Major changes in the technologies of power generation and distribution systems have been introduced in recent years due to concern over rapid climate change. Therefore, disturbances in the large-scale generation, transmission, and distribution of energy are expected to occur in the near future. This is due to the difficulty in controlling the transmission and distribution of energy produced from renewable energy sources (RESs), caused by the instability of these sources and the intermittent nature of their energy. As a result, maintaining the dynamic stability of wind power flow and control of the network frequency is becoming more challenging due to the high penetration impacts of RESs. In this paper, a control algorithm using the power-sharing method is proposed for a wind-based energy storage system to maintain the dynamic stability of wind power flow and control of frequency in the power network. To maintain the network stability, a storage system (battery) was installed to store the excess wind power without throwing it into the Secondary/Dump Load (SL) and minimize losses in power generated by the wind turbine. The results show, the transient time of wind power flow and the fluctuation rate of frequency are reduced significantly using a Fuzzy Logic (FL) controller compared to the Proportional Integral Derivative (PID) controller.

The increasing demand for energy in the building sector is mostly due to heat, ventilation and air conditioning (HVAC) systems. In the absence of the clear sky beam optical depth (CSBOD) and clear sky diffuse optical depth (CSDOD), there is a challenge to determine the solar heat gain for different orientations of the surface areas of buildings for HAVC design. The purpose of this research is to determine CSBOD and CSDOB from the available solar radiation data for the calculation of the cooling load in buildings. The numerical values of CSBOD and CSDOD are determined from simulations using three years of measured clear sky beam and diffuse irradiance data for the Umlazi area as a case study. From these results, the autoregressive integrated moving average (ARIMA) for both CSBOD and CSDOD was obtained, with ARIMA (2,1,1) (1,1,0) [12] and ARIMA (3,1,0) (1,1,0) [12] for CSBOD and CSDOD, respectively. The obtained values of 0.68073 and 2.64413 for CSBOD and CSDOD, respectively, were used to calculate the cooling load due to the solar irradiance heat gain for the hottest month of February in a newly built room in Mangosuthu University of Technology (MUT). The value of 1124 W was obtained using the radiant time series method (RTSM). A further study can be performed to use these models for the long-term forecasting of the solar radiation cooling load for optimal control of the HVAC systems.