Design of hybrid power generation systems connected to utility grid and natural gas distribution network: a new contribution

Hybrid power generation system (HPGS) is an active research area, which is in need of a continuous improvement. It represents the best solution for the most complex problems facing the world in the last decades. These problems are known as the shortage of energy, or lack of electricity, which logically are the results of the continuous increasing demand. Therefore, the researchers do their best to overcome all expected roadblocks facing the development, where the most applicable solutions to solve these problems are introduced. In this paper, the HPGS includes; wind turbine (WT), photovoltaic (PV), storage battery (SB), gas turbine (GT), and utility grid (UG). The GT of this system is fueled directly from the natural gas distribution network considering all operational conditions of it, which may be affected by fueling the natural gas for the GT. So, the natural gas distribution network is becoming an important component of the HPGS, and it is included in the HPGS for the first time. Multi meta-heuristic optimization techniques are applied to obtain the components sizing of this system, where cuckoo search algorithm (CSA), firefly algorithm (FA), and flower pollination algorithm (FPA) have been applied. Therefore, this paper introduces a new contribution not only to the new configuration of the HPGS, but also in applying the new optimization techniques as solving tools. The output results are compared to show the effectiveness and the superiority of the applied techniques as well as extract a recommendation for the best solving technique

COMPARISON OF MPPT ALGORITHMS FOR PHOTOVOLTAIC SYSTEMS UNDER UNIFORM IRRIADIANCE BETWEEN PSO AND P&O

This paper shows a Comparison between Conventional Method [P&O] and particle swarm optimization [PSO] Based on MPPT Algorithms for Photovoltaic Systems under uniform irradiance and temperature. The main idea is to show that PSO method has a very high tracking speed and has the ability to track MPP under different environmental conditions in addition to an easy hardware implementation using a low-cost microcontroller. MATLAB simulations are carried out under very challenging conditions, namely irradiance and temperature, which reflect a change in the load [KW]. The proposed PSO tracking method Results will be compared with conventional method called [P&O] through MATLAB/SIMULINK

Enhancing radial distribution system performance by optimal placement of DSTATCOM

In this paper, A novel modified optimization method was used to find the optimal location and size for placing distribution Static Compensator in the radial distribution test feeder in order to improve its performance by minimizing the total power losses of the test feeder, enhancing the voltage profile and reducing the costs. The modified grey wolf optimization algorithm is used for the first time to solve this kind of optimization problem. An objective function was developed to study the radial distribution system included total power loss of the system and costs due to power loss in system. The proposed method is applied to two different test distribution feeders (33 bus and 69 bus test systems) using different Dstatcom sizes and the acquired results were analyzed and compared to other recent optimization methods applied to the same test feeders to ensure the effectiveness of the used method and its superiority over other recent optimization mehods. The major findings from obtained results that the applied technique found the most minimized total power loss in system ,the best improved voltage profile and most reduction in costs due power loss compared to other methods

A probabilistic multi-objective approach for FACTS devices allocation with different levels of wind penetration under uncertainties and load correlation

This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the Multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller

FACTS allocation considering loads uncertainty, steady state operation constraints, and dynamic operation constraints

This study proposes an algorithm to allocate different types of flexible AC transmission system (FACTS) in power systems. The main objective of this study is to maximize profit by minimizing the system’s operating cost including FACTS devices (FDs) installation cost. Dynamic and steady state operating restrictions with loads uncertainty are included in the problem formulation. The overall problem is solved using both teaching learning based optimization (TLBO) technique for attaining the optimal allocation of the FDs as main-optimization problem and matpower interior point solver (MIPS) for optimal power flow (OPF) as the sub-optimization problem. The validation of the proposed approach is verified by applying it to test system of 59-bus; Simplified 14-Generator model of the South East Australian power system

Generalized optimal placement of PMUs considering power system observability, communication infrastructure, and quality of service requirements

This paper presents a generalized optimal placement of Phasor Measurement Units (PMUs) considering power system observability, reliability, Communication Infrastructure (CI), and latency time associated with this CI. Moreover, the economic study for additional new data transmission paths is considered as well as the availability of predefined locations of some PMUs and the preexisting communication devices (CDs) in some buses. Two cases for the location of the Control Center Base Station (CCBS) are considered; predefined case and free selected case. The PMUs placement and their required communication network topology and channel capacity are co-optimized simultaneously. In this study, two different approaches are applied to optimize the objective function; the first approach is combined from Binary Particle Swarm Optimization-Gravitational Search Algorithm (BPSOGSA) and the Minimum Spanning Tree (MST) algorithm, while the second approach is based only on BPSOGSA. The feasibility of the proposed approaches are examined by applying it to IEEE 14-bus and IEEE 118-bus systems

Population based optimization algorithms improvement using the predictive particles

A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the other unmodified population optimization algorithms based on the best solution, average solution, and convergence rate

A Novel Approach for the Power Ramping Metrics

One of the biggest concerns associated with incorporating a large amount of renewable energy into power systems is the need to cope with significant ramps in renewable power output. Power system operators need to have statistical information on the power ramping features of renewable generation, load, and net-load that can be used to mitigate ramping events in the case of a large forecast error to ensure the power system's flexibility and reliability; on the other hand, for economic considerations. So far, there is no consensus on a precise definition for the ramp event and so far there are hardly any metrics describing the ramping features of a power system. The paper introduces new metrics describing the power ramping features in a power system. The new metrics are ramp regularity factor (RRF), ramp intensity factor (RIF), and maximum ramp ratio (MRR). In addition, the coefficient of variation (CV) is used to characterize the average value of power ramps. The new ramp metrics are applied to the output power of Belgium's aggregated wind farms in 2017 and 2018. The results obtained by comparing the two years demonstrate that the two years have the same ramping behavior, although the average installed wind capacity has been increased. The new metrics can also be applied to other renewable sources (PV, tidal power, etc.), load, and net-load at any stage of operation

Sources and Mitigation of Harmonics in Industrial Electrical Power Systems: State of the Art

Abstract -Power systems are designed to operate at frequencies of 50 Hz or 60Hz. However, certain types of loads produce currents and voltages with frequencies that are integer multiples of the 50 or 60 Hz fundamental frequency. These higher frequencies represent a form of electrical network pollutions known as ''power system harmonics''. This paper presents an extensive literature review in the petrochemical sector for the power system harmonics such as harmonics fundamentals, harmonics harmful effects on various electrical equipment, harmonic distortion limits and harmonic mitigation techniques. This paper is intended as a guide for those interested in this problem or intending to perform further researches in this area

Green Synthesis, Characterization, and Antibacterial Activity of Silver/Polystyrene Nanocomposite

A novel, nontoxic, simple, cost-effective and ecofriendly technique was used to synthesize green silver nanoparticles (AgNPs). The AgNPs were synthesized using orange peel extract as a reducing agent for silver nitrate salt (AgNO3). The particle size distribution of AgNPs was determined by Dynamic Light Scattering (DLS). The average size of silver nanoparticles was 98.43 nm. The stable dispersion of silver nanoparticles was added slowly to polystyrene solution in toluene maintaining the temperature at 70°C. The AgNPs/polystyrene (PS) nanocomposite solution was cast in a petri dish. The silver nanoparticles encapsulated within polymer chains were characterized by X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM) equipped with Energy Dispersive Spectroscopy (EDS) in addition to Transmission Electron Microscopy (TEM). The green AgNPs/PS nanocomposite film exhibited antimicrobial activity against Gram-negative bacteria Escherichia coli, Klebsiella pneumoniae and Salmonella, and Gram-positive bacteria Staphylococcus aureus. Thus, the key findings of the work include the use of a safe and simple AgNPs/PS nanocomposite which had a marked antibacterial activity which has a potential application in food packaging