This paper performs a technoeconomic comparison of two hybrid renewable energy supplies (HRES) for a specific location in Ghana and suggests the optimal solution in terms of cost, energy generation capacity, and emissions. The two HRES considered in this paper were wind/hydrogen/fuel-cell and wind/battery storage, respectively. The necessity of this study was derived from the rise and expansion of hybrid renewable energy supply in a decentralised network. The readiness to embrace these new technologies is apparently high, but the best combination for a selected location that brings optimum benefits is not obvious and demands serious technical knowledge of their technical and economic models. In the methodology, an analytical model of energy generation by the various RE sources was first established, and data were collected about a rural-urban community in Doderkope, Ghana, to test the models. HOMER software was used to design the two hybrid systems based on the same load profiles, and results were compared. It turns out that the HRES 1 (wind/hydrogen/fuel-cell) had the lowest net present cost (NPC) and levelized cost of electricity (COE) over the project life span of 25 years. The energy reserve with the HRES 2 (wind/battery storage) was huge compared to that with the HRES 1, about 270% bigger. Furthermore, with respect to the emissions, the HRES 2 was environmentally friendlier than the HRES 1. Even though the battery storage seems to be more cost-effective than the hydrogen fuel-cell technology, the latter presents some merits regarding system capacity and emission that deserve greater attention as the world looks into more sustainable energy storage systems.

This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

This paper assesses the performance of electricity generation using wind/hydrogen/fuel-cell technology. The intermittency of renewables, especially wind, and the need for storage of excess energy make them unattractive for continuous generation of electricity. This paper focuses on the wind resource of Anloga (Ghana) and the potential of hydrogen production from water electrolysis. The assessment of this system covers three main areas including the potential energy generation, environmental impacts, and economic impacts. The paper adopted analytical models of energy generation of fuel cell and hydrogen technologies and further performs their assessment using HOMER software. It was revealed that the annual electricity production from the hydrogen fuel cell is 25,999kW/yr, with an annual capacity shortage of 392kW/yr representing a 10% capacity shortage. The levelized cost of electricity was 0.602$/kWh and the emissions have been completely minimized as compared to diesel generation plants.

Hybrid power systems that combine wind and solar PV technology have been widely employed for power generation, particularly for electrification in remote and islanding locations, because they are more cost-effective and reliable than traditional power systems. This article intends to develop an environmentally friendly and cost-effective hybrid power system for selected critical loads in the Avuto community of Ghana. Following the acquisition of site data, a hybrid solar PV, wind, diesel generator, and converter analysis was conducted using HOMER software to establish the appropriate sizing of system components based on technical and economic parameters such as load served, annual electricity production, net present cost (NPC), emission, Operating cost, Fuel consumption and energy cost (COE). Based on the optimization computational results, it can be stated that the combination of system components, including solar photovoltaic, wind turbine, and diesel generator, is a good fit for the application region and might be used for rural and island electrification in the future. The suggested energy system has an LCOE of 0.39 US$/kWh for the 1.08 US$/litre diesel fuel cost and a 3.33-year payback period, with 58.8 kW for PV, 7 units for 3 kW wind turbines, 10 kW for diesel generator, and 6.99 kW for the converter. In terms of emission reduction, the proposed case presented a 55% emission reduction from the base case scenario.

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