Genetic-convex model for dynamic reactive power compensation in distribution networks using D-STATCOMs
TL;DR: Numerical validations in the 33-node test feeder with radial configuration show the proposed genetic-convex model’s effectiveness to minimize the annual operative costs of the grid when compared with the optimization solvers available in GAMS software.
Abstract: This paper proposes a new hybrid master–slave optimization approach to address the problem of the optimal placement and sizing of distribution static compensators (D-STATCOMs) in electrical distribution grids. The optimal location of the D-STATCOMs is identified by implementing the classical and well-known Chu and Beasley genetic algorithm, which employs an integer codification to select the nodes where these will be installed. To determine the optimal sizes of the D-STATCOMs, a second-order cone programming reformulation of the optimal power flow problem is employed with the aim of minimizing the total costs of the daily energy losses. The objective function considered in this study is the minimization of the annual operative costs associated with energy losses and installation investments in D-STATCOMs. This objective function is subject to classical power balance constraints and device capabilities, which generates a mixed-integer nonlinear programming model that is solved with the proposed genetic-convex strategy. Numerical validations in the 33-node test feeder with radial configuration show the proposed genetic-convex model’s effectiveness to minimize the annual operative costs of the grid when compared with the optimization solvers available in GAMS software.
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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
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TL;DR: The numerical results indicate the positive effect of the dynamic reactive power injections in the power systems on annual operating cost reduction and the solution of the proposed model is reached with the general algebraic modeling system optimization package.
Abstract: The problem of the optimal siting and placement of static compensates (STATCOMs) in power systems is addressed in this paper from an exact mathematical optimization point of view. A mixed-integer nonlinear programming model to present the problem was developed with the aim of minimizing the annual operating costs of the power system, which is the sum of the costs of the energy losses and of the installation of the STATCOMs. The optimization model has constraints regarding the active and reactive power balance equations and those associated with the devices’ capabilities, among others. To characterize the electrical behavior of the power system, different load profiles such as residential, industrial, and commercial are considered for a period of 24 h of operation. The solution of the proposed model is reached with the general algebraic modeling system optimization package. The numerical results indicate the positive effect of the dynamic reactive power injections in the power systems on annual operating cost reduction. A Pareto front was built to present the multi-objective behavior of the studied problem when compared to investment and operative costs. The complete numerical validations are made in the IEEE 24-, IEEE 33-, and IEEE 69-bus systems, respectively.
10 citations
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11 Aug 2021
TL;DR: 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: 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.
9 citations
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TL;DR: In this article , a mixed-integer second-order cone programming (MI-SOCP) model is proposed to solve the problem of determining the optimal location and operation of distribution static synchronous compensators (D-STATCOMs) in power distribution networks.
8 citations
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TL;DR: In this article , the authors proposed a methodology for the optimal location and sizing of reactive compensation in an electrical transmission system through a deep neural network (DNN) by considering the smallest cost for compensation.
Abstract: This research proposes a methodology for the optimal location and sizing of reactive compensation in an electrical transmission system through a deep neural network (DNN) by considering the smallest cost for compensation. An electrical power system (EPS) is subjected to unexpected increases in loads which are physically translated as an increment of users in the EPS. This phenomenon decreases voltage profiles in the whole system which also decreases the EPS’s reliability. One strategy to face this problem is reactive compensation; however, finding the optimal location and sizing of this compensation is not an easy task. Different algorithms and techniques such as genetic algorithms and non-linear programming have been used to find an optimal solution for this problem; however, these techniques generally need big processing power and the processing time is usually considerable. That being stated, this paper’s methodology aims to improve the voltage profile in the whole transmission system under scenarios in which a PQ load is randomly connected to any busbar of the system. The optimal location of sizing of reactive compensation will be found through a DNN which is capable of a relatively small processing time. The methodology is tested in three case studies, IEEE 14, 30 and 118 busbar transmission systems. In each of these systems, a brute force algorithm (BFA) is implemented by connecting a PQ load composed of 80% active power and 20% reactive power (which varies from 1 MW to 100 MW) to every busbar, for each scenario, reactive compensation (which varies from 10 Mvar to 300 Mvar) is connected to every busbar. Then power flows are generated for each case and by selecting the scenario which is closest to 90% of the original voltage profiles, the optimal scenario is selected and overcompensation (which would increase cost) is avoided. Through the BFA, the DNN is trained by selecting 70% of the generated data as training data and the other 30% is used as test data. Finally, the DNN is capable of achieving a 100% accuracy for location (in all three case studies when compared with BFA) and objective deviation has a difference of 3.18%, 7.43% and 0% for the IEEE 14, 30 and 118 busbar systems, respectively (when compared with the BFA). With this methodology, it is possible to find the optimal location and sizing of reactive compensation for any transmission system under any PQ load increment, with almost no processing time (with the DNN trained, the algorithm takes seconds to find the optimal solution).
4 citations
References
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TL;DR: It is proved that convexification requires phase shifters only outside a spanning tree of the network and their placement depends only on network topology, not on power flows, generation, loads, or operating constraints.
Abstract: We propose a branch flow model for the analysis and optimization of mesh as well as radial networks. The model leads to a new approach to solving optimal power flow (OPF) that consists of two relaxation steps. The first step eliminates the voltage and current angles and the second step approximates the resulting problem by a conic program that can be solved efficiently. For radial networks, we prove that both relaxation steps are always exact, provided there are no upper bounds on loads. For mesh networks, the conic relaxation is always exact but the angle relaxation may not be exact, and we provide a simple way to determine if a relaxed solution is globally optimal. We propose convexification of mesh networks using phase shifters so that OPF for the convexified network can always be solved efficiently for an optimal solution. We prove that convexification requires phase shifters only outside a spanning tree of the network and their placement depends only on network topology, not on power flows, generation, loads, or operating constraints. Part I introduces our branch flow model, explains the two relaxation steps, and proves the conditions for exact relaxation. Part II describes convexification of mesh networks, and presents simulation results.
983 citations
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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
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TL;DR: In this article, the authors proposed a new method of scheduling for optimal placement and sizing of Distribution STATic COMpensator in the radial distribution networks to minimize the power loss, the proposed method Voltage Stability Index is used to search the optimal placement for installation of DSTATCOM.
134 citations
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10 Oct 2004
TL;DR: In this paper, a meshed network architecture for distribution networks has been investigated and compared with the radial scheme, and steady state and dynamic simulations have been performed, considering a portion of the real Italian distribution network, and voltage profile, power losses, short circuit currents and grid reliability have been analyzed.
Abstract: Until now, distribution networks are regarded as a passive termination of the transmission network, with a radial structure, unidirectional power flows, and a simple and efficient protection scheme. In presence of a large amount of distributed generation (DG), distribution networks will gradually, but ineluctably, change towards a new kind of active networks. The most revolutionary change, needed to efficiently allocate so much DG, could be represented by the adoption of a meshed network architecture. For this reason, in the paper new possible arrangements for distribution networks have been deeply investigated and compared with the radial scheme. Steady state and dynamic simulations have been performed, considering a portion of the real Italian distribution network, and voltage profile, power losses, short circuit currents and grid reliability have been analyzed.
119 citations
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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