A Proposal for a Multi-Agent based Synchronization Method for Distributed Generators in Micro-Grid Systems

Giammatteo, Paolo; Buccella, Concettina; Cecati, Carlo

doi:10.4108/eai.21-4-2016.151160

Cited by 6 publications

(4 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The known belief-desire-intention model (BDI) of intelligent agents is a model of intelligent agents suggesting that each agent has a "convinced" achievable goal, including excitation of particular states, stabilization, or mutual synchronization. The latter of particular interest [26,27] subject is frequently associated with the agents' self-organization in a network. The conception behindhand this consequence lies in the behavior of agents within a MAS.…”

Section: Multiagent Systemsmentioning

confidence: 99%

Cluster Flows and Multiagent Technology

2020

View full text Add to dashboard Cite

Multiagent technologies provide a new way for studying and controlling complex systems. Local interactions between agents often lead to group synchronization, also known as clusterization (or clustering), which is usually a more rapid process in comparison with relatively slow changes in external environment. Usually, the goal of system control is defined by the behavior of a system on long time intervals. As is well known, a clustering procedure is generally much faster than the process of changing in the surrounding (system) environment. In this case, as a rule, the control objectives are determined by the behavior of the system at large time intervals. If the considered time interval is much larger than the time during which the clusters are formed, then the formed clusters can be considered to be “new variables” in the “slow” time model. Such variables are called “mesoscopic” because their scale is between the level of the entire system (macro-level) and the level of individual agents (micro-level). Detailed models of complex systems that consist of a large number of elementary components (miniature agents) are very difficult to control due to technological barriers and the colossal complexity of tasks due to their enormous dimension. At the level of elementary components of systems, in many applications it is impossible to verify the models of the agent dynamics with the traditionally high degree of accuracy, due to their miniaturization and high frequency of control actions. The use of new mesoscopic variables can make it possible to synthesize fewer different control inputs than when considering the system as a collection of a large number of agents, since such inputs will be common for entire clusters. In order to implement this idea, the “clusters flow” framework was formalized and used to analyze the Kuramoto model as an attracting example of a complex nonlinear networked system with the effects of opportunities for the emergence of clusters. It is shown that clustering leads to a sparse representation of the dynamic trajectories of the system, which makes it possible to apply the method of compressive sensing in order to obtain the dynamic characteristics of the formed clusters. The essence of the method is as follows. With the dimension N of the total state space of the entire system and the a priori assignment of the upper bound for the number of clusters s, only m integral randomized observations of the general state vector of the entire large system are formed, where m is proportional to the number s that is multiplied by logarithm N/s. A two-stage observation algorithm is proposed: first, the state space is limited and discretized, and compression then occurs directly, according to which reconstruction is then performed, which makes it possible to obtain the integral characteristics of the clusters. Based on these obtained characteristics, further, it is possible to synthesize mesocontrols for each cluster while using general model predictive control methods in a space of dimension no more than s for a given control goal, while taking the constraints obtained on the controls into account. In the current work, we focus on a centralized strategy of observations, leaving possible decentralized approaches for the future research. The performance of the new framework is illustrated with examples of simulation modeling.

show abstract

Section: Multiagent Systemsmentioning

confidence: 99%

Cluster Flows and Multiagent Technology

2020

View full text Add to dashboard Cite

show abstract

“…At the same time, it can be noted that in the well-known works devoted to synchronization, the issues of multilevel behavioral synchronization have not been sufficiently studied, often in relation to specific implementation technologies [44][45][46] and much less often at the general structural-functional level [47]. This circumstance is even more pronounced in the case of a special behavior of the DIS, when solving problems that specialize the set of corresponding events and actions, in particular, during technical diagnostics.…”

Section: Introductionmentioning

confidence: 99%

Model of Process Synchronization in Through Analysis

Martynyuk¹

2021

ACPS

View full text Add to dashboard Cite

Synchronization of parallel processes of distributed information systems (DIS) has been largely determined by decisions taken at the stages of their design. Having already been in structural and functional models, when determining cause- and-effect relationships for events and actions in DIS components, it becomes necessary to coordinate them. In the proposed multilevel systemic, structural and functional synchronization model, a hierarchy of such causal relationships with interlevel mappings, inheritance and encapsulation of events and actions have been formed. The model has been also based on hierarchical extended Petri nets, which make it possible to represent various aspects of a special analysis of technical diagnostics, in particular, analysis of correctness, verification, testing, for the adopted display of the asynchronous-behavioral nature of the multilevel interaction of DIS processes. Features of the synchronization model include mapping operations for cross- level inheritance and encapsulations that synchronize events and actions, as well as end-to-end synchronized quasi-order relationships and compatibility for them. The synchronization model is also distinguished by the possibility of specializing its objects, operations and relations for the tasks of check and recognition of behavioral properties set for analysis and verification, basic in technical diagnostics, including in online and offline testing. The synchronization model has allowed one to determine the formal conditions for methods of end-to-end asynchronous coordination of events and actions of multi-level models, that represent design solutions for DIS, in particular, for technical diagnostics methods, and also to reduce the computational complexity of a special synchronization analysis due to an end-to-end decomposition approach. The dimension of the synchronization model has been estimated using the representation of Petri net graphs and special graphs of reachable states using list structures. The above estimates determine the limits of applicability of the formal synchronization model.

show abstract

“…A Maximum Power Tracker (MPPT) was used to monitor and maintain optimum operation of the solar system [20]- [22]. To monitor the power status, an Agent-Solar has been deployed.…”

Section: Solar Energy Agentmentioning

confidence: 99%

An Intelligent Power Management Investigation for Stand-alone Hybrid System Using Short-time Energy Storage

Sami¹

2017

IJPEDS

View full text Add to dashboard Cite

Stand-alone Hybrid systems become appreciating issues that ensure the required electricity to consumers. The development of a stand-alone Hybrid system becomes a necessity for multiple applications The enhance energy security. To achieve this objective, we have proposed an accurate dynamic model using Multi-Agent System (MAS) in which a solar energy System (SES) serves as the main load supply, an energy Backup System (ERS) is based on a fuel cell and Electrolyzer for long-term energy storage and an Ultra Capacitor (UCap) storage system deployed as a short-time storage. To cooperate with all systems, an Intelligent Power Management (IPM) based on a specific MAS is included. Thus, to prove the performance of the system, we tested and simulated it using the Matlab/Simulink environment.

show abstract

A Proposal for a Multi-Agent based Synchronization Method for Distributed Generators in Micro-Grid Systems

Cited by 6 publications

References 59 publications

Cluster Flows and Multiagent Technology

Cluster Flows and Multiagent Technology

Model of Process Synchronization in Through Analysis

An Intelligent Power Management Investigation for Stand-alone Hybrid System Using Short-time Energy Storage

Contact Info

Product

Resources

About