A framework for load shedding and demand response in DC microgrid using multi agent system

Rwegasira, Diana; Dhaou, Imed Ben; Anagnostou, Anastasia; Kondoro, Aron; Naiman, Shililiandumi; Kelati, Amleset; Taylor, Simon J. E.; Mvungi, Nerey H.; Tenhunen, Hannu

doi:10.23919/fruct.2017.8250194

Cited by 7 publications

(2 citation statements)

References 7 publications

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“…In addition to delivering a consistent and high-quality supply, every power system model must be prepared to satisfy demand while expending the least amount of operational and capital resources. e task described above is termed as economical load dispatch [1]. Economic load dispatch is an inventory management technology in which the system chooses discrete combination energy sources from all accessible sources to achieve the lowest possible costs [2].…”

Section: Introductionmentioning

confidence: 99%

Multithreaded Multiswarm Model for Intelligent Economic Prosumer Load Dispatch for Battery Supported DC Microgrid

Sarin

Mani

Stonier³

et al. 2022

Mathematical Problems in Engineering

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Economic load dispatch should be given special care even when the primary responsibility of any demand response model is to provide a consistent supply to the load. Demand can be satisfied by the utility grid as well as self-sustaining user sources. If a user generates excess power after meeting demand, the user can pool it and transfer it to the grid or neighboring consumers. This is referred to as the prosumer model, in which the user serves as both a producer and a consumer. Furthermore, some of the surplus power may be stored in energy storage devices. A sophisticated mathematical model is required to estimate how much power should be generated, pooled, pulled from the grid, gathered from close users, supplied to nearby customers, and so on. This paper tries to present a smart economic load dispatch model for a demand response system that combines a multithreaded swarm model with a reward-based reinforcement system to assure optimal source selection and power flow management. To identify the optimum cost-effective power sharing model among a user, the grid, and neighboring users, the system uses particle swarm optimization (PSO) and artificial bee colony (ABC) optimization. Both models have benefits and drawbacks, and not all models work well with all data input. Using two models at the same time consumes a significant amount of time and computational power. As a consequence, for each data input, an upper bound confidante (UBC) model is used in parallel to select the best economical swarm model based on a semisupervised reinforcement model. A weighted Boruvka’s algorithm based on transmission line cost and transmission loss is being used to construct an optimum economic power sharing model, which is backed by swarm models. The efficiency of each model is evaluated using the same data for both models, and error analysis is performed. It was discovered that each model performs differently for various data, and creating a reinforced multithreaded model helps to increase accuracy, reduce computing time, and improve efficiency.

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Section: Introductionmentioning

confidence: 99%

Multithreaded Multiswarm Model for Intelligent Economic Prosumer Load Dispatch for Battery Supported DC Microgrid

Sarin

Mani

Stonier³

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…This paper deals with a multi-source power system composed of wind turbine generators, photovoltaic panels, diesel generators and energy storage batteries. A hierarchical multiagent [82], [84] control solution is presented, which aims to predict the availability of sources, and based on these predictive probabilities a load shedding strategy will be established if needed. The multi-agent reconfiguration is composed of three models, where the first deals with sources, the second manages tests, and the last model copes with decisions.…”

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confidence: 99%

Modeling and Verification of a Reliable Multi-Agent Solution Promoting the Autonomy and Self-Sufficiency of Microgrids in an Isolated Location

et al. 2019

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This paper deals with microgrids in an islanded location where only local generator sources are deployed. However, microgrids in isolated mode are susceptible to unplanned meteorological changes. The defy is to improve the autonomy of these microgrids; thus we start first by checking the required availability of power in the microgrid after unexpected hardware conflicts, characterizing the faults in production sources by probabilistic distributions and modeling their occurrences by using a connection of Markov chains and Petri nets. We propose also a new multi-agent control strategy where agents are employed to check the availability of sources and supply power automatically to consumers in accordance with their priorities and power requirements. The whole architecture is modeled by a modeling and verification environment named ZIZO. The simulation and experimental results are based on data collected from a Tunisian petroleum platform. The availability of energy in the microgrid is increased to 99.68%, and it could be corrected up to 100%. Although this strategy can decrease the availability rate for the uncritical loads, it prevents the microgrid from any dangerous situation.INDEX TERMS Microgid in islanded mode, Markov chain, Petri net, reliability, verification.

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Voltage profile improvement in islanded DC microgrid using load shedding method based on DC bus voltage estimation

Basati,

Moradi,

Karimi

2024

Electr Eng

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A framework for load shedding and demand response in DC microgrid using multi agent system

Cited by 7 publications

References 7 publications

Multithreaded Multiswarm Model for Intelligent Economic Prosumer Load Dispatch for Battery Supported DC Microgrid

Multithreaded Multiswarm Model for Intelligent Economic Prosumer Load Dispatch for Battery Supported DC Microgrid

Modeling and Verification of a Reliable Multi-Agent Solution Promoting the Autonomy and Self-Sufficiency of Microgrids in an Isolated Location

Voltage profile improvement in islanded DC microgrid using load shedding method based on DC bus voltage estimation

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