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A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA.

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Solar cells are highly sensitive to temperature, which affects its operating parameters. The study has its aim in accessing the impact of temperature (in excess above the maximum operating cell temperature) and irradiance source on the efficiency of polycrystalline photovoltaic (PV) solar panels in an environment where the temperature and irradiance level can be fully controlled. For the study to achieve its aim, a solar box and tungsten light bulbs were used to create an environment where the irradiance level and the temperature can be controlled. The solar panel was placed inside the solar box facing the light source while the irradiance level and temperature were measured and held constant. Results show a steady decrease in voltage with increasing temperature while the performance ratio and efficiency of the photovoltaic module followed a similar trend as that of voltage once the temperature exceeds the maximum operating cell temperature. Results also show the output voltage of the photovoltaic to be higher under the tungsten light than the sun, but the efficiency achieved by the photovoltaic under the sun far exceeds that obtained under the tungsten light.

China intends to develop its renewable energy sector in order to cut down on its pollution levels. Concentrated solar power (CSP) technologies are expected to play a key role in this agenda. This study evaluated the technical and economic performance of a 100 MW solar tower CSP in Tibet, China, under different heat transfer fluids (HTF), i.e., Salt (60% NaNO3 40% KNO3) or HTF A, and Salt (46.5% LiF 11.5% NaF 42% KF) or HTF B under two different power cycles, namely supercritical CO2 and Rankine. Results from the study suggest that the Rankine power cycle with HTF A and B recorded capacity factors (CF) of 39% and 40.3%, respectively. The sCO2 power cycle also recorded CFs of 41% and 39.4% for HTF A and HTF B, respectively. A total of 359 GWh of energy was generated by the sCO2 system with HTF B, whereas the sCO2 system with HTF A generated a total of 345 GWh in the first year. The Rankine system with HTF A generated a total of 341 GWh, while the system with B as its HTF produced a total of 353 GWh of electricity in year one. Electricity to grid mainly occurred between 10:00 a.m. to 8:00 p.m. throughout the year. According to the results, the highest levelized cost of energy (LCOE) (real) of 0.1668 USD/kWh was recorded under the Rankine cycle with HTF A. The lowest LCOE (real) of 0.1586 USD/kWh was obtained under the sCO2 cycle with HTF B. In general, all scenarios were economically viable at the study area; however, the sCO2 proved to be more economically feasible according to the simulated results.

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