Review on renewable hybrid energy and power project design for sustainable development

Micro-grid, an innovative area in the power sector, has huge potential to diminish the cause of blackouts, power deficiencies and its independence help to deliver uninterruptable power to the customers. Micro-grids can offer a great choice for integrating localized renewable energy generation. Hybridizing renewable energy sources grant a realistic form of power production. Renewable and hybrid energy systems (HESs) are expanding due to environmental concerns of climate change, air pollution and depleting fossil fuels. Moreover, HESs can be cost-effective in comparison with conventional power plants. This paper reviews current methods for designing optimal HESs. The survey shows these systems are often developed on a medium scale in remote areas and standalone, but there is a global growing interest for larger scale deployments that are grid connected. Examples of hybrid energy systems are PV-Wind-Battery and PV-Diesel Battery. PV and wind energy sources are the most widely adopted. Diesel and batteries are often used but hydrogen is increasing as a clean energy carrier. The design of an efficient HES is challenging because HES models are non-linear, nonconvex, and composed of mixed-type variables which cannot be solved by traditional optimization methods. Alternatively, two types of approaches are typically used for designing optimal HESs: simulation-based optimization and metaheuristic optimization methods. Simulation-based optimization methods are limited in view of human intervention which makes them tedious, time consuming, and error-prone. Metaheuristics are more efficient because they can handle automatically a range of complexities. In particular, multi-objective optimization (MOO) metaheuristics are the most appropriate for optimal HES because HES models involves multiple objectives at the same time such as cost, performance, supply / demand management, grid limitations, and so forth. This paper shows that the energy research community has not fully utilized state-of-the-art MOO metaheuristics. More recent MOO metaheuristics could be used such as robust optimization and interactive optimization.

  • Authors Name: Ekaba S., Ekoko U. Chiadika M, Chiejine M

  • Email: ekaba.samuel@mydspg.edu.ng