Smart Microgrid (MG) effectively contributes to supporting the electrical power systems as a whole and reducing the burden on the utility grid by the use of unconventional energy generation resources, in addition to backup Diesel Generators (DGs) for reliability increasing. In this paper, potential had been done on day-ahead scheduling of diesel generators and reducing the energy cost reached to the consumers side to side with renewable energy resources, where economical energy and cost-effective MG has been used based on optimization agent called Energy Management System (EMS). Improved Particle Swarm Optimization (IPSO) technique has been used as an optimization method to reduce fuel consumption and obtain the lowest energy cost as well as achieving the best performance to the energy system. Three scenarios are adopted to prove the efficiency of the proposed method. The first scenario uses a 24 hour time horizon to investigate the performance of the model, the second scenario uses two DGs and the third scenario depends on a 48-hour time horizon to validating the performance. The superiority of the proposed method is illustrated by comparing it with PSO and simulation results show using the proposed method can reducing the fuel demand and the energy cost by satisfying the user’s preference.
The occurrence of Sub-Synchronous Resonance (SSR) phenomena can be attributed to the interaction that takes place between wind turbine generators and series-compensated transmission lines. The Doubly-Fed Induction Generator (DFIG) is widely recognized as a prevalent generator form employed in wind energy conversion systems. The present paper commences with an extensive exposition on modal analysis techniques employed in a series of compensated wind farms featuring Doubly Fed Induction Generators (DFIGs). The system model encompasses various components, including the aerodynamics of a wind turbine, an induction generator characterized by a sixth-order model, a second- order two-mass shaft system, a series compensated transmission line described by a fourth-order model, controllers for the Rotor-Side Converter (RSC) and the Grid-Side Converter (GSC) represented by an eighth-order model, and a first-order DC-link model. The technique of eigenvalue-based SSR analysis is extensively utilized in various academic and research domains. The eigenvalue technique depends on the initial conditions of state variables to yield an accurate outcome. The non-iterative approach, previously employed for the computation of initial values of the state variables, has exhibited issues with convergence, lack of accuracy, and excessive computational time. The comparative study evaluates the time-domain simulation outcomes under different wind speeds and compensation levels, along side the eigenvalue analysis conducted using both the suggested and non-iterative methods. This comparative analysis is conducted to illustrate the proposed approach efficacy and precision. The results indicate that the eigenvalue analysis conducted using the proposed technique exhibits more accuracy, as it aligns with the findings of the simulations across all of the investigated instances. The process of validation is executed with the MATLAB program. Within the context of the investigation, it has been found that increasing compensation levels while simultaneously decreasing wind speed leads to system instability. Therefore, modifying the compensation level by the current wind speed is advisable.
In developing nations, such as Iraq, supplying power to isolated and rural border areas that are not connected to the grid continues to be a problem. At present, fossil fuels, which are significant causes of pollution, supply around 80% of the world’s energy demands. Nonetheless, drastically reducing reliance on fossil fuels has many reasons, including depleting global fossil fuel supplies, increasing costs and growing energy needs. The present study examines the electrical requirements of the Al-Teeb area, a city situated in the eastern region of Iraq, close to the Iranian border. This region has not been researched despite its tourism and oil significance. Despite the unpredictable expansion of many isolated locations in Iraq in recent years, the number of generation stations has not changed. Supplying energy to these places will require considerable time and money. Photovoltaics (PV), wind turbines (WTs), diesel generators (DGs), batteries and converters combined on the basis of their compatibility under three distinct scenarios comprise the system’s components. Considering the lowest net present cost (NPC) and cost of energy (COE) of all the examined scenarios, PV, WTs, batteries and DGs are the most economical solutions for the Al-Teeb area. Number of PV (1,215), number of WTs (59), number of DGs (13), number of batteries (3,138), number of converters (47), COE (0.155 US$/kWh), NPC (14.2 million US$) and initial capital cost (4.91 million US$) are revealed by the results. Finally, the results are confirmed using another global optimization method, namely, modified particle swarm optimization.
This paper presents a novel approach, Equal Incremental fuel cost (λ-Concept) approach, for solving Multi Area Economic Dispatch (MAED) problem. It is a simple approach and developed from the basic observation of incremental fuel cost of an area. The proposed approach has been tested on 4-area system with four generators in each area and a large 2-area system consists of 120 generators. The suggested algorithm has been tested extensively by considering the different tie line power transfer limits and useful recommendations are provided. Further, the impact of tie line power transfer limits on total fuel cost is also discussed. It is found from the test cases that the proposed method is shown to be robust, very fast and extensible to include a large class of problems. The simulation results of the proposed method have been compared with the existing methods.
This paper presents a novel approach, Equal Incremental fuel cost (λ-Concept) approach, for solving Multi Area Economic Dispatch (MAED) problem. It is a simple approach and developed from the basic observation of incremental fuel cost of an area. The proposed approach has been tested on 4-area system with four generators in each area and a large 2-area system consists of 120 generators. The suggested algorithm has been tested extensively by considering the different tie line power transfer limits and useful recommendations are provided. Further, the impact of tie line power transfer limits on total fuel cost is also discussed. It is found from the test cases that the proposed method is shown to be robust, very fast and extensible to include a large class of problems. The simulation results of the proposed method have been compared with the existing methods.
Due to the nonlinear electrical properties of PV generators, the width and performance of these frames could be enhanced by carrying them to operate at ultimate energy mark tracking. In this study, a versatile maximum power point tracking (MPPT) model using a modified Flyback controller with artificial neural network (ANN) technique as our proposed system. The hybrid Flyback/ANN controller is based on teaching and training a neural network, where the dataset is utilized to adjust the levitation converter which is taken care of by a stand-alone photovoltaic generator (PVG) with a Flyback controller. It is assumed that the results will be obtained by the ANN-MPPT system with the Flyback controller which provides low motions and shows a great implementation around the maximum power point compared to the PVG used with traditional MPPT algorithms such as Perturbation and Observation (P & O).
Among all control methods for induction motor drives, Direct Torque Control (DTC) seems to be particularly interesting being independent of machine rotor parameters and requiring no speed or position sensors. The DTC scheme is characterized by the absence of PI regulators, coordinate transformations, current regulators and PWM signals generators. In spite of its simplicity, DTC allows a good torque control in steady state and transient operating conditions to be obtained. However, the presence of hysterics controllers for flux and torque could determine torque and current ripple and variable switching frequency operation for the voltage source inverter. This paper is aimed to analyze DTC principles, and the problems related to its implementation, especially the torque ripple and the possible improvements to reduce this torque ripple by using a proposed fuzzy based duty cycle controller. The effectiveness of the duty ratio method was verified by simulation using Matlab/Simulink software package. The results are compared with that of the traditional DTC models.
Some engineering applications requires constant engine speed such as power generators, production lines ..etc. The current paper focuses on adding a new closed loop based on engine torque. Load cells can be used to measure the torque of load applied , the electrical signal is properly handled to manipulate a special fuel actuator to compensate for the reduction in engine speed. The speed loop still acts as the most outer closed loop. This method leads to rapid speed compensation and lead control action.
This review article puts forward the phenomena of chaotic oscillation in electrical power systems. The aim is to present some short summaries written by distinguished researchers in the field of chaotic oscillation in power systems. The reviewed papers are classified according to the phenomena that cause the chaotic oscillations in electrical power systems. Modern electrical power systems are evolving day by day from small networks toward large-scale grids. Electrical power systems are constituted of multiple inter-linked together elements, such as synchronous generators, transformers, transmission lines, linear and nonlinear loads, and many other devices. Most of these components are inherently nonlinear in nature rendering the whole electrical power system as a complex nonlinear network. Nonlinear systems can evolve very complex dynamics such as static and dynamic bifurcations and may also behave chaotically. Chaos in electrical power systems is very unwanted as it can drive system bus voltage to instability and can lead to voltage collapse and ultimately cause a general blackout.
In this research we study the elevations of cities and the water resources specially at the dams reservoirs and the distance between them(dams & cities), we use the Google Earth program to determine these elevations and calculate the difference between the average level (elevation) of water at the dam and the average level of cities, which we want to supply it by water, in order to save electrical power by using the energy of supplied water through pipe line from dams to the cities, the pressure of supplied water must be calculated from the difference in elevations(head). The saving of energy can be achieved by two ways. The first is the energy saving by reduce the consumed power in the pumping water from river, which is used for different purposes. The second is the hydroelectric power generated by establishing a micro hydroelectric generator on the pipe line of the water supplied.