A review of hybrid renewable energies optimisation: design, methodologies, and criteria

Ajiboye, Olalekan Kunle; Ochiegbu, Chimere Victor; Ofosu, Eric Antwi; Gyamfi, Samuel

doi:10.1080/14786451.2023.2227294

Cited by 9 publications

(3 citation statements)

References 183 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among all these different methods, there is the linear programming [50,51] and mixed integer linear (PLNEM) solver (PLNEM) [32,33], which provides a suitable framework for obtaining highquality solutions [52] with acceptable computational effort and good convergence properties. The mixed integer linear programming solution method, therefore, is one that is widely employed for HRES (Hybrid Renewable Energy Systems) and is characterized by good convergence [53,54].…”

Section: Bibliographical Reviewsmentioning

confidence: 99%

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming and Onsset Method

Moyème,

Yao,

Sedzro

et al. 2024

Preprint

View full text Add to dashboard Cite

Optimal planning and design of microgrids is a priority for electrification of off-grid areas. Indeed, according to one of the Sustainable Development Goals (SDG) 7, the UN recommends universal access to electricity for all but, at the lowest cost. To achieve this goal, several optimization methods with different strategies have been proposed in the literature. This paper proposes a microgrid installation and planning model based on a combination of several techniques. Python 3.10, the programming language, has been used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. The results obtained enabled us to simulate the model obtained, with a cluster considered as a case study, based on the elbow and k-means clustering method; then, in a second phase, to size the microgrid with a capacity of 40 kW, by optimizing all the resources available on the site: the example of the various resources in the case of Togo was considered (solar, wind, hydropower). This study therefore highlighted the optimal resources obtained by integrating battery systems by the optimization model formulated on the basis of the various technology costs, such as investment, maintenance and operating costs, based on the technical limits of the various technologies. For the optimum results obtained, solar systems account for 80% of the maximum load considered, compared with 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed, based on the constraints of a voltage stability limit estimated at 10% of the maximum voltage drop. The substation capacity limit was also taken into account. The results obtained from the case studied enabled us again, to present selective results for load nodes in relation to the substation node. And finally, the spatial technology planning tool made it possible to obtain results on the different strategies for the various technologies required for the electrification planning study of the Togo case. The various results obtained from the different techniques provide the leads needed for a feasibility study for optimal electrification of off-grid areas, using microgrid systems.

show abstract

Section: Bibliographical Reviewsmentioning

confidence: 99%

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming and Onsset Method

Moyème,

Yao,

Sedzro

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Among all these different methods are linear programming [51,52] and the mixed integer linear (PLNEM) solver (PLNEM) [32,33], which provide a suitable framework for obtaining high-quality solutions [53] with acceptable computational effort and good convergence properties. The mixed integer linear programming solution method is, therefore, widely employed for HRESs (Hybrid Renewable Energy Systems) and is characterized by good convergence [54,55].…”

Section: Bibliographical Reviewsmentioning

confidence: 99%

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Kabe,

Bokovi,

Sedzro

et al. 2024

Energies

View full text Add to dashboard Cite

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

show abstract

“…Techno-economic optimization is a crucial step within the development of complex systems, as it helps assess their feasibility and viability. This procedure involves systematically examining numerous technical, economic, and financial aspects, which can often be conflicting [41][42][43].…”

Section: Setup Of the Optimization Problemmentioning

confidence: 99%

Mixing Renewable Energy with Pumped Hydropower Storage: Design Optimization under Uncertainty and Other Challenges

Zisos,

Sakki,

Efstratiadis

2023

Sustainability

View full text Add to dashboard Cite

Hybrid renewable energy systems, complemented by pumped hydropower storage, have become increasingly popular amidst the increase in renewable energy penetration. Such configurations are even more prosperous in remote regions that are typically not connected to the mainland power grid, where the energy independence challenge intensifies. This research focuses on the design of such systems from the perspective of establishing an optimal mix of renewable sources that takes advantage of their complementarities and synergies, combined with the versatility of pumped hydropower storage. However, this design is subject to substantial complexities, due to the multiple objectives and constraints to fulfill, on the one hand, and the inherent uncertainties, on the other, which span over all the underlying processes, i.e., external and internal. In this vein, we utilize a proposed hybrid renewable energy system layout for the Aegean Island of Sifnos, Greece, to develop and evaluate a comprehensive simulation-optimization scheme in deterministic and, eventually, stochastic settings, revealing the design problem under the umbrella of uncertainty. In particular, we account for three major uncertain elements, namely, wind velocity (natural process), energy demand (anthropogenic process), and wind-to-power conversion (internal process, expressed in terms of a probabilistic power curve). Emphasis is also given to the decision-making procedure regarding the system’s key design parameters (reservoir size and solar power capacity), which is achieved by thoroughly interpreting the uncertainty-aware optimization outcomes. Finally, since the proposed pumped hydropower storage uses the sea as the lower reservoir, additional technical challenges are addressed.

show abstract

A review of hybrid renewable energies optimisation: design, methodologies, and criteria

Cited by 9 publications

References 183 publications

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming and Onsset Method

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming and Onsset Method

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Mixing Renewable Energy with Pumped Hydropower Storage: Design Optimization under Uncertainty and Other Challenges

Contact Info

Product

Resources

About