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A Model Predictive Control based Peak Shaving Application for a Grid Connected Household with Photovoltaic and Battery Storage

  • The increase in households with grid connected Photovoltaic (PV) battery system poses challenge for the grid due to high PV feed-in as a result of mismatch in energy production and load demand. The purpose of this paper is to show how a Model Predictive Control (MPC) strategy could be applied to an existing grid connected household with PV battery system such that the use of battery is maximizedThe increase in households with grid connected Photovoltaic (PV) battery system poses challenge for the grid due to high PV feed-in as a result of mismatch in energy production and load demand. The purpose of this paper is to show how a Model Predictive Control (MPC) strategy could be applied to an existing grid connected household with PV battery system such that the use of battery is maximized and at the same time peaks in PV energy and load demand are reduced. The benefits of this strategy are to allow increase in PV hosting capacity and load hosting capacity of the grid without the need for external signals from the grid operator. The paper includes the optimal control problem formulation to achieve the peak shaving goals along with the experiment set up and preliminary experiment results. The goals of the experiment were to verify the hardware and software interface to implement the MPC and as well to verify the ability of the MPC to deal with the weather forecast deviation. A prediction correction has also been introduced for a short time horizon of one hour within this MPC strategy to estimate the PV output power behavior.show moreshow less

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Metadaten
Author:Deepranjan Dongol, Thomas Feldmann, Elmar BollinGND
Publisher:Science and Technology Publications
Year of Publication:2018
Page Number:10
ISBN:978-989-758-292-9
Language:English
Tag:Batteries
MPC; Photovoltaic; Smart Grid
Parent Title (English):Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS
First Page:54
Last Page:63
Document Type:Conference Proceeding
Open Access:Frei zugänglich
Institutes:Bibliografie
Release Date:2019/01/21
Licence (German):License LogoEs gilt das UrhG
Note:
Beitrag wurde peer-reviewed
DOI:https://doi.org/10.5220/0006685300540063