are to be presented in public and private contracts, as well as offering solutions to technical problems of electrical distribution companies within short periods of time. Based on these needs, this paper proposes the implementation of a Discrete–Continuous Parallel version of the Particle Swarm Optimization algorithm (DCPPSO) to solve the problem regarding the integration of photovoltaic (PV) distributed generators (DGs) in Direct Current (DC) grids, with the purpose of reducing the annual costs related to energy purchasing as well as the investment and maintenance cost associated with PV sources in a scenario of variable power demand and generation. In order to evaluate the effectiveness, repeatability, and robustness of the proposed methodology, four comparison methods were employed, i.e., a commercial software and three discrete–continuous methodologies, as well as two test systems of 33 and 69 buses. In analyzing the results obtained in terms of solution quality, it was possible to identify that the DCPPSO proposed obtained the best performance in relation to the comparison methods used, with excellent results in relation to the processing times and standard deviation. The main contribution of the proposed methodology is the implementation of a discrete–continuous codification with a parallel processing tool for the evaluation of the fitness function. The results obtained and the reports in the literature for alternating current networks demonstrate that the DCPPSO is the optimization methodology with the best performance in solving the problem of the optimal integration of PV sources in economic terms and for any kind of electrical system and size. [ABSTRACT FROM AUTHOR]

This paper deals with the problem regarding the optimal siting and sizing of distribution static compensators (D-STATCOMs) in electrical distribution networks to minimize the expected total annual operating costs. These costs are associated with the investments made in D-STATCOMs and expected energy losses costs. To represent the electrical behavior of the distribution networks, a power flow formulation is used which includes voltages, currents, and power as variables via incidence matrix representation. This formulation generates a mixed-integer nonlinear programming (MINLP) model that accurately represents the studied problem. However, in light of the complexities involved in solving this MINLP model efficiently, this research proposes a mixed-integer convex reformulation. Numerical results regarding the final annual operating costs of the network demonstrate that the proposed mixed-integer convex model is efficient for selecting and locating D-STATCOMs in distribution networks, with the main advantage that it is applicable to radial and meshed distribution grid configurations. A comparative analysis with respect to metaheuristic optimizers and convex approximations confirms the robustness of the proposed formulation. All numerical validations were conducted in the MATLAB programming environment with our own scripts (in the case of metaheuristics) and the CVX convex disciplined tool via the Gurobi solver. In addition, the exact MINLP model is solved using the GAMS software. [ABSTRACT FROM AUTHOR]

The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete–Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete–continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of quality of the solution, processing time, and repeatability in electrical networks of any size, since it showed a better performance as the size of the electrical system increased. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of the power flow analysis of bipolar direct current (DC) networks considering unbalanced loads and the effect of a neutral wire, which may be solidly grounded or non-grounded. The power flow problem is formulated using the nodal admittance representation of the system and the hyperbolic relations between power loads and voltages in the demand nodes. Using Taylor series expansion with linear terms, a recursive power flow method with quadratic convergence is proposed. The main advantage of the hyperbolic approximation in dealing with power flow problems in DC bipolar networks is that this method can analyze radial and meshed configurations without any modifications to the power flow formula. The numerical results in three test feeders composed of 4, 21, and 85 bus systems show the efficiency of the proposed power flow method. All of the simulations were conducted in MATLAB for a comparison of the proposed approach with the well-established successive approximation method for power flow studies in distribution networks. [ABSTRACT FROM AUTHOR]

This paper presents a new optimal power flow (OPF) formulation for monopolar DC networks using a recursive convex representation. The hyperbolic relation between the voltages and power at each constant power terminal (generator or demand) is represented as a linear constraint for the demand nodes and generators. To reach the solution for the OPF problem a recursive evaluation of the model that determines the voltage variables at the iteration t + 1 ( v t + 1 ) by using the information of the voltages at the iteration t ( v t ) is proposed. To finish the recursive solution process of the OPF problem via the convex relaxation, the difference between the voltage magnitudes in two consecutive iterations less than the predefined tolerance is considered as a stopping criterion. The numerical results in the 85-bus grid demonstrate that the proposed recursive convex model can solve the classical power flow problem in monopolar DC networks, and it also solves the OPF problem efficiently with a reduced convergence error when compared with semidefinite programming and combinatorial optimization methods. In addition, the proposed approach can deal with radial and meshed monopolar DC networks without modifications in its formulation. All the numerical implementations were in the MATLAB programming environment and the convex models were solved with the CVX and the Gurobi solver. [ABSTRACT FROM AUTHOR]

Power electronics converters are equipped with current controls that protect the converter from over-currents. This protection introduces non-differentiable equations into the power flow problem. The conventional Newton's method is not suitable in that conditions. This letter proposes a fixed-point iteration to overcome this difficulty. The technique is derivative-free, and hence, it can naturally include the saturation given by the converters’ current protection. Exact conditions for convergence and uniqueness of the solution are demonstrated using Banach's fixed point theorem. Numerical experiments in Matlab complement the analysis. [ABSTRACT FROM AUTHOR]

This paper focuses on minimizing the annual operative costs in monopolar DC distribution networks with the inclusion of solar photovoltaic (PV) generators while considering a planning period of 20 years. This problem is formulated through a mixed-integer nonlinear programming (MINLP) model, in which binary variables define the nodes where the PV generators must be located, and continuous variables are related to the power flow solution and the optimal sizes of the PV sources. The implementation of a master–slave optimization approach is proposed in order to address the complexity of the MINLP formulation. In the master stage, the discrete-continuous generalized normal distribution optimizer (DCGNDO) is implemented to define the nodes for the PV sources along with their sizes. The slave stage corresponds to a specialized power flow approach for monopolar DC networks known as the successive approximation power flow method, which helps determine the total energy generation at the substation terminals and its expected operative costs in the planning period. Numerical results in the 33- and 69-bus grids demonstrate the effectiveness of the DCGNDO optimizer compared to the discrete-continuous versions of the Chu and Beasley genetic algorithm and the vortex search algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a new solution methodology based on a mixed-integer conic formulation to locate and size photovoltaic (PV) generation units in AC distribution networks with a radial structure. The objective function comprises the annual expected energy costs of the conventional substation in addition to the investment and operating costs of PV sources. The original optimization model that represents this problem belongs to the family of mixed-integer nonlinear programming (MINLP); however, the complexity of the power balance constraints make it difficult to find the global optimum. In order to improve the quality of the optimization model, a mixed-integer conic (MIC) formulation is proposed in this research in order to represent the studied problem. Numerical results in two test feeders composed of 33 and 69 nodes demonstrate the effectiveness of the proposed MIC model when compared to multiple metaheuristic optimizers such as the Chu and Beasley Genetic Algorithm, the Newton Metaheuristic Algorithm, the Vortex Search Algorithm, the Gradient-Based Metaheuristic Optimization Algorithm, and the Arithmetic Optimization Algorithm, among others. The final results obtained with the MIC model show improvements greater than USD 100,000 per year of operation. All simulations were run in the MATLAB programming environment, using its own scripts for all the metaheuristic algorithms and the disciplined convex tool known as CVX with the Gurobi solver in order to solve the proposed MIC model. [ABSTRACT FROM AUTHOR]

Currently, with the quick increase in global population, the energetic crisis, the environmental problematic, and the development of the power electronic devices generated the need to include new technologies for supporting and potentiating electrical distributions systems; Distribution Static Compensators (D-STATCOMs) are highly used for this task due to the advantages that this technology presents: reduction in power loss, operation costs, and chargeability of branches, among others. The possibility to include this kind of technology within the electrical system has shown the need to develop efficient methodologies from the point of view of quality solution, repeatability and processing times by considering operation and investment costs as well as the technical conditions of the electrical grids under a scenario of variable power demand and then representing the real operation of the electrical grid. With the aim to propose a solution for this requirement, this paper presents a new Discrete-Continuous Particle Swarm Optimization (DCPSO) algorithm to solve the problem of the optimal integration of D-STATCOMs into Electrical Distribution Systems (EDSs). In this case, the objective function is the minimization of annual operating costs by using a weighted mono-objective function composed of the annual power loss and the investment cost and by including all constraints associated with the operation of an EDS in a distributed reactive compensation environmentinside the mathematical formulation. In order to evaluate the effectiveness and robustness of the proposed solution method, this study implemented two tests systems (i.e., 33- and 69-bus), as well as four comparison methods, and different considerations related to the inclusion of D-STATCOMs in the EDSs. Furthermore, for evaluating the repeatability of the solution obtained by each solution methods used, each algorithm was executed 100 times in Matlab software. The results obtained demonstrated that the proposed DCPSO/HSA methodology achieved the best trade-off between solution quality and processing time, with low standard deviation values for EDSs of any size. [ABSTRACT FROM AUTHOR]

The minimization of annual operating costs in radial distribution networks with the optimal selection and siting of fixed-step capacitor banks is addressed in this research by means of a two-stage optimization approach. The first stage proposes an approximated mixed-integer quadratic model to select the nodes where the capacitor banks must be installed. In the second stage, a recursive power flow method is employed to make an exhaustive evaluation of the solution space. The main contribution of this research is the use of the expected load curve to estimate the equivalent annual grid operating costs. Numerical simulations in the IEEE 33- and IEEE 69-bus systems demonstrate the effectiveness of the proposed methodology in comparison with the solution of the exact optimization model in the General Algebraic Modeling System software. Reductions of 33.04 % and 34.29 % with respect to the benchmark case are obtained with the proposed two-stage approach, with minimum investments in capacitor banks. All numerical implementations are performed in the MATLAB software using the convex tool known as CVX and the Gurobi solver. The main advantage of the proposed hybrid optimization method lies in the possibility of dealing with radial and meshed distribution system topologies without any modification on the MIQC model and the recursive power flow approach. [ABSTRACT FROM AUTHOR]

This paper proposes a sliding-mode controller to ensure both the global stability and maximum power generation of a photovoltaic system based on a flyback converter. The controller is based on an adaptive sliding-surface, which is designed to impose a constant frequency to the switching converter, thus simplifying the selection of both the passive and active elements of the device. Moreover, the controller stability is analyzed using the transversality, reachability and equivalent control conditions. The solution also includes an auto-tuning process for the parameters of the perturb and observe algorithm, which are calculated to ensure the global stability of the sliding-mode controller, thus ensuring the PV system stability. Finally, the performance of the complete solution is verified using detailed circuital simulations of a realistic application case. [ABSTRACT FROM AUTHOR]

This paper analyzes the power flow solution in bipolar direct current networks with radial structures considering multiple monopolar and bipolar constant power loads. The electrical configuration of the bipolar DC grid considers that the reference pole is non-grounded along the feeder, which produces important neutral currents and voltage imbalances along the DC grid. The power flow problem is formulated through the triangular-based representation of the grid topology, which generates a recursive formulation that allows determining the voltage values in the demand nodes through an iterative procedure. The linear convergence of the triangular-based power flow method is tested through multiple load variations with respect to the nominal grid operative condition. Numerical results in the 21- and the 85-bus grids reveal the relevant variations in the voltage profiles and total grid power losses when the neutral cable is solidly grounded or not. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article presents an optimal power flow (OPF) formulation based on the branch flow model for bipolar DC grids with asymmetric loading. The proposal considers the power injection formulation of the OPF to obtain the branch flow model equations that allow solving the power flow problem in bipolar DC networks. Furthermore, a nonlinear programming (NLP) optimization model is deduced and transformed into a second-order conic programming one, which involves convex optimization and guarantees a global optimum for the relaxed model, the uniqueness of this solution, and fast convergence. Finally, an approximation based on balanced operation is proposed to simplify the OPF. Several case studies in two test systems composed of 21 and 85 nodes with a bipolar structure demonstrate that the proposal is accurate, fast, and close to the exact solution of the NLP model when compared with competitive literature reports. All optimization models were implemented in the Python language. • Bipolar DC networks can operate with balanced and unbalanced conditions as a function of the demands connected between poles. • A convex approximation can be applied to bipolar DC networks via second order cone programming. • A simplification based on expected balanced operation ca be reached to obtain a relaxed convex model with low estimation errors. • Dispersed generators and battery energy storage systems can be added to bipolar DC networks using a optimal power flow formulation. [ABSTRACT FROM AUTHOR]

• An entire dynamic model of a synchronous machine with hydro-turbine systems is shown. • A decentralized controller of a hydro-turbine systems with surge tank based on Euler–Lagrange representation is proposed. • The proposed controller is based on standard passivity-based control. • A comparison of the proposed controller with a standard cascade PID controller and sliding-mode controller is presented. This paper addresses the problem of control design for hydro-turbine governing systems with surge tanks from the perspective of standard passivity-based control. The dynamic model of a synchronous machine is considered in conjunction with a model of the hydro-turbine to generate an eleventh-order nonlinear set of differential equations. An Euler–Lagrange representati of the system and its open-loop dynamics is developed. Then, the standard passivity-based control is applied to design a global and asymptotically stable controller in closed-loop operation. The proposed control is decentralized to avoid challenges of communication between the hydro-turbine governing systems. The proposed standard passivity-based control approach is compared with two control approaches. First, a classical standard cascade proportional-integral-derivative controller is applied for the governing system, the automatic voltage regulator, and the excitation system. Second, a sliding mode control is also implemented in the governing system. Two test systems were used to validate the performance of the proposed controller. The first test system is a single machine connected to an infinite bus, and the second test system is the well-known Western System Coordinating Council's multimachine system. Overall, simulation results show that the proposed controller exhibits a better dynamic response with shorter stabilization times and lower peaks during the transient periods. [ABSTRACT FROM AUTHOR]

• Passivity-based controllers for PWM-VSCs and PWM-CSCs were presented. • Asymptotic stability via Hamiltonian paradigm was guaranteed by implementing PBCs. • Active and reactive power control in single-phase MGs were proposed without PLLs. • Renewable and energy storage devices were integrated in MGs with CSC and VSCs. This paper presents a unified Hamiltonian formulation for controlling distributed energy resources (DERs) in ac single-phase microgrids (SP-MGs) via proportional-integral passivity-based control (PI-PBC), and interconnection and damping assignment passivity-based control (IDA-PBC). The proposed Hamiltonian formulation allows us to consider both pulse-width modulated voltage source converters (PWM-VSC) and pulse-width modulated current source converters (PWM-CSC) under a unified model. Renewable generation and supercapacitor energy storage systems are integrated via PWM-VSC technologies, while superconducting coils are integrated through PWM-CSC technologies. IDA-PBC and PI-PBC theories enable us to design control strategies begin that consider Lyapunov's stability theory combined with the well-known advantages of proportional and integral control actions. Our simulation's results corroborate the applicability of the proposed control approaches under stability paradigm. MATLAB/Simulink is employed for computational implementations via begin the SimPowerSystems toolbox. [ABSTRACT FROM AUTHOR]

Highlights • A mathematical model for a two-terminal VSC-HVDC system based on DPC is presented. • A passivity-based PI controller for a two-terminal VSC-HVDC system is described. • The performance of the PI-PBC for the VSC-HVDC system under conditions is tested. Abstract This paper proposes a direct power control (DPC) for a high-voltage direct-current system using voltage source converters (VSC-HVDC) by applying passivity-based control theory. This system allows doing an efficient and reliable integration of electrical network from renewable energy sources. The DPC model permits instantaneous control of the active and reactive power without employing the conventional inner-loop current regulator and the phase-locked loop, thus diminishing investment costs and increasing the reliability of the system. The proportional-integral passivity-based control (PI-PBC) is chosen to control the direct power model of the VSC-HVDC system since this system exhibits a port-Hamiltonian formulation in open-loop and as PI-PBC can exploit this formulation to design a PI controller, which guarantees asymptotically stable in closed-loop based on Lyapunov's theory. Passivity-based control is an active research subject in the control community which has gained a reputation of being a very theoretical subject. Nevertheless, it can have advantages from a practical point of view including an implementation similar to the conventional controls for power systems applications. The paper is oriented to the power & energy systems community, taking into account this practical approach. The proposed controller is assessed by simulations in a two-terminal VSC-HVDC system and compared with a PI direct power controller. Four simulation conditions using MATLAB/SIMULINK were conducted to verify the effectiveness of PI-PBC against a PI controller and a perturbation observer-based adaptive passive control under various operating conditions. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal pole-swapping problem in bipolar asymmetric distribution networks by proposing a mixed-integer conic approximation (MICA) model to minimize the total grid power losses for a particular load condition. The non-convex relation between voltage and power in the constant power loads is relaxed through equivalent cones. The effect of the neutral wire's connection is considered in the proposed MICA model via an auxiliary variable associated with the total current drained to the earth in the case of a solidly grounded connection. Numerical comparisons with three combinatorial optimization methods (black-hole optimization, sine-cosine algorithm, and the Chu & Beasley genetic algorithm) and a mixed-integer quadratic approximation in two test feeders composed of 21 and 85 buses demonstrate the effectiveness of the proposed MICA model. Reductions of about 3.9411 and 10.1706% were obtained after applying the MICA model to the optimal pole-swapping problem. All numerical validations were carried out in the MATLAB programming environment, using the convex disciplined tool known as CVX with the Gurobi solver. • Optimal load balancing in bipolar asymmetric DC networks minimizes its power losses. • The mixed-integer nonlinear model of the pole-swapping is transformed as a convex one. • The neutral floating connection presents the worst scenario for power losses. • Combinatorial methods find local solutions as the number of network nodes increases. [ABSTRACT FROM AUTHOR]

Highlights • Two convex optimal power flow reformulations are proposed. • Sequential quadratic programming allows global convergence. • Radial and mesh dc grids with multiple generation nodes are included. • Nonlinear model does not guarantee reaching the optimal solution. • SQP proposed models are faster than the exact non-convex OPF model. Abstract In this paper, we address the optimal power flow problem in dc grids (OPF-DC). Our approach is based on sequential quadratic programming which solves the problem associated with non-convexity of the model. We propose two different linearizations and compare them to a non-linear algorithm. The first model is a Newton-based linearization which takes the Jacobian of the power flow as a linearization for the optimization stage, and the second model uses the nodal currents as auxiliary variables to linearize over the inequality constraints. Simulation results in radial and meshed grids demonstrate the efficiency of the proposed methodology and allow finding the same solution given by the exact nonlinear representation of the OPF-DC problem. [ABSTRACT FROM AUTHOR]

This paper presents a reformulation of the power flow problem in low-voltage dc (LVDC) power grids via Taylor's series expansion. The solution of the original nonlinear quadratic model is achieved with this proposed formulation with minimal error when the dc network has a well defined operative conditions. The proposed approach provides an explicit solution of the power flow equations system, which avoids the use of iterative methods. Such a characteristic enables to provide accurate results with very short processing times when real operating scenarios of dc power grids are analyzed. Simulation results verify the precision and speed of the proposed method in comparison to classical numerical methods for both radial and mesh configurations. Those simulations were performed using C++ and MATLAB, which are programming environments commonly adopted to solve power flows. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are a clean energy source that allows for power generation integration into electrical networks without destructive environmental effects. PV systems are usually integrated into electrical networks only to provide active power during the day, without taking full advantage of power electronics devices, which can compensate for the reactive power at any moment during their operation. These systems can also generate dynamic reactive power by means of voltage source converters, which are called PV-STATCOM devices. This paper presents a convex formulation for the optimal integration (placement and sizing) of PV-STATCOM devices in electrical distribution systems. The proposed model considers reducing the costs of the annual energy losses and installing PV-STATCOM devices. A convex formulation was obtained to transform the hyperbolic relation between the products of the voltage into a second-order constraint via relaxation. Two simulation cases in the two IEEE test systems (33- and 69-node) with radial and meshed topologies were implemented to demonstrate the effectiveness of the proposed mixed-integer convex model. The results show that PV-STATCOM devices reduce the annual cost of energy losses of electrical networks in a more significant proportion than PV systems alone. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of determining the optimal location and operation of distribution static synchronous compensators (D-STATCOMs) in power distribution networks. Metaheuristic algorithms have been conventionally used to solve this mixed-integer nonlinear programming problem. In contrast, we propose a mixed-integer second-order cone programming (MI-SOCP) model that guarantees global optimum and fast convergence of the algorithms in optimization solvers. The model seeks to minimize the annual installation cost and the operating cost, subject to active and reactive power balance constraints, voltage regulation bounds, devices capabilities, and the number of D-STATCOMs available to be connected. The multiobjective nature of the problem is also analyzed and solved using the proposed MI-SOCP model. An extensive set of simulations on the IEEE-33 nodes test system demonstrate the advantages of this approach compared to conventional heuristic algorithms and with solutions given by algebraic modeling software. • D-STACOMs permit annual operative costs reduction in distribution grids. • MI-SOCP formulation allows finding the global optimal solution of the exact MINLP model through solution space convexification. • The proposed optimization model does not require statistical evaluation to validate its efficiency since it is mixed-integer convex in nature. [ABSTRACT FROM AUTHOR]

This paper proposes a new power flow (PF) formulation for electrical distribution systems using the current injection method and applying the Laurent series expansion. Two solution algorithms are proposed: a Newton-like iterative procedure and a fixed-point iteration based on the successive approximation method (SAM). The convergence analysis of the SAM is proven via the Banach fixed-point theorem, ensuring numerical stability, the uniqueness of the solution, and independence on the initializing point. Numerical results are obtained for both proposed algorithms and compared to well-known PF formulations considering their rate of convergence, computational time, and numerical stability. Tests are performed for different branch R / X ratios, loading conditions, and initialization points in balanced and unbalanced networks with radial and weakly-meshed topologies. Results show that the SAM is computationally more efficient than the compared PFs, being more than ten times faster than the backward–forward sweep algorithm. • Two novel efficient power flow algorithms for distribution systems. • Linearization using Laurent series expansion. • Convergence and stability analysis using Banach fixed-point theorem. • Proposed algorithms are at least three times faster than the backward–forward sweep. [ABSTRACT FROM AUTHOR]

This paper deals with the power flow problem in bipolar direct current distribution networks with unbalanced constant power loads. The effect of the neutral wire is considered in two prominent cases: (i) when the system is solidly grounded at each load point and (ii) when the neutral terminal is only grounded at the substation bus. The problem is solved using the successive approximation power flow method. Numerical results in two test feeders composed of 4 and 25 nodes demonstrate that the successive approximation power flow approach is adequate to solve the problem. It is also demonstrated that it is equivalent to the backward/forward power flow in matrix form. The main advantage of both power flow approaches is that they can work with radial and meshed distribution networks. Additionally, they do not require inverting matrices at each iteration, making them efficient in terms of computational processing times requirements. All the simulations are carried out in the MATLAB programming environment. • Bipolar DC networks have the double transference capability of their monopolar DC networks. • Convergence of the successive approximation power is ensured through the application of the Banach fixed point theorem. • Power losses increase for the topology with non-grounded neutral wire. • Monopolar unbalanced loads produce voltage imbalances between the positive and negative poles. [ABSTRACT FROM AUTHOR]

This paper addresses the problem concerning the efficient minimization of power losses in asymmetric distribution grids from the perspective of convex optimization. This research's main objective is to propose an approximation optimization model to reduce the total power losses in a three-phase network using the concept of electrical momentum. To obtain a mixed-integer convex formulation, the voltage variables at each node are relaxed by assuming them to be equal to those at the substation bus. With this assumption, the power balance constraints are reduced to flow restrictions, allowing us to formulate a set of linear rules. The objective function is formulated as a strictly convex objective function by applying the concept of average electrical momentum, by representing the current flows in distribution lines as the active and reactive power variables. To solve the relaxed MIQC model, the GAMS software (Version 28.1.2) and its CPLEX, SBB, and XPRESS solvers are used. In order to validate the effectiveness of load redistribution in power loss minimization, the initial and final grid configurations are tested with the triangular-based power flow method for asymmetric distribution networks. Numerical results show that the proposed mixed-integer model allows for reductions of 24.34 % , 18.64 % , and 4.14 % for the 8-, 15-, and 25-node test feeders, respectively, in comparison with the benchmark case. The sine–cosine algorithm and the black hole optimization method are also used for comparison, demonstrating the efficiency of the MIQC approach in minimizing the expected grid power losses for three-phase unbalanced networks. [ABSTRACT FROM AUTHOR]

This paper proposes an interconnection of the MATLAB and GAMS software interfaces, which were designed based on a master-slave methodology, to solve the mixed-integer nonlinear programming (MINLP) model problem associated with the problem regarding the optimal location and sizing of static distribution compensators (D-STATCOMs) in meshed and radial distribution networks, considering the problem of optimal reactive power flow compensation and the fact that the networks have commercial, industrial, and residential loads for a daily operation scenario. The objective of this study is to reduce the annual investment and operating costs associated with energy losses and the installation costs of D-STATCOMs. This objective function is based on the classical energy budget and the capacity constraints of the device. In the master stage, MATLAB software is used to program a discrete version of the sine-cosine algorithm (DSCA), which determines the locations where the D-STATCOMs will be installed. In the slave stage, using the BONMIN solver of the GAMS software and the known locations of the D-STATCOMs, the MINLP model representing the problem under study is solved to find the value of the objective function and the nominal power of the D-STATCOMs. To validate the effectiveness of the proposed master-slave optimizer, the 33-node IEEE test system with both radial and meshed topologies is used. With this test system, numerical comparisons were made with the exact solution of the MINLP model, using different solvers in the GAMS software, the genetic-convex strategy, and the discrete-continuous versions of the Chu and Beasley genetic algorithm and the salp swarm optimization algorithm. The numerical results show that DSCA-BONMIN achieves a global solution to the problem under study, making the proposed method an effective tool for decision-making in distribution companies. [ABSTRACT FROM AUTHOR]

Power losses (PL) are one of the most—if not the most—vital concerns in power distribution networks (DN). With respect to sustainability, distribution network reconfiguration (DNR) is an effective course of action to minimize power losses. However, the optimal DNR is usually a non-convex optimization process that necessitates the employment of powerful global optimization methods. This paper proposes a novel hybrid metaheuristic optimization (MO) method called the chaotic golden flower algorithm (CGFA) for PL minimization. As the name implies, the proposed method combines the golden search method with the flower pollination algorithm to multiply their benefits, guarantee the best solution, and reduce convergence time. The performance of the algorithm has been evaluated under different test systems, including the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus systems and the smart city (SC) network, each of which includes distributed-generation (DG) units and energy storage systems (ESS). In addition, the locations of tie-switches in the DN, which used to be considered as given information in previous studies, are assumed to be variable, and a branch-exchange adaption is included in the reconfiguration process. Furthermore, uncertainty analysis, such as bus and/or line fault conditions, are studied, and the performance of the proposed method is compared with other pioneering MO algorithms with minimal standard deviations ranging from 0.0012 to 0.0101. The case study of SC is considered and the obtained simulation results show the superiority of the algorithm in finding higher PL reduction under different scenarios, with the lowest standard deviations ranging from 0.012 to 0.0432. [ABSTRACT FROM AUTHOR]

This study addresses the problem of selecting the conductor sizes for medium-voltage distribution networks with radial configurations. The optimization model that represents this problem is part of the mixed-integer non-linear programming (MINLP) models, in which a power flow must be solved for each possible combination of conductor sizes. The main objective of this optimization problem is to find the best set of conductor sizes that minimize an economic objective function composed of the total costs of conducting materials added with the expected annual costs of the energy losses by proposing a new hybrid optimization methodology from the family of combinatorial optimization methods. To solve the MINLP model, a master–slave optimization method based on the modified version of the gradient-based metaheuristic optimizer (MGbMO) combined with the successive approximation power flow method for unbalanced distribution networks is presented. The MGbMO defines the set of conductor sizes assignable for each distribution line using an integer codification. The slave stage (three-phase power flow) quantifies the total power losses and their expected annual operating costs. Numerical results in the IEEE 8-, 27-, and 85-bus grids demonstrate the effectiveness of the proposed master–slave optimizer when compared with multiple combinatorial optimization methods (vortex search algorithm, the Newton-metaheuristic optimizer, the traditional and Chu and Beasley genetic algorithms, and the tabu search approaches). Two scenarios regarding the demand behavior were analyzed for the IEEE 8- and 27-bus grids: a peak load operation was considered, and, for the IEEE 85-bus grid, the daily demand behavior, including the presence of renewable generators, was considered. The 85-bus grid allowed showing that the most realistic operative scenario for selecting conductors is the case where a demand curve is implemented since reductions over 40% in the annual investment and operating costs were found when compared to the peak load operating condition. All numerical validations were performed in MATLAB software. [ABSTRACT FROM AUTHOR]

Optimal energy management has become a challenging task to accomplish in today's advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model's optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former's efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive control to stabilize the output voltage of a DC–DC boost converter that feeds an unknown constant power load (CPL). The proposed controller employs passivity-based control (PBC), which assigns a desired system energy to compensate for the negative impedance that may be generated by a CPL. A proportional-integral (PI) action that maintains a passive output is added to the PBC to impose the desired damping and enhance disturbance rejection behavior, thus forming a PI+PBC control. In addition, the proposed controller includes two estimators, i.e., immersion and invariance (I&I), and disturbance observer (DO), in order to estimate CPL and supply voltage for the converter, respectively. These observers become the proposed controller for an adaptive, sensorless PI+PBC control. Phase portrait analysis and experimental results have validated the robustness and effectiveness of the adaptive proposed control approach. These results show that the proposed controller adequately regulates the output voltage of the DC–DC boost converter under variations of the input voltage and CPL simultaneously. [ABSTRACT FROM AUTHOR]

The problem of voltage regulation in unknown constant resistive loads is addressed in this paper from the nonlinear control point of view for second-order DC-DC converters. The converters' topologies analyzed are: (i) buck converter, (ii) boost converter, (iii) buck-boost converter, and (iv) non-inverting buck-boost converter. The averaging modeling method is used to model these converters, representing all these converter topologies with a generalized port-Controlled Hamiltonian (PCH) representation. The PCH representation shows that the second-order DC-DC converters exhibit a general bilinear structure which permits to design of a passivity-based controller with PI actions that ensures the asymptotic stability in the sense of Lyapunov. A linear estimator based on an integral estimator that allows reducing the number of current sensors required in the control implementation stage is used to determine the value of the unknown resistive load. The main advantage of this load estimator is that it ensures exponential convergence to the estimated variable. Numerical simulations and experimental validations show that the PI passivity-based control allows voltage regulation with first-order behavior, while the classical PI controller produces oscillations in the controlled variable, significantly when the load varies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a master–slave methodology to address the problem of optimal integration (location and sizing) of Distributed Generators (DGs) in Direct Current (DC) networks. This proposed methodology employs a parallel version of the Population-Based Incremental Learning (PPBIL) optimization method in the master stage to solve the location problem and the Vortex Search Algorithm (VSA) in the slave stage to solve the sizing problem. In addition, it uses the reduction of power losses as the objective function, considering all the constraints associated with the technical conditions specific to DGs and DC networks. To validate its effectiveness and robustness, we use as comparison methods, different solution methodologies that have been reported in the specialized literature, as well as two test systems (the 21 and 69-bus test systems). All simulations were performed in MATLAB. According to the results, the proposed hybrid (PPBIL–VSA) methodology provides the best trade-off between quality of the solution and processing times and exhibits an adequate repeatability every time it is executed. [ABSTRACT FROM AUTHOR]

The optimal expansion of AC medium-voltage distribution grids for rural applications is addressed in this study from a heuristic perspective. The optimal routes of a distribution feeder are selected by applying the concept of a minimum spanning tree by limiting the number of branches that are connected to a substation (mixed-integer linear programming formulation). In order to choose the caliber of the conductors for the selected feeder routes, the maximum expected current that is absorbed by the loads is calculated, thereby defining the minimum thermal bound of the conductor caliber. With the topology and the initial selection of the conductors, a tabu search algorithm (TSA) is implemented to refine the solution with the help of a three-phase power flow simulation in MATLAB for three different load conditions, i.e., maximum, medium, and minimum consumption with values of 100%, 60%, and 30%, respectively. This helps in calculating the annual costs of the energy losses that will be summed with the investment cost in conductors for determining the final costs of the planning project. Numerical simulations in two test feeders comprising 9 and 25 nodes with one substation show the effectiveness of the proposed methodology regarding the final grid planning cost; in addition, the heuristic selection of the calibers using the minimum expected current absorbed by the loads provides at least 70% of the calibers that are contained in the final solution of the problem. This demonstrates the importance of using adequate starting points to potentiate metaheuristic optimizers such as the TSA. [ABSTRACT FROM AUTHOR]