Reviewing Microgrids from a Multi-Agent Systems Perspective

Gómez-Sanz, Jorge J.; García-Rodríguez, Sandra; Cuartero-Soler, Nuria; Hernández-Callejo, Luís

doi:10.3390/en7053355

Cited by 52 publications

(28 citation statements)

References 28 publications

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“…However, in some peak load intervals, even though all DG units are set at the maximum values, the power supply cannot fulfill the load amount. Some load is shed to maintain the power balance in the MG system (intervals [18][19][20][21][22][23]. Figure 13 represents the response of all components when the MG switches to the islanded mode.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Optimal Operation of Microgrids Considering Auto-Configuration Function Using Multiagent System

2017

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Monitoring the status of existing devices and identification of newly added devices is required in microgrids to adjust the operation schedule followed by any event or integration of a new device. Therefore, in this paper, automatic reconfiguration of microgrids is considered after the addition of a new device or change in the operation status of an existing device by using a multiagent system. This capability of the microgrid is named as auto-configuration function, which is performed by the auto-configurator agent. In case of addition of a new device, the auto-configurator agent is responsible for authorization and registration of the newly added device. In case of change in status of any existing device, the status information is updated. After integration of a new device or change in status of an existing device, re-optimization is carried out by the energy management system (EMS) agent. Agent communication language (ACL) is used to develop a modified contract net protocol (MCNP) for communication among different agents. EMS agent and auto-configurator agent exchange information for economic rescheduling of the microgrid components. Simulation results have shown that the proposed method can be used for optimal operation of microgrids when the configuration changes due to the addition/removal of a device.

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Section: Simulation Resultsmentioning

confidence: 99%

“…In order to solve the above-mentioned problem, the authors in [20] have summarized and introduced various agent-based control methods to operate the MG system in effective ways. An intelligent energy management have been developed to control an independent MG system based on multiagent system (MAS) [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Microgrids Considering Auto-Configuration Function Using Multiagent System

2017

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“…Finally, since the current centralized model of production and transmission is incredibly inefficient and places the grid under great pressure [29,30], novel multi-agent systems [31] have significantly advanced the development of the smart grid recently, especially demand response [21,28,32]. For example, Cha et al [32] proposed a multi-agent system to perform scheduling for maximum benefit in response to the electricity prices.…”

Section: Related Workmentioning

confidence: 99%

Efficient Energy Consumption Scheduling: Towards Effective Load Leveling

2017

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Different agents in the smart grid infrastructure (e.g., households, buildings, communities) consume energy with their own appliances, which may have adjustable usage schedules over a day, a month, a season or even a year. One of the major objectives of the smart grid is to flatten the demand load of numerous agents (viz. consumers), such that the peak load can be avoided and power supply can feed the demand load at anytime on the grid. To this end, we propose two Energy Consumption Scheduling (ECS) problems for the appliances held by different agents at the demand side to effectively facilitate load leveling. Specifically, we mathematically model the ECS problems as Mixed-Integer Programming (MIP) problems using the data collected from different agents (e.g., their appliances' energy consumption in every time slot and the total number of required in-use time slots, specific preferences of the in-use time slots for their appliances). Furthermore, we propose a novel algorithm to efficiently and effectively solve the ECS problems with large-scale inputs (which are NP-hard). The experimental results demonstrate that our approach is significantly more efficient than standard benchmarks, such as CPLEX, while guaranteeing near-optimal outputs.

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“…The size of a microgrid system can range from a single household to a large geographic area such as a campus. Energy consumers/producers such as a home, building, factory, or campus can use small-scale renewable energy systems to manage themselves either in island mode or connected to the main grid [3,4]. The customer may feed the excess power into the grid or store it using energy storage systems to be used when needed.…”

Section: Introductionmentioning

confidence: 99%

Communication Network Architectures for Smart-House with Renewable Energy Resources

2015

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Abstract:With the microgrid revolution, each house will have the ability to meet its own energy needs locally from renewable energy sources such as solar or wind. However, real-time data gathering, energy management and control of renewable energy systems will depend mainly on the performance of the communications infrastructure. This paper describes the design of a communication network architecture using both wired and wireless technologies for monitoring and controlling distributed energy systems involving small-scale wind turbines and photovoltaic systems. The proposed communication architecture consists of three layers: device layer, network layer, and application layer. Two scenarios are considered: a smart-house and a smart-building. Various types of sensor nodes and measurement devices are defined to monitor the condition of the renewable energy systems based on the international electrotechnical commission standard. The OPNET Modeler is used for performance evaluation in terms of end-to-end (ETE) delay. The network performance is compared in view of ETE delay, reliability and implementation cost for three different technologies: Ethernet-based, WiFi-based, and ZigBee-based. OPEN ACCESSEnergies 2015, 8 8717

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Reviewing Microgrids from a Multi-Agent Systems Perspective

Cited by 52 publications

References 28 publications

Optimal Operation of Microgrids Considering Auto-Configuration Function Using Multiagent System

Optimal Operation of Microgrids Considering Auto-Configuration Function Using Multiagent System

Efficient Energy Consumption Scheduling: Towards Effective Load Leveling

Communication Network Architectures for Smart-House with Renewable Energy Resources

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