Power system simulation: Difference between revisions

Revision as of 21:58, 4 April 2025

Revision as of 23:43, 25 July 2025

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==Load flow calculation==

The load-flow calculation<ref>{{Cite book|last=J. Arockiya|first=Xavier Prabhu |title=2016 IEEE 6th International Conference on Power Systems (ICPS) |chapter=Design of electrical system based on load flow analysis using ETAP for IEC projects |year=2016|journal=Power Systems (ICPS)|pages=1–6|doi=10.1109/ICPES.2016.7584103|isbn=978-1-5090-0128-6|s2cid=10118705 }}</ref> is the most common network analysis tool for examining the undisturbed and disturbed network within the scope of operational and strategic planning.

Using network topology, transmission line parameters, transformer parameters, generator ___location and limits, and load ___location and compensation, the load-flow calculation can provide voltage magnitudes and angles for all nodes and loading of network components, such as cables and transformers. With this information, compliance to operating limitations such as those stipulated by voltage ranges and maximum loads, can be examined. This is, for example, important for determining the transmission capacity of underground cables, where the influence of cable bundling on the load capability of each cable has to be taken also into account.

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The objective function in OPF can take on different forms relating to active or reactive power quantities that we wish to either minimise or maximise. For example we may wish to minimise transmission losses or minimise real power generation costs on a power network.

Other power flow solution methods like stochastic optimization incorporate the uncertainty found in modeling power systems by using the probability distributions of certain variables whose exact values are not known. When uncertainties in the constraints are present, such as for dynamic line ratings, chance constrained optimization can be used where the probability of violating a constraint is limited to a certain value.<ref>{{cite journal | last1=Giraldo, | first1=Juan S., | last2=Lopez | first2=Juan Camilo López,| last3=Castrillon | first3=Jhon A. Castrillon,| last4=Rider | first4=Marcos J. Rider,| andlast5=Castro | first5=Carlos A. Castro.| "title=Probabilistic OPF modelModel for unbalancedUnbalanced threeThree-phasePhase electricalElectrical distributionDistribution systemsSystems consideringConsidering robustRobust constraints."Constraints | journal=IEEE Transactions on Power Systems | date=2019 | volume=34, no.| issue=5 (2019):| 3443-3454.pages=3443–3454 | https://doi.org/=10.1109/TPWRS.2019.2909404 | bibcode=2019ITPSy..34.3443G }}</ref> Another technique to model variability is the [[Monte Carlo method]], in which different combinations of inputs and resulting outputs are considered based on the probability of their occurrence in the real world. This method can be applied to simulations for system security and unit commitment risk, and it is increasingly being used to model probabilistic load flow with renewable and/or distributed generation.<ref>Banerjee,{{cite Binayak,book and Syed Islam. "Modelling and Simulation of Power Systems."| title=Smart Power Systems and Renewable Energy System Integration. By Dilan Jayaweera. Vol. 57. Cham: Springer International, 2016. 15-26.| series=Studies in Systems, Decision and Control. Springer| Link.date=2016 Web.| 22volume=57 Nov.| 2016. http://link.springer.com/book/doi=10.1007%2F978/978-3-319-30427-4 | isbn=978-3-319-30425-0 }}</ref>

==Models of competitive behavior==