Modeling,simulation and energy management of solar PV-based Microgrinds using real-time residential data

Abstract:

The research aims at defining and analyzing an energy solution that incorporates

renewable energy, thereby giving rise to improving energy security and providing grid

stability for the grid networks located in urban residential areas. The urbanization growth

in Botswana coincides with the increase in electricity consumption. The electricity load

demand in the country outlasts the local supply and thereby the need for importing

electricity from the Southern Africa Power Pool (SAPP). To address grid stability and

reliable power supply issues, the research aims to design a microgrid system for an urban

settlement by matching the electric load demand with solar photovoltaic (PV) generation

in a residential district. The initial stages of the research include measuring electrical loads

in a single household for a certain period. The energy data collected from residential

homes were subjected to a smart metering examination. The analysis revealed high

variability in the daily energy usage of the household. The dataset was tabulated through

the two seasons experienced in Botswana, summer, and winter. Following a study using

clustering techniques, three clusters with outliers’ data identified the optimum monthly

energy use with the lowest Mean Squared Error (MSE) after ten iterations. The peak

hourly profiles from the metered residential household were used to represent a

cumulative 250-kW planned power solar PV microgrid system. The design and simulation

were conducted on the simulation environment MATLAB/Simulink with real-time daily

irradiation and temperature profiles from the metered household location.

Proportional Integral Derivative (PID) controllers could achieve a desired DC microgrid

voltage throughout the day. The boost converter through a signal from the Maximal Power

Point Tracking (MPPT) could achieve the maximum voltage of the solar PV module. For

energy management optimization, Fuzzy Logic Control (FLC) was incorporated for the

grid-connected microgrid with battery support. The FLC simulation analysis

demonstrated that the battery offered energy stability inside the microgrid system during

the shift from island mode to a grid-connected mode of operation. The economic study

was conducted in HOMERPro, and it revealed the levelized cost of electricity at USD

10.90/ kWh. The nature of the solar PV microgrid design revealed the system's lifetime

cost savings worth USD 99,248.6. A microgrid system is a subpart of a smart grid; thus,

the proposed system aids in achieving the quick restoration of electricity when a power

outage occurs while also enhancing local energy resiliency.

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APA

Boitshoko, S (2024). Modeling,simulation and energy management of solar PV-based Microgrinds using real-time residential data. Afribary. Retrieved from https://afribary.com/works/modeling-simulation-and-energy-management-of-solar-pv-based-microgrinds-using-real-time-residential-data

MLA 8th

Boitshoko, Seane "Modeling,simulation and energy management of solar PV-based Microgrinds using real-time residential data" Afribary. Afribary, 30 Mar. 2024, https://afribary.com/works/modeling-simulation-and-energy-management-of-solar-pv-based-microgrinds-using-real-time-residential-data. Accessed 26 May. 2024.

MLA7

Boitshoko, Seane . "Modeling,simulation and energy management of solar PV-based Microgrinds using real-time residential data". Afribary, Afribary, 30 Mar. 2024. Web. 26 May. 2024. < https://afribary.com/works/modeling-simulation-and-energy-management-of-solar-pv-based-microgrinds-using-real-time-residential-data >.

Chicago

Boitshoko, Seane . "Modeling,simulation and energy management of solar PV-based Microgrinds using real-time residential data" Afribary (2024). Accessed May 26, 2024. https://afribary.com/works/modeling-simulation-and-energy-management-of-solar-pv-based-microgrinds-using-real-time-residential-data