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Design, Management and Optimization of a Distributed Energy Storage System with the presence of micro generation in a smart house
Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology. (Fordonssystem, Vehicular Systems)
2012 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The owners of a house in today’s society do not know in real-time how much electricity they use. It could be beneficial for any residential consumer to have more control and overview in real-time over the electricity consumption. This could be done possible with a system that monitors the consumptions, micro renewables and the electricity prices from the grid and then makes a decision to either use or sell electricity to reduce the monthly electricity cost for the household and living a "Greener" life to reduce carbon emissions. In this thesis, estimations are made based on artificial neural network (ANN). The predictions are made for air temperature, solar insolation and wind speed in order to know how much energy will be produced in the next 24 hours from the solar panel and from the wind turbine. The predictions are made for electricity consumption in order to know how much energy the house will consume. These predictions are then used as an input to the system. The system has 3 controls, one to control the amount of sell or buy the energy, one to control the amount of energy to charge or discharge the fixed battery and one to control the amount of energy to charge or discharge the electric vehicle (EV). The output from the system will be the decision for the next 10 minutes for each of the 3 controls.

To study the reliability of the ANN estimations, the ANN estimations (SANN) are compared with the real data (Sreal ) and other estimation based on the mean values (Smean) of the previous week. The simulation during a day in January gave that the expenses are 0.6285 € if using SANN, 0.7788 € if using Smean and 0.5974 € if using Sreal. Further, 3 different cases are considered to calculate the savings based on the ANN estimations. The first case is to have the system connected with fixed storage device and EV (Scon;batt ). The second and third cases are to have the system disconnected (without fixed battery) using micro generation (Sdiscon;micro) and not using micro generation (Sdiscon) along with the EV. The savings are calculated as a difference between Scon;batt and Sdiscon, also between Sdiscon;micro and Sdiscon. The saving are 788.68 € during a year if Scon;batt is used and 593.90 € during a year if Sdiscon;micro is used. With the calculated savings and the cost for the equipment, the pay-back period is 15.3 years for Scon;batt and 4.5 years for Sdiscon;micro. It is profitable to only use micro generation, but then the owner of the household loses the opportunity to be part of helping the society to become "Greener". 

Place, publisher, year, edition, pages
2012. , 54 p.
Keyword [en]
Forecasting, ANN, Modelling, Optimization, Control, Energy, Micro renewables, Storage devices, Electric Vehicle
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-86818ISRN: LiTH-ISY-EX--12/4647--SEOAI: diva2:582826
External cooperation
DeustoTech, University of Deusto
Subject / course
Energy Systems
Available from: 2013-01-07 Created: 2013-01-07 Last updated: 2013-01-07Bibliographically approved

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