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dc.contributor.editorPinto, Tiago
dc.contributor.editorSoares, João
dc.contributor.editorLezama, Fernando
dc.date.accessioned2021-05-01T15:45:39Z
dc.date.available2021-05-01T15:45:39Z
dc.date.issued2020
dc.identifierONIX_20210501_9783039436491_1024
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/69278
dc.description.abstractThe synergy between artificial intelligence and power and energy systems is providing promising solutions to deal with the increasing complexity of the energy sector. Multi-agent systems, in particular, are widely used to simulate complex problems in the power and energy domain as they enable modeling dynamic environments and studying the interactions between the involved players. Multi-agent systems are suitable for dealing not only with problems related to the upper levels of the system, such as the transmission grid and wholesale electricity markets, but also to address challenges associated with the management of distributed generation, renewables, large-scale integration of electric vehicles, and consumption flexibility. Agent-based approaches are also being increasingly used for control and to combine simulation and emulation by enabling modeling of the details of buildings’ electrical devices, microgrids, and smart grid components. This book discusses and highlights the latest advances and trends in multi-agent energy systems simulation. The addressed application topics include the design, modeling, and simulation of electricity markets operation, the management and scheduling of energy resources, the definition of dynamic energy tariffs for consumption and electrical vehicles charging, the large-scale integration of variable renewable energy sources, and mitigation of the associated power network issues.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology
dc.subject.otherEV charging
dc.subject.othermulti-agent system
dc.subject.otherdigital twin
dc.subject.othercustomer satisfaction indicator
dc.subject.othersmart microgrid
dc.subject.otherenergy management system
dc.subject.otherreal-time optimization
dc.subject.otherimmune system algorithm
dc.subject.othereconomic dispatch
dc.subject.otherenergy consumption
dc.subject.otherwireless sensor network
dc.subject.othercooperation
dc.subject.othercollaboration
dc.subject.otherontology
dc.subject.otherenergy sector
dc.subject.otherscoping review
dc.subject.otherdecision-aid
dc.subject.otherdistributed energy resources
dc.subject.otherdistribution system operator
dc.subject.otherreactive power management
dc.subject.otheruncertainty
dc.subject.otherday-ahead market
dc.subject.otherbalancing market
dc.subject.otherbilateral trading
dc.subject.othermarket design
dc.subject.othervariable renewable energy
dc.subject.otheragent-based simulation
dc.subject.otherMATREM system
dc.subject.othercongestion management
dc.subject.otherdynamic tariff
dc.subject.otheragent-based distribution networks
dc.subject.otherdemand response
dc.subject.otherrouting protocols
dc.subject.otherperformance parameters
dc.subject.otherWireless Sensor Network (WSN)
dc.titleMulti-Agent Energy Systems Simulation
dc.typebook
oapen.identifier.doi10.3390/books978-3-03943-650-7
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783039436491
oapen.relation.isbn9783039436507
oapen.pages190
oapen.place.publicationBasel, Switzerland


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