An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm

Grisales-Noreña, Luis Fernando; Montoya, Oscar Danilo; Ramos‐Paja, Carlos Andrés

doi:10.1016/j.est.2020.101488

Cited by 88 publications

(76 citation statements)

References 37 publications

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“…In the case of the 69-bus test system, all the processing times required by the solution methods increased: MVO (132s), ALO (9.67s), BH (13.91s), CGA (17.38s), and PSO (13.67s). Again, the MVO exhibited the longest processing time in the 69-bus test system, with an average value close to two minutes [33,34]. Nevertheless, said time is also adequate for solving the problem of optimal power flow in the 69-bus test system because of the same reasons explained regarding its 21-bus counterpart.…”

Section: Processing Time Analysismentioning

confidence: 85%

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

et al. 2021

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This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

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Section: Processing Time Analysismentioning

confidence: 85%

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

et al. 2021

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“…Note that if the diagonal elements of the covariance matrix are equal and the off-diagonal elements in this matrix are zero, then the Gaussian distribution will produce hyperspheres in a d-dimensional space. An easy way to determine Σ, considering zero covariances and equal variances, is defined in (9).…”

Section: Vortex Search Algorithmmentioning

confidence: 99%

“…Additionally, the voltages of the end-users may be below the established limits. In order to improve the operation of the system, various strategies have been proposed, such as phase balancing [5], shunt capacitor placement [6], distributed generator placement [7], reconfiguration [8], location of energy storage systems [9], series compensation [10], etc.…”

Section: Introductionmentioning

confidence: 99%

Optimal Selection and Location of Fixed-Step Capacitor Banks in Distribution Networks Using a Discrete Version of the Vortex Search Algorithm

et al. 2020

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This paper deals with the problem of the optimal selection of capacitor banks in electrical AC distribution systems for minimizing the costs of energy losses during a year of operation through a discrete version of the vortex search algorithm (DVSA). This algorithm works with a hypersphere with a variable radius defined by an exponential function where a Gaussian distribution is used to generate a set of candidate solutions uniformly distributed around the center of this hypersphere. This center corresponds to the best solution obtained at the iteration t, which is initialized at the center of the solution space at the iterative search beginning. The main advantage of combining the exponential function with the Gaussian distribution is the correct balance between the exploration and exploitation of the solution space, which allows reaching the global optimal solution of the optimization problem with a low standard deviation, i.e., guaranteeing repeatability at each simulation. Two classical distribution networks composed of 33 and 69 nodes were used to validate the proposed DVSA algorithm. They demonstrated that the DVSA improves numerical reports found in specialized literature regarding the optimal selection and location of fixed-step capacitor banks with a low computational burden. All the simulations were carried out in MATLAB software.

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“…In the specialized literature, the analyses of DC networks are made in the context of providing electric distribution applications in urban and rural areas [1,10]. The main approaches correspond to: a) optimal location and sizing of distributed generators in DC grids [11], b) optimal location and operation of battery energy storage systems [12][13][14], c) optimal reconfiguration of DC feeders [15], d) and planning of DC networks [1], among others. Based on the importance of these studies, we focus on the problem of the optimal location and sizing of distributed generators in DC networks considering daily load and PV curves to minimize the number of daily energy losses and the harmful greenhouse gas emissions to the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

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

Molina

Montoya

Gil-González

2021

DYNA

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

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An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm

Cited by 88 publications

References 37 publications

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

Optimal Selection and Location of Fixed-Step Capacitor Banks in Distribution Networks Using a Discrete Version of the Vortex Search Algorithm

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

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