Energy Management of Microgrids Using Load Shifting and Multi-agent System

Abdelsalam, Abdelazeem A.; Zedan, Honey A.; ElDesouky, Azza A.

doi:10.1007/s40313-020-00593-w

Cited by 15 publications

(12 citation statements)

References 32 publications

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“…The multiple-agent system was considered a form of knowledge based on computation, that is being used to lower global market costs and total loads. Using a hybrid approach based on optimizing production rate, energy demand is reduced while costs are reduced by a percentage [ 41 ].…”

Section: Microgrid Market Segmentmentioning

confidence: 99%

A comprehensive review of DC microgrid in market segments and control technique

Kumar

Prabha

2022

Heliyon

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Section: Microgrid Market Segmentmentioning

confidence: 99%

A comprehensive review of DC microgrid in market segments and control technique

Kumar

Prabha

2022

Heliyon

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“…It utilises the load management techniques aimed to manipulate load profile curves by several ways: (1) peak clipping; (2) valley filling; (3) load shifting; (4) strategic conservation; (5) strategic load growth; (6) flexible load shape [10]. Although these load manipulation strategies were defined a few decades ago, the resent literature search [11][12][13][14][15] shows that they are still dominating in state-of-the-art DSM proposals and applications in both industrial and domestic sectors. The appropriate DSM technique from the list must be selected with respect to particular system parameters to ensure the most efficient operation of power supply sources.…”

Section: Domestic Demand Side Managementmentioning

confidence: 99%

“…For example, Abdelsalam et al [15] suggested a multi-agent optimisation algorithm (antlion algorithm) to analyse three domestic DSM scenarios. Two scenarios represented singe objective optimisation where the objective function is (1) to minimise the energy consumption cost and (2) maximise the load factor.…”

Section: Domestic Dsm Framework and Algorithmsmentioning

confidence: 99%

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Domestic demand-side management: analysis of microgrid with renewable energy sources using historical load data

et al. 2021

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This paper provides a high accuracy assessment of domestic demand-side management (DSM) approach in the context of distributed renewable energy sources (RES). To determine the potential of domestic DSM for households, a microgrid model of a typical UK residential estate was developed to simulate the impact of RES. The microgrid model comprises of 15 UK households with appropriate allocation of washing machines (WM), tumble dryers (TD) and dishwashers (DW) in accordance to the statistical data. In order to obtain a high accuracy result, the power consumption of the microgrid model utilises real historical high-resolution data of household energy consumption and RES generation. Thereafter, 40% of distributed wind and solar energy are implemented in the model to produce two individual scenarios. The operation of the white appliances in the model is controlled using a domestic DSM based on a load shifting algorithm. The primary criterion of the DSM considered in this paper is the reduction of energy feedback to the grid in order to decrease the utilisation of the grid and to reduce the transmission losses. The results obtained from the model simulation are compared to the baseline model and discussed with respect to the possible benefits of implementation of domestic DSM under the impact of RES. It has been shown that the self-consumption ratio of the microgrid operating under the DSM is increased by 3% for both scenarios. The model analysis provides highly realistic results which can be used for efficiency assessment of various load shifting methods.

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“…The classification, characterization, and tracking of loads necessitate systems for aligning loads with generation. Abdelsalam et al 8 suggested a hybrid demand-side management-multi-agent system energy management strategy for residential, commercial, and industrial microgrids. In 9 the authors used the idea of electric springs in series to manage bus voltage and fault ride-through.…”

Section: Introductionmentioning

confidence: 99%

Practical prototype for energy management system in smart microgrid considering uncertainties and energy theft

Saeed,

Sedhom,

Elbaghdadi

et al. 2023

Sci Rep

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The conventional electrical grid faces significant issues, which this paper aims to address one of most of them using a proposed prototype of a smart microgrid energy management system. In addition to relying too heavily on fossil fuels, electricity theft is another great issue. The proposed energy management system can simultaneously detect electricity theft and implement demand response tactics by employing time-of-use pricing principles and comparing real electricity consumption with grid data. The system uses the Al-Biruni earth radius (BER) optimization algorithm to make smart choices about how to distribute the load, intending to reduce energy consumption and costs without sacrificing comfort. As a bonus, it considers limitations imposed by battery charging/discharging and decentralized power generation. Incorporating sensors and SCADA-based monitoring, the system provides accurate measurement and management of energy usage through load monitoring and control. An intuitive mobile app also helps consumers connect, allowing for more active participation and better control over energy use. Extensive field testing of the prototype shows that by moving loads from peak period to another off-peak period, electricity expenditures can be reduced by up to 48.45%. The energy theft value was calculated to be 1199 W, proving that the system's theft detection model was effective.

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Energy Management of Microgrids Using Load Shifting and Multi-agent System

Cited by 15 publications

References 32 publications

A comprehensive review of DC microgrid in market segments and control technique

A comprehensive review of DC microgrid in market segments and control technique

Domestic demand-side management: analysis of microgrid with renewable energy sources using historical load data

Practical prototype for energy management system in smart microgrid considering uncertainties and energy theft

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