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Network-Constrained Transactive Control for Multi-Microgrids-based Distribution Networks with Soft Open Points

Published in IEEE Transactions on Sustainable Energy, 2023

Different from most transactive control studies only focusing on economic aspect, this paper develops a novel network-constrained transactive control (NTC) framework that can address both economic and secure issues for a multi-microgrids-based distribution network considering uncertainties. In particular, we innovatively integrate a transactive energy market with the novel power-electronics device (i.e., soft open point) based AC power flow regulation technique to improve economic benefits for each individual microgrid and meanwhile ensure the voltage security of the entire distribution network. In this framework, a dynamic two-timescale NTC model consisting of slow-timescale pre-scheduling and real-time corrective scheduling stages is formulated to work against multiple system uncertainties. Moreover, the original bilevel game problems are transformed into a single-level mixed-integer second-order cone programming problem through KKT conditions, duality, linearization and relaxation techniques to avoid iterations of transitional methods, so as to improve the solving efficiency. Finally, numerical simulations on modified 33-bus and 123-bus test systems with multi-microgrids verify the effectiveness of the proposed framework.

Recommended citation: Xiaodong Yang, Zehao Song, Jinyu Wen, Lijian Ding, Menglin Zhang, Qiuwei Wu, Shijie Cheng. "Network-Constrained Transactive Control for Multi-Microgrids-Based Distribution Networks With Soft Open Points," in IEEE Transactions on Sustainable Energy, vol. 14, no. 3, pp. 1769-1783, July 2023. https://ieeexplore.ieee.org/document/10048557

Low-carbon Scheduling Strategy of Distributed Energy Resources Based on Node Carbon Intensity for Distribution Networks

Published in High Voltage Engineering, 2023

The proposition of dual-carbon goal has accelerated the construction of new power systems. High penetration of renewable energy sources (RESs) and distributed energy sources (DERs) integrating into the power system pose a great challenge of how to improve the utilization of the flexibility potential provided by these various resources and to reduce the total carbon emission of the system. Consequently, we propose a low-carbon demand response (DR) strategy based on the carbon intensity for distribution networks (DNs), in which the low-carbon operation strategy can be realized by comprising both economic issue and low-carbon issue. First, we analyze the carbon intensity profile of the DN based on the carbon emission flow (CEF) theory. Then, we adjust the main grid power purchasing plan, the generation plan and guide the demand side resources to adjust their energy profile with the reference of carbon intensity. In order to ensure the safe and steady operation of the DN, the power system operation safety constraints are also included in the proposed strategy. Moreover, the numerical tests and comparison with other operation strategies have demonstrated that the proposed strategy can effectively improve the consumption of RES and reduce the branch loss, carbon emission and operation cost of DN.

Recommended citation: Zehao Song, et al. "Low-carbon Scheduling Strategy of Distributed Energy Resources Based on Node Carbon Intensity for Distribution Networks," in High Voltage Engineering, June, 2023, 49(06):2318-2328. https://www.researchgate.net/publication/371968866_Low-carbon_Scheduling_Strategy_of_Distributed_Energy_Resources_Based_on_Node_Carbon_Intensity_for_Distribution_Networks

Carbon-Aware Peer-to-Peer Joint Energy and Reserve Trading Market for Prosumers in Distribution Networks

Published in IEEE Internet of Things Journal, 2024

The increasing penetration of distributed energy resources (DERs) has facilitated the development of Peer-to-Peer (P2P) trading mechanism. An efficient P2P trading market framework is essential to integrate various kinds of DERs into new power systems while ensuring network security constraints (NSCs). This paper proposes a carbon-aware P2P trading market to realize joint energy and reserve trading for prosumers while satisfying NSCs of the distribution network (DN) simultaneously. A geometric series acceleration (GSA) method accelerated algorithm based on the consensus alternating direction method of multipliers (C-ADMM) is proposed to solve the distributed P2P trading problem. A data-driven method based on the two-sided distributionally robust chance constraint (TS-DRCC) is adopted to tackle with the uncertainty problem associated with DERs. Numerical tests on the IEEE 15-Bus distribution system and IEEE 141-Bus distribution system verify the advantages and effectiveness of the proposed method.

Recommended citation: Zehao Song, Yinliang Xu, Lun Yang, Hongbin Sun. "Carbon-aware Peer to Peer Joint Energy and Reserve Trading Market for Prosumers considering Network Security Constraints and Uncertainty," IEEE Internet of Things Journal, Early Access, Feb. 2024. https://ieeexplore.ieee.org/document/10440140

Low-carbon demand response program for power systems considering uncertainty based on the data-driven distributionally robust chance constrained optimization

Published in IET Renewable Power Generation, 2024

“The demand response (DR) program is an effective solution to promote the low-carbon operation of power systems with increasing penetration of renewable energy sources (RESs). This paper proposes a low-carbon DR program for power systems to enhance both the environmental friendliness and uncertainty resilience of the system operation. The system operator aims to minimize both the system’s operation cost and carbon trading cost. To handle the uncertainty associated with stochastic RES generation power and load consumption power, a data-driven method named the two-sided distributionally robust chance-constrained (TS-DRCC) approach is adopted to enhance the system’s uncertainty resilience. A ladder-type carbon trading scheme is utilized to calculate the carbon emission cost of the system. Comprehensive analyses of case studies have been conducted to validate that the proposed strategy can effectively reduce the total carbon emissions and total operation costs with good uncertainty resilience performance. The proposed low-carbon DR program is verified to achieve 63.64% more carbon emission reduction compared with the conventional DR program. Besides this, the proposed low-carbon DR program can also achieve 4.39% carbon-intensive generation power reduction and 5.52% RES power consumption compared with the conventional DR program.”

Recommended citation: Ruifeng Zhao, Zehao Song, Yinliang Xu, Jiangang Lu, Wenxin Guo, Haobin Li, "Low-carbon demand response program for power systems considering uncertainty based on the data-driven distributionally robust chance constrained optimization," IET Renewable Power Generation, Early Access, Jun. 2024. https://doi.org/10.1049/rpg2.13021

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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