Usefulness of DC power flow for active power flow analysis

Purchała, Konrad; Meeus, Leonardo; Dommelen, Daniel Van; Belmans, Ronnie

doi:10.1109/pes.2005.1489581

Cited by 301 publications

(242 citation statements)

References 6 publications

Supporting

Mentioning

233

Contrasting

Unclassified

Order By: Relevance

“…designed based on one of the two popular optimal power flow (OPF) models, namely, AC-OPF [17] and DC-OPF [18], [19]. Storage resources are assumed to be known a priori in these settings.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal large-scale storage placement in single generator single load networks

Thrampoulidis

Bose

Hassibi

2013

2013 IEEE Power &Amp; Energy Society General Meeting

View full text Add to dashboard Cite

Abstract-Large-scale storage will play an increasingly important role in future power grids. As a result, how to optimally place storage in such networks, is an important investment problem. Furthermore, since the allocation of storage resources is static, i.e., it is not feasible to move storage around in a dynamic fashion, it is important to derive optimal such allocations that are robust to the values of the load profiles and other network parameters, such as the line flow constraints. For a single generator single load network, and for a cost of generation that is quadratic in the generation power, we show that, for any given amount of storage resources, placing storage at the demand node is always optimal. This result is true regardless of the demand profile and flow constraints, and therefore is robust. As a byproduct of this result, for a fixed demand profile, we characterize the dependence of the optimal production cost on the flow constraints and on the available storage resources.

show abstract

“…designed based on one of the two popular optimal power flow (OPF) models, namely, AC-OPF [17] and DC-OPF [18], [19]. Storage resources are assumed to be known a priori in these settings.…”

Section: Introductionmentioning

confidence: 99%

“…Generator at bus 1 is connected to a load (or demand) at bus 2 using a single line. We adopt the DC power flow model [18] for our network and thus reactive power flows and power losses on the line are neglected.…”

Section: Introductionmentioning

confidence: 99%

Optimal large-scale storage placement in single generator single load networks

Thrampoulidis

Bose

Hassibi

2013

2013 IEEE Power &Amp; Energy Society General Meeting

View full text Add to dashboard Cite

show abstract

“…In transmission systems, branches are assumed to have a high X/R ratio, permitting application of the dc power flow approximations [62,63]. Under these assumptions, the |Z thev ij| distance predicts the change in voltage angle required to transmit a unit of active power from one bus, i, for reception at j, holding all other system quantities constant.…”

Section: ) Thevenin Impedance Distancementioning

confidence: 99%

Visualizing the Electrical Structure of Power Systems

Cuffe

Keane

2017

IEEE Systems Journal

106

View full text Add to dashboard Cite

“…In this section, a power distribution network is analyzed, under the Direct Current (DC) approximation (Purchala et al, 2005), in order to explore and discover possible critical scenarios (Mena et al, 2014). The network, represented in Fig.…”

Section: Conditional Expected Lofmentioning

confidence: 99%

Simulation-based exploration of high-dimensional system models for identifying unexpected events

Turati

Pedroni

Zio

2017

Reliability Engineering & System Safety

View full text Add to dashboard Cite

. Simulation-based exploration of highdimensional system models for identifying unexpected events. Reliability Engineering and System Safety.http://dx.doi.org/10.1016/j.ress.2017.04.004Simulation-based exploration of high-dimensional system models for identifying unexpected events AbstractMathematical numerical models are increasingly employed to simulate system behavior and identify sequences of events or configurations of the system's design and operational parameters that can lead the system to extreme conditions (Critical Region, CR). However, when a numerical model is: i) computationally expensive, ii) high-dimensional, and iii) complex, these tasks become challenging.In this paper, we propose an adaptive framework for efficiently tackling this problem: i) a dimensionality reduction technique is employed for identifying the factors and variables that most affect the system behavior; ii) a meta-model is sequentially trained to replace the computationally expensive model with a computationally cheap one; iii) an adaptive exploration algorithm based on Markov Chain Monte Carlo is introduced for exploring the system state space using the meta-model; iv) clustering and other techniques for the visualization of high dimensional data (e.g., parallel coordinates plot) are employed to summarize the retrieved information.The method is employed to explore a power network model involving 20 inputs. The CRs are properly identified with a limited computational cost, compared to another exploration technique of literature (i.e., Latin Hypercube Sampling).

show abstract

Usefulness of DC power flow for active power flow analysis

Cited by 301 publications

References 6 publications

Optimal large-scale storage placement in single generator single load networks

Optimal large-scale storage placement in single generator single load networks

Visualizing the Electrical Structure of Power Systems

Simulation-based exploration of high-dimensional system models for identifying unexpected events

Contact Info

Product

Resources

About