Abstract. The evolution of the energy markets has been accelerating the use of distributed energy resources (DERs) all over the world. Virtual power plant (VPP) is a new method to management this increasing two-way complexity. In this paper, a bidding model for a VPP via robust optimization in the uncertain environment of the electricity market is presented. The flexible feature embedded in the model with respect to solution accuracy and computation burden would be advantageous to the VPP. Results of a case study are provided to show the applicability of the proposed bidding model.
The dynamic characteristics of active distribution networks (ADNs) need to be concerned if a large number of distributed generators are connected. A highly efficient and reliable simulation algorithm with good numerical stability is therefore essential for the stability analysis of ADNs. This paper proposes a novel dynamic simulation algorithm of ADNs based on the implicit projective method, which is a second order integration algorithm. The proposed method is an equivalent A-
Stable method and the calculation efficiency is increased significantly compared with the traditional integration algorithms. It is especially suitable for the dynamic simulation and stability analysis of the ADNs with a large number of DGs. Case studies based on the IEEE 123-node test feeder show the feasibility and effectiveness of the proposed method, which is verified through the comparison with the commercial simulation tool DIgSI-LENT/PowerFactory and the traditional trapezoidal method.Index Terms-active distribution network (ADN), differentialalgebraic equation (DAE), dynamic simulation, equivalent AStable, implicit projective method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.