Distributed Optimization of Standalone PV Components in Microgrid

Estimation of the potential array output charge in the performance analysis of stand-alone photovoltaic systems without MPPT (Case study: Mediterranean climate)

Photovoltaic (PV) sources are inherently nonlinear power devices and maximum power derivation depends on Maximum Power Point Tracking (MPPT) algorithms. The algorithms require testing in real time situation using PV source which will be troublesome due to higher dependency on environmental factors and larger space requirements for higher power rating. The increased and faster testing lead to develop a PV emulator with desired performance and lower price compare to the commercially available PV emulators. In this paper, design of a digitally controlled PV emulator with varying resistive load is proposed to replicate the behaviour of real PV sources. The power stage of the PV emulator is realized using dc-dc buck converter topology and the controller stage uses an algorithm-in-loop Newton-Raphson (NR) method. Finally, the PV emulator is tested with MPPT controlled dc-dc boost converter for battery charging. The design of the power and control stages of the PV emulator has been compared with the real PV source and it is observed that the obtained V-I characteristic is in good agreement. Moreover, an analysis is presented for different switching frequencies of the boost converter connected as load to the fixed switching frequency buck converter, i.e., PV emulator. Further, a schematic hardware circuit diagram has been given to realize the proposed digitally controlled PV emulator.

Off-grid systems based on Solar Photovoltaic (PV) source have emerged as a promising solution for electrification in grid in-extensible areas. Major design challenges faced are requirement of: (a) operating PV at Maximum Power Point (MPP) to extract utmost possible power from the PV source, (b) protecting the battery damage due to over-charge and over-discharge, (c) converting the dc voltage obtained from PV side to requisite ac voltage with good efficiency, and (d) achieving the requisite voltage boosting operation in the system as using a low voltage battery and PV source is desirable for domestic application. As multiple requirements are to be met, majority of the previously worked-upon schemes for off-grid system required a minimum of three power converters involving several components leading to considerable system reliability and efficiency fall. A very few two-stage schemes are also proposed but they need higher component count clubbed to complex control. In an attempt to address this issue, a reduced component count off-gird system with only two stages, one bidirectional converter (BDC) and one dc-ac inverter followed by a step-up transformer is proffered. A suitable control strategy is also proposed. Matlab/Simulink platform is employed to validate the overall scheme, and relevant results are presented.

This paper addresses the problem of the optimal location and sizing of photovoltaic (PV) sources in direct current (DC) electrical networks considering time-varying load and renewable generation curves. To represent this problem, a mixed-integer nonlinear programming (MINLP) model is developed. The main idea of including PV sources in the DC grid is minimizing the total greenhouse emissions produced by diesel generators in isolated areas. An artificial neural network is employed for short-term forecasting to deal with uncertainties in the PV power generation. The general algebraic modeling system (GAMS) package is employed to solve the MINLP model by using the CONOPT solver that works with mixed and integer variables. Numerical results demonstrate important reductions of harmful gas emissions to the atmosphere when PV sources are optimally integrated (size and location) to the DC grid.

To verify the design of a standalone photovoltaic (PV) system, a test facility has been installed. This paper describes a platform to test the stand-alone solar system according to the IEC62124 standard. Essentially, this entire system involves the integration of a Personal Computer (PC), Data Acquisition (DAQ), an AC load and a solar array simulator (SAS) to test and simulate a stand-alone PV system. The main functions of this test facility are data acquisition, display, save and analysis data and print reports. The information obtained by monitoring parameters, such as average battery’s temperature, voltage and current is fed to the PC via the DAQ for analysis. This customized control interface has been developed by utilizing Labview software. This new system boasts of high accuracy measurements coupled with the commercial viability of low cost.

Nowadays, Photovoltaic (PV) energy sources are responsible for more than 720TWh of the global energy consumed. Therefore, among all the arising research topics regarding PV sources, Maximum Power Point tracking (MPPT) algorithms are widely studied, since they have critical importance in order to properly track and acquire the Maximum Power Point (MPP) of energy that can be harvested from the PV source; where, metaheuristic optimization algorithms have been adapted as MPPT solutions, showing an improved settling time with steady-state oscillations reduction. Yet, many of the Metaheuristic-based MPPTs have issues against dynamically variable MPP through time, since they cannot work properly without a reset signal upon temperature and irradiance parametric changes, due the fact that those algorithms converge on a global solution after some iterations and need to know when to start looking for the MPP again in order to avoid getting stuck in old MPP points. Hence, this work shows the implementation of Artificial Neural Networks (ANN) for pattern recognition through the power data acquisition, which enables an efficient solution that allows detecting changes in solar irradiation, which leads to a reliable reinitialization signal through the detection of solar irradiation through the ANN; where, results show over 99% of accuracy from the change in solar irradiation detection. Moreover, the ANN was trained using the metaheuristic Earthquake Algorithm (EA), which was again validated as a training method for ANN. In addition, this work enables other users to suite the EA algorithm to other applications and the implementation of ANN into the Simulink environments; both achievements, through the presentation and exploration of a complete MATLAB code example for the implementation of the EA as ANN training method, and the complete Simulink testbed design with a full description of the ANN implementation for the Simulink environment.KeywordsArtificial neural networksMetaheuristicsPhotovoltaic systems

A case study of a typical 2.32 kW P stand-alone photovoltaic (SAPV) in composite climate of New Delhi (India)

A line-frequency zig-zag transformer is beneficial to integrate ac grid and photovoltaic (PV) source in a unified ac-dc (UACDC) system. In this paper, the zig-zag transformer efficiency is characterized during UACDC operation. For reliable estimation of efficiency, winding resistances and hence copper loss are accurately determined with inclusion of eddy current effects. Furthermore, an optimal power sharing ratio between the ac grid and PV source, is deduced to maximize the transformer efficiency at a given output power. A 1.0 kW, 415/130 V, 50 Hz, delta/zig-zag transformer is used to experimentally validate these attributes of UACDC operation. At rated output of 1.0 kW, a transformer efficiency of 93.55% is achieved, an improvement of 1.34% with respect to conventional ac power transfer. For the same secondary current capacity, UACDC operation allows an additional 17.64% of output power transfer without any thermal overloading of the secondary winding. Following benefits are therefore derived from the UACDC operation e.g. integration of PV with the ac grid, improved efficiency and effective utilization of installed thermal capacity of the zig-zag transformer.

Monitoring PWM signals in stand-alone photovoltaic systems

This study deals with the comprehensive modelling, analysis, and control of a DC microgrid (MG) in islanded mode. The proposed DC MG comprises a wind turbine, a photovoltaic (PV) source, battery storage, DC/DC source, and load side converters with DC loads. To this aim, a circuit‐oriented modelling of the whole system is developed. The PV source is modelled with a single‐diode electrical circuit. Afterward, a mathematical model of the system with state‐space representation was derived. A detailed analysis of PV system design is performed because the parameters of PV are appearing in the dynamic model of DC MG. For the purpose of controller development, the dynamic model of the DC MG, which is modelled by a non‐linear‐non‐affine eight‐order system, is linearized around an equilibrium point using the Jacobian matrix framework, while stability using eigenvalue is carried out showing that the stability is guaranteed under operating condition. Finally, for the first time, the state‐dependent Riccati equation (SDRE) technique is proposed to find the optimal regulation problem for the DC MG with non‐linear‐non‐affine dynamics. The numerical simulation studies first confirm the validity of the performed mathematical, then the effectiveness of the proposed non‐linear controller is evaluated under illumination and load change.

Microgrids are highly compatible with photovoltaic (PV) sources because of their ability to internally aggregate and balance multiple PV sources without imposing restrictions on the penetration of such intermittent power sources. There are two major types of inverter control configurations that are used in photovoltaic inverters to provide an interface to a CERTS microgrid. These control configurations exhibit important duality characteristics, and both are capable of tracking maximum input power while abiding by the CERTS droop algorithms. This paper investigates and demonstrates the comparative performance characteristics of these two major controller types: 1) a grid-forming droop-style controller similar to those used for controlling distributed generators; and 2) a current-regulated grid-follower controller. It is shown that only the grid-forming controller allows a PV source to operate alone in an islanded CERTS microgrid, but the grid-follower controller enjoys some inherent advantages with regard to faster dynamic response.

Overcurrent faults and excessive dc bus ripple are key challenges for three-phase photovoltaic (PV) sources during unbalanced grid voltage sags. Compared to widely-used grid-following PV sources, droop-controlled sources with voltage and frequency regulation capabilities can deliver much improved responses. The low-voltage ride-through (LVRT) capability of grid-forming PV sources is investigated. A fast-responding active power control for grid-forming PV sources is proposed that includes fast current-limiting to avoid protection circuit trip events. In addition, negative sequence current compensation is added to the basic droop control to minimize PV dc-bus power oscillations. MATLAB/Simulink-based simulations and controller hardware-in-the-loop (CHIL) results demonstrate the enhanced LVRT capability of the proposed control scheme for grid-forming PV sources under both balanced and unbalanced grid voltage sags.

Coordinated active power control of Wind, Solar and Diesel Generator in a Microgrid

The subject of variable step size maximum power point tracking (MPPT) algorithms has been addressed in the literature. However, most of the addressed algorithms tune the variable step size according to two variables: the photovoltaic (PV) array voltage (<TEX>$V_{PV}$</TEX>) and the PV array current (<TEX>$I_{PV}$</TEX>). Therefore, both the PV array current and voltage have to be measured. Recently, maximum power point trackers that arc based on a single variable (<TEX>$I_{PV}$</TEX> or <TEX>$V_{PV}$</TEX>) have received a great deal of attention due to their simplicity and ease of implementation, when compared to other tracking techniques. In this paper, two methods have been proposed to design a variable step size MPPT algorithm using only a single current sensor for stand-alone battery storage PV systems. These methods utilize only the relationship between the PV array measured current and the converter duty cycle (D) to automatically adapt the step change in the duty cycle to reach the maximum power point (MPP) of the PV array. Detailed analyses and flowcharts of the proposed methods are included. Moreover, a comparison has been made between the proposed methods to investigate their performance in the transient and steady states. Finally, experimental results with field programmable gate arrays (FPGAs) are presented to verify the performance of the proposed methods.

This paper aims to obtain suitable control for the Standalone Photo Voltaic (SAPV) system to meet consumer demands in weather conditions. A SAPV system is developed with a solar array with Maximum Power Point Tracking (MPPT), a Boost converter, and an inverter. This system uses controllers for maximum power output tracking, constant DC output of the Boost converter, and constant output voltage and frequency with Pulse Width Modulation (PWM) control mode. MPPT, boost converter, and inverter controller parameters are designed and tuned, respectively. Proportional gain (Kp) and Integral gain (Ki) values and the Sliding Mode Method (SMC) tune the controller parameters. The innovative aspect of this work is to propose a standalone PV system with controllers based only on the sliding mode control approach. Moreover, the current controller provides an output current of high quality with a THD of 1.6 %. Effectiveness and robustness of the proposed scheme modeled and simulated under OPAL-RT real-time Software in Loop (SIL) platform with MATLAB Simulink under fast variations of irradiance and temperature. Various analyses have been carried out for examining the proposed Sliding Mode Controller-based standalone photovoltaic system.