A compact nanogrid for home applications with a behaviour-tree-based central controller

Burgio, Alessandro; Menniti, Daniele; Sorrentino, N.; Pinnarelli, Anna; Motta, M.

doi:10.1016/j.apenergy.2018.04.082

Cited by 19 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The structure of the controller should be selected based on the purpose of the nanogrid and the available resources. For example, a nanogrid has been proposed in [20], which is aimed at small-scale residential buildings, such as houses. The adoption of a central controller instead of a distributed one simplifies the installation for residential applications, as it offers compactness, quick and easy installation, and requires minor changes to the existing infrastructure.…”

Section: Nanogrid Control Structuresmentioning

confidence: 99%

Design of a Smart Nanogrid for Increasing Energy Efficiency of Buildings

et al. 2021

View full text Add to dashboard Cite

Distributed generation (DG) systems are growing in number, diversifying in driving technologies and providing substantial energy quantities in covering the energy needs of the interconnected system in an optimal way. This evolution of technologies is a response to the needs of the energy transition to a low carbon economy. A nanogrid is dependent on local resources through appropriate DG, confined within the boundaries of an energy domain not exceeding 100 kW of power. It can be a single building that is equipped with a local electricity generation to fulfil the building’s load consumption requirements, it is electrically interconnected with the external power system and it can optionally be equipped with a storage system. It is, however, mandatory that a nanogrid is equipped with a controller for optimisation of the production/consumption curves. This study presents design consideretions for nanogrids and the design of a nanogrid system consisting of a 40 kWp photovoltaic (PV) system and a 50 kWh battery energy storage system (BESS) managed via a central converter able to perform demand-side management (DSM). The implementation of the nanogrid aims at reducing the CO2 footprint of the confined domain and increase its self-sufficiency.

show abstract

Section: Nanogrid Control Structuresmentioning

confidence: 99%

Design of a Smart Nanogrid for Increasing Energy Efficiency of Buildings

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Papers Workflow and human activity modelling [39], [68], [63], [10], [69], [13], [151] Camera Control [132], [98] Smart homes and Power Grids [49], [15], [18], [60] Engineering Design Tasks [175], [121] was used to get input for a Fallback node, to take advantage of an experienced operator's (brain surgeon) expertise in choosing the correct action for a brain tumor ablation task. This has been demonstrated in both simulation [68] and in real experiments on mice [69].…”

Section: Areamentioning

confidence: 99%

“…Finally, apart from encoding task execution for autonomous agents and humans, BTs have been also been used to run simpler automated systems, such as smart homes. The reactivity of the BTs can be exploited to control a domestic power grid, by balancing loads and engaging local back-up power production when needed [15,18,60], or control the activation of lighting or smart appliances based on human actions [49].…”

Section: Areamentioning

confidence: 99%

A Survey of Behavior Trees in Robotics and AI

Iovino¹,

Scukins²,

Styrud³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Such highly diverse factors, when coupled with the intermittency of connected renewables, make the hourly load of a nanogrid even more volatile. Thus, to maintain an equilibrium between load and generation, a nanogrid employs either a centralized or a decentralized dynamic controller [2,3].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction

2022

View full text Add to dashboard Cite

Increased focus on sustainability and energy decentralization has positively impacted the adoption of nanogrids. With the tremendous growth, load forecasting has become crucial for their daily operation. Since the loads of nanogrids have large variations with sudden usage of large household electrical appliances, existing forecasting models, majorly focused on lower volatile loads, may not work well. Moreover, abrupt operation of electrical appliances in a nanogrid, even for shorter durations, especially in “Peak Hours,” raises the energy cost substantially. In this paper, an ANN model with dynamic feature selection is developed to predict the hour-ahead load of nanogrids based on meteorological data and a load lag of 1 h (t-1). In addition, by thresholding the predicted load against the average load of previous hours, peak loads, and their time indices are accurately identified. Numerical testing results show that the developed model can predict loads of nanogrids with the Mean Square Error (MSE) of 0.03 KW, the Mean Absolute Percentage Error (MAPE) of 9%, and the coefficient of variation (CV) of 11.9% and results in an average of 20% daily energy cost savings by shifting peak load to off-peak hours.

show abstract

A compact nanogrid for home applications with a behaviour-tree-based central controller

Cited by 19 publications

References 35 publications

Design of a Smart Nanogrid for Increasing Energy Efficiency of Buildings

Design of a Smart Nanogrid for Increasing Energy Efficiency of Buildings

A Survey of Behavior Trees in Robotics and AI

Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction

Contact Info

Product

Resources

About