Multiagent Genetic Algorithm: An Online Probabilistic View on Economic Dispatch of Energy Hubs Constrained by Wind Availability

Moeini‐Aghtaie, Moein; Dehghanian, Payman; Fotuhi‐Firuzabad, Mahmud; Abbaspour, Ali

doi:10.1109/tste.2013.2271517

Cited by 172 publications

(80 citation statements)

References 25 publications

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“…Before that, several conceptual approaches for an integrated view of transmission and distribution systems with dispersed generation and storage have been published, such as "energyservices supply systems" [10], "basic units" [11] and "micro grids" [12]. There are few studies that discuss the hub design issue, while the majority are focused on the different operational issues in the multi-carrier energy systems, such as the economic dispatch [13]- [14], the optimal power flow [15]- [16], the unit commitment [17]- [18], and the optimal coupling of the energy carriers [19]. An approach in [20] considers the optimization of couplings among multiple energy networks consisting of electricity, natural gas and district-heating loads, while [21] presents a financial investment valuation method for energy hubs which includes conversion, storage and Demand Side Management capabilities (DSM).…”

Section: B Literature Review and Contributionsmentioning

confidence: 99%

Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

Kampouropoulos

Andrade

Sala

et al. 2018

IEEE Trans. Smart Grid

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Abstract-This paper presents a novel method for the energy optimization of multi-carrier energy systems. The presented method combines an adaptive neuro-fuzzy inference system, to model and forecast the power demand of a plant, and a genetic algorithm to optimize its energy flow taking into account the dynamics of the system and the equipment's thermal inertias. The objective of the optimization algorithm is to satisfy the total power demand of the plant and to minimize a set of optimization criteria, formulated as energy usage, monetary cost and environmental cost. The presented method has been validated under real conditions in the car manufacturing plant of SEAT in Spain in the framework of an FP7 European research project.

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Section: B Literature Review and Contributionsmentioning

confidence: 99%

Multiobjective Optimization of Multi-Carrier Energy System Using a Combination of ANFIS and Genetic Algorithms

Kampouropoulos

Andrade

Sala

et al. 2018

IEEE Trans. Smart Grid

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“…Genetic Algorithm is widely utilized in economic dispatch, such as [43] does. A Multi-Agent Genetic Algorithm is carried out in [44] , emulation results indicates that this method outperforms generatl genetic algorithms in calculation quality. Detailed information and implementation of genetic algorithm can be found in [45] .…”

Section: Economic Dispatchmentioning

confidence: 99%

A Review on Multi-Agent Technology in Micro-Grid Control

Ding

Yin

2018

Electron Sci Technol Appl

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Micro-Grid (MG) integrates renewable generation, storage devices and controllable generations, it provides efficent utilization of clean energy while keeping stable external characteristics. Capability of continuous power supply, high scalability and flexible operation modes can satifiy the current demand of joint operation of renewable generation and Macro-Grid, and will provide a solid foundation for smart grid technology in the future. Thus, MG is an excellent integration of renewable energy utilization with a bright future, Multi-Agent System (MAS) is a new hierarchical control platform and can completely cover all the devices within a MG, its flexible control modes meet the needs of various operations of MG, and the capability of distributed computing supports intelligent functions of MG in the future. Therefore, developing premium functions for MAS in MG control will promote the development of both MG and Smart Grid technologies. This paper reviews the current applications of MAS technology for MG both in basic and advanced control demands. For basic demands concerning safe operations for MG, functions of MAS are available, but a further improvement of performance is essential for future researches to increase penetration of MAS in MG control; For advanced demands, MAS should increase calculation speed to meet the complex need of MG. In the last part, the future focuses are also depicted.

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“…Some real facilities which can be modeled through the energy hub concept are big building complexes (airports, hospitals, and shopping malls), rural and urban domains, industrial plants (steel works, paper mills), and small isolated systems (trains, ships, aircrafts) [4]. Within the hub, energy is converted and conditioned using combined heat and power technology, electrical transformers, power electronic devices, gas compressors, heat exchangers or boilers, and other equipment [2].…”

Section: Energy Hubsmentioning

confidence: 99%

“…Traditionally, common energy infrastructures such as electricity, natural gas and local district heating systems are mostly planned and operated independently [1][2][3][4][5]. The independent approaches applied in dealing with these energy carriers, however, could overshadow the optimal energy operation [5][6][7].…”

Section: Introductionmentioning

confidence: 99%