K
Kanzumba Kusakana
Researcher at Central University of Technology
Publications - 170
Citations - 2844
Kanzumba Kusakana is an academic researcher from Central University of Technology. The author has contributed to research in topics: Renewable energy & Hybrid system. The author has an hindex of 26, co-authored 154 publications receiving 2132 citations. Previous affiliations of Kanzumba Kusakana include Tshwane University of Technology & Central University, India.
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Optimal energy management of a grid-connected dual-tracking photovoltaic system with battery storage: Case of a microbrewery under demand response
TL;DR: An optimal energy management model to minimize the energy cost of a microbrewery, under demand response, supplied with a grid-connected photovoltaic system with battery storage system is developed.
Proceedings ArticleDOI
Pumped storage-based standalone hydrokinetic system: Feasibility and techno-economic study
TL;DR: In this article, a pumped hydro storage is proposed to be used in conjunction with a standalone hydrokinetic system in off-grid power supply to achieve 100% energy autonomy in remote and isolated communities.
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Optimal energy management of a residential grid-interactive Wind Energy Conversion System with battery storage
TL;DR: An optimal energy management model of a 2 kW residential grid-interactive Wind Energy Conversion System with battery storage is presented and results have shown that up to 95% daily operation cost reduction is achievable.
Proceedings ArticleDOI
Energy dispatching of an isolated diesel-battery hybrid power system
TL;DR: The results show that using the developed optimal energy dispatch models, significant fuel saving can be achieved compared to the case where the diesel generator is used alone to supply the same load requirements.
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Optimal energy management of a retrofitted Rubber Tyred Gantry Crane with energy recovery capabilities
TL;DR: In this article, an optimal energy management model for the grid-powered electric RTG, with a battery storage system, is developed to reduce the operation cost, by minimizing the component linked to the maximum demand charges from the grid, as well as the componentlinked to the Time of Use (ToU) pricing structure.