Multi-agent based multi objective renewable energy management for diversified community power consumers

Xiong, Liansong; Li, Penghan; Wang, Ziqiang; Wang, Jie

doi:10.1016/j.apenergy.2019.114140

Cited by 63 publications

(24 citation statements)

References 32 publications

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“…It makes it possible to distinguish the agents in contact with the external environment without however being precise on the other agents of the system. It also has the merit of giving an overview of all interactions between agents [41].…”

Section: Multi-agent Architecture By Detailmentioning

confidence: 99%

Multi-agent System for Management of Data from Electrical Smart Meters

Yacouba¹,

Training²,

Diabagaté³

et al. 2021

IJITCS

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The smart meter can process sensor data in a residential grid. These sensors transmit different parameters or measurement data (index, power, temperature, fluctuation of voltage and electricity, etc.) to the smart meter. All of these measurement data can come in different ways at the smart meter. The sensors transmit each measurement data to the smart meter. In addition, the collection of this data to a central system is a significant concern to ensure data integrity and protect the privacy of residents. The complexity of these data management also lies in their volume, frequency, and scheduling. This work presents a scheduling and a collection mechanism in private power consumption data between both sensors and smart meters on one hand and between smart meters and the central data collection system on other hand. We have found several approaches to intelligent meter data management in scientific researches. We propose another approach in response to this concern for the scheduling and collection of measurement data to a central system from residential areas of sensors’ network connected to smart meters. This work is also an example of a link between data collection and data scheduling in intelligent information management, transmission, and protection. We also propose a modeling of the measurement objects of smart grid and highlight the changes made to these objects throughout the process of data processing. It should be noted that this smart grid system consists of three main active systems namely sensors, smart meters and central system. In addition to these three systems, there are other systems that communicate with the smart meters and the central system. We have identified three implementation models for the smart metering system. We also present an intelligent architecture based on multi-agent systems for the smart grid. Most current electricity management systems are not adapted to the new challenges imposed by social and economic development in Africa. The objectives of this study are to initiate the design of a smart grid system for the management of electricity data.

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Section: Multi-agent Architecture By Detailmentioning

confidence: 99%

Multi-agent System for Management of Data from Electrical Smart Meters

Yacouba¹,

Training²,

Diabagaté³

et al. 2021

IJITCS

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“…The managed microgrid architecture, based on a multiagent approach, is used to improve the adequacy of microgrid management and ensure the delivery of the required power, and also implements intelligent microgrid management at the distributed generation level. In [16], the authors propose a multipurpose renewable energy management scheme to meet the electricity demand of a variety of consumers, in which a multiagent system is used to coordinate and control the generation and consumption of energy. In accordance with the proposed scheme, a multiagent energy grid and energy consumption control system is first built.…”

Section: Introductionmentioning

confidence: 99%

Application of a multiagent approach for optimal load distribution between sources in an integrated energy system taking into account the operation of active consumers

2021

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In the context of the energy transition, research on the creation of integrated energy systems and their control is an actual task. They combine a significant share of renewable energy sources, contribute to the overall efficiency of the system, and enable active consumers to participate in the energy supply process. At the same time, in connection with the growing capacity of distributed energy sources, new problems arise related to the operation of distribution networks and difficulties in adapting the operation of active consumers in the centralized energy system. Active consumers can regulate their energy consumption by distributing the load between centralized and distributed energy sources, and as a result, they can provide flexibility, maneuverability in the operation of the system and increase the efficiency of its redundancy. To organize the work of active consumers in an integrated energy system, a multiagent approach is used. This approach is widely used by researchers to solve various practical tasks. Allows us to represent active consumers in the form of agents with an individual behavior algorithm and organize their interaction with the energy system to ensure optimal energy supply. The proposed mathematical model for finding the optimal composition of generating capacity takes into account the structural organization of centralized and distributed energy sources, as well as the participation of active consumers in the process of energy supply. Modeling of an integrated energy system and carrying out a computational experiment based on multiagent technologies are performed in the AnyLogic software environment.

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“…In [14], the battery bank voltage level is used to operate the battery backup. In the optimization-based control technique, cost minimization has been taken as an objective function to handle the microgrid energy management [15,16]. The battery management strategies focus is towards peak demand management for economical energy cost, optimizing cost by operating at minimal tariff.…”

Section: Introductionmentioning

confidence: 99%

A Novel Battery Supported Energy Management System for the Effective Handling of Feeble Power in Hybrid Microgrid Environment

Gangatharan¹,

Rengasamy²,

Elavarasan³

et al. 2020

IEEE Access

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One of the crucial challenges in the present power distribution system is the conversion loss phenomenon. Modern microgrid integrates various converters for varieties of applications, such as distributed power generation interconnection, energy storage management system, grid integration, demand management, etc. The increased usages of power converters further worsen the existing situation. Any initiatives taken towards energy conservation go in vain due to the excessive conversion loss phenomenon in the present distribution schemes. In this regard, a novel microgrid energy management scheme is proposed and developed to reduce the conversion losses in the residential distribution system. It uses a new control algorithm that finds the strength of power available in the DC side before being transferred. The conversion process is invoked only if the power is adequate, and if found feeble, then the conversion process is withdrawn and stored in an auxiliary battery. Conversion of feeble power would result in high loss across the converters and transformers. In this scheme, the AC loads are supplied by the utility grid, and the DC loads are fed by a solar PV and an auxiliary battery bank. The power conversion is done only during unavoidable circumstances. A prototype hardware setup has been developed, and the objective of the proposed research task has been validated. Further, the proposed scheme would gain importance in reducing the cost of the electricity for a time-of-use tariff system by optimization. A genetic algorithm is proposed to optimize the energy management of the microgrid system.

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Multi-agent based multi objective renewable energy management for diversified community power consumers

Cited by 63 publications

References 32 publications

Multi-agent System for Management of Data from Electrical Smart Meters

Multi-agent System for Management of Data from Electrical Smart Meters

Application of a multiagent approach for optimal load distribution between sources in an integrated energy system taking into account the operation of active consumers

A Novel Battery Supported Energy Management System for the Effective Handling of Feeble Power in Hybrid Microgrid Environment

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