Search Results for author: Zechun Hu

Found 11 papers, 3 papers with code

Flexibility Pricing in Distribution Systems: A Direct Method Aligned with Flexibility Models

no code implementations22 Apr 2024 Yilin Wen, Yi Guo, Zechun Hu, Gabriela Hug

We then propose a flexibility cost formulation aligned with the aggregated flexibility model and design a DSO flexibility market model to activate the aggregated flexibility in the distribution system to participate in the transmission system operation for energy arbitrage and ancillary services provision.

Improved Inner Approximation for Aggregating Power Flexibility in Active Distribution Networks and its Applications

no code implementations3 Mar 2023 Yilin Wen, Zechun Hu, Jinhua He, Yi Guo

Concise and reliable modeling for aggregating power flexibility of distributed energy resources in active distribution networks (ADNs) is a crucial technique for coordinating transmission and distribution networks.

Decision Making

Aggregated Feasible Region of Heterogeneous Demand-Side Flexible Resources---Part I: Theoretical Derivation of the Exact Model

no code implementations9 Nov 2021 Yilin Wen, Zechun Hu, Shi You, Xiaoyu Duan

The number of constraints is linear with the number of resources and is exponential with the number of time intervals, respectively.

Modeling of Frequency Security Constraints and Quantification of Frequency Control Reserve Requirements for Unit Commitment

no code implementations26 Oct 2021 Likai Liu, Zechun Hu

The UC simulation is conducted on IEEE 118-bus system to test the proposed optimal PFC droop gain strategy and SFC reserve requirement quantification method.

Data-Driven Scheduling of Electric Boiler with Thermal Storage for Providing Power Balancing Service

no code implementations12 Aug 2021 Likai Liu, Zechun Hu, Jian Ning, Yilin Wen

Through the case study, it is found that the proposed method can save the total operation cost of the EBTS compared with the deterministic EBTS operation optimization model.

Clustering Scheduling

An Extreme Learning Machine-Based System Frequency Nadir Constraint Linearization Method

no code implementations12 Aug 2021 Likai Liu, Zechun Hu, Nikhil Pathak, Haocheng Luo

Therefore, it is essential to consider the frequency nadir constraint (FNC) in power system scheduling.

Scheduling

Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model

no code implementations12 Aug 2021 Likai Liu, Zechun Hu, Asad Mujeeb

First, the historical power system operation data following large power disturbances are used to identify the FRM key parameters offline.

Data-Driven Distributionally Robust Optimization for Real-Time Economic Dispatch Considering Secondary Frequency Regulation Cost

no code implementations20 Jan 2021 Likai Liu, Zechun Hu, Xiaoyu Duan, Nikhil Pathak

With the large-scale integration of renewable power generation, frequency regulation resources (FRRs) are required to have larger capacities and faster ramp rates, which increases the cost of the frequency regulation ancillary service.

Data-based Distributionally Robust Stochastic Optimal Power Flow, Part II: Case studies

1 code implementation17 Apr 2018 Yi Guo, Kyri Baker, Emiliano Dall'Anese, Zechun Hu, Tyler H. Summers

Here, we present extensive numerical experiments in both distribution and transmission networks to illustrate the effectiveness and flexibility of the proposed methodology for balancing efficiency, constraint violation risk, and out-of-sample performance.

Optimization and Control Systems and Control

Data-based Distributionally Robust Stochastic Optimal Power Flow, Part I: Methodologies

1 code implementation17 Apr 2018 Yi Guo, Kyri Baker, Emiliano Dall'Anese, Zechun Hu, Tyler H. Summers

We propose a data-based method to solve a multi-stage stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.

Optimization and Control Systems and Control

Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization

1 code implementation13 Jun 2017 Yi Guo, Kyri Baker, Emiliano Dall'Anese, Zechun Hu, Tyler Summers

We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.

Optimization and Control Systems and Control

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