arXiv subjects

Fushuan Wen

Publications and source records attributed to Fushuan Wen.

6 recordsLinked to original sources

arXiv2025

Aggregation Model and Market Mechanism for Virtual Power Plant Participation in Inertia and Primary Frequency Response

The declining provision of inertia by synchronous generators in modern power systems necessitates aggregating distributed energy resources (DERs) into virtual power plants (VPPs) to unlock their potential in delivering inertia and primary frequency response (IPFR) through ancillary service markets. To facilitate DER participation in the IPFR market, this paper proposes an aggregation model and market mechanism for VPPs participating in IPFR. First, an energy-reserve-IPFR market framework is developed, in which a VPP acts as an intermediary to coordinate heterogeneous DERs. Second, by taking into account the delay associated with inertial response, an optimization-based VPP aggregation method is introduced to encapsulate the IPFR process involving a variety of DERs. Third, an energy-reserve-IPFR market mechanism with VPP participation is introduced, aiming to minimize social costs, where stochastic deviations of renewable energy generation are explicitly modeled through chance-constrained reformulations, ensuring that the cleared energy, reserve, and IPFR schedules remain secure against forecast errors. Case studies on IEEE 30-bus and IEEE 118-bus systems show that the nadir and quasi-steady-state frequencies are reproduced by the VPP aggregation model with a mean absolute percentage error <= 0.03%, and the proposed market mechanism with VPP participation reduces the total system cost by approximately 40% and increases the net profit by about 30%.

econ.GN↗

arXiv2024

Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation

Carbon footprint accounting is crucial for quantifying greenhouse gas emissions and achieving carbon neutrality.The dynamic nature of processes, accounting rules, carbon-related policies, and energy supply structures necessitates real-time updates of CFA. Traditional life cycle assessment methods rely heavily on human expertise, making near-real-time updates challenging. This paper introduces a novel approach integrating large language models (LLMs) with retrieval-augmented generation technology to enhance the real-time, professional, and economical aspects of carbon footprint information retrieval and analysis. By leveraging LLMs' logical and language understanding abilities and RAG's efficient retrieval capabilities, the proposed method LLMs-RAG-CFA can retrieve more relevant professional information to assist LLMs, enhancing the model's generative abilities. This method offers broad professional coverage, efficient real-time carbon footprint information acquisition and accounting, and cost-effective automation without frequent LLMs' parameter updates. Experimental results across five industries(primary aluminum, lithium battery, photovoltaic, new energy vehicles, and transformers)demonstrate that the LLMs-RAG-CFA method outperforms traditional methods and other LLMs, achieving higher information retrieval rates and significantly lower information deviations and carbon footprint accounting deviations. The economically viable design utilizes RAG technology to balance real-time updates with cost-effectiveness, providing an efficient, reliable, and cost-saving solution for real-time carbon emission management, thereby enhancing environmental sustainability practices.

cs.IR↗

arXiv2024

Applying Large Language Models to Power Systems: Potential Security Threats

Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency. However, this action may also incur potential security threats, which have not been fully recognized so far. To this end, this article analyzes potential threats incurred by applying LLMs to power systems, emphasizing the need for urgent research and development of countermeasures.

cs.AI↗

arXiv2024

Power System Fault Diagnosis with Quantum Computing and Efficient Gate Decomposition

Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers. However, most classical methods suffer from significant time-consuming, memory overhead, and computational complexity issues as the scale of the power system concerned increases. With rapid development of quantum computing technology, the combinatorial optimization method based on quantum computing has shown certain advantages in computational time over existing methods. Given this background, this paper proposes a quantum computing based power system fault diagnosis method with the Quantum Approximate Optimization Algorithm (QAOA). The proposed method reformulates the fault diagnosis problem as a Hamiltonian by using Ising model, which completely preserves the coupling relationship between faulty components and various operations of protective relays and circuit breakers. Additionally, to enhance problem-solving efficiency under current equipment limitations, the symmetric equivalent decomposition method of multi-z-rotation gate is proposed. Furthermore, the small probability characteristics of power system events is utilized to reduce the number of qubits. Simulation results based on the test system show that the proposed methods can achieve the same optimal results with a faster speed compared with the classical higher-order solver provided by D-Wave.

eess.SY↗

arXiv2022

Cyber-physical interdependent restoration scheduling for active distribution network via ad hoc wireless communication

This paper proposes a post-disaster cyber-physical interdependent restoration scheduling (CPIRS) framework for active distribution networks (ADN) where the simultaneous damages on cyber and physical networks are considered. The ad hoc wireless device-to-device (D2D) communication is leveraged, for the first time, to establish cyber networks instantly after the disaster to support ADN restoration. The repair and operation crew dispatching, the remote-controlled network reconfiguration and the system operation with DERs can be effectively coordinated under the cyber-physical interactions. The uncertain outputs of renewable energy resources (RESs) are represented by budget-constrained polyhedral uncertainty sets. Through implementing linearization techniques on disjunctive expressions, a monolithic mixed-integer linear programming (MILP) based two-stage robust optimization model is formulated and subsequently solved by a customized column-and-constraint generation (C&CG) algorithm. Numerical results on the IEEE 123-node distribution system demonstrate the effectiveness and superiorities of the proposed CPIRS method for ADN.

eess.SY↗

arXiv2021

Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection

Non-intrusive load monitoring (NILM) is essential for understanding customer's power consumption patterns and may find wide applications like carbon emission reduction and energy conservation. The training of NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners during the NILM model training. To prevent such potential risks, a novel NILM method named Fed-NILM which is based on Federated Learning (FL) is proposed in this paper. In Fed-NILM, local model parameters instead of local load data are shared among multiple data owners. The global model is obtained by weighted averaging the parameters. Experiments based on two measured load datasets are conducted to explore the generalization ability of Fed-NILM. Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained NILM is conducted. The experimental results show that Fed-NILM has superior performance in scalability and convergence. Fed-NILM outperforms locally-trained NILMs operated by local data owners and approximates the centrally-trained NILM which is trained on the entire load dataset without privacy protection. The proposed Fed-NILM significantly improves the co-modeling capabilities of local data owners while protecting power consumers' privacy.

cs.LG↗