In this paper, correlation between uncertainties related to loads is addressed in transmission network expansion planning (TNEP). The TNEP problem is formulated in a multi-objective form with objective functions of minimum investment cost, minimum congestion cost, and minimum risk cost. Load correlation is modeled using unscented transformation method and compared to a simulation method. Initially, the impact of load correlation on congestion cost and risk cost is shown. Then, using the Pareto solutions of this multi-objective TNEP problem, impact of load correlation on the TNEP solutions is illustrated. The Pareto solutions are obtained using the Non-dominated Sorting Genetic Algorithm II (NSGA II) that is capable in handling non-convex and non-linear problems with mixed integer structures. Test results are provided for the IEEE 24-bus Reliability Test System (RTS) and a representation of the actual Iranian 400 kV transmission network.
Transmission network expansion planning considering load correlation using unscented transformation / Abbasi, Shahriar; Abdi, Hamdi; Bruno, Sergio; La Scala, Massimo. - In: INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS. - ISSN 0142-0615. - STAMPA. - 103:(2018), pp. 12-20. [10.1016/j.ijepes.2018.05.024]
Transmission network expansion planning considering load correlation using unscented transformation
Bruno, Sergio;La Scala, Massimo
2018-01-01
Abstract
In this paper, correlation between uncertainties related to loads is addressed in transmission network expansion planning (TNEP). The TNEP problem is formulated in a multi-objective form with objective functions of minimum investment cost, minimum congestion cost, and minimum risk cost. Load correlation is modeled using unscented transformation method and compared to a simulation method. Initially, the impact of load correlation on congestion cost and risk cost is shown. Then, using the Pareto solutions of this multi-objective TNEP problem, impact of load correlation on the TNEP solutions is illustrated. The Pareto solutions are obtained using the Non-dominated Sorting Genetic Algorithm II (NSGA II) that is capable in handling non-convex and non-linear problems with mixed integer structures. Test results are provided for the IEEE 24-bus Reliability Test System (RTS) and a representation of the actual Iranian 400 kV transmission network.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
