Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems:A Review

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

Experimental study on lightning discharge attachment to the modern wind turbine blade with lightning protection system

Lightning strike is one of the most severe threats to the wind turbine blades and causes huge damage. Mostly wind turbines are struck by lightning when the blades are rotating. The effect of blade rotation on a lightning discharge attachment is unclear. Therefore, a rod electrode was used in a wind turbine lightning discharge test to investigate the difference in lightning triggering ability when blades are rotating and stationary. A standard switching waveform of 250/2500μs was applied to the rod electrode. Lightning discharge tests of a 1:30 scale wind turbine model with 3m air gaps were performed and the discharge process was observed. Three side receptors were used for Lightning protection system (LPS) of wind turbine blade (WTB). Distance between each receptor was 40cm and 1 st receptor had 5cm distance from tip of the WTB. Standard switching impulses (negative and positive) were applied to the WTB with different orientations and rotating speed. The experimental results demonstrated that when negative switching impulses were applied to the wind turbine blade, all the lightning discharges hit on the 1 st receptor; however, in the case of positive switching impulse, some discharges also hit on the other receptors, blade surface and nacelle of WTB. The attachment points remain same when the blade is stationary or rotating. The analysis revealed that polarity of switching impulse has significant influence on attachment point, and the rotation has little influence during the attachment process. The results can contribute to optimize the design of LPS of wind turbine blade

Analysis of entropy generation in biomimetic electroosmotic nanofluid pumping through a curved channel with Joule dissipation

Biomimetic designs are increasingly filtering into new areas of technology in recent years. Such systems exploit characteristics intrinsic to nature to achieve enhanced adaptivity and efficiency in engineering applications. Peristaltic propulsion is an example of such characteristics and in the current article it is explored as a feasible mechanism for deployment in electrokinetic pumping of nanofluids through a curved distensible conduit as a model for a bioinspired smart device. The unsteady mass, momentum, energy and nanoparticle concentration conservation equations for a Newtonian aqueous ionic fluid under an axial electrical field are formulated and simplified using lubrication approximations and low zeta potential (Debye H¨uckel linearization). A dilute nanofluid is assumed with Brownian motion and thermophoretic body forces present. The reduced non-dimensional conservation equations are solved with the symbolic software, Mathematica 9 via the NDSolve algorithm for velocity, temperature, nano-particle concentration distributions for low zeta potential. An entropy generation analysis is also conducted. The influence of curvature parameter, maximum electroosmotic velocity (Helmholtz-Smoluchowski velocity), inverse EDL thickness parameter, zeta potential ratio and Joule heating parameter on transport characteristics is evaluated with the aid of graphs and contour plots. Temperature profiles are elevated with positive Joule heating and reduced with negative Joule heating whereas the opposite behaviour is observed for the nano-particle concentrations

Online monitoring of Electricity Data through wireless transmission using Radio Frequency

Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT: Power system is becoming more complex with the passage of time, as non-linearity of the system invite major dynamic kind of problems. One of major problems in power system is the acquisition of electricity data. Energy meter reading is a tiresome and pricey concern. Planned system of energy meter data reading will allow to control room to access the customer’s energy meter and also allow the service provider to monitor and control the whole energy consumption, acquisition of energy data and fault or energy theft case in its zone. Digital wireless meter is technological enhanced and improved version of meter reading with safe prestigious time of energy providing company employees as there is no need of manpower for manual meter reading while visiting home to home. Radio frequency is proposed source of wireless communication for data integration. Online monitoring of electricity is being addressed first time in this research paper fo

mining recent frequent itemsets in data streams

Shandong Univ, Int Nat Computat & Knowledge Discovery AssocMining frequent itemsets in data streams is a hot research topic in recent years. Due to the continuous, highspeed and unbounded properties of data streams, traditional algorithms on static dataset are not suitable for mining in data streams

A Modified Hybrid Particle Swarm Optimization With Bat Algorithm Parameter Inspired Acceleration Coefficients for Solving Eco-Friendly and Economic Dispatch Problems

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

Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

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

Forecasting of Wind Speed and Power through FFNN and CFNN Using HPSOBA and MHPSO-BAACs Techniques

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