Aggregate thermostatically controlled loads (TCLs) are good candidates for providing load following services in power systems. This paper is concerned with the modeling, evaluation and control problems of a population of heterogeneous TCLs. Specifically, the heterogeneous population is divided into multiple homogeneous clusters and each cluster, i.e., TCL aggregator, is modeled by an approximated three-input single-output (TISO) state space model. Here, the aggregators serve as a bridge connecting the load utility and the terminal TCLs, which have their own decision makers and are responsible for aggregate estimation and command issuing. And aggregate evaluation is carried out for the aggregator so as to provide the aggregate regulation capacities and ramping rates, which is useful for setting of the reference power trajectory. Based on the established control model, we furthermore propose a hierarchical centralized control algorithm for a bus load utility to regulate all TCLs inside it so as to provide load following service, while not affecting the customers' comfort levels. Finally, simulation results with respect to a common bus load are provided to demonstrate the effectiveness of the proposed aggregate modeling and the centralized load following strategy.

Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework and a Gate Recurrent Unit (GRU) model to predict vessel trajectories. First, series of trajectories are extracted from Automatic Identification System (AIS) ship data (i.e., longitude, latitude, speed, and course). Secondly, main trajectories are derived by applying the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Next, a trajectory information correction algorithm is applied based on a symmetric segmented-path distance to eliminate the influence of a large number of redundant data and to optimize incoming trajectories. A recurrent neural network is applied to predict real-time ship trajectories and is successively trained. Ground truth data from AIS raw data in the port of Zhangzhou, China were used to train and verify the validity of the proposed model. Further comparison was made with the Long Short-Term Memory (LSTM) network. The experiments showed that the ship’s trajectory prediction method can improve computational time efficiency even though the prediction accuracy is similar to that of LSTM.

This study proposes an incentive-based demand response (IDR) unit commitment model considering different types of demand response (DR) resources. In the proposed IDR dispatch model, (i) different load characteristic patterns of DR users can be included, such as transfer-type, shift-type and clip-type electricity users, and (ii) the uncertainty of DR participation behaviour is considered in the system reserve electricity using chance constrained programming. Simulation results for the Pennsylvania-New Jersey-Maryland (PJM) 5-bus system and the Institute of Electrical and Electronics Engineers (IEEE) 118bus system indicate that the proposed model can achieve optimal DR scheduling while considering both economics and system reliability when high-quality DR resources are limited. Moreover, the unique scheduling features of DR must be considered in addition to economics and flexibility when dispatching DR resources; the uncertainty of DR can affect the highest confidence level of system operation. In addition, some significant coefficients of the special dispatch constraints of IDR clearly influence the performance of IDR resources.

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Inspired by the collective behaviour of ant colonies, a stigmergic cooperation mechanism for shop floor control is proposed. In stigmergic shop floor control systems, one piece agent takes charges of one work piece and chooses a manufacturing resource for it in the light of pheromones stored in information environment. Piece agents also update these pheromones to guide subsequent agents' routing. Experiments confirm that a stigmergic cooperation mechanism has an excellent scheduling performance in a static environment, good suitability for the stochastic machining-time problem and good adaptability to shop floor changes.