ABSTRACT
The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.
Subject(s)
Solar Energy , Wind , Uncertainty , Monte Carlo Method , Algorithms , Renewable Energy , Stochastic Processes , Models, TheoreticalABSTRACT
The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.
ABSTRACT
This paper proposes a novel robust controller for frequency stabilization of electrical systems taken into consideration a high renewable energy sources (RESs) penetration. The suggested controller, robust PID (RPID) controller, is combination of a proportional-integral-derivative (PID) controller and a linear quadratic gaussian (LQG) controller. Furthermore, the Improved lightning attachment procedure optimization (ILAPO) technique is applied for determining the optimal setting of the parameters of the introduced RPID controller. A studied power system with RESs is used as a test system to justify the performance of the RPID controller. The superiority of the introduced robust controller is verified through a comparison of its performance under system uncertainties with those of other robust controllers presented in the literature. The results elucidate that the RPID controller can effectually enhance frequency stability and guarantee reliable performance for power grids supplied with high share of renewable energy for all studied scenarios. Consequently, the proposed RPID controller purveys creditable for modern power systems considering RESs.
ABSTRACT
L18 Hunter design was used to investigate the practicability of applying QbD approaches to fluidized hot melt granulation (FHMG) in preparing oral controlled release systems. Eight high-risk variables obtained from risk analysis were classified into chemical factors (type and percentage of meltable binder, matrix viscosity and percentage and filler type) and process variables (size fraction of meltable binder, inlet air volume and fluidization time). The variables were screened for their impacts on pellets characteristics. The obtained results showed that the meltable binder percentage was the significant variable affecting most responses. Flow properties, size distribution, bulk, and tapped densities were significantly (P<0.05) affected by the filler type, inlet air volume, and fluidization time. On the other hand, the matrix variables were non-significant to the dissolution parameters. Out of eight critical variables, it was found that the meltable binder percentage and size fraction, inlet air volume had the most significant effects and will be optimized in the second part of the study. In conclusion, QbD paradigm not only offered a robust FHMG technique to formulate controlled release formulations of hydrophilic drugs but also provided a time and cost saving advantage to pharmaceutical industry.
