Engineering topics

Hao, Jun

Publications and source records attributed to Hao, Jun.

4 records

DOE OSTI2024

Learning-Based Building Flexibility Estimation and Control to Improve Microgrid Economics and Resilience: Preprint

This paper proposes a learning-based building flexibility estimation and control framework to improve system economics and resilience. A data-driven building load flexibility model consisting of weather forecasting and estimating load consumption is proposed to quantify building heating, ventilation, and air conditioning (HVAC) load flexibility. A reinforcement learning-based microgrid controller is proposed to dispatch distributed generators, distributed energy resources, and build HVAC loads while taking flexibility information as one of the inputs. Simulation analysis is conducted on the model of a real microgrid in California. The effectiveness of the proposed learning-based building flexibility estimation and control in reducing microgrid energy costs and improving the sustainability of critical loads is demonstrated.

building load flexibility↗

DOE OSTI2024

Integrated Transmission and Distribution Co-Simulation Platform for Demonstration of Bulk Grid Services Using Distributed Energy Resources

In September 2020, the Federal Energy Regulatory Commission (FERC) released Order 2222, which opens wholesale markets to small-capacity distributed energy resources (DERs), recognizing their potential in improving operational efficiency by providing bulk grid services. Therefore, a co-simulation capability that can connect transmission and distribution (T&D) simulations and evaluate the impacts of DER provision of bulk grid services is needed. In this paper, we present a new integrated T&D co-simulation platform that incorporates T&D system simulators, DER aggregator/group strategies, and a co-simulation coordinator. Industry-standard communication protocols are employed to mimic real-world conditions. Secondary frequency regulation is selected as the representative bulk grid service, and we simulate the responses of DERs to the frequency regulation signals. The simulation results for a solar-rich distribution feeder in Colorado, USA, demonstrate how the T&D co-simulation setup is used to evaluate the contributions of DERs to minimize the bulk grid frequency deviation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

A Generation-Storage Coordination Dispatch Strategy for Power System Based on Causal Reinforcement Learning

In the backdrop of global energy transformation, power systems integrating high proportions of renewable energy sources are facing unprecedented challenges in operational stability and dispatch efficiency. To address these challenges, this study introduces a generation-storage coordination real-time dispatch strategy based on Causal Power System Dynamic Reinforcement Learning (CPSDRL). Diverging from traditional reinforcement learning approaches, CPSDRL innovatively incorporates causal inference within the state prediction model - the crux of model-based reinforcement learning - thereby establishing the Power Causal Dynamic Model (PCDM). Assisted by the prior knowledge of power systems, the model significantly enhances prediction accuracy and reliability through a two-stage training process. Utilizing PCDM, this study further applies a direct policy search algorithm to optimize the real-time dispatch strategy. Experimental results indicate that the proposed method improves the stability of generation-storage coordination real-time dispatch and exhibits competitive advantages in sample efficiency and computational speed, compared to traditional model-based and model-free reinforcement learning algorithms. This method is expected to enhance the practicality and adaptability of causal reinforcement learning techniques in power system scheduling and control.

causal reinforcement learning↗

DOE OSTI2024

TFDA (Transient Fault Detection Algorithm) [SWR-24-23]

The Transient Fault Detection Algorithm (TFDA) is an algorithm to detect the non-fault and transient fault conditions, and classify the fault types. This is part of OEDI-SI project (https://data.openei.org/search?q=oedi%20si).

Hao, Jun↗