Simultaneous Placement and Sizing of DG and Capacitor to Minimize the Power Losses in Radial Distribution Network

Manikanta, G.; Mani, Ashish; Singh, H. P.; Chaturvedi, D. K.

doi:10.1007/978-981-13-0589-4_56

Cited by 19 publications

(7 citation statements)

References 19 publications

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“…Also, the birth and the death of a coyote are considered in COA. The birth of a new coyote is written as a combination of the social conditions of two random parents plus an environmental factor, as shown in (22).…”

Section: B Overview Of Coyote Optimization Algorithmmentioning

confidence: 99%

“…On the other hand, the deterministic approaches generally do not consider PV generation or load uncertainties. These methods include analytic [12], classical [13], metaheuristic-based [14]- [17], [19]- [22] and hybrid types [23]- [25]. The analytical type is more suitable for smaller systems with a limited number of PV-DG units in the planning.…”

Section: Introductionmentioning

confidence: 99%

“…However, the selection of the optimal number of DGs in the distribution networks is only limited to three units. In [22], the adaptive quantum-inspired evolutionary algorithm was proposed for placing and sizing DGs and capacitors. Nevertheless, the planning study only considers a given load demand at a specific time instant and only up to three DGs can be placed in the system.…”

Section: Introductionmentioning

confidence: 99%

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Coyote Optimization Algorithm-Based Approach for Strategic Planning of Photovoltaic Distributed Generation

Chang

Chinh

2020

IEEE Access

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The optimal planning for distributed generations (DGs) associated with photovoltaics (PVs) in the utility-owned distribution system is crucial for increasing high penetration of renewables while against practical system operation constraints. Such PV-DG planning is categorized as a complicated mixed-integer nonlinear programming (MINLP) problem and is extremely difficult to solve by using conventional methods. In recent years, several bio-inspired metaheuristic algorithms have been proposed to tackle various complicated real-parameter optimization problems. This paper proposes a two-stage approach including a new bio-inspired algorithm, Coyote Optimization Algorithm (COA), to solve the large-scale MINLP PV-DG sizing problem considering different load levels. The objective function terms under consideration include the total system power loss and voltage regulator tap changes at different load levels while against limits of rms bus voltages, tap changes, and PV-DG constraints at each candidate bus. The proposed method is tested using the IEEE 123-bus unbalanced benchmark system and an actual utility distribution network. Results obtained are then compared with those obtained by a classic MINLP solver-based and four other bio-inspired methods. Moreover, results also show that the proposed method leads to lower loss, a minimum number of regulator tap changes, and higher PV penetration capacity among the compared methods and is suitable for solving the large-scale PV-DG planning problem in distribution systems.

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Section: B Overview Of Coyote Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coyote Optimization Algorithm-Based Approach for Strategic Planning of Photovoltaic Distributed Generation

Chang

Chinh

2020

IEEE Access

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“…Load flow results are included in various tools which monitor, analyze and control the power system. Load flow analysis is directly or indirectly used in various power system applications such as distributed generator and capacitor placements [2][3][4], economic dispatch [5,6], power quality improvements, network reconfiguration and service restorations [7,8], power systems optimization and other applications. Different load flow methods have been devised for power flow analysis in both distribution networks and transmission network.…”

Section: Introductionmentioning

confidence: 99%

An improved backward/forward sweep power flow method based on network tree depth for radial distribution systems

Kawambwa

Mwifunyi

Mnyanghwalo

et al. 2021

Journal of Electrical Systems and Inf Technol

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This paper presents an improved load flow technique for a modern distribution system. The proposed load flow technique is derived from the concept of the conventional backward/forward sweep technique. The proposed technique uses linear equations based on Kirchhoff’s laws without involving matrix multiplication. The method can accommodate changes in network structure reconfiguration by involving the parent–children relationship between nodes to avoid complex renumbering of branches and nodes. The IEEE 15 bus, IEEE 33 bus and IEEE 69 bus systems were used for testing the efficacy of the proposed technique. The meshed IEEE 15 bus system was used to demonstrate the efficacy of the proposed technique under network reconfiguration scenarios. The proposed method was compared with other load flow approaches, including CIM, BFS and DLF. The results revealed that the proposed method could provide similar power flow solutions with the added advantage that it can work well under network reconfiguration without performing node renumbering, not covered by others. The proposed technique was then applied in Tanzania electric secondary distribution network and performed well.

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“…Moreover, AQiEA doesn't require any other operators like mutations or local heuristics to drive the individuals toward convergence. Recently, AQiEA is applied to some engineering optimization problems like ceramic grinding [4], optimal location and size of Distributed Generators (DG) [5], Network Reconfiguration [6], Siting and sizing of Capacitors [7], Cost analysis of DG & Capacitor [8] and simultaneous implementation of both DGs and capacitors [9], and has shown better performances as compared with other approaches.…”

Section: Introductionmentioning

confidence: 99%

Adaptive quantum-inspired evolutionary algorithm for optimizing power losses by dynamic load allocation on distributed generators

et al. 2019

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In this paper, an Adaptive Quantum-inspired Evolutionary Algorithm (AQiEA) has been applied for minimizing the power losses in the distribution network by suitable placement, sizing and subsequent allocation of load on Distributed Generators (DG) for a varying load with a time horizon of twentyfour hours. Many efforts have been reported in the literature to minimize power losses. However, they have mostly used a fixed load, i.e., nonvarying load, whereas it is well known that load in distribution network varies during the day. An investigation was undertaken to find the reduction in power losses on a timevarying load. It has been found that the average power losses for dynamic load allocation on DGs for every hour have a maximum reduction in power loss as compared with other well-known cases in the literature. Optimal location and size of DG is a difficult nonlinear, non-differentiable combinatorial optimization problem. AQiEA is used to find the appropriate location and capacity of DG for a varying load with a time horizon of twenty-four hours to minimize the power losses. AQiEA doesn't require additional operators like local search and mutation to improve the convergence rate and avoid the premature convergence. A Quantum Rotation inspired Adaptive Crossover operator is used as a variation operator, which is parameter free. The effectiveness of AQiEA is demonstrated on two test bus systems viz., 33 bus system and 69 bus system, which are used as benchmark problems for validating the proposed methodology as well as for comparative testing amongst existing techniques. Wilcoxon signed rank test is also used to demonstrate the effectiveness of AQiEA. The experimental results show that AQiEA has better performance as compared to some existing 'state of art' techniques.

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Simultaneous Placement and Sizing of DG and Capacitor to Minimize the Power Losses in Radial Distribution Network

Cited by 19 publications

References 19 publications

Coyote Optimization Algorithm-Based Approach for Strategic Planning of Photovoltaic Distributed Generation

Coyote Optimization Algorithm-Based Approach for Strategic Planning of Photovoltaic Distributed Generation

An improved backward/forward sweep power flow method based on network tree depth for radial distribution systems

Adaptive quantum-inspired evolutionary algorithm for optimizing power losses by dynamic load allocation on distributed generators

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