Network Capacity Assessment of Combined Heat and Power-Based Distributed Generation in Urban Energy Infrastructures

Zhang, Xianjun; Karady, G.G.; Piratla, Kalyan R.; Ariaratnam, Samuel T.

doi:10.1109/tsg.2013.2264832

Cited by 37 publications

(21 citation statements)

References 14 publications

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“…Three DGs are suggested at node 76, two DGs at node 48, and following 43 locations are suggested a single DG: node 10,101,104,105,108,11,111,113,114,16,19,2,20,25,27,29,33,35,36,4,42,46,47,49,5,50,53,6,62,64,65,66,67,71,72,75,80,82,86,89,9,93 and 99.…”

Section: Results and Analysis Of Continual Sensitivity Analysismentioning

confidence: 99%

“…The nexus between distributed generation, urban gas and water network infrastructure has been discussed in [29], [30] and [31]. An integrated dispatch model is discussed in [29] where optimal DG capacity is assessed for integrated electricity and water network in urban setting.…”

Section: Network Dg Capacity Assessmentmentioning

confidence: 99%

“…An integrated dispatch model is discussed in [29] where optimal DG capacity is assessed for integrated electricity and water network in urban setting. Reference [32] analyzes the impacts of gas and water distribution networks on the optimal locations and capacity of distributed generation.…”

Section: Network Dg Capacity Assessmentmentioning

confidence: 99%

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Optimal capacity and location assessment of natural gas fired distributed generation in residential areas

Kamdar

Karady

2014

2014 Clemson University Power Systems Conference

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With ever increasing use of natural gas to generate electricity, installed natural gas fired microturbines are found in residential areas to generate electricity locally. This research work discusses a generalized methodology for assessing optimal capacity and locations for installing natural gas fired microturbines in distribution residential network.The overall objective is to place microturbines to minimize the system power loss occurring in the electrical distribution network; in such a way that the electric feeder does not need any up-gradation. The IEEE 123 Node Test Feeder is selected as the test bed for validating the developed methodology. Three-phase unbalanced electric power flow is run in OpenDSS through COM server, and the gas distribution network is analyzed using GASWorkS. The continual sensitivity analysis methodology is developed to select multiple DG locations and annual simulation is run to minimize annual average losses.The proposed placement of microturbines must be feasible in the gas distribution network and should not result into gas pipeline reinforcement. The corresponding gas distribution network is developed in GASWorkS software, and nodal pressures of the gas system are checked for various cases to investigate if the existing gas distribution network can accommodate the penetration of selected microturbines. The results indicate the optimal locations suitable to place microturbines and capacity that can be accommodated by the system, based on the consideration of overall minimum annual average losses as well as the guarantee of nodal pressure provided by the gas distribution network. The proposed method is generalized and can be used for any IEEE test feeder or an actual residential distribution network.ii ACKNOWLEDGMENTS

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Section: Results and Analysis Of Continual Sensitivity Analysismentioning

confidence: 99%

Section: Network Dg Capacity Assessmentmentioning

confidence: 99%

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Optimal capacity and location assessment of natural gas fired distributed generation in residential areas

Kamdar

Karady

2014

2014 Clemson University Power Systems Conference

Self Cite

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“…Otherwise, it can attain the maximum value of f t,n • d t,n , which is the total amount of flexible load available at time period . The same logic applies for (22).…”

Section: Mathematical Formulationmentioning

confidence: 93%

“…A disjunctive formulation has been implemented to capture the effect that reconductoring has on a line's characteristics and . Different variables are used to model the flow at the sending and receiving ends of each line similar to [22]; differences between these variables represent losses over the line. Constraints (14)- (15) state that real and reactive power flows are bounded by the line's thermal rating.…”

Section: Mathematical Formulationmentioning

confidence: 99%

Strategic valuation of smart grid technology options in distribution networks

Konstantelos

Giannelos

Štrbac

2017

2017 IEEE Power &Amp; Energy Society General Meeting

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-The increasing penetration of renewable distributed generation (DG) sources in distribution networks can lead to violations of network constraints. Thus, significant network reinforcements may be required to ensure that DG output is not constrained. However, the uncertainty around the magnitude, location and timing of future DG capacity renders planners unable to take fully-informed decisions and integrate DG at a minimum cost. In this paper we propose a novel stochastic planning model that considers investment in conventional assets as well as smart grid assets such as demandside response, coordinated voltage control and soft open points (SOPs). The model also considers the possibility of active power generation curtailment of the DG units. A node-variable formulation has been adopted to relieve the substantial computational burden of the resulting mixed integer non-linear programming (MINLP) problem. A case study shows that smart technologies can possess significant strategic value due to their inherent flexibility in dealing with different system evolution trajectories. This latent benefit remains undetected under traditional deterministic planning approaches which may hinder the transition to the smart grid.

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Optimal day-ahead scheduling of integrated urban energy systems

et al. 2016

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10An optimal day-ahead scheduling method (ODSM) for the integrated urban energy system (IUES) is 11 introduced, which considers the reconfigurable capability of an electric distribution network. The 12 hourly topology of a distribution network, a natural gas network, the energy centers including the model. Numerical studies demonstrate that the proposed ODSM is able to provide the IUES with an 21 effective and economical day-ahead scheduling scheme and reduce the operational cost of the IUES.

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Network Capacity Assessment of Combined Heat and Power-Based Distributed Generation in Urban Energy Infrastructures

Cited by 37 publications

References 14 publications

Optimal capacity and location assessment of natural gas fired distributed generation in residential areas

Optimal capacity and location assessment of natural gas fired distributed generation in residential areas

Strategic valuation of smart grid technology options in distribution networks

Optimal day-ahead scheduling of integrated urban energy systems

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