Search

This paper presents a distribution system operator (DSO) framework for wholesale and retail market participation of distributed energy resources (DERs) aggregators. The DSO coordinates aggregators’ energy and regulation offers as well as end-users’ energy consumption through the unbalanced retail market and submits balanced energy and regulation offers to the wholesale market on behalf of all the aggregators and end-users within its territory. Various kinds of DER aggregators including demand response aggregators (DRAGs), energy storage aggregators (ESAGs), electric vehicle (EV) charging stations (EVCSs), dispatchable distributed generation aggregators (DDGAGs), and renewable energy aggregators (REAGs) are modeled. To handle unbalanced distribution grids with single-phase aggregators, a linearized unbalanced power flow is tailored to model operating constraints of the distribution grid with various aggregators. A market settlement approach is proposed for the DSO, which coordinates with wholesale market clearing process and ensures the DSO’s non-profit characteristic. It is proved that at the wholesale-DSO coupling substation, the total payment received/compensated by the DSO under the wholesale price is identical to that under three single-phase retail prices for each phase at the substation. Case studies are performed on the modified IEEE 33-node and 240-node distribution test systems to investigate the market outcomes of the proposed DSO. [ABSTRACT FROM AUTHOR]

Market and network integration of distributed energy resources can be facilitated by their coordination within a virtual power plant (VPP). However, VPP operation subject to network limits and different market and physical uncertainties is a challenging task. This paper introduces a framework that co-optimizes the VPP provision of multiple market (e.g., energy, reserve), system (e.g., fast frequency response, inertia, upstream reactive power), and local network (e.g., voltage support) services with the aim of maximizing its revenue. To ensure problem tractability, while accommodating the uncertain nature of market prices, local demand, and renewable output and while operating within local network constraints, the framework is broken down into three sequentially coordinated optimization problems. Specifically, a scenario-based robust optimization for day-ahead resource scheduling, with linearized power flows, and two receding horizon optimizations for close-to-real-time dispatch, with a more accurate second-order cone relaxation of the power flows. The results from a real Australian case study demonstrate how the framework enables effective deployment of VPP flexibility to maximize its multi-service value stack, within an uncertain operating environment, and within technical limits. [ABSTRACT FROM AUTHOR]

This paper proposes a stochastic optimization model for preparatory operation of distributed energy resources that enhance the resilience of critical loads against extreme weather events. Adopting hurricanes as a case study of extreme weather events, a high-precision spatio-temporal hurricane impact analysis model is first developed to enhance the situational awareness of automated distribution systems and generate a set of hurricane-induced outage scenarios. A stochastic mixed-integer conic programming model is then developed for co-optimizing the preparatory schedule of distributed energy storage and distributed generation ahead of hurricane along with post-event decisions of restoring services to customers, while minimizing the expected cost of energy not served as the index to measure the resilience. The proposed model prioritizes the service restoration of critical loads by integrating customer interruption cost function, which captures the customer type and outage costs as well as interruption duration in making restoration decisions. Additionally, a conic power flow model is adopted to accurately capture network operation constraints in pre- and post-outage scenarios. Numerical results conducted on the IEEE 33-bus system show that the proposed model can enhance the performance of automated distribution systems in responding to hurricane-induced outages by efficiently utilizing grid resources to improve the resilience of critical loads. [ABSTRACT FROM AUTHOR]