Omar Mohamed

Supercritical generation technologies have emerged as a promising pathway toward clean and flexible energy conversion. Advancing these technologies demands highly accurate mathematical models capable of supporting innovative control strategies and enhancing energy efficiency. The modeling of industrial systems has evolved from simple first-principle formulations to sophisticated frameworks that preserve their physical foundations while being enhanced through optimization algorithms. As these optimization problems increase in size, sensitivity analysis becomes essential for identifying the most influential parameters, thereby reducing computational complexity without sacrificing accuracy. These critical parameters are then refined using an adaptive Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization method. The results demonstrate notable gains in both predictive accuracy and computational efficiency. The proposed model holds significant potential for power generation applications, particularly in advanced control system design and real-time performance monitoring. This study developed a supercritical power plant (SCPP) model that achieved improved accuracy while maintaining simplicity compared to existing models. A model of a 600 MW SCPP is first constructed and systematically analyzed to capture its key characteristics. Subsequently, a sensitivity analysis is conducted to identify the parameters with the greatest influence on model performance. These critical parameters are then tuned using an adaptive Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization method. The results demonstrate notable gains in accuracy. The proposed model offers promising potential for power generation applications, particularly in the areas of advanced control system design and real-time performance monitoring.

Applied mathematical modeling of the various energy resources has contributed widely in their design and operation improvements. Among renewable energy sources, wind energy systems have gained a significant attention for their rapid and continuous growth, which resulted from their cost effectiveness and clean power generation. However, research effort on the industry and university levels is still needed to enhance the characteristics of existing wind turbines and allow larger amounts of their share. This thesis presents an accurate model of a wind turbine (TWT-1.65) system for time-based dynamic simulations. The parameters of the power coefficient have been identified, calibrated and verified using different meta-heuristic optimization techniques, which are the Wild Horse Optimizer (WHO), Whale Optimizer (WO), and Genetic Algorithm (GA) to enhance the model accuracy over the previously related studies. The new version of the parameters has resulted in a higher accuracy while being practically meaningful, with the best achieved MSE of 0.006. The system model has been then integrated with two predictive controllers which are the linear Model Predictive Control (MPC) and Neural Network Predictive Control (NNPC) to regulate the pitch angle trends for the target of maximum energy harvesting. With proper selection of the controllers’ parameters, off-line simulation studies have shown improved production trends of the power output with constrained and safe trajectory of the pitch angle, which can be translated to be an improve in the total average harvested power of 34% and 44.5% over the complete time window of 5760 min using NNPC and Linear MPC respectively.

Mathematical modeling of combined-cycle units has produced a variety of valuable results, primarily significant advances in their energy efficiency and education of power plant operators. These positive results motivate the need for more complex modeling and simulation techniques with the goal of greater accuracy and accessibility. This study presents a simplified and enhanced version of a gas turbine model for dynamic performance simulation. The theoretical improvements have been proved through modeling philosophy and simulation accuracy attained by metaheuristic optimization techniques. The model has been constructed using thermodynamic and mathematical concepts, with parameter optimization and calibration performed using metaheuristic optimizers. The suggested model’s resilience has been demonstrated by simulation results.

Coal power plants have been a major source of undesirable emissions. Despite the technological advancements in renewable energies, coal units are still in-service in many developed and developing countries due to their reliability, adequacy, and flexibility for power delivery. There are some promising technologies for cleaner operation during power production from coal, including supercritical boiler (SC) design and carbon capture and storage (CCS), however, the challenging in innovating effective methods is still open to expand the boundary of knowledge in this speciality. This paper introduces a novel and simple method for reducing CO₂ emissions and improving the dynamic responses of a 600 MW SC coal power plant by Artificial Neural Network (ANN) technique. A wide-range data-driven feedforward ANN model has been identified and verified for the various operations recorded as closed-loop data-sets, which covers all situations of startup, once-through mode, and even emergency shutdown of the unit. The closed-loop SC plant model has been augmented with an inverse multivariable coordinate NN controller, developed by analogous learning algorithm to improve the plant automation. With precisely selected setpoints, as operational rules, of temperature, pressure, and earliest possible power demand signals, the automated SC plant has been capable to operate with lower coal consumption - and thus lower emissions – than the existing operation strategy during startup, normal operation, and emergency shutdown modes. The improvement in dynamic responses have been quantified through simulations with comparison with existing performance, which have resulted in an overall average reduction of 2.143 Kg/s in coal consumption.

Gas turbines are used in the energy sectors as propulsion and power generation technologies. Despite technological advances in power generation and the emergence of numerous energy resources, gas turbine technology remains important due to its flexibility in load demand following, dynamical behavior, and the ability to work on different fuels with minor design changes. However, there would be no ambitious progress for gas turbines without reliable modeling and simulation. This paper describes a novel approach for modeling, identifying, and controlling a running gas turbine power plant. A simplified nonlinear model structure composed of s-domain transfer functions and nonlinear blocks represented by rate limiters, saturations, and look-up tables has been proposed. The model parameters have been optimized to fit real-world data. The verified model was then used to design a multiple PI/PD control to regulate the gas turbine via the inlet guide vane and fuel vales. The aim is to raise and stabilize the compressor’s differential pressure or pressure ratio, as well as raise the set-point of the temperature exhausted from the combustion turbine; as a result, energy efficiency has been improved by an average of 237.16 MWh saving in energy (or 8.96% reduction in fuel consumption) for a load range of 120 MW to 240 MW.

Energy demand forecasting has been an indispensable research target for academics, which has led to creative solutions for energy utilities in terms of power system design, control, and planning. The usefulness of energy demand forecasting is confined to the power engineering industry but globally exceeds such outcomes to contribute to the environment and health sectors. Despite the large number of research projects published on this topic, the challenge of energy demand forecasting still exists, especially with the developments in modeling concepts via artificial intelligence, which motivates more attractive solutions for the variables involved in energy demand forecasting. Mathematical correlation or extrapolation-like methods cannot be effective in all situations; however, when a time series neural network is presented, most statistical, empirical, and theoretical problems can be easily handled. This paper presents a simple and easy-to-understand method for the next decade of energy demand forecasting based on a nonlinear autoregressive (NAR) neural network. From its time series past values, NAR structurally is an optimal predictor for a future variable. A publicly available data set for global energy consumption has been used to construct the network model with sufficiently accurate results. The evidence has appeared in precisely following the exponential trend of energy consumption as well as the regressions for training, testing, and validation, which ensures the model's robustness and avoids getting involved in overfitting. The proposed model concepts and results can be easily used in undergraduate engineering education, training graduates, and future research.

Computer simulation of energy resources has led to significant achievements in the interdisciplinary fields of energy and environment. Apart from renewable resources, fossil-fuel power generation plants can be made cleaner to satisfy the future climate targets while keeping secure and stable grid. Clean coal power plants are still among the dominant options for power generation, which are committed through energy-efficient operation, carbon capture and storage, or combination of both strategies. On the other hand, machine learning and deep learning techniques have a leading integrity in the field of simulation. This paper presents accurate models of a cleaner coal-fired supercritical (SC) unit using two types of artificial neural network, which are Elman neural network (ENN) and generalized regression neural network (GRNN). The models newly embed higher coverage range and more accurate results than previously published models. Each subsystem of the models has been structured as a multi-input single-output (MISO) component to predict the behavior of significant variables in the plant, mainly the supercritical pressure in MPa, the steam temperature in °C and the production in MW. Those variables have been intentionally selected as they are clear indicators for the energy-efficient and cleaner production. Simulation results of four sets of data have indicated satisfactory performance of both models with a bit higher superiority of the GRNN that has given negligible or zero Mean Squared Error (MSE) for all outputs, whereas the minimum MSE of the deep ENN is 3.131 × 10⁻³.

With increased interests in affordable energy resources, a cleaner environment, and sustainability, more objectives and operational obligations have been introduced to recent power plant control systems. This paper presents a verified load frequency model predictive control (MPC) that aims to satisfy the load demand of three practical generation technologies, which are wind energy systems, clean coal supercritical (SC) power plants, and dual-fuel gas turbines (GTs). Simplified state-space models for the two thermal units were constructed by concepts of subspace identification, whereas the individual wind turbine integration was implicated by the Hammerstein–Wiener (HW) model and then augmented from the output to simulate the effect of a wind farm, assuming similar power harvesting from all turbines in the farm. A practical strategy of control was then suggested, which was as follows: with a changing load demand, the available harvested wind energy must be fully admitted to the network to cover part of the load demand with the free energy, and the resultant load signal will then be instructed to the MPCs designed for the coal and gas units for the coordination of generation. The load signal, after being penetrated by wind, has more transients and faster changes, and needs a more sophisticated control in order to follow the load demand of the flexible coal and gas units. Furthermore, as the level of wind penetration increases, the power system frequency excursions are higher. The simulation results show an acceptable performance for linear MPCs embedded to the GT and coal units, with around a 90 MW share of wind without exceeding the safe restrictions of the plants and allowable reasonable frequency excursions. The complete simulation framework can be used to facilitate wind energy penetration in such power systems and train the operators and future engineers with subsequent power system frequency simulation studies.

This paper introduces shortcut and effective approaches for energy auditing and conservation for educational buildings with application to Princess Sumaya University for Technology. The building electricity is significantly covered by a photovoltaic system, which is included in the building assessments and solutions. The short and simplified computations of the Normalized Performance Indicator of the building have shown fair performance, but some conservation opportunities are still possible. Two feasible energy conservation approaches are presented; the first is by optimization of the operating times of 357 air conditioners in the building using a Genetic Algorithm optimizer. Rigorous effort was done to get an accurate relationship between the comfortable time spans and the average temperatures to ensure the users’ convenience with considering their willingness to exceed the pre-selected optimized time span. Nineteen scenarios over 6 months have attained an average saving of 12.5%, which leads to a payback period of 9.27 years. The second approach is by ThermaCote® building insulation, which attains 38% cost savings with a combined payback period of 19.16 years, including external replacement. Although the existing photovoltaic system supplies a considerable free energy proportion and reduces the billing cost, the proposed two methods have improved the normalized performance indicator from the “fair” range in the existing situation to the “good” range.