Integration of distributed generation for assessment of distribution system reliability considering power loss, voltage stability and voltage deviation

Dixit, Mukul; Kundu, Prasanta; Jariwala, Hitesh R.

doi:10.1007/s12667-017-0248-6

Cited by 26 publications

(10 citation statements)

References 43 publications

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“…Numerous goals are suggested for the reconfiguration problem in [29][30][31]. There are promoting the use of the reconfiguration method to alleviating the reliability and power loss costs.…”

Section: Cost Of Power Lossmentioning

confidence: 99%

A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

Abdolahi

Taghizadegan

Banaei

et al. 2021

IET Science Measure & Tech

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A reliability-based optimal μ-PMU (micro-phasor measurement unit) placement scheme is suggested for efficient observability enhancement of smart distribution grids at steady-state and contingencies conditions. This article introduces a unique method for the μ-PMU allocation in reconfigurable smart distribution grids in which communication system requirements and zero injection nodes (ZINs) are considered. The original objective function and limitations are proposed aiming at minimizing the capital cost, including communication links and installation costs of μ-PMU, optical power ground wire cost, power losses cost, and reliability cost as well as obtaining the maximum number of measurement redundancy constrained to full system observability in the presence of ZINs and tie switches. The suggested method is formulated as a mixed-integer linear programming problem applied to find optimal μ-PMU locations considering the cost of communication infrastructure and co-optimize the system switching plan simultaneously. In this regard, CPLEX-a high-performance mathematical solver-is used to solve the proposed mixed-integer linear optimization problem to reach the global optimality. The simulations are performed on 33, 69, and 85-bus radial distribution networks, and comprehensive simulation studies show the effectiveness of the suggested method.

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Section: Cost Of Power Lossmentioning

confidence: 99%

A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

Abdolahi

Taghizadegan

Banaei

et al. 2021

IET Science Measure & Tech

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“…Multi‐objective function of a DG allocation problem involves combination or addition of many single objectives in Section with an inevitable conflict in which a single solution is incapable of solving all the diverse objectives. These multi‐objective functions are required to be minimised or maximised simultaneously into a single‐objective formulation . The multi‐objective formulations are grouped into (i)Multi‐objective weighted sum formulation: weighted sum of each objective is used to transform multi‐objective formulation into a single objective function problem with the use pre‐specified weights.…”

Section: Formulation Of Rehdg Allocation Planning Problemmentioning

confidence: 99%

“…These multi-objective functions are required to be minimised or maximised simultaneously into a single-objective formulation. 33,[38][39][40][41] The multi-objective formulations are grouped into 5,7 (i) Multi-objective weighted sum formulation: weighted sum of each objective is used to transform multi-objective formulation into a single objective function problem with the use pre-specified weights. Weighted sum approach is simple to formulate but difficult to apply in non-convex optimisation problems.…”

Section: Multi-objectives Of Rehdg Allocation Problemmentioning

confidence: 99%

“…The algorithm is adapted from the swarm of honey bee's natural behaviour for finding nectar . ABCAs are used in Mohandas et al and Dixit et al for solving optimal DGs placement and sizing problem to minimise power losses and improve the voltage stability of the distribution system. In Abu‐Mouti and El‐Hawary, optimal allocation of DGs is achieved using ABCA by tuning control inputs, colony size, and iteration number.…”

Section: Optimisation Algorithms For Dg Allocation Planningmentioning

confidence: 99%

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Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

2020

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Summary Integrating renewable energy hybrid distributed generation (REHDG) into distribution network systems (DNSs) has become increasingly important because of various technical, economic, and environmental advantages accruing from it. However, the output power of REHDGs from photovoltaic (PV) and wind is highly variable because of its dependency on intermittent parameters such as solar irradiance, temperature, and wind speed. Such variability of generated power from large‐scale REHDGs or load introduces small signal instabilities (oscillations). Meanwhile, different locations of integration and sizes of REHDGs in the DNS affect the system oscillation modes by either improving or depriving the small‐signal stability (SSS) of the network. Consequently, a significant number of research has been conducted on the planning of optimal allocation of REHDGs in DNS. In this regard, this paper reviews the existing planning models, optimisation techniques, and resources' uncertainty modelling employed in REHDGs allocations in terms of their capability in obtaining optimal solutions and enhancing SSS of the system. Planning models with optimisation algorithms are evaluated for modelling renewable resource uncertainties and curtailing SSS variables. Research works on planning of optimal allocation of these generations attain minimum cost, but were unable to satisfy the SSS requirements of the system. The existing models for the planning and design of optimal timing, sizing, and placement of REHDGs will need to be improved to optimally allocate REHDGs and satisfy the SSS of the DNS after the integration.

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“…GA-based Tabu Search method (GATS) has been applied for investigating the problem of optimal placement of multi types DG units [10]. A novel meta-heuristic technique Modified Gbest-guided artificial bee colony (MGABC) is used for the multiple objective of power loss reduction, voltage stability improvement and enhancement of voltage level [11]. To identify the ultimate DG location, loss sensitivity analysis (LSA) is used whereas hybrid Artificial Bee Colony and Cuckoo search (ABC-CS) algorithm is implemented for optimal sizing DG [12].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimal sizing of distributed generation by application of Taguchi desirability function analysis

2019

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Distributed Generation (DG) is modular power generating technology. DG has beneficial impact on tail end voltages, line losses and operating cost. The sizing and location of DG in distribution system is a vital undertaking. In this paper Taguchi Desirability Function Analysis (TDFA) is presented. The remarkable highlight of TDFA is the skillful handling of simultaneous optimization of nearly thousand objectives and its ability to generate multiple optimal solutions. It facilitates the effortless addition of each objective. TDFA is used for finding the optimal size of single as well as multiple DG. Size estimation is carried out for multiple objectives of minimizing the line losses, improving the voltage profile and improving voltage stability index (VSI). The objectives may be minimized, maximized or assigned target values simultaneously. In the scope of this paper more than nine objectives have been optimized simultaneously. TDFA has been implemented to determine optimal size of DG for different load conditions. The proposed approach is tested and verified on IEEE 33-bus and IEEE 85-bus radial distribution system (RDS). Keywords Distributed generation • Radial distribution system • VSI • TDFA List of symbols DG Distributed generation RDS Radial distribution system F No. of input factors l No. of levels FFD Full factorial design df Degree of freedom Y Response R No. of responses x 1, x 2 Input factors ß Regression coefficients ɛ Error term 2FI Two-factor interaction R 2 Coefficient of determination d Desirability function ID Individual desirability CD Composite desirability L i Minimum acceptable value of response Ui Maximum acceptable value of response

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Integration of distributed generation for assessment of distribution system reliability considering power loss, voltage stability and voltage deviation

Cited by 26 publications

References 43 publications

A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

A reliability‐based optimal μ‐PMU placement scheme for efficient observability enhancement of smart distribution grids under various contingencies

Towards maximising the integration of renewable energy hybrid distributed generations for small signal stability enhancement: A review

Multi-objective optimal sizing of distributed generation by application of Taguchi desirability function analysis

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