Renewables and energy storage to optimise the amount of electrical energy consumed in a building.
| dc.contributor.advisor | Swanson, Andrew Graham. | |
| dc.contributor.author | Naidoo, Ulika Devina. | |
| dc.date.accessioned | 2023-07-12T07:36:11Z | |
| dc.date.available | 2023-07-12T07:36:11Z | |
| dc.date.created | 2022 | |
| dc.date.issued | 2022 | |
| dc.description | Masters Degree. University of KwaZulu-Natal, Durban. | en_US |
| dc.description.abstract | The rising cost of electricity and fuel, along with the looming threat of load shedding has frustrated not only the business owner but the homeowner as well. The need to reduce costs, and the growing pressure for companies and individuals to become more environmentally friendly, is becoming more apparent. To reduce costs and the effects of load shedding, and to become more sustainable, the integration of renewable energy is a clear solution. The solution has led to the investigation of a hybrid system that uses grid-supplied power and renewable energy supplied power which will achieve an effective and efficient optimization of cost. This dissertation is centred on minimising the total cost of ownership over twenty years. This is done by comparing different optimisation algorithms and identifying a cost-effective way of integrating a source of renewable energy, specifically solar energy, with an existing grid-supplied building. The zoning of buildings was found to have an impact on the total cost of ownership as the tariffs were different. By developing a function, the efficiency of a system was quantified based on the load, and what type of building it was. The load has a direct impact on the total cost of ownership. The electrical energy used in a building, and the property type, whether industrial, commercial or residential zone, affects the optimisation algorithm that is used. To minimise the total cost of ownership over twenty years, consideration was given to trade-offs between the available solar, oversizing the PV installation, the cost of electricity at different hours and the use of a storage system. To ensure that the total cost of ownership was correct, financial equations for growing annuity and the prescribed rates for assets, maintenance, and electricity were used. Further to this, South African energy tariffs, actual prices of inverters, solar panels, batteries and solar data of South Africa was used. MATLAB was the application of choice of software due to its optimisation capabilities. Examples of each type of building were analysed to find the optimisation that returned the lowest TCO. Particle Swarm Optimisation, when used for industrial buildings produced the lowest TCO, while smaller loads from commercial buildings and a residential housing, showed that the lowest TCO came from Teaching-Learning Based Optimisation. In each case, the fastest and slowest optimisation technique was Pattern Search and Firefly Optimisation respectively. | en_US |
| dc.identifier.uri | https://researchspace.ukzn.ac.za/handle/10413/21867 | |
| dc.language.iso | en | en_US |
| dc.subject.other | Solar data. | en_US |
| dc.subject.other | Particle Swarm Optimisation. | en_US |
| dc.subject.other | Residential housing. | en_US |
| dc.subject.other | Invasive Weed Optimisation. | en_US |
| dc.subject.other | Battery information. | en_US |
| dc.title | Renewables and energy storage to optimise the amount of electrical energy consumed in a building. | en_US |
| dc.type | Thesis | en_US |