Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm.

Due to focal liberality in electricity market projection, researchers try to suggest powerful and successful price forecasting algorithms. Since, the accurate information of future makes best way for market participants so as to increases their profit using bidding strategies, here suggests an algorithm for electricity price anticipation. To cover this goal, separate an algorithm into three steps, namely; pre-processing, learning and tuning. The pre-processing part consists of Wavelet Packet Transform (WPT) to analyze price signal to high and low frequency subseries and Variational Mutual Information (VMI) to select valuable input data in order to helps the learning part and decreases the computation burden. Owing to the learning part, a new Least squares support vector machine based self-adaptive fuzzy kernel (LSSVM-SFK) is proposed to extract best map pattern from input data. A new modified HBMO is introduced to optimally set LSSVM-SFK variables such as bias, weight, etc. To improve the performances of HBMO, two modifications are proposed that has high stability in HBMO. Suggested forecasting algorithm is examined on electricity markets that has acceptable efficiency than other models.

A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources.

The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses' search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non-dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue.

Hybrid Application of Nanoparticles and Polymer in Enhanced Oil Recovery Processes.

Nowadays, the addition of nanoparticles to polymer solutions would be of interest; however, the feasible property of nanoparticles and their impact on oil recovery has not been investigated in more detail. This study investigates the rheology and capillary forces (interfacial tension and contact angle) of nanoparticles in the polymer performances during oil recovery processes. Thereby, a sequential injection of water, polymer, and nanoparticles; Nanosilica (SiO2) and nano-aluminium oxide (Al2O3) was performed to measure the oil recovery factor. Retention decrease, capillary forces reduction, and polymer viscoelastic behavior increase have caused improved oil recovery due to the feasible mobility ratio of polymer-nanoparticle in fluid loss. The oil recovery factor for polymer flooding, polymer-Al2O3, and polymer-SiO2 is 58%, 63%, and 67%, respectively. Thereby, polymer-SiO2 flooding would provide better oil recovery than other scenarios that reduce the capillary force due to the structural disjoining pressure. According to the relative permeability curves, residual oil saturation (Sor) and water relative permeability (Krw) are 29% and 0.3%, respectively, for polymer solution; however, for the polymer-nanoparticle solution, Sor and Krw are 12% and 0.005%, respectively. Polymer treatment caused a dramatic decrease, rather than the water treatment effect on the contact angle. The minimum contact angle for water and polymer treatment are about 21 and 29, respectively. The contact angle decrease for polymer treatment in the presence of nanoparticles related to the surface hydrophilicity increase. Therefore, after 2000 mg L-1 of SiO2 concentration, there are no significant changes in contact angle.

Parametric Study of Polymer-Nanoparticles-Assisted Injectivity Performance for Axisymmetric Two-Phase Flow in EOR Processes.

Among a wide range of enhanced oil-recovery techniques, polymer flooding has been selected by petroleum industries due to the simplicity and lower cost of operational performances. The reason for this selection is due to the mobility-reduction of the water phase, facilitating the forward-movement of oil. The objective of this comprehensive study is to develop a mathematical model for simultaneous injection of polymer-assisted nanoparticles migration to calculate an oil-recovery factor. Then, a sensitivity analysis is provided to consider the significant influence of formation rheological characteristics as type curves. To achieve this, we concentrated on the driving mathematical equations for the recovery factor and compare each parameter significantly to nurture the differences explicitly. Consequently, due to the results of this extensive study, it is evident that a higher value of mobility ratio, higher polymer concentration and higher formation-damage coefficient leads to a higher recovery factor. The reason for this is that the external filter cake is being made in this period and the subsequent injection of polymer solution administered a higher sweep efficiency and higher recovery factor.

Feasible analysis of reusing flowback produced water in the operational performances of oil reservoirs.

Water reuse is considered one of the most efficient and optimum ways in petroleum industries to address the water scarcity problem. The effluents which are made by the petroleum operations are supposed to be one of the hazardous materials when they are discharged to the environment. The objective of this study is to measure the volume of the required water for the operational performances of the studied oil field. To do this, the necessary water and the volume of provided treated water for the waterflooding, tertiary flooding, and hydraulic fracturing procedures are appropriately measured and by the utilization of photo-Fenton/flotation are administered to remove the oil droplets. According to the observational measurements, it is clarified that hydraulic fracturing has supplied approximately 93% of its required water by the treatment of flowback water and it virtually eliminated the necessity of fresh water from local or domestic water resources. Moreover, the total freshwater that has been saved in this oil field is investigated about 80% of the total required water for their performances. Consequently, the lower need of fresh water from local resources would reduce the unnecessary expenses to provide this volume of water and would save fresh water for about 2750 inhabitants for 1 year to overcome the issue of water scarcity in the world.