Photovoltaic Power Integration Research Articles

Statistical Evaluation of Sustainable Urban Planning: Integrating Renewable Energy Sources, Energy-Efficient Buildings, and Climate Resilience Measures

In the face of increasing climate-related challenges, sustainable urban energy networks must grapple with transmission congestion in deregulated power systems. This study introduces a novel statistical approach that synergizes two powerful methodologies for evaluating and managing urban energy networks: the integration of photovoltaic (PV) renewable energy sources and Gated Recurrent Unit (GRU)-based PV forecasting. By combining evolutionary optimization techniques with GRU forecasting, we aim to bolster the sustainability and reliability of urban energy networks while addressing climate change adaptation. In this study, we employ statistical methods, including generator sensitivity ratios and bus sensitivity ratios (BSR), to pinpoint optimal locations for PV power integration within urban networks. The Lion Optimization Algorithm (LOA), an evolutionary optimization method, is employed within a digital twin framework to optimize active power rescheduling values and congestion prices. The incorporation of GRU-based PV forecasting enhances the precision and resilience of our energy management approach. Through extensive simulations conducted on the New England 39-bus system, our findings reveal that the combination of the LOA method and GRU forecasting outperforms traditional optimization techniques such as the Ant Lion Optimizer (ALO) and Particle Swarm Optimization (PSO). This dual-optimization strategy leads to significantly reduced active power rescheduling values and congestion prices, underlining the potential of our statistical model to effectively manage congestion in transmission lines within sustainable urban energy networks. In the context of integrating renewable PV sources and adapting urban energy infrastructure to climate change impacts, the synergy of evolutionary optimization and GRU-based forecasting offers a robust pathway to enhance sustainability and reliability in urban energy networks.

Itô-theory-based multi-time scale dispatch approach for cascade hydropower-photovoltaic complementary system

The complementary operation of cascade hydropower (CHP) and photovoltaic (PV) can increase the integration of PV power and has become a trend in modern power systems. However, the randomness and variability of PV power output might cause challenges in precisely tracking the submitted generation plan of the CHP-PV complementary system. The safety and stability of the receiving-end power grid might deteriorate. In this paper, a multi-time scale dispatch framework with day-ahead and intra-day dispatch is established for the CHP-PV complementary system to address the PV uncertainties. The day-ahead dispatch is employed to provide reliable reference results and sufficient adjustment reservations for the intra-day dispatch. An Itô-theory-based stochastic optimization (ITB-SO) approach is proposed for the intra-day dispatch by modeling the PV prediction error with stochastic differential equations (SDE). The SDE model can be embedded into the ITB-SO approach without scenario generation, and thus computational burden can be significantly reduced compared with scenario-based methods. A complementary system in southwest China is chosen as a detailed case study. The simulation results demonstrate that real-time random PV fluctuations could be compensated by adjusting CHPs. Meanwhile, the target water level of cascade reservoirs can be tracked under the proposed approach.

Voltage Profile Improvement by Integrating Renewable Resources with Utility Grid

There are three main parts of an electric power system—power generation, transmission, and distribution. For electric companies, it is a tough challenge to reduce losses of the power system and deliver lossless and reliable power from the generating station to the consumer end. Nowadays, modern power systems are more complex due to gradually increasing loads. In the electrical power system, especially in transmission and distribution networks, there are power losses due to many reasons such as overloading of the line, long distribution lines, low power factors, corona losses, and unsuitable conductor size. The main performance factor of the power system is reliability. Reliability means continuity of the power supply without any interruptions from the generating station to the demand side. Thus, due to these power losses, there are voltage stability problems and economic losses in the electrical system. The voltage stability of the power system can be increased by improving the voltage profile. In this paper, different techniques are analyzed that include the integration of wind power, the integration of photovoltaic power, and reactive power injection by integrating FACTS devices. These techniques are applied to the IEEE 57 bus system with standard data using simulation models developed in MATLAB. Thus, the results of the analysis of these techniques have been compared with each other.

Analytical Assessment of Time-Varying Reliability and Penetration Limit of PV-Integrated Systems

This article presents a discrete convolution-based method for reliability evaluation of a grid-connected photovoltaic (PV) system, where special consideration is given to the variable availability of PV array components. Along with the negative impacts of variable solar irradiance and ambient temperature on the PV power generation, they affect the reliability of PV system power electronic components. A variable availability model based on the power loss in the semiconductors of PV system components is needed to account for this effect in the reliability analysis. The article proposes a 3-D capacity outage model of a PV farm that accounts for the variable availability and the redundancies associated with the topologically complex multistring PV arrays. The outage model is used to construct a PV capacity outage probability and frequency table (COPAFT), which along with conventional generation COPAFT, is convolved with the distribution of load to determine system reliability. An integration limit assessment tool is also developed within the proposed framework, ensuring that the PV power integration does not violate the system frequency stability. Simulations on the modified IEEE RTS-79 system demonstrate the efficacy of the proposed method.

Model for Rating a Vanadium Redox Flow Battery Stack through Constant Power Charge–Discharge Characterization

A method for estimating the stack rating of vanadium redox flow batteries (VRFBs) through constant power characterization was developed. A stack of 22 cells, each with 1500 cm2 of nominal electrode area, was constructed and tested using constant current and constant power protocols. Typical ratios of charging to discharging power that prevail in various applications (e.g., peak shaving, wind power/solar photovoltaic power integration) were employed in the test protocols. The results showed that fractional energy storage capacity utilization and round-trip energy efficiency varied linearly with the power at which the energy was charged or discharged. A zero-dimensional electrochemical model was proposed based on the area-specific resistance to account for the energy stored/extracted during constant power discharge in the state of charge (SoC) window of 20% to 80%. It was shown that this could be used to rate a given stack in terms of charging and discharging power from the point of view of its application as a power unit. The proposed method enables stack rating based on a single polarization test and can be extended to flow battery systems in general.

Expert knowledge based proportional resonant controller for three phase inverter under abnormal grid conditions

ABSTRACT Integration of photovoltaic (PV) power to the grid is achieved using three-phase inverters with high-quality current waveforms. The new grid codes impose a limit on the total harmonic distortion (THD) value of the inverter’s current waveforms. Due to simple structure and ease of design, proportional integral (PI) controllers are the most widely adopted methods to control three-phase grid-connected inverters; however under abnormal grid conditions, PI controllers may suffer from instability problems. To address the aforementioned problem, this article proposes an expert knowledge-based proportional resonant (PR) control for the integration of PV power to the grid under abnormal grid conditions. The proposed control method adjusts the control parameters based on rules generated from expert knowledge. Initially, parameters of the proposed control methods are optimized using particle swarm optimization (PSO) method. The proposed control is tested with a 100 kW grid-tied three-phase inverter unit under normal and abnormal grid conditions and its performance are compared with PI and fixed gain PR control methods. Moreover, the proposed control is tested experimentally with a scaled down 300 W grid-tied inverter lab prototype module. From the simulation and experimental results, it is verified that the proposed adaptive PR control technique is capable of providing low THD in the injected current under abnormal grid conditions compared with fixed gained PR controllers.

A methodology of photovoltaic power integration in air conditioning system – An inverter‐less approach

The photovoltaic (PV) power generation and cooling demand of the air conditioner are increased along with an increase in solar irradiation. Therefore, considering such fact, in this paper, PV power is integrated with the air conditioner to support the grid. With recent developments in power electronics, the air conditioning systems are operated in variable speed using variable frequency drive (VFD) technology. In this paper, taking the advantage of the VFD technology, PV power is directly injected into the DC bus of VFD using an isolated DC-DC converter. In this methodology, due to the high-frequency DC-DC conversion, high power DC-AC (50 Hz) stage is eliminated, and seamless power is transferred from PV generation to the load without interrupting the main operation of the air conditioner. Thus, the reliability of the system is enhanced with the reduction in overall cost, conversion losses and bulkiness. With the PV power support, the peak amplitude of the grid current is reduced and consequently the power consumption, reactive power intake from the grid, as well as the harmonics component of the grid current, are reduced. This scheme is used in rural or suburban areas where the solar profile is significant and air conditioner is extensively used.

Data driven approach to forecast the next day aggregate production of scattered small rooftop solar photovoltaic systems without meteorological parameters

Photovoltaic (PV) power has became an attractive research subject, despite the variability and uncertainty of the phenomenon, PV power forecasting is considered as a promising solution for successful PV power integration, and also an important decision making information for both, grid operators and energy traders. Forecasting the aggregated PV power in different spatiotemporal scales is very relevant for grid operators, but unfortunately, in the state of the art, few works deal with this subject. In the aim to forecast directly the next day aggregate photovoltaic power of scattered small rooftop installations based only on historical production data, we propose in this paper a hybrid nonlinear autoregressive regression model, composed from a combination of empirical mode decomposition (EMD), stepwise regression (SWR) and least square support vector regression (LsSVR) techniques. To address the lack of meteorological parameters, we propose a different data preprocessing strategy, this strategy will also allow capturing the diurnal component of the PV power time series in an efficient way. An in depth analysis of the proposed approach using publicly available database was conducted. We compared the results of the proposed approach with three other models, two nonlinear autoregressive models based on LsSVR (AR-LsSVR) and FFNN (AR-FFNN), and the third one is a persistent forecaster. Three accuracy metrics were adopted to compare the different models, the nMAE, nMBE and nMRSE. In general good results were obtained in clear and partially cloudy days, but there are few days characterized by sudden change in the time series pattern that the model cant handle. But still competitive results are obtained, the hybrid model gives an nMAE = 4.12% compared to 4.94%, 4.95% and 164.98% for AR-LsSVR, AR-FFNN and the persistent forecaster respectively. The projection of the obtained results on five recently developed models shows the effectiveness of the proposed approach.

A Novel Deep Learning Approach for Short Term Photovoltaic Power Forecasting Based on GRU-CNN Model

The integration of photovoltaic power brings the key to clean energy. However, the increasing proportion of photovoltaic (PV) energy in power systems due to the random and intermittent nature of solar energy resources is causing difficulties for system operators to dispatch PV power stations. To reduce the negative influence of the use of PV power, it is great significant to predict PV power accurately. In this paper, we propose a high-precision hybrid neural network model that employs Gated Recurrent Units (GRU) and Convolution Neural Network (CNN) to build a GRU-CNN model to forecast PV system output power. The proposed framework has two major phases. Firstly, the sample data is divided into training set and test set. For this, the temporal characteristics of the data set are extracted using a GRU model and the spatial characteristics are obtained using the CNN model. Secondly, the final predicted PV power is obtained through the output layer. The forecasting accuracy of GRU-CNN is determined by the mean absolute error (MAE), mean square error (MSE), determination coefficient (R2) and root mean square error (RMSE) values. The findings of the comparison experiments show that the GRU-CNN model has better accuracy than some deep learning methods, including, GRU, CNN and long-short term memory model (LSTM).

Optimal integration of photovoltaic power into the electricity network using Slime mould algorithms: Application to the interconnected grid in North Cameroon

This document proposes a method for determining the optimal point of integration of PV Generated into the electricity grid. The Slime mould optimization algorithm ( SMOA) is applied to determine the best node in the case of one and two injection points. The problem has been modelled as an optimization problem where the objective is to minimize joule losses and the main constraint is to regulate the voltage at each point. The proposed method was implemented in MATLAB and applied in the nodes of the IEEE networks 33 and 69 and the north interconnected grid (NIG) of north Cameroon. The results allowed us to determine the optimal points of the NIG allowing us to inject with less losses. Comparison of the results obtained with other algorithms showed that the SMOA has the best reduction in power losses and a good improvement in the voltage profile.

Optimal Operation of a Photovoltaic Integrated Captive Cogeneration Plant with a Utility Grid Using Optimization and Machine Learning Prediction Methods

The World Energy Council, in its 2019 World Energy Scenarios Report, advised policymakers to identify innovative opportunities for the integration of renewable energy resources into existing electrical power systems to achieve a fast and affordable solution. However, large-scale industries with cogeneration units are facing problems in handling the higher penetration levels of intermittent renewable energies. This paper addresses large-size photovoltaic power integration problems and their optimal operation. This work considers the case of a chemical industry having both cogeneration power and solar photovoltaics. Here, a modified firefly algorithm and a hybrid power resource optimization solver are proposed. The results of the proposed method are compared with other benchmark techniques, to confirm its advantages. The proposed techniques can be used in industries having cogeneration power plants with photovoltaics for better optimization and to meet the guidelines specified in IEEE 1547. The voltage ramp index is proposed to determine the voltage ramp up and down with intermittent solar irradiance. Additionally, a machine learning technique is used to predict the cogeneration plant efficiency at different loads and the solar irradiance under varying weather conditions. Finally, this paper proposes the effectiveness of the modified heuristic technique and certain guidelines, including solvers for industrial use.

Predicción y modelado del comportamiento de corrientes armónicas en sistemas fotovoltaicos conectados a red basados en redes NARX

This research presents the modeling and prediction of the harmonic behavior of current in an electric power supply grid with integration of photovoltaic power by inverters. The methodology used was based on the use of recurrent artificial neural networks of the nonlinear autoregressive with external input type. Work data was obtained from experimental sources through the use of a test bench, measurement, acquisition and monitoring equipment. The input-output parameters for the neural network were the current values in the inverter and in the supply grid respectively. The results showed that the neural network can capture the dynamics of the analyzed system. The generated model presented flexibility in data handling, allowing to represent and predict the behavior of the harmonic phenomenon. The obtained algorithm can be transferred to physical or virtual systems for the control or reduction of harmonic distortion.

Modeling of Harmonic Current in Electrical Grids with Photovoltaic Power Integration Using a Nonlinear Autoregressive with External Input Neural Networks

This research presents the modeling and prediction of the harmonic behavior of current in an electric power supply grid with the integration of photovoltaic power by inverters using artificial neural networks to determine if the use of the proposed neural network is capable of capturing the harmonic behavior of the photovoltaic energy integrated into the user’s electrical grids. The methodology used was based on the use of recurrent artificial neural networks of the nonlinear autoregressive with external input type. Work data were obtained from experimental sources through the use of a test bench, measurement, acquisition, and monitoring equipment. The input–output parameters for the neural network were the current values in the inverter and the supply grid, respectively. The results showed that the neural network can capture the dynamics of the analyzed system. The generated model presented flexibility in data handling, allowing to represent and predict the behavior of the harmonic phenomenon. The obtained algorithm can be transferred to physical or virtual systems for the control or reduction of harmonic distortion.

Reduction of Power Imbalances Using Battery Energy Storage System in a Bulk Power System with Extremely Large Photovoltaics Interactions

Power imbalances such as power shortfalls and photovoltaic (PV) curtailments have become a major problem in conventional power systems due to the introduction of renewable energy sources. There can be large power shortfalls and PV curtailments because of PV forecasting errors. These imbalances might increase when installed PV capacity increases. This study proposes a new scheduling method to reduce power shortfalls and PV curtailments in a PV integrated large power system with a battery energy storage system (BESS). The model of the Kanto area, which is about 30% of Japan’s power usage with 60 GW grid capacity, is used in simulations. The effect of large PV power integration of 50 GW and 100 GW together with large BESS capacity of 100 GWh and 200 GWh has been studied. Mixed integer linear programming technique is used to calculate generator unit commitment and BESS charging and discharging schedules. The simulation results are shown for two months with high and low solar irradiance, which include days with large PV over forecast and under forecast errors. The results reveal that the proposed method eliminates power shortfalls by 100% with the BESS and reduce the PV curtailments by 69.5% and 95.2% for the months with high and low solar irradiance, respectively, when 200 GWh BESS and 100 GW PV power generation are installed.

A Hysteresis Current Controller PWM Scheme Applied to 3-Level NPC Inverter for Distributed Generation Interface

In this article, a novel pulsewidth modulation (PWM) technique is proposed for an existing space vector hysteresis current controller (SVHCC) scheme. A three-level neutral-point-clamped (NPC) inverter is used for dual purpose of shunt active power filtering and solar photovoltaic (PV) power integration to a distribution grid. This dual-purpose NPC inverter utilizes the proposed PWM technique for solving the neutral-point voltage-balancing issue in a unique way. Due to the inherent advantages of the SVHCC, the current control is characterized by fast action and optimal switching. A single reference current required for both filtering and maximum PV power integration is generated using instantaneous power theory, where a diode bridge rectifier load is considered to demonstrate the controller performance while compensating nonlinear current components. Thus, not only a unique space-vector-based PWM technique is adopted for fast-tracking of currents, but also a sliding current error boundary is used for the first time. This sliding technique tackles the dc-link balance issue of the NPC inverter while controlling the output currents simultaneously. The simulation and hardware results prove the aforementioned performance and demonstrate the efficacy of the proposed PWM current controller.

Optimal Integration of Photovoltaic Power into the Electricity Network Using Slime Mould Algorithms: Application to the Interconnected Grid in North Cameroon

This document proposes a method for determining the optimal point of integration of PV Generated into the electricity grid. The Slime mould optimization algorithm ( SMOA) is applied to determine the best node in the case of one and two injection points. The problem has been modeled as an optimization problem where the objective is to minimize joule losses and the main constraint is to regulate the voltage at each point. The proposed method was implemented in MATLAB and applied in the nodes of the IEEE networks 33 and 69 and the north interconnected grid (NIG) of north Cameroon. The results allowed us to determine the optimal points of the NIG allowing us to inject with less losses. Comparison of the results obtained with other algorithms showed that the SMOA has the best reduction in power losses and a good improvement in the voltage profile

Research on the Influence of Distributed Generation on Voltage in Rural Distribution Network

With the development of photovoltaic projects for poverty alleviation, large number of distributed photovoltaic power plant is incorporated rural grid. On the one hand, photovoltaic poverty project can increase the income of farmers, on the other hand, it can improve the low voltage problem of rural distribution network. While photovoltaic power plant incorporating makes the distribution network from traditional radial network become active network, and causes a change of voltage distribution in feeder. Rural grid distribution network is the weakest link in the entire grid. So that it is necessary to analyse the influence factors of voltage distribution and the interconnection principles of photovoltaic power station. In this paper, through the modeling and simulation of distributed photovoltaic access to rural distribution network, analyses the law of the capacity and access location of photovoltaic power integration in the distribution network, and summarizes the interconnection requirements of the photovoltaic power station. The results show that only reasonable and proper use the distributed photovoltaic power plant, can play the role of voltage support and solve the problem of low voltage in rural distribution network.

Interval prediction of photovoltaic power generation based on cloud theory

With the increasing integration of photovoltaic power into the power system, the reliable photovoltaic power generation prediction is of significant to the security and economics of the power system operation. However, the prediction deviation is unavoidable, therefore this paper presents an interval prediction model for the photovoltaic power generation based on cloud theory. Based on the error analysis of the photovoltaic power prediction, the training samples can be selected to establish the cloud model for each small power generation bin. In this way, the predictive cloud distributions for different predictive power generation can be obtained to generate the prediction intervals at each time slot. To take the photovoltaic power plant in German as example, the proposed model is validated. The results show that the proposed model outperforms the other benchmark method. And the calculation process of the proposed model is simple and short in computation time. The analysis results are expected to be used in the field of power grid dispatching and decision making.

Study of trackside photovoltaic power integration into the traction power system of suburban elevated urban rail transit line

The trackside of suburban elevated urban rail transit (URT) line is a potential platform for placing Photovoltaic (PV) panels. This paper has made a comprehensive study of trackside PV power integration into the direct current (DC) traction power supply system of URT. With the elevated section of Metro Line 11 in suburban Shanghai as an example, the potential PV installation capacity has been evaluated. Based on the unique feature of the DC traction power supply system, a DC side PV integration scheme and control strategy has been proposed. The simulation models based on the electrical characteristics of URT power system and the moving train have been especially developed as an effective tool to perform the scenario analysis of different PV integration schemes, and an energy saving parameter “k” has been proposed to evaluate the energy saving effect. It concludes that DC side PV integration can help to compensate the traction voltage and reduce the catenary transmission loss in the traction stage of trains, thereby it has a higher energy saving rate. Even better performance can be achieved with both PV and energy storage system (ESS) integrated into the DC traction power system. The energy saving result can achieve the effect of “1 + 1 > 2”, which means the total amount of energy savings is even larger than the sum of PV or ESS working alone. Moreover, the traction power quality and safety will also be improved.

A review on voltage control for distribution systems with large-scale distributed photovoltaic power integration

In order to solve the problems of voltage wide-range fluctuations and over-limitation after large-scale distributed photovoltaic (DPV) integrated into distribution networks, this paper conducts a comprehensive and detailed analysis of voltage control strategy by summarizing existing voltage control technology and experience at home and abroad. The technical points and a comprehensive comparison from the perspectives of active power curtailment (APC), reactive power control (RPC) and active-reactive power coordination control (A/RC) are made. Through comparative analysis, it is found that adopting active-reactive power coordination control is a key measure to effectively improve voltage quality and increase the PV hosting capacity of distribution networks. The optimization of the control strategy can effectively improve the friendliness of large-scale PV system integration. The method and control strategy can provide a theoretical basis and practical reference for voltage management of distribution networks with high penetration PV integration.