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This paper presents a day-ahead forecasting method for photovoltaic (PV) power plants in commercial sectors. The method is based on numerical weather prediction (NWP) models from open weather maps and power plant specifications. The output of the model is the predicted power output from the PV power plant, which is incorporated into an optimal control strategy of the PV plant using battery storage. The use of optimal algorithms assists in the PV power plant curtailment in cases of over-generation and reduces the dependence on conventional power sources such as generators in cases of under-generation by the PV plant. It was found that most forecasting methods do not incorporate PV power and storage systems for proper optimization and demand management. This can be seen as a gap for further research of forecasting models integrated with battery storage systems to improve PV power system performance. Results obtained show a good performance of the developed model. A root means square error (RMSE) of 425.79 W and 595.10 W and a mean absolute error (MAE) of 246.26 W and 238 W were achieved for a summer and winter day, respectively. Furthermore, an excellent positive correlation exists between the predicted output power and the observed results, with R2 values over 90%.

Fossil fuels are rapidly running out, and with the demand for environmentally friendly energy sources increasing, power grids are looking for distributed power generation-based renewable resources. The distribution of these energy sources is significantly linked to the development of smart microgrids, which are also extensively connected with the energy internet. This paper explores the energy internet operation, focusing on developing a routing algorithm for an energy router. The energy routing algorithm is further substantiated with the aid of simulations. This algorithm can find all the paths available for energy transmission between two nodes and selects the track with the most negligible losses as the path for transmission. All the possible routes are displayed along with the losses associated with each direction to ensure that the approach with the lowest losses is taken. The algorithm is also tested for 24 h at an hourly interval to observe the change in power transmitted on the system.