Efficient operative cost reduction in distribution grids considering the optimal placement and sizing of d-statcoms using a discrete-continuous vsa
TL;DR: Numerical results in two radial test feeders demonstrate that the proposed DCVSA can solve the mixed-integer nonlinear programming model (MINLP) model with best results when compared with the MINLP solvers available in the GAMS software.
Abstract: The problem of reactive power compensation in electric distribution networks is addressed in this research paper from the point of view of the combinatorial optimization using a new discrete-continuous version of the vortex search algorithm (DCVSA). To explore and exploit the solution space, a discrete-continuous codification of the solution vector is proposed, where the discrete part determines the nodes where the distribution static compensator (D-STATCOM) will be installed, and the continuous part of the codification determines the optimal sizes of the D-STATCOMs. The main advantage of such codification is that the mixed-integer nonlinear programming model (MINLP) that represents the problem of optimal placement and sizing of the D-STATCOMs in distribution networks only requires a classical power flow method to evaluate the objective function, which implies that it can be implemented in any programming language. The objective function is the total costs of the grid power losses and the annualized investment costs in D-STATCOMs. In addition, to include the impact of the daily load variations, the active and reactive power demand curves are included in the optimization model. Numerical results in two radial test feeders with 33 and 69 buses demonstrate that the proposed DCVSA can solve the MINLP model with best results when compared with the MINLP solvers available in the GAMS software. All the simulations are implemented in MATLAB software using its programming environment.
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TL;DR: The proposed discrete-continuous version of the Chu–Beasley genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates and minimizes the annual operating costs of the grid.
Abstract: In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.
26 citations
TL;DR: In this article, a fractional-order sliding mode control (FOSMC) was proposed for a D-STATCOM to compensate the low power distribution system by injecting/absorbing a specific extent of the reactive power under disturbances.
Abstract: At present, the disturbances like the voltage fluctuations, resulting from the grid’s complexities and unbalanced load conditions, create severe power quality concerns like total harmonic distortion (THD) and voltage unbalance factor (VUF) of the grid voltage. Though the custom power devices such as distribution-static compensators (D-STATCOMs) improve these power quality concerns, however, the accompanying controller plays the substantial role. Therefore, this paper proposes a fractional-order sliding mode control (FOSMC) for a D-STATCOM to compensate the low power distribution system by injecting/absorbing a specific extent of the reactive power under disturbances. FOSMC is a non-linear robust control in which the sliding surface is designed by using the Riemann-Liouville ( RL ) function and the chattering phenomenon is minimized by using the exponential reaching law. The stability of FOSMC is evidenced by employing the Lyapunov stability criteria. Moreover, the performance of the proposed FOSMC is further accessed while doing its parametric variations. The complete system is demonstrated with a model of 400V, 180kVA radial distributor along with D-STATCOM under two test scenarios in MATLAB/Simulink environment. The results of the proposed controller are compared with the fixed frequency sliding mode control (FFSMC) and conventional proportional-integral (PI) control. The results validate the superiority of the proposed controller in terms of rapid tracking, fast convergence, and overall damping with very low THD and VUF.
22 citations
27 May 2021
TL;DR: Numerical results present optimal solutions with processing times lower than 5 s, which confirms its applicability in large-scale optimization problems employing embedding master–slave optimization structures.
Abstract: The power flow problem in three-phase unbalanced distribution networks is addressed in this research using a derivative-free numerical method based on the upper-triangular matrix. The upper-triangular matrix is obtained from the topological connection among nodes of the network (i.e., through a graph-based method). The main advantage of the proposed three-phase power flow method is the possibility of working with single-, two-, and three-phase loads, including Δ- and Y-connections. The Banach fixed-point theorem for loads with Y-connection helps ensure the convergence of the upper-triangular power flow method based an impedance-like equivalent matrix. Numerical results in three-phase systems with 8, 25, and 37 nodes demonstrate the effectiveness and computational efficiency of the proposed three-phase power flow formulation compared to the classical three-phase backward/forward method and the implementation of the power flow problem in the DigSILENT software. Comparisons with the backward/forward method demonstrate that the proposed approach is 47.01%, 47.98%, and 36.96% faster in terms of processing times by employing the same number of iterations as when evaluated in the 8-, 25-, and 37-bus systems, respectively. An application of the Chu-Beasley genetic algorithm using a leader–follower optimization approach is applied to the phase-balancing problem utilizing the proposed power flow in the follower stage. Numerical results present optimal solutions with processing times lower than 5 s, which confirms its applicability in large-scale optimization problems employing embedding master–slave optimization structures.
19 citations
TL;DR: Numerical validations in the IEEE 33- and IEEE 69-bus systems demonstrate that the proposed discrete-continuous version of the vortex search algorithm (DCVSA) presents a better numerical performance regarding the objective function value when compared with the BONMIN solver in the GAMS software.
Abstract: This paper deals with the optimal siting and sizing problem of photovoltaic (PV) generators in electrical distribution networks considering daily load and generation profiles. It proposes the discrete-continuous version of the vortex search algorithm (DCVSA) to locate and size the PV sources where the discrete part of the codification defines the nodes. Renewable generators are installed in these nodes, and the continuous section determines their optimal sizes. In addition, through the successive approximation power flow method, the objective function of the optimization model is obtained. This objective function is related to the minimization of the daily energy losses. This method allows determining the power losses in each period for each renewable generation input provided by the DCVSA (i.e., location and sizing of the PV sources). Numerical validations in the IEEE 33- and IEEE 69-bus systems demonstrate that: (i) the proposed DCVSA finds the optimal global solution for both test feeders when the location and size of the PV generators are explored, considering the peak load scenario. (ii) In the case of the daily operative scenario, the total reduction of energy losses for both test feeders are 23.3643% and 24.3863%, respectively; and (iii) the DCVSA presents a better numerical performance regarding the objective function value when compared with the BONMIN solver in the GAMS software, which demonstrates the effectiveness and robustness of the proposed master-slave optimization algorithm.
13 citations
TL;DR: In this article, the equivalence of the matricial form of the classical backward/forward load flow formulation for distribution networks with the recently developed successive approximations (SA) load flow approach was demonstrated.
Abstract: This paper shows the equivalence of the matricial form of the classical backward/forward load flow formulation for distribution networks with the recently developed successive approximations (SA) load flow approach. Both formulations allow solving the load flow problem in meshed and radial distribution grids even if these are operated with alternating current (AC) or direct current (DC) technologies. Both load flow methods are completely described in this research to make a fair comparison between them and demonstrate their equivalence. Numerical comparisons in the 33- and 69-bus test feeder with radial topology show that both methods have the same number of iterations to find the solution with a convergence error defined as 1×10−10.
13 citations
References
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TL;DR: In this article, a particle swarm optimization algorithm for finding the optimal location and sizing of DSTATCOM with the aim of reducing the total power loss along with voltage profile improvement of Radial Distribution System is proposed.
216 citations
TL;DR: An effective biologically inspired algorithm (Immune Algorithm) is used to search the best location and determine the size of DSTATCOM and minimum cost of installation and maximum power loss reduction are integrated into an objective function through appropriate weighting factors.
216 citations
TL;DR: The results proved that simultaneous reconfiguration and optimal allocation of PV array and DSTATCOM unit leads to significantly reduced losses, improved VP, and increased LB.
Abstract: In this paper, a combination of a fuzzy multiobjective approach and ant colony optimization (ACO) as a metaheuristic algorithm is used to solve the simultaneous reconfiguration and optimal allocation (size and location) of photovoltaic (PV) arrays as a distributed generation (DG) and distribution static compensator (DSTATCOM) as a distribution flexible ac transmission system (DFACT) device in a distribution system. The purpose of this research includes loss reduction, voltage profile (VP) improvement, and increase in the feeder load balancing (LB). The proposed method is validated using the IEEE 33-bus test system and a Tai-Power 11.4-kV distribution system as a real distribution network. The results proved that simultaneous reconfiguration and optimal allocation of PV array and DSTATCOM unit leads to significantly reduced losses, improved VP, and increased LB. Obtained results have been compared with the base value and found that simultaneous placement of PV and DSTATCOM along with reconfiguration is more beneficial than separate single-objective optimization. Also, the proposed fuzzy-ACO approach is more accurate as compared to ACO and other intelligent techniques like fuzzy-genetic algorithm (GA) and fuzzy-particle swarm optimization (PSO).
204 citations
TL;DR: In this paper, a combinatorial process based on reconfiguration and DSTATCOM allocation is implemented to mitigate losses and improve voltage profile in power distribution networks, where differential evolution algorithm (DEA) has been used to solve and overcome the complicity of this combinatorsial nonlinear optimization problem.
134 citations
TL;DR: In this article, an up-to-date survey of the literature on the optimal allocation of D-STATCOM in distribution networks is presented, which can be classified into five categories including analytical methods, artificial neural network-based approaches, metaheuristic methods, sensitivity approaches, and a combination of sensitivity approaches and metaheuristics.
Abstract: With the growth and development of power grids, optimal utilization of electric networks is very important. Because of the high cost of construction and development of power networks, mitigation of existing issues, such as excessive power losses, voltage profile problems, voltage instabilities, reliability problems and etc. is inevitable. To obviate these problems, Distribution Synchronous Static Compensator (D-STACTOM) as a shunt compensator device can be used in electric distribution networks. Optimal location and size of D-STACOM, should be determined on the basis of economic viability, required quality, reliability and availability. In recent years, several papers have concentrated on the techniques used for finding optimal location and size of the D-STATCOM units considering different aspects. However, to date, no review paper has been published in this field. This paper presents an up to date survey of the literature on the optimal allocation of D-STATCOM in distribution networks. The existing research works have been classified into five categories including analytical methods, artificial neural network-based approaches, metaheuristic methods, sensitivity approaches, and a combination of sensitivity approaches and metaheuristic methods. Moreover, it was found that in D-STATCOM allocation problem, the objectives may be alleviation of power loss, mitigation of voltage deviations, improvement of reliability metrics and enhancement of voltage stability. All methods, objectives and constrains have been compared and discussed in details. Eventually, an overall review on the reviewed works has been provided and some directions for future research were suggested.
100 citations