As the proportion of new energy access increases year by year, the resulting energy imbalance and voltage/trend distribution complexity of the distribution network system in the spatio-temporal dimension become more and more prominent. The joint introduction of electromagnetic rotary power flow controller (RPFC) and energy router (ER) can improve the high proportion of new active distribution network (ADN) consumption and power supply reliability from both spatial and temporal dimensions. To this end, the paper proposes an ADN expansion planning method considering RPFC and ER access. A two-layer planning model for RPFC and ER based on spatio-temporal characteristics is established, with the upper model being the siting and capacity-setting layer, which takes the investment and construction cost of RPFC and ER as the optimization objective, and the lower model being the optimal operation layer, which takes the lowest operating cost of the distribution network as the objective. The planning model is solved by a hybrid optimization algorithm with improved particle swarm and second-order cone planning. The proposed planning model and solving algorithm are validated with the IEEE33 node example, and the results show that the joint access of RPFC and ER can effectively improve the spatial-temporal distribution of voltage in the distribution network and has the lowest equivalent annual value investment and operation cost.

This study proposes a two-level optimization scheduling method for multi-region integrated energy systems (IESs) that considers dynamic time intervals within the day, addressing the diverse energy characteristics of electricity, heat, and cooling. The day-ahead scheduling aims to minimize daily operating costs by optimally regulating controllable elements. For intra-day scheduling, a predictive control-based dynamic rolling optimization model is utilized, with the upper-level model handling slower thermal energy fluctuations and the lower-level model managing faster electrical energy fluctuations. Building on the day-ahead plan, different time intervals are used for fast and slow layers. The slow layer establishes a decision index for command cycle intervals, dynamically adjusting based on ultra-short-term forecasts and incremental balance corrections. Case studies demonstrate that this method effectively leverages energy network characteristics, optimizes scheduling intervals, reduces adjustment costs, and enhances system performance, achieving coordinated operation of the IES network and multi-energy equipment.

With the large-scale integration of renewable energy sources, traditional distribution networks are gradually evolving into a new form of flexible interconnection distribution networks. To enhance the rapidity and accuracy of power flow control through flexible interconnection devices, there is an increasing demand for precise grid equivalent models. Existing grid equivalent models predominantly adopt single-port configurations for radial networks, while there is limited research on two-port network equivalent models tailored for flexible interconnection distribution networks. Focusing on the scenario of flexible interconnection distribution networks integrated with Rotary Power Flow Controllers (RPFCs), this paper proposes an equivalent modeling method of two-port networks based on the superposition theorem under small disturbance conditions. A flexible interconnection distribution network model incorporating RPFCs and its corresponding two-port equivalent model are developed. The parameters of the two-port equivalent model are derived through superposition theorem calculations, enabling the realization of power decoupling control functionality for RPFCs. The simulation results show that the deviations between the set value of active power and the actual value remains at about 3%, and the deviations between the set value of reactive power and the actual value is between 4% and 7%, thereby verifying the effectiveness of the constructed two-port model in power flow control and further supporting the accuracy of the proposed method.

The energy router (ER) is a crucial component in smart distribution networks, and its optimal configuration is essential for enhancing the operational efficiency, economy, and security of the grid. However, existing research rarely considers both the location and sizing costs of the ER in conjunction with flexible load demand response. Therefore, this paper proposes an optimal configuration method for the energy router in active distribution networks, incorporating demand response. First, to balance the comprehensive operational characteristics of the active distribution network throughout the year with computational efficiency, an improved K-means clustering algorithm is employed to construct multiple representative scenarios. Then, a bi-level programming model is established for ER location and sizing, considering demand response. The upper level optimizes the location and capacity configuration of the ER to minimize the overall cost of the distribution network. The lower level focuses on multi-objective optimization, including peak shaving, valley filling, network losses, and voltage deviations, to achieve energy scheduling within the distribution network. Finally, an improved bi-level particle swarm optimization algorithm is employed to solve the model. Simulation results based on the IEEE 33-node system demonstrate that the peak shaving and valley filling optimization rate after ER integration into the active distribution network is at least 9.19%, and it is improved to 14.35% when combined with demand response. Concurrently, the integration of the ER enhances the distribution network’s ability to absorb renewable energy, reduces network losses, and improves power quality.

As distributed power sources, agricultural electrical equipment, and user-side energy storage are scaled up in rural distribution networks, agricultural integrated energy systems play a key role in promoting distributed energy accommodation and economic operation. However, the uncertainty of electricity load and the diversity of connected equipment pose challenges to system optimization and scheduling. To address this, this paper proposes an optimal integrated energy scheduling method for agricultural parks that takes short-term load forecasting results into account. Firstly, a prediction model based on improved Bayesian optimization and ensemble learning is established. Compared to individual base learners, this model can effectively enhance reliability and accuracy, thus achieving a robust description of the scheduling scheme. Then, Interruptible Load (IL) resources and a reward mechanism are introduced to shift peak loads and improve the economic operation of agricultural parks. Finally, simulation analysis is carried out based on an actual microgrid in an agricultural park in North China. Results indicate that the proposed method reduces electricity costs by 1.02%, validating its effectiveness and advancement.

To address the issues of prediction failure and energy storage regulation saturation caused by drastic source-load variations under extreme scenarios, a novel voltage optimization strategy based on the coordination of a Volt/Var Regulator and an Energy Router is proposed. Firstly, the mechanism by which the accumulation of prediction errors leads to integral saturation of energy storage and subsequent failure in voltage regulation is elucidated. Subsequently, by constructing a refined model, the proposed approach integrates the series voltage regulation capability of the VVR—which alters the electrical distance—with the cross-node load transfer capability of the ER, achieving an organic synergy between discrete coarse adjustment and continuous fine-tuning. Simulations based on the IEEE 33-node system demonstrate that the proposed method exhibits excellent robustness under extreme operating conditions such as photovoltaic surges and abrupt load changes. It does not rely on prediction accuracy and effectively overcomes the regulation blind spots caused by prediction failures, thereby significantly mitigating voltage violations and effectively maintaining the system voltage strictly within safe operational limits.