A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems

Injeti, Satish Kumar; Kumar, Nitesh

doi:10.1016/j.ijepes.2012.08.043

Cited by 269 publications

(124 citation statements)

References 20 publications

(19 reference statements)

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“…Moreover, in recent years, due to shar-ply increased loads and the demand for higher system security, DG allocation for voltage stability at the distribution system level has attracted the interest of some recent research efforts. For instance, DG units are located and sized using different methods: iterative techniques based on Continuous Power Flow (CPF) [8] and a hybrid of model analysis and CPF [28], power stability index-based method [29], numerical approach [30,31], simulated annealing algorithm [32] and PSO [33][34][35]. However, the cost-benefit analyses of DG planning have been ignored in the works presented above.…”

Section: Introductionmentioning

confidence: 99%

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

2014

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highlights DG allocation for minimizing energy loss and enhancing voltage stability. Expressions to find the optimal power factor of DG with commercial standard size. A methodology for DG planning to recover investment for DG owners.Impact of technical and environmental benefits on DG investment decisions. Benefit-cost analysis to specify the optimal location, size and number of DG units. Keywords:Distributed generation; Emission reduction; Loss reduction; Network upgrade deferral; Optimal power factor; Voltage stability abstract This paper presents new analytical expressions to efficiently capture the optimal power factor of each Distributed Generation (DG) unit for reducing energy losses and enhancing voltage stability over a given planning horizon. These expressions are based on the derivation of a multi-objective index (IMO), which is formulated as a combination of active and reactive power loss indices. The decision for the optimal location, size and number of DG units is then obtained through a benefit-cost analysis. Here, the total benefit includes energy sales and additional benefits, namely energy loss reduction, network upgrade deferral and emission reduction. The total cost is a sum of capital, operation and maintenance costs. The methodology was applied to a 69-bus industrial distribution system. The results showed that the additional benefits are imperative. Inclusion of these in the analysis would yield faster DG investment recovery.

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Section: Introductionmentioning

confidence: 99%

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

2014

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“…Detailed data of the 118-node radial distribution system are given in [18]. [19]. The capacity of the DG of renewable energy systems is 1.5 MW and the power factor is 0.95.…”

Section: Simulation Results Of Case Studymentioning

confidence: 99%

Two-stage optimization method for power loss and voltage profile control in distribution systems with DGs and EVs using stochastic second-order cone programming

TANG¹,

Jiekang²,

Wu³

et al. 2018

Turk J Elec Eng & Comp Sci

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This paper introduces a stochastic second-order cone programming (SSCOP) approach to solve the distributed generation coordination problem considering the uncertainty of electric vehicle charging in distribution networks. To minimize the total power loss in the distribution system, the problem is formulated to coordinate the output power of distributed generations (DGs). Two stages are presented to solve the optimization problem: the first stage is to optimize the output power of distributed generators without electric vehicle charging in the distribution system, and the second stage is to optimize the output power of distributed generators according to the stochastic increased load due to the uncertainty of electric vehicle charging. The proposed approach is tested on 69-node and 118-node large-scale distribution systems. The simulated results demonstrate the feasibility and effectiveness of SSCOP.

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“…Most traditional methods have assumed that DG units are dispatchable and placed at the peak load [20]. Typical examples for such researches are analytical methods [21][22][23][24][25], numerical approaches [19,26,27] and a wide range of heuristic algorithms such as Simulated Annealing (SA) [28], Genetic Algorithm (GA) [29], Particle Swarm Optimization (PSO) [30,31], Artificial Bee Colony (ABC) algorithm [32], Modified Teaching-Learning Based Optimization (MTLBO) [33], and Harmony Search Algorithm (HSA) [34]. However, such traditional methods may not address a practical case of the time-varying characteristics of demand and renewable generation (e.g., nondispatchable wind output) as the optimum DG size at the peak demand may not remain at other loading levels.…”

Section: Technical Benefits Energy Lossesmentioning

confidence: 99%

“…Like the DG allocation for minimising power losses, most traditional methods for enhancing voltage stability have assumed that DG units are dispatchable and placed at the peak load. Typical examples of such studies are iterative techniques based on Continuous Power Flow (CPF) [43,44], a hybrid of model analysis and CPF [45], a power stability index-based method [46], a numerical approach [47] and heuristic algorithms such as SA [28] and PSO [48][49][50]. Although well-suited to accommodate dispatchable DG units such as gas turbines, the approaches presented above may not solve a practical scenario that considers the time-characteristics of the varying demand and nondispatchable renewable DG output.…”

Section: Voltage Stabilitymentioning

confidence: 99%

See 1 more Smart Citation

Smart integration of distributed renewable generation and battery energy storage

Hung¹

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Renewable energy (i.e., biomass, wind and solar) and Battery Energy Storage (BES) are emerging as sustainable solutions for electricity generation. In the last decade, the smart grid has been introduced to accommodate high penetration of such renewable resources and make the power grid more efficient, reliable and resilient. The smart grid is formulated as a combination of power systems, telecommunication communication and information technology. As an integral part of the smart grid, a smart integration approach is presented in this thesis. The main idea behind the smart integration is locating, sizing and operating renewable-based Distributed Generation (DG) resources and associated BES units in distribution networks strategically by considering various technical, economical and environmental issues. Hence, the aim of the thesis is to develop methodologies for strategic planning and operations of high renewable DG penetration along with an efficient usage of BES units.The first contribution of the thesis is to present three alternative analytical expressions to identify the location, size and power factor of a single DG unit with a goal of minimising power losses. These expressions are easily adapted to accommodate different types of renewable DG units for minimizing energy losses by considering the time-varying demand and different operating conditions of DG units. Both dispatchable and nondispatchable renewable DG units are investigated in the study. Secondly, a methodology is also introduced in the thesis for the integration of multiple dispatchable biomass and nondispatchable wind units. The concept behind this methodology is that each nondispatchable wind unit is converted into a dispatchable source by adding a biomass unit with sufficient capacity to retain the energy loss at a minimum level. Thirdly, the thesis studies the determination of nondispatchable photovoltaic (PV) penetration into distribution systems while considering time-varying voltage-dependent load models and probabilistic generation. The system loads are classified as an industrial, commercial or residential type or a mix of them with different normalised daily patterns. The Beta probability density function model is used to describe the probabilistic nature of solar irradiance. An analytical expression is proposed to size a PV unit. This expression is based on the derivation of a multiobjective index (IMO) that is formulated as a combination of iii three indices, namely active power loss, reactive power loss and voltage deviation. The IMO is minimised in determining the optimal size and power factor of a PV unit. Fourthly, the thesis discusses the integration of PV and BES units considering optimal power dispatch. In this work, each nondispatchable PV unit is converted into a dispatchable source by adding a BES unit with sufficient capacity. An analytical expression is proposed to determine the optimal size and power factor of PV and BES units for reducing energy losses and enhancing voltage stability. A self-correction algorith...

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A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems

Cited by 269 publications

References 20 publications

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

Two-stage optimization method for power loss and voltage profile control in distribution systems with DGs and EVs using stochastic second-order cone programming

Smart integration of distributed renewable generation and battery energy storage

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