Extension of Distribution Transformer Life in the Presence of Smart Inverter-based Distributed Solar Photovoltaic Systems

A transformer is an essential but expensive power delivery equipment for a distribution utility.In many distribution utilities worldwide,a sizable percentage of transformers are near the end of their designed life.At the same time,distribution utilities are adopting smart inverter-based distributed solar photovoltaic(SPV)systems to maximize renewable generation.The central objective of this paper is to propose a methodology to quantify the effect of smart inverter-based distributed SPV systems on the aging of distribution transformers.The proposed method is first tested on a modified IEEE-123 node distribution feeder.After that,the procedure is applied to a practical distribution system,i.e.,the Indian Institute of Technology(IIT)Roorkee campus,India.The transformer aging models,alongside advanced control functionalities of grid-tied smart inverter-based SPV systems,are implemented in MATLAB.The open-source simulation tool(OpenDSS)is used to model distribution networks.To analyze effectiveness of various inverter functionalities,time-series simulations are performed using exponential load models,considering daily load curves from multiple seasons,load types,current harmonics,etc.Findings show replacing a traditional inverter with a smart inverter-based SPV system can enable local reactive power generation and may extend the life of a distribution transformer.Simulation results demonstrate,simply by incorporating smart inverter-based SPV systems,transformer aging is reduced by 15%to 22%in comparison to SPV systems operating with traditional inverters.

Annual performance analysis of different maximum power point tracking techniques used in photovoltaic systems

This paper presents an annual performance evaluation of three maximum power point tracking (MPPT) methods. The used MPPT techniques (Perturb and Observe, Incremental Inductance and Sliding mode) are evaluated under an annual data of atmospheric conditions of the target site. The main contribution of this work is to consider real fluctuation conditions of solar irradiations, ambient temperatures and wind velocities. It was found that the Sliding mode provides higher energy yields independently of the period. Compared to the basic P&O and the IC techniques, sliding mode has the potential of generating up to 8.18% more electrical energy than other techniques.

Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels

The Problem of Photovoltaic(PV)defects detection and classification has been well studied.Several techniques exist in identifying the defects and localizing them in PV panels that use various features,but suffer to achieve higher performance.An efficient Real-Time Multi Variant Deep learning Model(RMVDM)is presented in this article to handle this issue.The method considers different defects like a spotlight,crack,dust,and micro-cracks to detect the defects as well as loca-lizes the defects.The image data set given has been preprocessed by applying the Region-Based Histogram Approximation(RHA)algorithm.The preprocessed images are applied with Gray Scale Quantization Algorithm(GSQA)to extract the features.Extracted features are trained with a Multi Variant Deep learning model where the model trained with a number of layers belongs to different classes of neurons.Each class neuron has been designed to measure Defect Class Support(DCS).At the test phase,the input image has been applied with different operations,and the features extracted passed through the model trained.The output layer returns a number of DCS values using which the method identifies the class of defect and localizes the defect in the image.Further,the method uses the Higher-Order Texture Localization(HOTL)technique in localizing the defect.The pro-posed model produces efficient results with around 97%in defect detection and localization with higher accuracy and less time complexity.

Generating capacity adequacy evaluation of large-scale, grid-connected photovoltaic systems

Large-scale, grid-connected photovoltaic sys- tems have become an essential part of modem electric power distribution systems. In this paper, a novel approach based on the Markov method has been proposed to investigate the effects of large-scale, grid-connected photovoltaic systems on the reliability of bulk power systems. The proposed method serves as an applicable tool to estimate performance （e.g., energy yield and capacity） as well as reliability indices. The Markov method frame- work has been incorporated with the＇ multi-state models to develop energy states of the photovoltaic systems in order to quantify the effects of the photovoltaic systems on the power system adequacy. Such analysis assists planners to make adequate decisions based on the economical expectations as well as to ensure the recovery of the investment costs over time. The failure states of the components of photovoltaic systems have been considered to evaluate the sensitivity analysis and the adequacy indices including loss of load expectation, and expected energy not supplied. Moreover, the impacts of transitions between failures on the reliability calculations as well as on the long- term operation of the photovoltaic systems have been illustrated. Simulation results on the Roy Billinton test system has been shown to illustrate the procedure of the proposed frame work and evaluate the reliability benefits of using large-scale, grid-connected photovoltaic system on the bulk electric power systems. The proposed method can be easily extended to estimate the operating and maintenance costs for the financial planning of the photovoltaic system projects.

A digital twin model-based approach to cost optimization of residential community microgrids

This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system.Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency.This study was conducted along two main axes.The first axis focuses on designing a digital twin for a residential community microgrid platform.This phase involves data collection,cleaning,exploration,and interpretation.Moreover,it includes replicating the functionality of the real platform and validating the results.The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid,while maintaining the concept of peer-to-peer energy trading.Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid.Meanwhile,a single prosumer with a storage system actively engages in energy exchange to maximize the community’s profit.This is achieved by optimizing battery usage using a cost optimization solution.The proposed solution is validated using the developed digital twin.

赛德西港用于船舶减排的陆上可再生能源供电评估

Air pollution from shipping is becoming a critical issue,particularly in dense hub port cities.One proposed solution to minimize ship-based emissions at ports is the implementation of an Onshore Power Supply(OPS)system.OPS allows ships to shut off their auxiliary engines and instead connect to the port grid.While there have been numerous studies conducted on ports in Europe and the United States,little research has been done on Egyptian ports.Therefore,this paper aims to investigate the feasibility of implementing OPS at Port Said West Port in Egypt,aligning with Egypt Vision 2030’s goals for addressing climate change.The research primarily focuses on analyzing data collected from calling ships to generate socio-economic and cost-effectiveness analyses of OPS.To further enhance the environmental benefits of OPS,the paper proposes the use of solar energy as the OPS electricity source.The findings of the study revealed that by relying on the national grid,emissions can be reduced by 28%.Moreover,it is predicted that this reduction could reach 100%if electricity generation is solely based on solar energy.Additionally,the economic analysis demonstrates promising profitability,with a payback period of approximately two years.

Networked Control Approach for Distributed Generation Systems

Microgrid has emerged as an answer to growing demand for distributed generation(DG) in power systems. It contains several DG units including microalternator, photovoltaic system and wind generation. It turns out that sustained operation relies on the stability of these constituent systems. In this paper, a microgrid consisting of microalternator and photovoltaic system is modeled as a networked control system of systems(So S)subjected to packet dropouts and delays. Next, an observerbased controller is designed to stabilize the system in presence of the aforementioned communication constraints and simulation results are provided to support the control design methodology.

Fuzzy Adaptive P&O Control for MPPT of a Photovoltaic Module

In this work a Maximum Power Point Tracker (MPPT) for photovoltaic modules is developed using fuzzy logic. As it is well known, the output of the photovoltaic module is a non-linear curve which has a unique point of maximum power (MPP) for a given condition of radiation and temperature. When a load is connected to the module, only in very specifics cases, the operation point will coincide with the MPP, for any other conditions the system will not operate with maximum power. Thus MPPT circuits must guarantee that photovoltaic modules operate with its maximum power at most of the time, independently to the radiation and temperature conditions. In order to achieve this objective, in this paper the input variables of the controller are transformed into linguistic variables, which associate with a set of rules results the displacement of the operation point so as to transfer the maximum power from the photovoltaic module to the load.

An Overview about the Brazilian Photovoltaic Market Development

There are five main institutions that develop research and provide data regarding photovoltaic energy generation in Brazil, they are: Brazilian Electricity Regulatory Agency (ANEEL);Energy Research Office (EPE);International Renewable Energy Agency (IRENA);Institute for the Development of Alternative Energies in Latin America (IDEAL);and Greener (a research and consultancy company specialized in the photovoltaic solar energy sector). The reports provided by these institutions present a large volume of data and information, this factor makes hard task of understanding the Brazilian photovoltaic market. Therefore, this paper purposes to present an overview about the development of photovoltaic generation in Brazil, through of an unpublished compilation and analysis of the data provided by the institutions previously cited. For this, initially the factors that contributed to the implementation and expansion of this sector are presented. Following, it is presented the main resolutions for the implementation of distributed generation, as well as organizations responsible for the standardization, operation, testing and expected requirements for connection of renewable sources in the electrical system. Quantitative data about energy installed, number of installations approvals, distribution of installations by sector of society, number homologations by power range and cost distribution for the implementation of these systems are provided. Finally, the incentive policies, credit lines and future perspectives for the development of the photovoltaic sector in Brazil are presented.

Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

Renewable energy has become a solution to the world’s energy concerns in recent years.Photovoltaic(PV)technology is the fastest technique to convert solar radiation into electricity.Solar-powered buses,metros,and cars use PV technology.Such technologies are always evolving.Included in the parameters that need to be analysed and examined include PV capabilities,vehicle power requirements,utility patterns,acceleration and deceleration rates,and storage module type and capacity,among others.PVPG is intermit-tent and weather-dependent.Accurate forecasting and modelling of PV sys-tem output power are key to managing storage,delivery,and smart grids.With unparalleled data granularity,a data-driven system could better anticipate solar generation.Deep learning(DL)models have gained popularity due to their capacity to handle complex datasets and increase computing power.This article introduces the Galactic Swarm Optimization with Deep Belief Network(GSODBN-PPGF)model.The GSODBN-PPGF model predicts PV power production.The GSODBN-PPGF model normalises data using data scaling.DBN is used to forecast PV power output.The GSO algorithm boosts the DBN model’s predicted output.GSODBN-PPGF projected 0.002 after 40 h but observed 0.063.The GSODBN-PPGF model validation is compared to existing approaches.Simulations showed that the GSODBN-PPGF model outperformed recent techniques.It shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day.

Influence of Taxation on Supply and Demand in Tomorrow's Crowd Energy Paradigm

The emergence of the energy self-sufficient home presents a new role for government taxation. Policymakers now face the challenge of reflecting this technological change in their decision-making and must assume a greater level of engagement. This paper proposes a number of original fiscal concepts for policymakers to implement in the support of micro-grid development. These are designed to optimise a sustainable transition away from the centralised energy system whilst creating shared value among stakeholders throughout the value chain. Concepts are based on residential micro-grid schemata in Switzerland and are applicable in other countries.

Grid Voltage Sensor-less Protection Scheme for One Cycle-Controlled Single-phase Photovoltaic Inverter Systems

One-cycle-controlled(OCC)inverters are suitable for small single-phase photovoltaic distributed-generator systems because of their simplicity,phase-locked-loop free structure,grid voltage sensor-less operation,and cost-effectiveness.Grid voltage sensor-less control helps reduce cost and increases reliability in operation.However various sensors are used for implementation of a protection mechanism.In this paper,a grid voltage sensorless protection scheme for OCC based single-phase inverter systems is proposed.The estimated value of voltage at point of common coupling(VPCC)is used for protecting the system during over/under voltage conditions of the grid,implementing of voltage ride through conditions,and for disconnecting the grid during islanded conditions.The VPCC is estimated from the measured inverter current,switching pulses,and the measured dc-link voltage using a second-order filter.Simulation and experimental studies are performed to verify the efficacy of the proposed voltage sensor-less protection mechanism triggered using estimated VPCC.

Sensitivity analysis of an HgCdTe based photovoltaic receiver for long-wavelength free space optical communication systems

We examine theoretically the performance of an Hg0.77Cd0.23Te based p-n photodetector/HFET optical receiver due to its possible application at 10.6 μm free space optical communication system at high bit rate.A rigorous noise model of the receiver has been developed for this purpose.We calculate the total noise and sensitivity of the receiver.The front-end of the receiver exhibits a sensitivity of -45 dBm at a bit rate of 1 Gb/s and -30 dBm at a bit rate of 10 Gb/s,and the total mean-square noise curren t〈i2n〉=5×10-15 A2 at a bit rate of 1 Gb/s an d〈i2n〉 =10-12 A2 at a bit rate of 10 Gb/s,and a 3-dB bandwidth of 10 GHz.

Electrical and thermal performances of photovoltaic/thermal systems with magnetic nanofluids:A review

Enhancing solar photovoltaic and thermal conversion performances may help develop more environmentally friendly hybrid photovoltaic/thermal(PV/T)systems that can be used in applications ranging from household to industrial scales.Owing to their enhanced thermal and optical properties,nanofluids have proven to be good candidates for designing PV/T systems with superior performances.As smart nanofluids,magnetic nanofluids(MNFs)can further enhance the performances of PV/T systems under external magnetic fields.This paper reviews recent developments in enhancing the electrical and thermal performances of PV/T systems using magnetic nanofluids.Various parameters affecting the performances are highlighted,and some areas for further investigations are discussed.The reviewed literature shows that PV/T systems with MNFs are promising.However,their performances need further investigation before they can be used in applications.

Bayesian bootstrap quantile regression for probabilistic photovoltaic power forecasting

Photovoltaic(PV)systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing.Because of the uncertain nature of the solar energy resource,PV power forecasting models are crucial in any energy management system for smart distribution networks.Although point forecasts can suit many scopes,probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies.This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model,in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model.A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients,raising the predictive ability of the final forecasts.Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2%when compared to relevant benchmarks.

Optimization Methods of MPPT Parameters for PV Systems:Review,Classification,and Comparison

To obtain efficient photovoltaic(PV)systems,optimum maximum power point tracking(MPPT)algorithms are inevitable.The efficiency of MPPT algorithms depends on two MPPT parameters,i.e.,perturbation amplitude and perturbation period.The optimization of MPPT algorithms affect both the tracking speed and steady-state oscillation.In this paper,optimization methods of MPPT parameters are reviewed and classified into fixed and variable methods.The fixed MPPT parameters are constant during MPPT performance,and a trade-off should be made between the tracking speed and steady-state oscillation.However,the variable MPPT parameters will be changed to improve both the tracking speed and the steadystate oscillations.Moreover,some of them are simulated,compared,and discussed to evaluate the real contributions of the optimization methods to the MPPT efficiency.Furthermore,significant features of the optimization methods,i.e.,noise immunity,robustness,and computation effort,are investigated.

Technical design and environmental analysis of 100-kWp on-grid photovoltaic power plant in north-western Iran

Due to the fuel security and environmental concerns of traditional energy resources like fossil fuels,grid operators are tending to use renewable energies as the primary energy supply.This paper presents the study of designing,simulation and analysis of a 100-kWp on-grid photovoltaic power plant(PV-PP)in north-western Iran.Accurate meteorological data,satellite images and local knowledge from this region have narrowed down the options to three highly irradiated cities of Maragheh,Mahabad and Khalkhal in this region.PVsyst and MATLAB software are used in this paper to obtain the performance results.Environmental effects and carbon-emission savings from the execution of the proposed PV-PP are also available in this paper.The result of this study shows that PV-PP installation in Maragheh will have higher energy output than the two other cities.This study is insightful for the academics and the grid stakeholders in finding optimal spots in north-western Iran to construct a PV-PP.Also,recommendations are available for future studies.

A new method for estimating the longevity and degradation of hotovoltaic systems considering weather states

The power output of solar photovoltaic （PV） systems is affected by solar radiation and ambient temperature. The commonly used evaluation techniques usually overlook the four weather states which are clear, cloudy, foggy, and rainy. In this paper, an ovel analytical model of the four weather conditions based on the Markov chain is proposed. The Markov method is well suited to estimate the reliability and availability of systems based on a continuous stochastic process. The proposed method is generic enough to be applied to reliability evaluation of PV systems and even other applications. Further aspects investigated include the new degradation model for reliability predication of PV modules. The results indicate that the PV module degradation over years, failures, and solar radiation must be considered in choosing an efficient PV system with an optimal design to achieve the maximum benefit of the PV system. For each aspect, a method is proposed, and the complete focusing methodology is expounded and validated using simulated point targets. The results also demonstrate the feasibility and applic- ability of the proposed method for effective modeling of the chronological aspects and stochastic characteristics of solar cells as well as the optimal configuration and sizing of large PV plants in terms of cost and reliability.

Stochastic maximum power point tracking of photovoltaic energy system under partial shading conditions

A large portion of the available power generation of a photovoltaic (PV) array could be wasted due to partial shading, temperature and irradiance effects, which create current/voltage imbalance between the PV modules. Partial shading is a phenomenon which occurs when some modules in a PV array receive non-uniform irradiation due to dust, cloudy weather or shadows of nearby objects such as buildings, trees, mountains, birds etc. Maximum power point tracking (MPPT) techniques are designed in order to deal with this problem. In this research, a Markov Decision Process (MDP) based MPPT technique is proposed. MDP consists of a set of states, a set of actions in each state, state transition probabilities, reward function, and the discount factor. The PV system in terms of the MDP framework is modelled first and once the states, actions, transition probabilities, and reward function, and the discount factor are defined, the resulting MDP is solved for the optimal policy using stochastic dynamic programming. The behavior of the resulting optimal policy is analyzed and characterized, and the results are compared to existing MPPT control methods.

A sliding-neural network control of induction-motor-pump supplied by photovoltaic generator

Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies, solar energy represents the most important source, the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun’s energy typically characterized by their low efficiency. However, it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever, the weather conditions, we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature, we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition, the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally, simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study, we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.