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A Brattle Group study finds virtual power plants are cheaper than other viable options for resource adequacy, namely storage and natural gas peaking plants.
The resilience of an active distribution network (ADN) is continuously threatened by extreme incidents. Cascading failures are the cause of most large-scale network outages in such events. Planning for resilient ADN operations under catastrophic emergencies is thus necessary for the distribution system operator. Fortunately, grid-edge flexibility can be a valuable tool in enabling resilience. This paper proposes a cooperative planning approach for the optimal partitioning and operation of ADNs with integrated VPPs in the form of multiple interconnected microgrids capable of successful islanded operation. On the grid edge, virtual power plants operate and control the distributed energy resources (DERs) and thus facilitate the ADN operation during emergencies. The uncertainty of the renewable DERs is, however, a challenge. Therefore, the proposed ADN partitioning model is formulated as a stochastic linear programming problem. Further, the operation model is based on the stochastic model predictive control technique. It has two parts: 1) the receding horizon day-ahead operation, and 2) the feedback correction for real-time operation. In the first part, the receding horizon day-ahead schedule is calculated following the latest forecast-based scenarios. In the second part, the receding schedule is adjusted based on the real-time forecast. Its effectiveness is assessed on IEEE 123 node ADN.
Extreme incidents can cause diverse and dynamic disruptions to active distribution networks (ADNs). Thus, ADN operators (DNOs) must plan to prepare for and adapt to changes in conditions to withstand and quickly recover from such disruptions. This paper proposes a cooperative planning framework to design optimal microgrids (MGs) in smart ADNs integrated with virtual power plants (VPPs) to quickly reconfigure and recover during such events. The proposed framework has two parts: 1) optimal partitioning of ADNs with integrated VPPs into supply-sufficient MGs; and 2) the scheduling of the partitioned MGs. Since the VPPs are autonomous, a smart DNO and VPP interface and operating model for dynamic operating envelopes (DOEs) is proposed to quantify and integrate the supply capacity of VPPs in the operation of MGs. As part of this framework, three optimization models are formulated including VPP optimization, ADN partitioning, and a partitioned ADN scheduling model, where the uncertainty of renewables, loads, and prices is modeled as stochastic scenarios. In part one, the VPP optimization model quantifies the available supply of VPPs. Then, the ADN partitioning model derives the optimal MGs by utilizing the supply of VPPs and network resources. In part two, based on the available supply of VPPs, the partitioned ADN scheduling model derives a day-ahead schedule for the MGs and the DOEs for VPPs. VPPs can then re-optimize to maximize their export within the DOEs. Finally, the proposed framework is validated on IEEE 13 and 123 node test networks, and the results are presented.