This paper proposes an optimal strategy by using line hardening and energy storage system (ESS) deployment to enhance the distribution system (DS) resilience in natural disasters. A multi-stage multi-zone DS line state set is established, based on the conceptual resilience quantification, to reflect spatial and temporal characteristics of the severe contingencies. To address random contingencies, the proposed problem is formulated as a defender-attacker-defender (DAD) model that minimizes the weighted load shedding in the worst-case scenario, and the column-and-constraint generation (C&CG) algorithm is used to solve the model efficiently. The effectiveness of the proposed strategy is verified on the modified IEEE 33-node DS and a modified power grid representing a province in China.

Photovoltaic (PV) power generation is affected by many meteorological factors and environmental factors, which has obvious intermittent, random, and volatile characteristics. To improve the accuracy of short-term PV power prediction, a hybrid model (VMD-ISSA-GRU) based on variational mode decomposition (VMD), improved sparrow search algorithm (ISSA) and gated recurrent unit (GRU) is proposed. First of all, the PV time series is decomposed into a series of different subsequences by VMD to reduce the non-stationarity of the original data. Then, the main factors affecting PV power generation are obtained by using the correlation coefficients of Spearman and Pearson, which reduces the computational complexity of the model. Finally, the GRU network optimized by ISSA is used to predict all the subsequences and residual error of VMD, and the prediction results are reconstructed. The results show that the hybrid VMD-ISSA-GRU model has stronger adaptability and higher accuracy than other traditional models. The mean absolute error (MAE) in the whole test set is 1.0128 kW, the root mean square error (RMSE) is 1.5511 kW, and the R _adj ² can reach 0.9993.

The coordinated operation of an integrated electricity and heating system (IEHS) can improve overall energy efficiency and provide additional flexibility. However, the inherent uncertainties pertaining to pipeline parameters and ambient temperature in a district heating network (DHN) have not been addressed in the literature, although such uncertainties could affect the quality or even security of IEHS operation. To address this issue, we propose a two-stage robust IEHS scheduling model that considers the uncertainties of the heat load, ambient temperature and heat dissipation coefficients of heating pipelines. The proposed model embedded with bilinear terms is equivalently transformed via the big-M method into an adaptive linear robust optimization problem that can be solved by the column-and-constraint generation (C&CG) algorithm. Case studies are conducted for two test systems. The simulation results show that the proposed method can effectively handle the uncertainties in DHN and improve the robustness of the IEHS.

From the study of dispatcher power flow in a distributed energy management system (DEMS) among multiple control centers, a fixed-point iteration scheme for boundary bus states is constructed to modify the exterior network equivalent through data exchange. Implementation details such as determination of combination parameters, independent choice of reference bus, and distribution of unbalanced power are analyzed. Using this method, each subsystem involved in distributed computing can maintain its method of modeling the exterior network. Adverse effects of communication interference on computing can also be eliminated. Results consistent with integrated power flow are obtained with little loss of accuracy. Thus, the proposed algorithm is especially suitable for application in DEMS, in which control centers can only communicate by WAN. Tests are conducted on the IEEE 118-bus system partitioned into five subsystems, the IEEE 300-bus system, and an actual power system, which verifies the favorable convergence and effectiveness of the method.