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The ERTMS/ETCS is the newest automatic train protection system. This is a system that supports the driver in driving the train. It is currently being implemented throughout the European Union. This system’s latest specifications also provide additional functions to increase the energy efficiency of train driving in the form of ATO (automatic train operation). These functions of the ETCS will be valuable, provided they operate without failure. To achieve errorless configuration of the ETCS, a methodology for automatic system verification using the IMDS (Integrated Model of Distributed Systems) formalism and the temporal tool Dedan was applied. The main contribution is asynchronous and timed verification, which appropriately models the distributed nature of the ETCS and allows the designer not only to analyze time dependencies but also to define the range of train velocities in which the operational scenario is valid. Additionally, the novelties of the presented verification methodology are the graphical design of the system components and automated verification freeing the designer from using textual design. We express the verified properties as observer automata rather than in temporal logic. Moreover, we check partial properties related to system fragments, which is crucial in distributed systems. This paper presents the verification of an example ETCS system application. The verification results are presented as sequence diagrams leading to a correct/incorrect final state.

Relay-based traffic control systems are still used in railway control systems. Their correctness is most often verified by manual analysis, which does not guarantee correctness in all conditions. Passenger safety, control reliability, and failure-free operation of all components require formal proof of the control system’s correctness. Formal evidence allows certification of control systems, ensuring that safety will be maintained in correct conditions and the in event of failure. The operational safety of systems in the event of component failure cannot be manually checked practically in the event of various types of damage to one component, pairs of components, etc. In the article, we describe the methodology of automated system verification using the IMDS (integrated model of distributed systems) temporal formalism and the Dedan tool. The novelty of the presented verification methodology lays in graphical design of the circuit elements, automated verification liberating the designer from using temporal logic, checking partial properties related to fragments of the circuit, and fair verification preventing the discovering of false deadlocks. The article presents the verification of an exemplary relay traffic control system in the correct case, in the case of damage to elements, and the case of an incorrect sequence of signals from the environment. The verification results are shown in the form of sequence diagrams leading to the correct/incorrect final state.

Zhao, X.; Gu, B.; Gao, F., and Chen, S., 2020. Matching model of energy supply and demand of the integrated energy system in coastal areas. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 983–989. Coconut Creek (Florida), ISSN 0749-0208. Due to the uncertainty of the selection range of the main equipment capacity of the distributed energy system in coastal areas, the matching ability of energy supply and demand is relatively low. From the two directions of “power by heat” and “heat by electricity”, the operation and output modes of energy in the system are studied; the selection range of the main equipment capacity of the distributed energy system is determined by calculating the load of energy supply and demand; according to the selection range, the necessary mapping conditions of the matching relationship between energy supply and demand are analyzed, and the matching model of energy supply and demand is constructed by using the matching relationship between energy supply and demand, so as to complete the matching of energy supply and demand of the integrated energy system in coastal areas. The experimental results show that the total energy output of the integrated energy system in coastal areas reaches 1867 kJ in unit time, but the proportion occupancy rate between the output nodes is the lowest, which has a good matching ability of energy supply and demand.

The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process.

This paper investigates a data-based distributed model predictive control (DMPC) method for large-scale systems composed of a number of isolated subsystems. Under the circumstances that the system dynamics knowledge is unknown, the proposed method includes the learning phase of the system model and the design phase of the model-based controller in serial. First, a practical learning algorithm is designed utilizing the observed input–output data of the controlled plant for model learning. which consists of the projection identification procedure and neural networks (NNs) estimation procedure. Thus, the linear model of the system and the unmodeled dynamics are obtained. Based on the learned system model, the DMPC method is developed for constrained large-scale systems. The controller is derived by solving the optimization problem of isolated sub- systems, which minimizes the local cost function of each subsystem. Moreover, sufficient conditions are established for the stability of the overall system. The recursive feasibility and convergence are guaranteed by rigorous derivation. The effectiveness of the DMPC approach proposed in this paper is demonstrated by applying it to the vehicle platoon system.

Energy management is crucial for various activities in the energy sector, such as effective exploitation of energy resources, reliability in supply, energy conservation, and integrated energy systems. In this context, several machine learning and deep learning models have been developed during the last decades focusing on energy demand and renewable energy source (RES) production forecasting. However, most forecasting models are trained using batch learning, ingesting all data to build a model in a static fashion. The main drawback of models trained offline is that they tend to mis-calibrate after launch. In this study, we propose a novel, integrated online (or incremental) learning framework that recognizes the dynamic nature of learning environments in energy-related time-series forecasting problems. The proposed paradigm is applied to the problem of energy forecasting, resulting in the construction of models that dynamically adapt to new patterns of streaming data. The evaluation process is realized using a real use case consisting of an energy demand and a RES production forecasting problem. Experimental results indicate that online learning models outperform offline learning models by 8.6% in the case of energy demand and by 11.9% in the case of RES forecasting in terms of mean absolute error (MAE), highlighting the benefits of incremental learning.

An energy hub (EH) provides an effective solution to the management of local integrated energy systems (IES), supporting the optimal dispatch and mutual conversion of distributed energy resources (DER) in multi-energy forms. However, the intrinsic stochasticity of renewable generation intensifies fluctuations in the system’s energy production when integrated into large-scale grids and increases peak-to-valley differences in large-scale grid integration, leading to a significant reduction in the stability of the power grid. A distributed privacy-preserving energy scheduling method based on multi-agent deep reinforcement learning is presented for the EH cluster with renewable energy generation. Firstly, each EH is treated as an agent, transforming the energy scheduling problem into a Markov decision process. Secondly, the objective function is defined as minimizing the total economic cost while considering carbon trading costs, guiding the agents to make low-carbon decisions. Lastly, differential privacy protection is applied to sensitive data within the EH, where noise is introduced using energy storage systems to maintain the same gas and electricity purchases while blurring the original data. The experimental simulation results demonstrate that the agents are able to train and learn from environmental information, generating real-time optimized strategies to effectively handle the uncertainty of renewable energy. Furthermore, after the noise injection, the validity of the original data is compromised while ensuring the protection of sensitive information.

The cooperative output regulation problem has been studied by two approaches: the distributed observer (DO) approach and the distributed internal model (DIM) approach, respectively. Each of these two approaches has its own merits and weaknesses. Recently, we presented an overview on the cooperative output regulation problem by the DO approach. This paper further surveys the cooperative output regulation problem by the DIM approach. We first summarize the constructions and the roles of two different versions of the internal models: the distributed p -copy internal model and the distributed canonical internal model. Then, we describe an integrated framework that combines the DO approach and the DIM approach. Extensions and variants of the DIM and their applications will also be highlighted.

This paper presents a model predictive control (MPC)-based energy management system (EMS) for optimizing cooperative operation of networked microgrids (MGs). While the isolated operation of individual MGs limits system-wide optimization, the proposed approach enhances both stability and efficiency through integrated control. The system employs mixed-integer quadratic constrained programming (MIQCP) to model complex operational characteristics of MGs, facilitating the optimization of interactions among distributed energy resources (DERs) and power exchange within the MG network. The effectiveness of the proposed method was validated through a series of case studies. First, the performance of the algorithm was evaluated under various weather conditions. Second, its robustness against prediction errors was tested by comparing scenarios with and without disturbance prediction. Finally, the cooperative operation of MGs was compared with the independent operation of a single MG to analyze the impact of the cooperative approach on performance improvement. Quantitatively, integrating predictions reduced operating costs by 19.23% compared to the case without predictions, while increasing costs by approximately 3.7% compared to perfect predictions. Additionally, cooperative MG operation resulted in an average 46.18% reduction in external resource usage compared to independent operation. These results were verified through simulations conducted on a modified version of the IEEE 33-bus test feeder.

Demand response has gradually evolved into integrated demand response (IDR) as energy integration technology develops in integrated energy systems (IESs). The IES has a large amount of data interaction and an increasing concern for users’ privacy protection. Based on the combined cooling, heating, and power model, our study established an IDR management model considering demand-side energy coupling, focusing on cost optimization. In terms of privacy protection in the IDR management process, an optimization method based on the Adam algorithm was proposed. Only nonsensitive data, such as gradients, were transmitted during the processing of the Adam-based method by relying on a centralized iterative computing architecture similar to federated learning. Thus, privacy protection was achieved. The final simulation results proved that the proposed IDR management model had a cost reduction of more than 9% compared with a traditional power demand response. Further simulations based on this model showed that the efficiency and accuracy of the proposed Adam-based method are better than those of other distributed computing algorithms.