In DC distribution networks, the uncertainties of renewable energies and loads cause frequent voltage fluctuations. In this paper, a voltage regulation strategy based on the combined coordination of centralized optimal control (COC) and adaptive droop control (ADC) is proposed for droop-controlled DC distribution networks. In the COC strategy, the operation reference power of voltage source converters (VSCs) is optimized using the bus injection power measurements to improve the voltage profile and balance the power loading rates of different VSCs. The VSC capacity margin constraint is established to ensure that the capacity margin of each VSC is sufficient to cope with the unexpected fluctuation of loads and renewable energies. Then, an ADC strategy is adopted to handle voltage fluctuation between adjacent two COCs. In the ADC strategy, the operation reference power of the VSCs is further adjusted to recover the voltages. The voltage regulation is achieved based on the relationship among the VSC voltages and the minimum/maximum voltages of the whole network, which does not depend on the information of each nodal voltage and thus real-time network communication is avoided. The simulation results show that the proposed voltage regulation strategies can effectively improve the voltage profile under various uncertain environments.

The increasing frequency and severity of hurricaneinduced disasters on power distribution networks highlight the growing importance of conducting thorough risk assessments. This paper introduces a hurricane risk assessment framework tailored for large-scale synthetic power distribution systems, aiming to enhance the practicality and accuracy of assessment significantly. The framework provides a refined method for synthesizing power distribution networks by assigning specific building types and importance levels to individual load points. This refinement enables the accurate modeling of the topological structure and physical characteristics of power distribution systems within specific geographic areas. By utilizing real historical hurricane data, the study simulates hurricane scenarios and outages closely resembling actual events, offering a more realistic portrayal of hurricane risks. Incorporating a novel risk indicator for quantitative assessment, each hurricane scenario in the assessment model undergoes evaluation through calculating weighted load reduction. Synthetic distribution networks are established for Emporia and Portsmouth to validate the approach, followed by risk assessment based on historical hurricane occurrences between 1940 and 2024 in both regions. The results underscore the importance of considering city-scale power distribution networks in hurricane risk assessment, as emphasized by the proposed method. Furthermore, the method's ability to accurately capture the impact of spatial heterogeneity of hurricane effects on risk assessment accuracy is demonstrated through comparisons of different hurricane trajectories.

Multienergy load forecasting (MELF) is the premise of regional integrated energy systems (RIES) production planning and energy dispatch. The key of MELF is the consideration of multienergy coupling and the improvement of prediction accuracy. Therefore, a MELF method considering the multienergy coupling of variation characteristic curves (MELF_MECVCC) for RIES is proposed. The novelty of MELF_MECVCC lies in the following three aspects. 1) For the trend stripping and volatility extraction of multienergy load time series, the extreme-point symmetric mode decomposition-sample entropy (ESMD-SE) method is introduced to decompose and reconstruct the variation characteristic curves of multienergy, including trend curve and fluctuation curve. 2) The multienergy coupling of the variation characteristic curves is considered to reflect the variation characteristics of the multienergy loads. 3) Different methods are applied according to different variation characteristics; i.e., the combined method based on multitask learning and long short-term memory network (MTL-LSTM) is applied to predict the trend curve with strong correlation and the least square support vector regression (LSSVR) method is applied to predict the fluctuation curve with strong volatility and high complexity. Based on the actual data set of the University of Texas in Austin, the MELF_MECVCC model is simulated and verified, which shows that the model reduces the mean absolute percentage error (MAPE) and the root mean square error (RMSE) and fits better with the original load and has higher prediction accuracy, compared with current advanced algorithms.

The Linear Quadratic Regulator (LQR) has been frequently employed for controlling islanded microgrids (MGs). However, it shows limited effectiveness in transient response regulation and resilience. To enhance the robustness and performance of three-phase inverters operating in islanded mode, a power-sharing control technique integrating Linear Quadratic Regulator (LQR) and Proportional Integral (PI) controllers is presented in this paper. Traditional Single Input Single Output (SISO) controllers are unable to handle transient responses and stability issues. The proposed hybrid LQR-PI control technique minimizes the quadratic value of power error and control inputs during transients while also optimizing robustness margins by including an adaptive tuning mechanism. The methodology involves a comprehensive analysis and application across multiple operational domains, which are used to develop a model that integrates voltage and current dynamics, as well as power sharing, in a synchronous reference frame. The proposed approach was thoroughly tested, and the suggested strategy demonstrates a significant reduction in settling time, as well as improved system responsiveness and stability. The results indicate that this hybrid control method is highly effective in providing precise and efficient control under varying operational conditions, as it ensures rapid convergence to the desired state with minimum overshoot. Under dynamic conditions, the research proposes a process for creating an islanded microgrid (MG) model in a synchronous reference frame, with robustness analysis confirming the stability of the LQR and PI controllers. The validation is conducted entirely in a Matlab/Simulink environment. This study is focused on simulation-based validation under ideal conditions. Simulation results validate the accuracy and efficiency of the proposed model and control technique, showing significant enhancements in transient response and robustness in islanded mode operations. It is very effective when requiring precise and efficient control under varying operational conditions since it guarantees rapid convergence to the desired state with minimum overshoot. Under dynamic conditions, the research proposes a process for creating an islanded MG model in a synchronous reference frame with robustness analysis confirming the stability of the LQR and PI controllers. The validation is conducted entirely in a Matlab/Simulink environment. This study focuses on simulation based validation under ideal conditions. Simulation results validate the accuracy and efficiency of the proposed model and control technique showing significant enhancements in transient response and robustness in islanded mode operations.