Loading...
Modelling and control of hybrid photovoltaic and micro-hydro system
Author(s)
Melamu, Moteane Thabo
Date Issued
2021
Type
Thesis
Publisher
Cape Peninsula University of Technology
Abstract
The increase in population growth increased standard of living, and the advancement of technologies has resulted in exponential growth in electrical consumption. Reduction in fossil fuel reserves is therefore diminishing due to the energy demand increase. The combustion of fossil fuels as energy sources leads to pervasive environmental degradation especially the global climate change that is caused by greenhouse gas emissions. As a result, there is an urgency to curb carbon emissions without compromising universal access to modern energy, socio-economic development, employment creation, poverty reduction which are all in pursuit of realizing sustainable development goals. The deficiency in access to energy is linked to a lack of socio-economic development and this is one of the factors that lead to poverty. Globally there are approximately 1.4 billion people who lack access to electricity, 85% reside in rural areas and the majority are in Sub-Saharan Africa.
The power plants utilize fossil fuels and nuclear energy sources which for most of the 20th century was less expensive and in abundance. These are certainly reliable ways of providing energy however they are located closer to the load which implies the rural population is often not connected to the national electricity network. These are some of several reasons why there is an increased interest in a microgrid that utilizes renewable energy sources. However, renewable resources are volatile since they are mostly dependent on weather conditions. To circumvent this, more than one energy source for continuous power flow to meet the load is required. This too requires additional energy storage devices with quick responses to mitigate against disturbances. The inclusion of multitudes of sources requires energy management control for energy flow among the sources to secure reliable and optimal use of the system. The research aims to develop energy management for the system so to maintain power imbalance and mitigate the fluctuation of power that is fed to the load.
The system is designed as a standalone hybrid microgrid system on the Matlab/Simulink environment which comprises of Photovoltaic (PV) array, Lithium-ion (Li-ion) battery storage device Microhydropower (MHP) system. All the sources are supplying the AC load through a three-phase inverter. The DC-DC bidirectional converter interfaces the renewable resources and a battery bank to allow power going in both directions to absorb excess and dispatch power to prevent mismatch due to variation of power from PV array and micro-hydro plant. The load considered in this research is of 50 remote households amounting to 180kW. The PV account for about 102.2kW and Micro-hydropower contributes around 96.18kW. The weather conditions in terms of irradiance will be considered to mimic real-life situations for the analysis of the power system. The mathematical modeling of components energy management algorithms to prioritize energy demand with regards to the demand is implemented.
The system provides quick response time where PV power transient time is 15ms to stabilize and the MHP takes 0.8s to reach a steady state. This is because of the high mechanical torque on turbine blades due to water kinetic energy that impacts the blades. The fuzzy logic control provides smooth system operation as the energy management system where there is a continuous power flow provided to the load and voltage. This is only true if SOC is neither bigger than SOCmax(80%) nor smaller than SOCmin(20%). This is to protect the lifespan of the battery. The system provides over 99.4% of accuracy with calculated values compared to the ones generated by Matlab/Simulink. The battery response time to any mismatches is 5ms before it dispatches power or before it can absorb excess power.
The power plants utilize fossil fuels and nuclear energy sources which for most of the 20th century was less expensive and in abundance. These are certainly reliable ways of providing energy however they are located closer to the load which implies the rural population is often not connected to the national electricity network. These are some of several reasons why there is an increased interest in a microgrid that utilizes renewable energy sources. However, renewable resources are volatile since they are mostly dependent on weather conditions. To circumvent this, more than one energy source for continuous power flow to meet the load is required. This too requires additional energy storage devices with quick responses to mitigate against disturbances. The inclusion of multitudes of sources requires energy management control for energy flow among the sources to secure reliable and optimal use of the system. The research aims to develop energy management for the system so to maintain power imbalance and mitigate the fluctuation of power that is fed to the load.
The system is designed as a standalone hybrid microgrid system on the Matlab/Simulink environment which comprises of Photovoltaic (PV) array, Lithium-ion (Li-ion) battery storage device Microhydropower (MHP) system. All the sources are supplying the AC load through a three-phase inverter. The DC-DC bidirectional converter interfaces the renewable resources and a battery bank to allow power going in both directions to absorb excess and dispatch power to prevent mismatch due to variation of power from PV array and micro-hydro plant. The load considered in this research is of 50 remote households amounting to 180kW. The PV account for about 102.2kW and Micro-hydropower contributes around 96.18kW. The weather conditions in terms of irradiance will be considered to mimic real-life situations for the analysis of the power system. The mathematical modeling of components energy management algorithms to prioritize energy demand with regards to the demand is implemented.
The system provides quick response time where PV power transient time is 15ms to stabilize and the MHP takes 0.8s to reach a steady state. This is because of the high mechanical torque on turbine blades due to water kinetic energy that impacts the blades. The fuzzy logic control provides smooth system operation as the energy management system where there is a continuous power flow provided to the load and voltage. This is only true if SOC is neither bigger than SOCmax(80%) nor smaller than SOCmin(20%). This is to protect the lifespan of the battery. The system provides over 99.4% of accuracy with calculated values compared to the ones generated by Matlab/Simulink. The battery response time to any mismatches is 5ms before it dispatches power or before it can absorb excess power.
Additional information
Thesis (MEng (Energy))--Cape Peninsula University of Technology, 2021
File(s)![Thumbnail Image]()
Loading...
Name
MELAMU__MOTEANE_THABO__214252450.pdf
Size
3.62 MB
Format
Adobe PDF
Checksum
(MD5):d636e08a5dbf5dc717be4f3542be8f71