Abstract
More and more distributed energy resources (DERs) are being integrated into the distribution networks raising the new concerns of secure and economic operations. Some traditional distribution networks are upgraded to active distribution networks (ADNs) which can communicate with and control the DERs. A microgrid connected to an ADN can be coordinated with the ADN. In this paper, a two-stage hierarchical congestion management mechanism is proposed for an ADN connected with multi-type DERs and microgrids. At the first stage, a hierarchical optimization model is built considering the dispatch of direct control resources (DCRs) and microgrids. An analytical target cascading (ATC) method is employed to optimize the microgrid autonomy model and the ADN optimization model simultaneously. A second stage optimization is designed to deal with the case when the control of DCRs and microgrids is not enough to completely eliminate the congestion. A congestion management model calling for the ancillary services provided by DERs is established, with an objective of minimizing the operational cost of distribution system operator (DSO). Case studies are carried out on modified IEEE 33 and PG&E 69 bus systems. The simulation results show that the proposed method can balance the interests of different stakeholders and eliminate the network congestion efficiently.
Highlights
In recent years, the rapid development of customer-side distributed energy resources (DERs) in distribution networks has played a positive role in the development and utilization of renewable energy, fossil energy consumption reduction and carbon emission decrease
This method relies on the power flow control capabilities of direct control resources (DCRs) and microgrids, and the ancillary services for congestion management provided by DERs
NETWORK CONGESTION MANAGEMENT MODEL OF DERS AT THE SECOND STAGE When network congestion is severe and the first stage model cannot eliminate the congestion, the second stage will enable the ancillary services for congestion management provided by DERs
Summary
T = 24 Time interval, t = 1h Price of electricity transaction between distribution network and the microgrid connecting to node i at time t. CtEV Interaction cost between distribution network and EV charging stations at time t. CtIL Interaction cost between distribution network and flexible load aggregators at time t. Discharge power of energy storage at time t Charging power of energy storages at time t Residual energy of energy storage at time t Charging/Discharging states of energy storages at time t, binary variables Power from renewable energy power station d to the distribution network at time t Actual charging demand of EV charging station e at time t Actual load demand of flexible load aggregator l at time t
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
