Model-Free Energy Management System for Hybrid Alternating Current/Direct Current Microgrids

Brandão, Danilo I.; Santos, R.P.; Silva, Waner Wodson A. G.; Oliveira, Thiago Ribeiro de; Donoso-Garcia, P.F.

doi:10.1109/tie.2020.2984993

Cited by 23 publications

(11 citation statements)

References 27 publications

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“…While previously, like in [24]- [26], the ac/dc power models were developed for higher-level distributed microgrids ignoring significant component's power losses. 2) Moreover, in comparison to [25], [27], [28], where only proportional power sharing was addressed. In this work, a novel two-stage co-simulation framework is adopted to implement multitime scale energy management and control strategy.…”

Section: A Our Contributionmentioning

confidence: 99%

“…Our proposed timetriggered data transmission between the offline scheduler and the local controller will significantly reduce the transmission latency, jitter, and computing power associated with the communication by using the time stamp feature. 4) As compared to the conventional hierarchical control structure of microgrids [24]- [27], the proposed architecture is comprised of both secondary predictive control and primary distributed robust control layers. It improves the system predictability, redundancy and enables the plugand-play feature in HAPN.…”

Section: A Our Contributionmentioning

confidence: 99%

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Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

Minhas

Frey

2021

IEEE Trans. on Ind. Applicat.

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An energy-efficient smart home generally integrates renewable energy sources (RESs) with intelligent energy devices (IEDs) to make itself cost-efficient. However, the stochastic nature of the RESs and IEDs makes the power network uncertain, and it degrades the economic efficiency of the smart home. Hence a co-simulation based intelligent home energy management system (HEMS) is proposed in this article. It interconnects the rolling horizon-based model predictive energy scheduling mechanism with robust control strategies. It manages the load following energy supply entities (ESEs) optimally and efficiently. A time-ahead scheduler integrates an optimal algorithm to estimate the cost-optimal (e.g., minimizing total energy cost) decision signals for various ESEs by incorporating component based losses and efficiencies. Besides, a real-time distributed robust control strategy is established executing a coordinated energy sharing mechanism by incorporating an auxiliary power source. Time-triggered low-jitter wireless communication architecture is adopted to transfer the optimal decision signals from the central scheduler to the distributed device level local controller. A scenario based comparison of various ac/dc models of a home area power network evaluates the strength of the prospective HEMS.

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Section: A Our Contributionmentioning

confidence: 99%

Section: A Our Contributionmentioning

confidence: 99%

Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

Minhas

Frey

2021

IEEE Trans. on Ind. Applicat.

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“…Raspberry Pi is a microcomputer that is broadly used in IoT, cloud computing, wireless communication, and big data. Recently, Raspberry Pi has been used in energy management [147], [148] and in big data-based IoT solutions [149] [150], [155], [156], [157]. This architecture has been developed from the CoT, virtual power plant, and parallel computing point of view.…”

Section: Ddl-based Distributed Load Forecasting Architecturementioning

confidence: 99%

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

et al. 2020

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Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

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“…In on-grid systems, the effects of the fluctuations of these sources can be avoided by exchanging the fluctuations of the extracted power with electric utility. [3][4][5][6][7] The utilization of energy storage systems (ESS) and traditional standby power sources in off-grid systems help mitigate the consequences of these sources' inherent volatility. [8][9][10][11] The ESS used in the off-grid systems adds a substantial cost and for this reason, it is important to have smart demand-side management (DSM) to reduce loads during times of scarcity while urging consumers to raise their loads in case of higher generation than the loads need periods which increases the fit between loads and renewable energy generation.…”

Section: Introductionmentioning

confidence: 99%

A novel energy storage and demand side management for entire green smart grid system for NEOM city in Saudi Arabia

Eltamaly

2023

Energy Storage

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Hybrid renewable energy systems (HRES) are gaining high interest in supplying electric energy for remote communities. Energy storage systems (ESS) are utilized by green autonomous HRESs to accommodate the variability of renewable resources such as wind and solar energy systems. The lack of any traditional energy source is adding a great reliability challenge which should be compensated using expensive ESS. This challenge can be avoided by using a pumped hydro energy storage system (PHES) in harmony with batteries. The PHES is an excellent option to be used in NEOM city due to the perfect topographical characteristic of this site. The minimum cost of energy and the highest reliability is used as an objective for sizing the proposed entire green HRES. Using smart grid principles (SGP) and demand‐side management (DSM) in the design and operation stages will minimize system size and cost, which can result in a significant reduction in consumer bills. As a result, this paper introduces an innovative DSM based on a dynamic tariff. The suggested DSM technique was developed utilizing a unique fuzzy logic that takes into account the present and day‐ahead ESS situations to intelligently determine the ideal tariff for the lowest cost and maximum reliability of the HRES. This paper introduces a modified grey wolf optimization (MGWO) technique to shorten convergence time while preserving the best accuracy. The suggested MGWO is assessed against 10 swarm optimization techniques. The payback period of the project is 7 years. The findings acquired from this unique program demonstrated its superiority, with conversion times reduced by 22% to 80% when compared to previous optimization procedures. Furthermore, as compared to the flat rate pricing tariff, the usage of the dynamic tariff lowered the LCOE by 53%.

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Model-Free Energy Management System for Hybrid Alternating Current/Direct Current Microgrids

Cited by 23 publications

References 27 publications

Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

A novel energy storage and demand side management for entire green smart grid system for NEOM city in Saudi Arabia

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