Photovoltaic Generation Output Research Articles

Advanced Nonlinear Control of Shunt Active Power Filter Connected to PV System via DC/DC Converter

We address the challenge of controlling a single-phase shunt active power filter in the presence of nonlinear loads. The active power filter is linked to a photovoltaic (PV) system, which supplies active power to the load. The main control objectives are: (i) mitigation of harmonics current and reactive power induced by the non-linear load; (ii) regulation the PV generator output voltage to follow its reference provided by the Maximum Power Point Tracking (MPPT) block, (iii) regulation the DC bus voltage to a constant value that must be greater than the amplitude of the grid voltage in order to guarantee full power injection to the non-linear load. To solve this problem, we use a nonlinear multi-loop controller. The inner loop is designed using the Backstepping method to cope with compensation problems. The external loop is responsible for regulating the dc bus voltage. The DC-DC boost control loop is designed to achieve the MPPT objective. The efficiency of the proposed controller is validated by simulation in MATLAB/Simulink.

Distributionally Robust Model Predictive Control for Smart Electric Vehicle Charging Station With V2G/V2V Capability

This paper proposes a distributionally robust model predictive control (DRMPC) for energy management of a vehicle-to-grid (V2G)/vehicle-to-vehicle (V2V)-enabled smart electric vehicle charging station (EVCS) with a photovoltaic (PV) system and an energy storage system. The proposed DRMPC method aims to reduce the operational cost of the EVCS while ensuring the desired charging demands of electric vehicle (EV) users under uncertainties in electricity buying/selling prices, PV generation outputs, and future EV charging demands. To cope with these uncertainties, the proposed method includes the following three features: i) tractable reformulation of the worst-case expected buying cost and selling revenue using a Wasserstein metric and duality theory, ii) determination of a distributionally robust bound on the random PV generation output using its support information, and iii) a scenario-based approach to predicting the future EV charging demand. To improve computational efficiency, a penalty method is proposed to relax the complementarity constraints, while still ensuring nonsimultaneous charging and discharging of EVs under the derived sufficient conditions. Numerical examples using a real-world operational dataset of the EVCS are provided to demonstrate the effectiveness of the proposed DRMPC method under uncertain environments in terms of the EVCS cost saving via V2G/V2V capability, data utilization, and computational complexity.

Three-Stage Stochastic Unit Commitment for Microgrids Toward Frequency Security via Renewable Energy Deloading

Renewable energy is boosting the deployment of microgrids (MGs) with stochastic and low inertia nature. To improve the operational efficiency of MGs while guaranteeing frequency security, a three-stage stochastic unit commitment problem is proposed, where renewable energy can be deloaded. In the first stage, the diesel generators (DGs) are scheduled, responding to uncertainties of loads and photovoltaic generator output. In the second stage, the outputs of DGs are optimized to reduce the operational cost under uncertain disturbances. In the third stage, a novel PV deloading strategy is proposed to test the frequency security of MGs. The three-stage optimization problem is formulated as a multi-stage stochastic optimization problem with recourse. This problem is then solved using a novel nested Benders decomposition algorithm with both dual cuts and partial primal cuts. Simulations are performed on an AC MG under different PV penetration levels and disturbances. The results verify the effectiveness of the proposed model in balancing operational efficiency, renewable energy utilization, and frequency security.

Lightning Shielding with Shading Avoidance for a Photovoltaic (PV) Farm

The special requirement of a PV farm lightning shielding system is that it should not introduce shadow on the active PV surfaces so as to avoid impaired PV generation output. By using a simple, repeatable-unit PV row made up of single or multiple PV strings, the problem of finding a cost-effective and efficient PV row lightning finial arrangement can be resolved. The shading analysis combined with the MS IEC 62305 implementation of the Rolling Sphere Method (RSM) is able to give a simple procedure that lends itself to spreadsheet calculations pertaining to PV farm lightning shielding design.

Short-term Forecasting Approach Based on bidirectional long short-term memory and convolutional neural network for Regional Photovoltaic Power Plants

Accurate photovoltaic (PV) generation output prediction is one of the effective ways to ensure the safe operation of power grid, develop reasonable dispatching plan and improve the efficiency of clean energy. With the large-scale operation of PV power plants in recent years, forecasting regional PV output becomes more significant. We proposed a short-term forecasting approach based on bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) for regional PV power plants. First, the k-means algorithm is used to divide power plants with similar generation characteristics into the same output subregion. Second, a representative power plant in each subregion is selected based on three correlation coefficients. Then, we develop a regional prediction model based on BiLSTM-CNN method. This model takes historical operation and meteorological data of the representative power plant as input, and takes the total subregional power generation as output. Finally, this short-term forecasting approach is tested using real data from PV power plants in Chuxiong and Dali region, Yunnan province, China. The comparison of numerical results shows this proposed method can effectively improve the short-term prediction accuracy of regional PV generation output.

Analysis of output coupling characteristics among multiple photovoltaic power stations based on correlation coefficient

The rapid development of photovoltaic generation has brought great challenges to operation and planning of power systems. Based on actual operation data of several photovoltaic power stations, this paper analyzes the fluctuation of photovoltaic generation output first. Then a correlation coefficient based quantitative calculation method for coupling characteristics among multiple photovoltaic power stations is proposed, and the load-following level under various photovoltaic generation capacities is given at last. This paper illustrates that the output of photovoltaic generation presents complementary coupling characteristics, which reduces the fluctuation of the overall output and can deteriorate the load-following level. The results of this paper can provide a theoretical basis for the planning of power systems with a high penetration of renewable energy such as photovoltaic generation.

Adaptive output-feedback control design for maximum power point tracking of uncertain photovoltaic systems

The problem of maximum power point tracking (MPPT) control of uncertain photovoltaic (PV) systems is addressed in this paper, on the basis of an adaptive Kalman-like observer. The system is composed of a photovoltaic generator (PVG) supplying power to a DC centrifugal pump driven by a DC–DC boost converter. The PVG is connected to the converter by a long PV cable. Indeed, many PV power plant situations require that the PV panels be installed at a great distance from the converter for several reasons including the security and the property of the site and its exposure to good daily solar irradiance. This obviously leads to the difficulty of PVG output current and voltage measurement using ordinary sensors. Such quantities measurement being in fact necessary for MPPT algorithms and controllers design. Furthermore, the specific parameters of the long PV cable used, namely its resistance and inductance, could have a significant effect on the MPPT control efficiency if instead of PVG delivered voltage and current measurements, only those accessible at the converter side of the cable are used in MPPT controllers design. Therefore, this work aims at overcoming the two aforementioned issues by proposing an adaptive output-feedback control-based MPPT for PV systems. An adaptive Kalman-like observer providing online estimates of inaccessible state variables as well as of PV cable unknown parameters is firstly designed. Then, a backstepping control law is synthesized to meet the MPPT objective. The convergence of both adaptive observer and closed-loop system control has been established using Lyapunov approach and the effectiveness of the developed adaptive MPPT controller in achieving an accurate and robust MPPT control towards uncertainties of the PV cable parameters, has been validated through numerical simulations.

Analysis of the Impact of Particulate Matter on Net Load and Behind-the-Meter PV Decoupling

With the increasing penetration of the photovoltaic (PV) generator, uncertainty surrounding the power system has increased simultaneously. The uncertainty of PV generation output has an impact on the load demand forecast due to the presence of behind-the-meter (BtM) PV generation. As it is hard to assess the amount of BtM PV generation, the load demand pattern can be distorted depending on the solar irradiation level. In several literature works, the influence of the load demand pattern from BtM PV generation is modeled using environmental data sets such as the level of solar irradiation, temperature, and past load demand data. The particulate matter is a severe meteorological event in several countries that can reduce the level of solar irradiation on the surface. The accuracy of the forecast model for PV generation and load demand can be exacerbated if the impact of the particulate matter is not properly considered. In this paper, the impact of particulate matter to load demand patterns is analyzed for the power system with high penetration of BtM PV generation. Actual meteorological data are gathered for the analysis and correlations between parameters are built using Gaussian process regression.

Joint optimization of Volt/VAR control and mobile energy storage system scheduling in active power distribution networks under PV prediction uncertainty

Mobile energy storage systems (MESSs) are becoming crucial devices to maintain stable power distribution system operations under the operation of voltage regulators including on-load tap changers, capacitor banks, and smart inverters of photovoltaic (PV) systems. This study proposes a joint framework in which the operation of voltage regulators and the road routing of MESSs are co-optimized for Volt/VAR control (VVC) in both power distribution and transportation networks. The proposed framework aims to minimize real power loss and voltage deviation along with peak shaving in power distribution networks, and to reduce the MESS traveling cost in transportation networks. Under the uncertainty of the predicted error of the PV generation output, the proposed framework is formulated as a chance-constrained optimization problem using mixed-integer linear programming. The proposed joint optimization algorithm was simulated in the IEEE 13-bus and IEEE 33-bus systems, which were coupled with 9-node and 15-node transportation systems, respectively. The results show that, in contrast with a VVC method without MESS, the proposed algorithm using MESS reduces the total real power loss and peak load by 9.7% and 15.92%, respectively, in the IEEE 13-bus system, and 2.55% and 3.47%, respectively, in the IEEE 33-bus system.

A Learning-Based Bidding Approach for PV-Attached BESS Power Plants

Large-scale renewable photovoltaic (PV) and battery energy storage system (BESS) units are promising to be significant electricity suppliers in the future electricity market. A bidding model is proposed for PV-integrated BESS power plants in a pool-based day-ahead (DA) electricity market, in which the uncertainty of PV generation output is considered. In the proposed model, we consider the market clearing process as the external environment, while each agent updates the bid price through the communication with the market environment for its revenue maximization. A multiagent reinforcement learning (MARL) called win-or-learn-fast policy-hill-climbing (WoLF-PHC) is used to explore optimal bid prices without any information of opponents. The case study validates the computational performance of WoLF-PHC in the proposed model, while the bidding strategy of each participated agent is thereafter analyzed.

Fine-Grained Photovoltaic Output Prediction Using a Bayesian Ensemble

Local and distributed power generation is increasingly relianton renewable power sources, e.g., solar (photovoltaic or PV) andwind energy. The integration of such sources into the power grid ischallenging, however, due to their variable and intermittent energyoutput. To effectively use them on alarge scale, it is essential to be able to predict power generation at afine-grained level. We describe a novel Bayesian ensemble methodologyinvolving three diverse predictors. Each predictor estimates mixingcoefficients for integrating PV generation output profiles but capturesfundamentally different characteristics. Two of them employ classicalparameterized (naive Bayes) and non-parametric (nearest neighbor) methods tomodel the relationship between weather forecasts and PV output. The thirdpredictor captures the sequentiality implicit in PV generation and uses motifsmined from historical data to estimate the most likely mixture weights usinga stream prediction methodology. We demonstrate the success and superiority of ourmethods on real PV data from two locations that exhibit diverse weatherconditions. Predictions from our model can be harnessed to optimize schedulingof delay tolerant workloads, e.g., in a data center.

An online optimization method for extracting parameters of multi-parameter PV module model based on adaptive Levenberg-Marquardt algorithm

This paper proposes a novel method to estimate the optimal parameters of a single-diode photovoltaic (PV) module model as well as the active power of PV generation. First, a multi-parameter PV module model is established considering the changes of temperature and solar irradiance; thus, the model is adaptive to different environmental conditions. Second, the model parameter estimation is designed as an optimization problem. An online optimization scheme that only requires the normal operation information is proposed, which avoids the need for an offline experiment to determine the current–voltage (I-V) curve as in conventional PV modeling methods. Third, the adaptive Levenberg-Marquardt (ALM) method with an adaptative damping factor selection strategy is developed to solve the parameter optimization problem, which overcomes the singular issue of traditional LM methods. Moreover, the global sensitivity analysis (GSA) is leveraged to identify critical parameters affecting the PV generation output power, which provides information on model simplification. Finally, the proposed method is tested on data from real-world PV systems in Bend, Oregon, USA, under diverse conditions and scenarios. It is revealed that compared with commonly used methods, the proposed method obtains more accurate PV module parameters and offers PV generation outputs closer to the true outputs.

Optimum Parametric Identification of a Stand-Alone Photovoltaic System with Battery Storage and Optimization Controller Using Averaging Approach

The dimensioning of photovoltaic systems is the major concern of researchers and power industry practitioners. This aims to improve energy efficiency and protect the conversion units by a consistent assessment of power conditioning circuits and interconnections for the PV application. In this context, this paper sets out to fulfill detailed modeling and control steps of a standalone photovoltaic (PV) power system with energy storage, according to practical specifications of the load, PV generation unit, and battery pack. The main goal is to estimate all unknown parameters, as the diode ideality factor and revers saturation current, the controller, and the PV link. The PV link interfacing the PV source circuit to the PV-side converter (PVSC) provides a filtering function to maintain a steady voltage at the link. The charge controller used in the PV-side converter is a DC/DC buck converter. It transfers the PV power to the battery and supplies the load. Using pulse- width modulation (PWM) technical, of which the switching duty cycle is the control-input variable; the PVSC power-conditioning circuit is permanently controlled by the maximum power point tracking (MPPT) algorithm to achieve the maximum energy. The battery pack voltage is properly maintained by the charge controller and specified to match the load voltage rating, to avoid a high ratio of voltage conversion. A method is proposed to integrate both the MPPT function and the battery cycle charge. The PV generator output and the power conditioning circuits, mainly constructed from switching- mode power converters, are nonlinear. An averaged model is then derived for dynamic analysis and controller synthesis, using the state-space averaging and linearization method. A PV array of nine PV modules configured into three strings is used in this application to demonstrate the effectiveness of modeling, design, control, and simulation. Simulation model for the controller and power interface is built and developed in short term, using the fundamental blocks of Matlab Simulink.

Regulation capability of small cascade hydropower stations for photovoltaic generation

The stochasticity of photovoltaic (PV) generation output makes it necessary to smooth the output using other flexible power sources to realize the scale development of PV power. Small cascade hydropower stations (SCHSs) are also widely distributed in space, like PV generation. Therefore, it is proposed to utilize the regulation capability of SCHSs to complement PV generation. Targeting the maximization of the utilization rate of outgoing lines (utilization rate for short) of the system, the minimization of the curtailment rate, and the maximization of the minimum output within a time frame, a model for exploring the capability of SCHSs for regulating PV generation was established and solved by a hierarchical optimization method. By taking two small hydropower stations (the Mupo and Yangjiawan stations) on the Fabian River with a total installed capacity of 105 MW and the surrounding PV power source in China's Sichuan Province as examples, it was found that SCHSs can regulate a certain level of PV generation through the combined operation of cascade reservoirs and reduce the fluctuations and stochasticity of the PV generation output. The results have far-reaching significance on the large-scale development of PV generation.

Reducing Emissions and Costs with Vehicle-to-Grid

Determining when and how to charge electric vehicles to minimize costs and potential greenhouse gas emissions is a complex optimization problem. Recently in Applied Energy, Brinkel et. al. developed a multi-objective smart charging method that can reduce costs and cut carbon emissions without upgrading existing grid infrastructure in model case scenarios.

Performance Assessment of Photovoltaic Generator(PVG) Power System Using Maximum Power Point Tracking (MPPT) DC to DC Boost Converter and DC to AC Inverter

Output power of Photovoltaic Generator (PVG) in a power system was tracked using duty cycle perturbation pulse applied into Maximum Power Point Tracking (MPPT) DC to DC boost converter. Perturbation pulse was controlled by Perturb and Observe (P&O) algorithm which is written in Arduino Uno microcontroller. Power system consist of PVG connected directly with MPPT DC to DC boost converter and then with DC to AC inverter and selected load is 20 W bulb. PVG efficiency was determined by the ratio between the PVG output electric power and the amount sunshine irradiation power received on the surface of PVG. MPPT efficiency was calculated base on output power of PVG work as an input power of MPPT and both input and output power of MPPT was measured using voltage sensor and ACS 712 current sensor. Voltage and current measurements was carried out in online system using Parallax Data Acquisition (PLX-DAQ) software and then voltage, current and power was tabulated in excell table. The graph of voltage, current and power was plotted as a function of time and shows the occurrence of power tracking of MPPT to extract maximum power from PVG after duty cycle of perturbation was applied. The performance of power system was determined by multiplying efficiency of PVG, MPPT and Inverter which gives the performance value around 93 percent of PVG efficiency.

Integration of Deep Boltzmann Machine and Generalized Radial Basis Function Network for Photovoltaic Generation Output Forecasting

In this paper, an efficient method is proposed to deal with photovoltaic generation output forecasting with Deep Boltzmann Machine. In recent years, the penetration of photovoltaic generation has been widely spread in the world due to clean energy. However, it has brought about uncertainties for generation schedules in a way that power system operators have to consider the significant variations of generation output. As a result, the forecasting model of the generation output with high accuracy is required in the industries. This paper proposes a Deep Neural Network (DNN) model that integrates Deep Boltzmann Machine with Generalized Radial Basis Function Network (GRBFN) of Artificial Neural Network (ANN). The proposed model is tested for real data of photovoltaic generation output.

Optimal Volt–Var Curve Setting of a Smart Inverter for Improving Its Performance in a Distribution System

We propose an algorithm to set the parameters of volt-var curves to improve the performance of distributed generation. Specifically, we optimally set the volt-var curves of the smart inverter for proper control of the distributed generation output. To improve the overall distribution system performance, we consider the minimization of voltage deviation, system loss, and peak of reactive power in an objective function, thereby providing optimal volt-var curve parameters. The proposed algorithm overcomes various limitations of existing studies. First, we use distribution system factors in a multiobjective optimization function to adjust volt-var curve parameters that deviate from the default settings. Second, we consider the service transformers that affect the system voltage according to the photovoltaic generation output in the test model during optimization. Third, we analyze the system improvement according to the number of parameters to be optimized. Fourth, we analyze the implementation of the optimized volt-var curve according to the practical periods when the smart inverter settings can be updated. Fifth, we evaluate different suitable volt-var curves for multiple photovoltaic generators grouped using clustering. Using the proposed algorithm, the distribution system operator can set the optimal volt-var curve according to the system conditions and requirements. We conducted simulations of the proposed method using OpenDSS, a quasistatic time-series simulation, on a test model reflecting the characteristics of a South Korean distribution system. The simulation results confirm that the overall system performance increases when the optimal volt-var curve settings obtained from the proposed algorithm are used.

Short-Term Photovoltaic Generation Forecasting Based on LVQ-PSO-BP Neural Network and Markov Chain Method

With the rapid development of solar photovoltaic generation, the effective prediction of photovoltaic is of great significance to mitigate its impact on power system. According to the analysis of main factors which affect power output of photovoltaic system, a short-term power forecasting model based on back propagation(BP) neutral network and LVQ-PSO-BP neural network and Markov chain method was established. The weather is clustered and distinguished by using learning vector quantization(LVQ) and the particle swarm optimization(PSO) is used to optimize BP neural network weights and thresholds, improving forecasting network training speed. Finally, daily predictive value is corrected by Markov chain method to improve short-term photovoltaic generation forecasting precision. The simulation results indicate that the proposed method can accelerate the speed of searching optimums, improving the classification accuracy of weather types and the precision of the photovoltaic generation output effectively.

Battery Energy Storage System Control for Mitigating PV Penetration Impact on Primary Frequency Control and State-of-Charge Recovery

Increasing photovoltaic (PV) penetration significantly diminishes system inertia that affects systems' damping capability to regulate primary frequency control. Unlike wind turbine, PV energy system is incapable of providing under-frequency support because of no stored kinetic energy and could cause penalties for violating regulatory requirements. Therefore, a droop-type, lead- lag controlled battery energy storage system (BESS) with a novel adaptive state-of-charge (SOC) recovery strategy is proposed in this paper to provide additional damping, enhance the inertial ability of the system with 18.18% PV penetration and which satisfy Australian National Electricity Market regulatory requirements. The adaptive SOC recovery aims to maintain flexible battery SOC value according to load/PV generation forecast and comply with future events, such as peak PV generation or lower PV output during the passing cloud periods. The proposed adaptive SOC strategy regulates SOC based on the value of charging current and moreover, adaptive SOC recovery does not affect the maximum SOC limit for the regular network event. Simulation results demonstrate BESS efficacy in mitigating the adverse inertial impact of PV and accomplishing mandatory grid requirements. Moreover, the proposed adaptive SOC recovery shows the flexibility of BESS for SOC management planning in accordance with future events forecast.