The paper presents a modified hybrid particle swarm optimization with bat algorithm parameter inspired acceleration coefficients (MHPSO-BAAC) without and with the constriction factor to find the optimal solution of the economic dispatch problems (EDPs) incorporating conventional as well as hybrid and renewable energy sources (RESs) based plants. The algorithm is designed by modifying the recently presented hybrid PSO and BA (HPSOBA) algorithm applied for the achievement of the optimal solution of the EDPs. The modified algorithm is implemented to solve EDPs of all RESs-based power systems for three scenarios, without constraints, with time-varying demand, and with the consideration of regional load sharing dispatch (RLSD). The performance of the algorithm is also verified through the implementation of various combinations of hybrid as well as thermal power plants (TPPs). The case of TPPs consists of three different scenarios: 1) a small-scale system with constraints like ramp-rate limits (RRLs), prohibited operating zones (POZs), and power losses; 2) a medium-scale power system with consideration of emission-economic dispatch (EED); 3) a large-scale power system with valve-point loading (VPL) effect. The results of the designed MHPSO-BAAC algorithm are compared with the various metaheuristic algorithms available in the literature and the comparative analysis shows the superior performance of the developed algorithm in terms of fuel cost reduction, fast convergence, and computational time.

Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs.

This paper presents a hybrid metaheuristic optimization algorithm developed to solve the Economic Dispatch Problem (EDP) encountered in different combinations of power plants. The algorithm is developed by assimilating the prominent features of Particle Swarm Optimization (PSO) and Bat Algorithm (BA) and improves cost reduction and convergence with lesser computational time. The developed algorithm is employed for the solution of EDP consisting of only Renewable Energy Sources (RESs) implemented at various locations in Pakistan. The all RES based EDP consists of scenarios composed of sub-scenarios having no constraints, with time-varying loads and multi-area economic dispatch (MAED). The algorithm is also tested for three different combinations of power plants, comprising of RES integrated with thermal power plants (TPPs), the small-scaled thermal power system with constraints, and a large-scaled power system with Valve Point Loading (VPL) effect. The comparative analysis of the results for the developed metaheuristic algorithm with various existing techniques shows a reasonable reduction in the cost, improved computational time, and fast convergence.

Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed and power using the implementation of the Feedforward and cascaded forward neural networks (FFNNs and CFNNs, respectively). The one and half year’s dataset for Jhimpir, Pakistan, is used to train FFNNs and CFNNs with recently developed novel metaheuristic optimization algorithms, i.e., hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) named HPSOBA, along with a modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), without and with the constriction factor (MHPSO-BAAC-χ). The forecasting results are made for June–October 2019. The accuracy of the forecasted values is tested through the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The graphical and numerical comparative analysis was performed for both feedforward and cascaded forward neural networks that are tuned using the mentioned optimization techniques. The feedforward neural network was achieved through the implementation of HPSOBA with a mean absolute error, mean absolute percentage error, and root mean square error of 0.0673, 6.73%, and 0.0378, respectively. Whereas for the case of forecasting through a cascaded forward neural network, the best performance was attained by the implementation of MHPSO-BAAC with a MAE, MAPE and RMSE of 0.0112, 1.12%, and 0.0577, respectively. Thus, the mentioned neural networks provide a more accurate prediction when trained and tuned through the given optimization algorithms, which is evident from the presented results.

The secrecy and security of patients' details are among the biggest concerns in Healthcare Information Systems. The Electronic Patient Records (EPR) data, along with doctors' comments can be embedded inside carrier DICOM Images using the proposed scheme. The confidential information is scattered into different sets, and rather than embedding it in a single DICOM image, it is embedded into multiple carriers for enhanced security. These scans can be used together to hide the confidential patient data using the proposed technique. The prototype steganography scheme is tested utilizing LSB Substitution and dummy secret data is embedded inside DICOM Images. The achieved results are imperceptible to the human visual system (HVS). Performance matrices i.e., PSNR, MSE, RMSE, SSIM, NR-IQA parameters (BRISQUE, NIQE, PIQE), as well as entropy, are calculated for cover and stego images. The proposed scheme has been found to be resilient and computationally secure.

The renewable energy sources (RESs)-based economic dispatch problem (EDP) is of vital importance for modern power systems. Environmental pollution, climatic degradation, and rapidly growing prices of continuously depleting fossil fuels have encouraged researchers to consider mechanisms for RES implementation and optimal operations. This paper presents a quasi-oppositional population-based global particle swarm optimizer with inertial weights (QPGPSO-ω) to solve environment friendly EDPs. The optimization technique is applied to solve the EDP under different scenarios including cases where only renewable energy sources (RESs) are used and the cases where combined emission–economic dispatch (CEED) problem is taken into account. The scenario for RESs includes a combination of six wind, five solar PV, and four biofuel systems for power generation. EDPs are considered without any constraints, and the variability of resources is depicted over time, along with the regional load-sharing dispatch (RLSD). The case of CEED considers ten thermal units with the valve point loading (VPL) effect and transmission losses. The results obtained by the proposed QPGPSO-ω algorithm are better than the reported results employing other optimization methods. This is shown by the lower costs achieved up to USD 8026.1439 for the case of only RES-based EDPs, USD 1346.8 for the case of RES-based EDPs with RLSD, and USD 111,533.59 for the case of CEED. Thus, the proposed QPGPSO-ω algorithm was effective in solving the various adopted power dispatch problems in power system.