Due to the addition of the drag reducer in refined oil pipelines for increasing the pipeline throughput as well as reducing energy consumption, the classical method based on the Darcy-Weisbach Formula for precise pressure loss calculation presents a large error. Additionally, the way to accurately calculate the pressure loss of the refined oil pipeline with the drag reducer is in urgent need. The accurate pressure loss value can be used as the input parameter of pump scheduling or batch scheduling models of refined oil pipelines, which can ensure the safe operation of the pipeline system, achieving the goal of energy-saving and cost reduction. This paper proposes the data-driven modeling of pressure loss for multi-batch refined oil pipelines with the drag reducer in high accuracy. The multi-batch sequential transportation process and the differences in the physical properties between different kinds of refined oil in the pipelines are taken into account. By analyzing the changes of the drag reduction rate over time and the autocorrelation of the pressure loss sequence data, the sequential time effect of the drag reducer on calculating pressure loss is considered and therefore, the long short-term memory (LSTM) network is utilized. The neural network structure with two LSTM layers is designed. Moreover, the input features of the proposed model are naturally inherited from the Darcy-Weisbach Formula and on adaptation to the multi-batch sequential transportation process in refined oil pipelines, using the particle swarm optimization (PSO) algorithm for network hyperparameter tuning. Case studies show that the proposed data-driven model based on the LSTM network is valid and capable of considering the multi-batch sequential transportation process. Furthermore, the proposed model outperforms the models based on the Darcy-Weisbach Formula and multilayer perceptron (MLP) from previous studies in accuracy. The MAPEs of the proposed model of pipelines with the drag reducer are all less than 4.7% and the best performance on the testing data is 1.3627%, which can provide the calculation results of pressure loss in high accuracy. The results also indicate that the model’s capturing sequential effect of the drag reducer from the input data set contributed to improving the calculation accuracy and generalization ability.

As smart grid develops and renewables advance, challenges caused by uncertainties of renewables have been seriously threatening the energy system’s safe operation. Nowadays, the integrated electric-gas system (IEGS) plays a significant role in promoting the flexibility of modern grid owing to its great characteristic in accommodating renewable energy and coping with fluctuation and uncertainty of the system. And hydrogen, as an emerging and clean energy carrier, can further enhance the energy coupling of the IEGS and promote carbon neutralization with the development of power-to-hydrogen (P2H) technology and technology of blending hydrogen in the natural gas system. Dealing with the uncertainty of renewables, a robust schedule optimization model for the integrated electric and gas systems with blending hydrogen (IEGSH) considering the dynamics of gas is proposed and the iterative solving method based on column-and-constraint generation (C&CG) algorithm is implemented to solve the problem. Case studies on the IEGSH consisting of IEEE 39-bus power system and 27-node natural gas system validate the effectiveness of the dynamic energy flow model in depicting the transient process of gas transmission. The effectiveness of the proposed robust day-ahead scheduling model in dealing with the intra-day uncertainty of wind power is also verified. Additionally, the carbon emission reduction resulting from the blending of hydrogen is evaluated.

Air conditioner (AC) loads offer promising flexibility for active distribution networks to manage uncertainties, such as those in renewable energy generation, electricity prices, and load demand. However, real-time scheduling of ACs is challenging due to their massive temporal coupling constraints and time-causal uncertainties. To address this, a novel time-causal aggregation-based approximate dynamic programming (TCA-ADP) algorithm is proposed for efficient scheduling. The time-causality requirements for aggregating state variables are first analyzed to align with the real-time sequential decision-making process. Subsequently, an enhanced aggregation model is developed to ensure both high accuracy and adherence to time causality. The aggregation process is further reformulated as a linear program to optimize aggregation parameters and enable tractable computation. Accordingly, the TCA-ADP leverages aggregated state variables to approximate the value function as a new way, balancing computational efficiency and economy against the large value function space of massive ACs. By training the value function offline using historical data, the TCA-ADP efficiently achieves near-optimal real-time scheduling of massive ACs through parallel and closed-form disaggregation. Case studies demonstrate the effectiveness and scalability of the TCA-ADP, highlighting its aggregation accuracy, uncertainty handling, and the trade-off between economy and tractability.

Aggregating distributed energy resources (DERs) aims to encode their collective flexibility into a single set for efficient grid dispatch. However, existing aggregation methods are overly conservative for heterogeneous DERs due to two main challenges: 1) dimensional heterogeneity, which complicates the combination of flexibilities across different time dimensions, and 2) type heterogeneity, where diverse and irregular DER profiles hinder accurate approximations, resulting in significant flexibility loss. To resolve these challenges, this paper propose a novel preference-oriented aggregation method for reserve dispatch. For dimensional heterogeneity, we extend existing techniques by reformulating the Minkowski sum as a polytope projection problem using a matrix transformation technique. By unifying DERs in a higher-dimensional space and projecting them back into the aggregate feasible region, the proposed technique effectively aggregates dimensionally heterogeneous DERs. For type heterogeneity, we further develop a distributed aggregation-dispatch coordination framework that incorporates reserve dispatch preferences into aggregation. This framework effectively captures the critical, active aggregate flexibility prioritized in optimal reserve dispatch, thereby significantly reducing the flexibility loss when aggregating type-heterogeneous DERs. Numerical tests validate the effectiveness of our method in addressing both heterogeneities and highlight its promising potential for power systems with high reserve requirements.

Wind power generation (WPG) has been increasingly integrated into power systems to reduce carbon emissions. However, its inherent volatility and stochasticity have significantly challenged the reliable system operation and effective accommodation. In this paper, a robust scheduling method with a quantitative accommodation guarantee for power systems with WPG integration is proposed. First, a dynamic uncertainty set (DUS) of WPG is introduced, and a stochastic accommodation rate (SAR) indicator is accordingly proposed and derived to quantify the attainable accommodation level. Based on the DUS, a robust scheduling method is proposed, in which the implicit affine strategy (IAS) is adapted to guarantee the nonanticipativity of scheduling strategies. Moreover, a SAR constraint is carefully built according to the assessment result of the power system’s maximal and minimal SAR and embedded into the scheduling model to provide a quantitative accommodation guarantee. An illustrative case is presented to validate the effectiveness of the SAR indicator. Numerical simulations in the modified 6-bus, 14-bus, and 118-bus power systems validate the superiority of the proposed scheduling method.