Qiuwei Wu’s research while affiliated with Queen's University Belfast and other places

Grid-forming wind turbines (GFM-WTs) provide a promising solution for the restoration of wind-integrated power systems owing to their fast restart and grid support capabilities. However, individual GFM-WTs rely on external GFM sources for self-start, and their GFM support capability may be hindered by wind power shortages, potentially causing restoration failures. To address these challenges, this paper combines GFM-WTs and GFM energy storage systems (ESSs) to construct a black-start source for power system restoration. The GFM-ESS can provide self-start conditions for the GFM-WT while also improving its GFM support capability to withstand wind power shortages and load restoration transients. To ensure the stability of the restored system, a coordinated control strategy is proposed. First, a pre-synchronization control is designed to enable the GFM-WT to seamlessly connect with the GFM-ESS. Second, a power system stabilizer is developed to mitigate the power oscillation between the parallel GFM-WT and GFM-ESS. Third, to coordinate their power outputs during the load restoration, a model predictive secondary frequency control considering the stability of the WT rotor speed and system frequency is presented. The simulation results demonstrate that the proposed strategy can improve the stability and security of the system restoration process under wind power shortages.

In the published publication [...]

With the increasing emphasis on managing line losses, accurately locating and analyzing abnormal line losses in distribution networks has become a critical challenge in implementing effective loss reduction strategies. Aiming at locating and analyzing abnormal line losses caused by equipment aging in the distribution network, an accurate location method considering topology matching and parameter estimation in the grid is proposed. Firstly, a topology matching model based on a support vector machine in the grid is established to identify the real-time topology connection relationship within the distribution network. The accuracy of SVM is enhanced through an optimized parameter selection strategy. Secondly, a multi-objective optimization model is built employing the operation data collected by the measurement equipment, focusing on voltage and power estimation to form a parameter estimation model. This model focuses on voltage and power estimation, improving the accuracy of parameter estimation compared to single-parameter optimization methods. The weighting coefficient is selected to minimize the solution error. Finally, by comparing the deviation between the estimated values of the branch parameters and the theoretical values, the aging degree of each branch is evaluated, and branches with abnormal line losses are accurately located. The effectiveness of the proposed method is verified using the IEEE 33-bus distribution network, demonstrating its potential for improving the accuracy of identifying abnormal line losses caused by equipment aging and supporting enhanced distribution network management.

With the rapid advancements in transportation electrification, the proliferation of electric vehicles (EVs) has interconnected power and transportation networks, forming the vehicle-traffic-power nexus. By setting charging prices, charging station operators (CSOs) can effectively guide the charging behavior of EVs, alleviate grid stress, and enhance profitability. This paper proposes a Nash-Stackelberg-Nash (N-S-N) game model to investigate the competitive charging pricing strategy for CSOs. We establish the stochastic user equilibrium with the elastic demand traffic assignment problem (SUE-ED-TAP) model to account for users' incomplete rationality and perception errors regarding trip costs. Furthermore, to protect the privacy of both CSOs and EV users, a federated multi-agent deep reinforcement learning-based solution method is proposed to solve this problem. In this method, a non-profit aggregator is introduced to exchange neural network parameters among agents, enabling privacy-preserving and collaborative learning without sharing CSOs' data. Case studies on two test systems show that the proposed method achieves higher profits compared to existing algorithms.

Negative impacts such as unstable power supply and stranded coal power assets caused by coal phase-out have frequently occurred. As an important regulatory measure to achieve the goal of carbon neutrality, whether carbon pricing can effectively deal with the negative impacts of coal phase-out has become a key issue of widespread concern. In this paper, a hybrid bottom-up real option (RO) and top-down system dynamic (SD) model is developed to assess the negative impacts of coal phase-out in China. The simulation results based on different carbon pricing mechanisms indicate that the total stranded coal power asset in China may range between 1.4 and 1.7 trillion yuan under all simulation scenarios. Regardless of the carbon pricing mechanisms adopted, China will probably encounter short-term electricity shortages before 2027. The aggressive carbon pricing mechanism may not necessarily decrease the value of stranded assets but could potentially escalate electricity shortage risk between 2033 and 2040. Compared with the altering initial carbon price or carbon price drift rate, the adjustment of the free carbon quota reduction rate may be a more feasible approach to achieving carbon neutrality target, and it will lead to a lower electricity shortage risk and stranded coal power assets in China.

Global warming has motivated the world's major countries to actively develop technologies and make policies to promote carbon emission reduction. Focusing on interconnected multi-regional power systems, this paper proposes a coordinated planning model for interconnected power systems considering energy storage system planning and transmission expansion. A market-based carbon emission quota trading market that helps reduce carbon emissions is built and integrated into the coordi-nated planning model, where entities can purchase extra or sell surplus carbon emission quotas. Its effects on promoting carbon emission reduction are analyzed. Considering the limitations on information exchange between interconnected regional power systems, the proposed model is decoupled and solved with the analytical target cascading algorithm. A modified two-region 48-bus system is used to verify the effectiveness of the proposed model and solving method.