This paper presents the technical and economic analysis of a solar–wind electricity generation system to meet the power requirements of a rural community (Okorobo-Ile Town in Rivers State, Nigeria) using the Renewable—energy and Energy—efficiency Technology Screening (RETScreen) software. The entire load estimation of the region was classified into high class, middle class, and lower class. Two annual electricity export rates were considered: 0.1 USD/KWh and 0.2 USD/KWh. The results from the proposed energy model comprising a 600 kW PV system and a 50 kW wind system showed that with a USD 870,000 initial cost and USD 9600 O&M cost, the annual value of the electricity generated was 902 MWh. The simple payback was 5.1 years with a net present value of USD 3,409,532 when 0.2 USD/KWh was used as the annual export rate instead of 10.8 years for simple payback and an NPV of USD 1,173,766 when 0.1 USD/KWh was used. Thus, there is a potential to install a wind–solar system with average weather conditions of 4.27 kWh/m²/d for the solar irradiance and 3.2 m/s for the wind speed at a 10 m hub height using a rate of 0.2 USD/KWh as the electricity export rate.

This work presents an investigation of the optimal tilt angles for mounting pv panels in South –South and South - East States in Nigeria when no tracking system exists using the photovoltaic geographic information system (PVGIS). Experiments were carried out to verify results obtained from the PVGIS database. The experimental set up consist of five PV panels each rated 10Watts inclined at five different angles including the optimal angle obtained from the PVGIS database. South-South Nigeria consists of six states (Rivers, Delta, Bayelsa, Edo, Cross-Rivers and Akwa-Ibom). South-East Nigeria consists of five states (Abia, Enugu, Imo, Ebonyi and Anambra). The optimal installation angle of selected cities within each of the States was identified. Results from experiments conducted showed that from January to April and September to December, the yearly optimal tilt angle (11o for Port Harcourt, 12o for Yenagoa, 11o for Benin City, 10o for Calabar, 11o for Asaba and 10o for Uyo) is suitable for use in the South – South while from January to April and September to December, the yearly optimal tilt angle (10o for Umuahia, 11o for Awka, 12o for Enugu, 10o for Owerri, and 11o for Abakaliki) is suitable for use in the South – East. The horizontal plane (0o) is suitable from May to August in both South –South and South – Eastern States. The untapped solar potential in the South – South was observed to be as high as 286.62kWh/m2 for Port Harcourt and a low value of 19.34kWh/m2 was recorded in Calabar when solar panels are not installed at the optimal angles. For the South –East, the untapped solar potential was observed to be as high as 222.11kWh/m2 for Umuahia and a lowest value of 21.31kWh/m2 was also recorded in Umuahia when solar panels were not installed at the optimal angles. Installing PV panels at the optimal tilt angle can improve the efficiency of solar energy generation, making it more cost-effective by maximizing the total amount of diffused and direct radiation.

This work utilizes the particle swarm optimization (PSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two-step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained are then compared with results from the Non-dominant Sorting Genetic Algorithm II (NGSA-II), hybrid genetic algorithm–particle swarm optimization (GA-PSO) and the proprietary derivative-free optimization algorithm. An energy management system monitors the energy balance and ensures that the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consists of solar panels (151), wind turbine (3), inverter (122) and batteries (31). This results in a minimized TEC, TAC and LCOE of USD 469,200, USD 297,100 and 0.007/kWh, respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient conditions.