Solar photovoltaic (PV) systems continue to be the most prevalent renewable energy resource despite the presence of numerous limitations. A power discrepancy between PV modules on a large scale may result in power dissipation throughout the entire PV system. This particular paper proposes an efficient multi-port converter for distributed maximum power point tracking operation (D-MPPT) for a solar PV system. The operation details of the proposed multi-port converter along with analytical waveforms are presented in this paper. To implement the D-MPPT approach in the proposed multi-port converter, a detailed analysis of mathematical modeling of solar PV systems with a mismatch of PV power and voltage stabilization approach is done. In addition, the proposed approach eliminates the need for additional current sensors and semiconductor components to overcome the effect of mismatched power in the PV system. To validate this, the prototype has been built and integrated with the real environment of the solar PV system. To verify the operation, a detailed simulation study and experimental investigation have been carried out and presented in this paper which reveals that the proposed system offers 24% improved power extraction compared to the centralized converter and MPPT method under partially shaded conditions. After a detailed investigation and discussion of measured results and analysis, it is concluded that the proposed multi-port DC-DC converter is the most suitable solution for solar PV applications.

Background: Microgrid is the recent decade terminology that surpasses the long-run issues associated with the public and utility grids. Among the renewable energy sources, solar PV units have gained greater importance owing to their huge potential availability and laidback operating characteristics on technological grounds. Conversely, it offers pollution-free electricity and perhaps the dependability is volatile in most situations. The literature study accumulates the foresaid setback and presents the fluctuation-less and controlled standard quality of power outputs. Objective: The aim of this particular research is to propose an assessment of Power Quality enhancement in a Grid-tied photovoltaic (PV) network via ANN-based UPQC. The novel idea behind this proposed approach is the UPQC component which deliberately regulates and controls the power system to achieve higher levels of power quality, ultimately meeting the recent IEEE standards. Method: This particular research enhances the performances of UPQC employed in the microgrid unit by replacing the traditional PI controller with a multi-layered feed-forward-type ANN controller for the current regulation of the series active filter. Additionally, a training algorithm for the ANN controller is built, trained and simulated via MATLAB/Simulink platform. The ANN-based UPQC is proposed to alleviate the power quality challenges like sag and swell in voltage, harmonic distortion, the time required for voltage compensation, and power factor. Therefore, UPQC is equipped to enrich the standard of power transfer at the point of common coupling inside the power frameworks, respectively. Result: Finally, the simulation results are presented to validate the operation of the grid-tied PV network via an ANN-based UPQC system. To show the enriched performance of the proposed topology, a comparative analysis is made with PI controller-based UPQC, and outcomes infer to be in agreement with the theoretical discussions. Also, the ANN-based proposed approach reduces the restoration time and THD as well under both sag and swell conditions, respectively. Conclusion: In this articulated work, a PV power system network with a DC-DC converter and three-phase inverter is employed for grid integration. The peak power extraction is ensured via a DC-DC converter with an incremental conductance algorithm. Both UPQCs are analysed and experimented via MATLAB/Simulink platform with inconstant nonlinear loads to investigate the indices mentioned above and corroborate the same within the operating regions.

Aims: Aim of this research is to propose a novel concurrent UPQC scheme for improving the power quality issues in grid integrated solar photovoltaic (PV) systems. Background: The power quality is a major issue for the grid integration of renewable energy sources. Issues like voltage sag, voltage swell, harmonics & non-linear load variations are certainly observed in the distributed energy system and it is mandated that any system has to depend on advanced controllers to improve power quality and stabilize the electrical parameters. Controller related power quality improvements are a bit easier to design but the tuning of power is difficult in this aspect. Objective: In order to overcome the aforesaid limitation, this particular paper proposes a new concurrent UPQC scheme for improving the four different power quality issues in grid integrated solar photovoltaic (PV) system such as voltage sag, voltage swell, non-linear load variations and current harmonics. Methods: The operating regions of each power quality issues are examined in the I-V curves of PV specifications and the new operating modes of PV systems are mapped for every quality improvement considering the power, current and frequency of the grid and load as well. The pool of solutions is developed from the real power, reactive power and converter duty cycle and verified with the proposed solutions. Additionally, the designed switching frequency of the proposed system has a 5% variation for practical irradiance. The PV uniform irradiance profile matches the real power for various proposed concurrent UPQC schemes. Results: Finally, the simulation results are presented to validate the operation of the proposed concurrent UPQC schemes for PV system. The comparative study of the proposed concurrent UPQC scheme for PV system with appropriate literature is presented. The superiority of the proposed schemes infers studying the odd harmonic components up to 100th order after implementing the proposed concurrent UPQC scheme for PV system. Conclusion: From the measured results, it is concluded that a new concurrent optimization scheme enhances the operation of solar PV system that integrates with the grid. The power quality issues like voltage & current swell, voltage & current sag, voltage imbalance, and harmonics are reduced compared to existing methods. The validation of the schemes is achieved through the group constraints and the operating slopes in each region.

This article proposes a switched Z source DC/DC converter based dual stator winding induction generator-based wind-energy-conversion-system (WECS) using an artificial neural network (ANN) maximum power point tracking (MPPT) control technique. Nowadays, multiphase machines are widely preferred for their increased power density, efficiency and improved reliability. In this article, a dual stator winding induction generator is proposed for WECS. A DC-DC converter plays a vital role in the peak power extraction and wide wind speed range operation of DSWIG in WECS. WECS is a high voltage and high-power application that necessitates high gain. A conventional boost converter may lead to an instability issue under a higher duty ratio for high gain. Hence, in this analysis, a switched Z -source DC/DC converter is proposed to avoid instability, which operates with a minimum duty ratio. The proposed topology utilises a backpropagation based neural network control approach to achieve the most accessible energy from the speed of the rotor and actual power. The results are compared with a classical power signal feedback method and an ANN-based MPPT with a boost DC-DC converter. Various topologies are analyzed in terms of voltage quality, power tracking, and tracking time using Matlab.

Solar PV-connected distributed utility grid often faces several issues due to variable penetration of the generated power. It creates frequent disturbance in load side and increases the voltage instability. It is a great challenge to maintain the stability at distributed low-voltage grid and improve the quality of power. In order to overcome this problem, this paper proposes an adaptive voltage and current regulatory approach to improve the power quality in a solar PV-integrated low-voltage utility grid. It supplies auto-adjustable reactive power during the small and large voltage deviations in the grid. The proposed approach assures that the load bus voltage is maintained at 1 p.u. under variable environmental conditions. In addition, the power quality gets improved by injecting the power with improved quality. Three cases of standalone mode, grid-connected modes with and without STATCOM have been investigated and reported in this paper. To validate the proposed adaptive voltage and current regulatory approach, the dynamic results of regulated grid voltage under poor environmental conditions are analyzed and the measured results are presented in this paper. Furthermore, the obtained results are evaluated with the existing approaches such as BAT, firefly and elephant herding optimization (EHO) algorithms and reported in this paper.

This paper deals with a Unit Commitment (UC) problem of a power plant aimed to find the optimal scheduling of the generating units involving cubic cost functions. The problem has non convex generator characteristics, which makes it very hard to handle the corresponding mathematical models. However, grasshopper optimization algorithm (GOA) has reached a high efficiency, in terms of solution accuracy and computing time for such non convex problems. Hence, GOA is applied for scheduling of generators with higher order cost characteristics, and turns out to be computationally solvable. In particular, we represent a model that takes into account the accurate higher order generator cost functions along with ramp limits, and turns to be more general and efficient than those available in the literature. The behavior of the model is analyzed through proposed technique on modified IEEE-24 bus system and IEEE-30 bus system.

In today's competitive electricity market, managing transmission congestion is a major challenging issue due to operational constraints. Flexible AC transmission system (FACTS) device can be a choice to control the power flow in the congested transmission lines. This paper explores the use of thyristor controlled series compensator (TCSC) for power flow control in congested network using firefly algorithm (FA). The optimal location of TCSC is identified based on real power performance index and reduction of total system reactive power loss methods. Further in the congestion management (CM) problem, single line outage analysis is also performed. FA is used to determine the minimum total cost which includes production cost and TCSC cost. Results of five bus, IEEE 14 and IEEE 30 bus test systems indicate that FA provides minimum cost compared to the previous literature. The efficiency of the proposed FA for obtaining the high quality solution is also established.

In this paper, grasshopper optimization algorithm is presented to resolve the combined economic emission dispatch (CEED) problem involving cubic functions considering power flow constraints. Electric power system wants to satisfy its customers load demand with minimum fuel cost and emission. Fuel cost and emission has instantly association with energy cost. In CEED problem, the price penalty factor occupies a cardinal role to fetch the optimal results. The various types of price penalty factor available in the literature are analyzed to determine the optimal one for the test cases considered. The test systems used in this CEED problem are 3 unit system considering transmission loss and 13 unit system considering valve point effects. The leading requirement in both the test cases is to optimize the total cost, fuel cost and emission. The numerical and statistical results affirm the high degree of the solution founded by GOA and its superiority is compared with already existing algorithms employed in solving CEED problems.

In the deregulation of electricity market, the transmission congestion management (CM) has become extremely important in order to ensure security and reliability of the system. This paper proposes a method to manage congestion by optimal rescheduling of the active powers of generators based on firefly algorithm (FA). However, all generators in the system need not take part in CM. Thus, in this article, generators are selected based on the magnitude of generator sensitivities to the congested line. In this paper, the proposed FA is tested on standard IEEE 30 bus, 118 bus systems and a practical Indian utility 62 bus system for the solution of CM problem. The results of these test systems provide minimum rescheduling cost and are compared with that of CPSO, PSO-TVIW, PSO-TVAC, VEPSO and PSO-ITVAC methods. Results prove that FA is indeed capable of getting a high quality solution for the CM problem.

Economic dispatch aims to make the minimal operating cost of power plant by determining the optimal power produced by each generating unit under constrained circumstances. At the present time, power utilities have stumble upon a fresh dispatch problem, because of crucial concern over fuel shortages. Fuel suppliers increase constraints in their fuel supply contracts that force the utilities to schedule the generation on the basis of fuel availability. With the ever increasing proportion of the fuel budget in the total operating costs, the fuel restricted economic dispatch problem has popped up. A new methodology based on a teaching learning-based optimization algorithm is proposed for solving fuel restricted economic dispatch problems. The potential of the proposed method is tested with standard test systems which include different cost characteristics. The obtained results are compared to other algorithms surfaced in the recent state-of-the-art literature, confirming the effectiveness of the developed methodology.

The Combined Heat and Power Dispatch (CHPD) is an important optimization task in power system operation for allocating generation and heat outputs to the committed units. This paper presents a Grey Wolf Optimization (GWO) algorithm for CHPD problems. The effectiveness of the proposed method is validated by carrying out extensive tests on three different CHPD problems such as static economic dispatch, environmental-economic dispatch and dynamic economic dispatch. Valve-point effects, ramp-rate limits and spinning reserve constraint along with network loss are considered. Standard test systems containing 4, 7, 11 and 24 units are used for demonstration purpose. To validate the performance of the GWO, statistical measures like best, mean, worst, standard deviation, epsilon, iter and sol-iter over 50 independent runs are taken. The simulation experiments reveal that GWO performs better in terms of solution quality and consistency. (C) 2015 Elsevier Ltd. All rights reserved.

This paper deals with a Unit Commitment (UC) problem of a power plant aimed to find the optimal scheduling of the generating units involving cubic cost functions. The problem has non convex generator characteristics, which makes it very hard to handle the corresponding mathematical models. However, Teaching Learning Based Optimization (TLBO) has reached a high efficiency, in terms of solution accuracy and computing time for such non convex problems. Hence, TLBO is applied for scheduling of generators with higher order cost characteristics, and turns out to be computationally solvable. In particular, we represent a model that takes into account the accurate higher order generator cost functions along with ramp limits, and turns to be more general and efficient than those available in the literature. The behavior of the model is analyzed through proposed technique on modified IEEE-24 bus system.

Economic dispatch (ED) solution accuracy can be improved with cubic cost models and optimisation algorithms. This article proposes a new methodology for solving ED problem with cubic cost models using teaching learning-based optimisation (TLBO) algorithm. The key aspects of ED scenario such as valve point effects, environmental factors, transmission losses, spinning reserve, ramp rate, prohibited operating zones and fuel limitations are considered in this study. The proposed methodology is applied to test systems involving cubic cost equations in 3, 5, 6, 13, 26 and a large-scale system containing 156 units, in order to evaluate its efficiency and feasibility. Convergence characteristics of the TLBO has been assessed and investigated through comparison with results reported in the literature. Many trials with different initial values have been carried out for all the test systems in order to justify the robustness of the proposed methodology. Considering the quality of the solution and convergence speed obtained, this method seems to be a promising alternative approach for solving the ED problems with cubic functions.

This paper compares two different cost functions involved in fuel cost modelling of the Economic Dispatch (ED) problem. A nature inspired Teaching Learning Based Optimization (TLBO) is used to perform economic dispatch for the second and third order cost models and the algorithm fetches the best schedules. Further, economy deviation reveals the need of a model representing the practical generator characteristic. A comparative study has been made on the solutions obtained by TLBO for both cost function models on a 13-unit system with valve point effects and a 26-unit large scale system. The solution evolved is quite encouraging and useful in identifying accurate cost models of units that lead to exact dispatch. The different degrees of performance analysis such as convergence, robustness, standard deviation, epsilon, iter and computation time for the ED has also been presented.