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The demand for energy and the electricity consumption have been growing for decades. This increase in demand is partially explained by the electrification of all that surrounds us. This is even more marked nowadays with the emergence of electric vehicles. The past decades have also seen the emergence and development of new energy sources. In Europe, it is impossible to take a trip on the highway without seeing wind turbines and it is increasingly rare to see a residential area without any photovoltaic panel. However, the development of renewable energy sources has also led to new kinds of problems related to electrical networks.

In this thesis, the implementation of an algorithm constructed to provide efficient distribution network management is described. This algorithm aims to meet the requirements of the system operators defined within a European project. The implementation choices and the discussion around these choices are provided. Finally, the behaviour of the algorithm is analysed in different test cases.

A review of the active network management topic and of the tools and models used in this context is done. The importance of active network management is highlighted through this review and through the full description of the European project in which this work takes place.

flexibility --- flexibility market --- active network management --- distribution network --- communication platform --- distribution system operator --- DSO --- Ingénierie, informatique & technologie > Ingénierie électrique & électronique --- Ingénierie, informatique & technologie > Energie

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The 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.

History of engineering & technology --- EV charging --- multi-agent system --- digital twin --- customer satisfaction indicator --- smart microgrid --- energy management system --- real-time optimization --- immune system algorithm --- economic dispatch --- energy consumption --- wireless sensor network --- cooperation --- collaboration --- ontology --- energy sector --- scoping review --- decision-aid --- distributed energy resources --- distribution system operator --- reactive power management --- uncertainty --- day-ahead market --- balancing market --- bilateral trading --- market design --- variable renewable energy --- agent-based simulation --- MATREM system --- congestion management --- dynamic tariff --- agent-based distribution networks --- demand response --- routing protocols --- performance parameters --- Wireless Sensor Network (WSN)

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The Special Issue Distributed Energy Resources Management 2018 includes 13 papers, and is a continuation of the Special Issue Distributed Energy Resources Management. The success of the previous edition shows the unquestionable relevance of distributed energy resources in the operation of power and energy systems at both the distribution level and at the wider power system level. Improving the management of distributed energy resources makes it possible to accommodate the higher penetration of intermittent distributed generation and electric vehicle charging. Demand response programs, namely the ones with a distributed nature, allow the consumers to contribute to the increased system efficiency while receiving benefits. This book addresses the management of distributed energy resources, with a focus on methods and techniques to achieve an optimized operation, in order to aggregate the resources namely in the scope of virtual power players and other types of aggregators, and to remunerate them. The integration of distributed resources in electricity markets is also addressed as an enabler for their increased and efficient use.

n/a --- virtual power plant --- bidding strategy --- local flexibility market --- multi-period optimal power flow --- flexibility service --- occupant comfort --- unbalanced networks --- decentralized energy management system --- autonomous control --- optimization --- energy storage --- microgrids --- energy efficiency --- distributed energy --- control system --- DSM --- optimal scheduling --- adaptability --- synergistic optimization strategy --- teaching-learning --- distributed generation --- energy storage system --- stackelberg dynamic game --- IoT (Internet of Things) --- supply and demand --- comprehensive benefits --- distributed generator --- frequency bus-signaling --- active distribution networks --- swarm intelligence --- wind --- multi-agent technology --- solar --- power system management --- fault-tolerant control --- indoor environment quality --- multi-temporal optimal power flow --- multi-agent synergetic estimation --- smart grids --- local energy trading --- active power control --- prosumer --- microgrid --- trade agreements --- healthy building --- smart grid --- nonlinear control --- algorithm design and analysis --- batteries --- droop control --- distributed energy resources --- aggregator --- multi-agent system --- frequency control --- particle swarm optimization --- distribution system operator --- building climate control --- low voltage networks --- demand Response --- clustering --- distributed coordination --- demand-side management --- demand response