Hierarchical Flexibility Offering Strategy for Integrated Hybrid Resources in Real-time Energy Markets
Bio
I am currently pursuing a Ph.D. degree in Electrical and Computer engineering at the University of Utah in Salt Lake City, Utah. As a Graduate Research Assistant at Utah Smart Energy Laboratory, my research has particularly focused on developing optimization and artificial intelligence models for power system operation and planning with green technologies.
Abstract
This study proposes a hierarchical model for determining the energy flexibility offering strategy of integrated hybrid resources (IHRs) in power distribution systems to participate in real-time energy markets.
The proposed model utilizes the scalability, fast response time, and uncertainty observation of deep reinforcement learning (DRL) to overcome the scalability issue of operating numerous flexible resources and deliverability of energy flexibility to the real-time markets in the presence of network constraints.
To that end, the power distribution system is divided into multiple IHRs, where different types of flexible loads, energy storage systems, and solar plants with controllable inverters are operated through local IHR controllers, trained by deep deterministic policy gradient (DDPG) algorithm.
Active power request and reactive power capacity of IHRs are then transmitted to a central flexibility controller, where a quadratic optimization model ensures the deliverability of the energy flexibility to the real-time energy market by satisfying the distribution network constraints.
The proposed model is implemented on the 123-bus test power distribution system, demonstrating the capability of DRL-based hierarchical model for the scalable operation of IHRs in order to offer deliverable energy flexibility to the real-time energy market.
