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With the development of port automation and artificial intelligence, coordination with multi-geographic data centers (Geo-DCs) has become a viable solution to address the issue of limited port computing resources. This study proposes a distributed energy dispatch method for the port microgrid coordinated with Geo-DCs (Geo-DCPM), aimed at reducing port carbon emissions and operational costs. Consider the single point of failure problem and high construction costs of centralized data centers. Geo-DCs are first introduced to solve the problem of insufficient computing resources in ports. An energy consumption calculation model for Geo-DCs is established, considering the data load delay constraint and the data space transfer constraint caused by specific delay-sensitive loads in the port microgrid. Then, an energy dispatch model (EDM) is constructed for the Geo-DCPM, taking into account carbon capture costs. Moreover, based on mixed-integer linear programming, a distributed algorithm is proposed to solve the EDM problem. Finally, the simulation results verify the effectiveness of the proposed method. Compared with the centralized algorithm, the packet loss rate of the distributed algorithm combined with Geo-DCs is significantly lower, reduced by about 70%. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed collaborative energy management of distributed cyber-energy system with consideration of multiple types of coupled energy sources and loads. Firstly, the energy structure of We-Energy (WE) and the characteristics of multi-energy interconversion are studied. With this effort, the concept of general loads are modeled based on transform loss and compensation coefficient, which enables multiple controllable loads interconverting with each other to reduce energy consumption. As a result, the system capacity costs can be greatly reduced. Furthermore, a new fully distributed neurodynamics-based algorithm is proposed to solve the economic dispatch problem of multiple WEs. The proposed method can only use seldom shared variables to obtain the optimal solutions and does not require the objective function to be strictly convex and smooth in solving convex optimization problems. Meanwhile, the proposed possesses faster convergence speed by employing application-specific integrated circuits. Moreover, we prove that the proposed neurodynamics-based algorithm can asymptotically converge to the global optimal point based on Lyapunov analysis method. Finally, simulation results demonstrate the validity of the algorithm and the effect of general load on the overall operating cost of the WE system. [ABSTRACT FROM AUTHOR]

The security and economy of multienergy systems (MESs) are directly threatened by the potential cyberattacks. It is of great importance to investigate the effects of cyberattacks, e.g., denial of service (DoS) attacks, on distributed energy management algorithms. To this end, this article focuses on exploring how the frequency and the time of duration of DoS attacks influence the behavior of Newton–Raphson-based distributed energy management (NRBDEM) algorithm for MES and in which condition the optimal operations can still be obtained. First, a switched NRBDEM algorithm is presented, which is composed of the normal operation mode and the attack mode. In the attack mode, the attackers are able to change the communication structure at will and make it unconnected to destroy the convergence of the switched NRBDEM algorithm. Then, by making use of automatons to generate the hybrid time domain, the switched NRBDEM algorithm is further modeled and formulated as a hybrid dynamical system, which provides a mathematical model for the subsequent convergence analysis. Therein, the generated hybrid time domain satisfies the average dwell-time constraint and time-ratio constraint to limit the persistent attacks. Furthermore, we analyze the restrained conditions for persistent attacks, under which the optimality and convergence of the switched NRBDEM algorithm can be guaranteed still. Finally, simulation results demonstrate the effectiveness of the proposed method. Note to Practitioners—The multienergy system (MES) is viewed as a typical cyber-physical system. Since it works in a networked environment, the convergence and optimality of the corresponding distributed energy management algorithms are easily destroyed by various cyberattacks, such as the DoS attack. Few noticeable studies have been documented to investigate the distributed energy management problem for MES under persistent DoS attacks. To address this issue, this article proposes a switched NRBDEM algorithm subject to persistent DoS attacks, which is made up of normal operation mode and attack mode. Moreover, sufficient conditions are derived for the tolerable frequency of attack and the time of duration. It is helpful for the practitioners to know in which conditions the global convergence and optimality can be maintained still. Several simulations are provided to verify the correctness of the proposed theoretical analysis results. Our future work will aim at analyzing the performance of different energy management algorithms under different cyberattacks. [ABSTRACT FROM AUTHOR]

This article investigates the problem of distributed cooperative energy management of multiple energy bodies with the consideration of both the optimal energy generation/consumption of each participant within single energy body and the optimal energy distribution on the interconnected lines between any pair of energy bodies. First, we define the physical and communication structure of the system formed by many energy bodies, each of which is viewed as a multienergy prosumer. Then, a distributed energy management model is proposed to achieve not only maximum profits of overall energy generation and consumption, but also minimum cost of energy delivery. To address this issue, a distributed double-Newton descent (DDND) algorithm is proposed, which possesses two advantages. On the one hand, by employing second-order information, the concept of Newton descent is embedded into the implementation of the proposed algorithm, resulting in faster convergence speed. On the other hand, the proposed algorithm performs in a fully distributed fashion. As a consequence, each participant can locally obtain its optimal operation as well as the global energy market clearing prices; meanwhile, each energy router can locally obtain the optimal exchanged energy with its neighbor energy routers. Moreover, we prove that the proposed DDND algorithm can asymptotically converge to the global optimal point. As a result, the correctness of the DDND algorithm can be guaranteed in theory. Finally, simulation results validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The islanded and network-connected modes are expected to be modeled into a unified form as well as in a distributed fashion for multi-energy system. In this way, the adaptability and flexibility of multi-energy system can be enhanced. To this aim, this paper establishes a double-mode energy management model for the multi-energy system. It is formed by many energy bodies. With such a model, each participant is able to adaptively respond to the change of mode switching. Furthermore, a novel distributed dynamic event-triggered Newton-Raphson algorithm is proposed to solve the double-mode energy management problem in a fully distributed fashion. In this method, the idea of Newton descent along with the dynamic event-triggered communication strategy are introduced and embedded in the execution of the proposed algorithm. With this effort, each participant can adequately utilize the second-order information to speed up convergence. The optimality is not affected. Meanwhile, the proposed algorithm can be implemented with asynchronous communication and without needing special initialization conditions. It exhibits better flexibility and adaptability especially when the system modes are changed. In addition, the continuous-time algorithm is executed with discrete-time communication driven by the proposed dynamic event-triggered mechanism.It results in reduced communication interaction and avoids needing continuous-time information transmission. It is also proved that each participant can asymptotically converge to the global optimal point. Finally, simulation results show the effectiveness of the proposed model and illustrate the faster convergence feature of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distributed continuous-time Newton–Raphson algorithm for distributed convex optimization problem, where the components of the goal are obtainable at different agents. To accelerate convergence speed, we focus on introducing Newton descent idea in our algorithm and extending it in a distributed setting. It is proved that the proposed algorithm can converge to the global optimal point with exponential convergence rate under weight-balanced directed graphs. Motivated by practical considerations, an event-triggered broadcasting strategy is further developed for each agent. Therein, the implementation of communication is driven by the designed triggered condition monitored by agents. Consequently, the proposed continuous-time algorithm can be executed with discrete-time communication, thus being able to greatly save communication expenditure. Moreover, the strategy is proved to be free of Zeno behavior. Eventually, the simulation results illustrate the advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the economic dispatch problem of microgrids in a distributed fashion. To address this issue, a delay-free-based distributed algorithm is presented to optimally assign the whole energy demand among local generation units with the objective of minimizing the agminated operation cost. By implementing the proposed algorithm, each component can find its own optimal operations by only requiring local computation and communication. As a result, it enhances the system robustness, flexibility, privacy, etc. More importantly, the time-varying delays model is considered and embedded into the design of our distributed algorithm, such that the components can employ the delays information to achieve the collaborative operation, which are more general and applicable for practical power systems. In addition, we have proved that the proposed algorithm can converge to the global optimal point under some sufficient conditions. Finally, several simulations are provided to demonstrate the correctness and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the issues of day-ahead and real-time cooperative energy management for multienergy systems formed by many energy bodies. To address these issues, we propose an event-triggered-based distributed algorithm with some desirable features, namely, distributed execution, asynchronous communication, and independent calculation. First, the energy body, seen as both energy supplier and customer, is introduced for system model development. On this basis, energy bodies cooperate with each other to achieve the objective of maximizing the day-ahead social welfare and smoothing out the real-time loads variations as well as renewable resource fluctuations with the consideration of different timescale characteristics between electricity and heat power. To this end, the day-ahead and real-time energy management models are established and formulated as a class of distributed coupled optimization problem by felicitously converting some system coordinates. Such problems can be effectively solved by implementing the proposed algorithm. With the effort, each energy body can determine its owing optimal operations through only local communication and computation, resulting in enhanced system reliability, scalability, and privacy. Meanwhile, the designed communication strategy is event-triggered, which can dramatically reduce the communication among energy bodies. Simulations are provided to illustrate the effectiveness of the proposed models and algorithm. [ABSTRACT FROM AUTHOR]