Exploring the effects of adaptive reactive support capabilities by DGs in optimal operational scheduling of smart active distribution networks

Abstract

This paper proposes an optimal day-ahead (DA) operational scheduling framework to be implemented in distribution management systems (DMSs) as the core of decision makings in active distribution networks (ADNs). Belonging to the new emerging and prosperous technology namely smart distribution grids, the contextualized ADN is composed of active elements including both renewable-based and conventional distributed generations (DGs) as well as demand side management programs. Also, the information and communication technology (ICT) infrastructures are well-equipped in the network which enriches the distribution system operator (DSO) to have a remote and online control on active elements whenever needed. The proposed day-ahead optimal operational framework firstly schedules the next 24-hour dispatches of DGs, responsive loads (RLs) as well as electricity purchase from wholesale market aiming to minimize the total operation costs. In the first strategy, DGs are supposed to be operated within the mandatory range of reactive support without any financial compensation. Subsequently as an innovative point, the costs of reactive power purchases from both wholesale market and active elements of ADN are judicially included in the scheduling process wherein DGs are contemplated to be utilized in adaptive power factor mode up to a pre-specified minimum value considering financial reimbursements. In contradiction to the conventional fixed pricing mechanisms, a more practical approach for reactive support of DGs is considered and the effect of higher participation of active elements in reactive power provision are thoroughly interrogated in enhancing the economical and technical issues. Also, having a proper control on DGs operating power factor has resulted in extra released capacity which could be exploited to cover the network uncertainties such as wind speed or load variation during a day. The established model is formulated as a mixed integer non-linear problem and solved using binary genetic algorithm. A 33-bus ADN is considered to verify the performance of the proposed optimal operation framework.