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Precise forecasting of solar power output is crucial for integrating renewable energy into power networks, improving efficiency and dependability. This study assesses the efficacy of several Machine Learning (ML) algorithms in predicting solar power generation through a detailed performance comparison. This paper analyzes six algorithms: CatBoost, Gradient Boosting Machines (GBMs), Multilayer Perceptron (MLP) regressor, Support Vector Machines (SVMs), XGBoost, and Random Forest (RF). Using a dataset of 4213 sets of solar power generation data, each model was trained and tested, with performance evaluated based on R-squared (R2) scores for the whole dataset, training set, and test set. Also, this study examined the mean and standard deviation of test set predictions to gauge how consistent each model was. The results showed that RF had the highest overall R2 score of 0.940 and a training set score of 0.971. XGBoost demonstrated exceptional performance on the test set, attaining a high R2 score of 0.822. CatBoost and GBMs exhibited strong performance, albeit with slightly lower R2 values of 0.786 and 0.829, respectively. Although the MLP regressor and SVMs exhibited high training scores, they encountered difficulties in generalizing to unfamiliar data. This paper highlights the effectiveness of combining XGBoost and RF techniques in improving the accuracy of solar power forecasts. The investigation focuses on enhancing the precision and reliability of renewable energy projections through a comprehensive comparison of various contemporary ML techniques.

Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation’s attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach’s prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy.

With the increase of photovoltaic penetration, the power-electronic converter (PEC)-interfaced generators with traditional control strategy may cause severe power oscillations after being disturbed due to lack of inertia and damping. The photovoltaic power plant controlled by virtual synchronous generator (VSG) technology, while introducing virtual inertia and virtual damping to improve the stability of the system, will also inherit its oscillation characteristics and cause a new oscillation mode. Aiming at this problem, this paper analyzes the difference between the system low-frequency oscillation (LFO) modes before and after the VSG is connected to the two-area system by the modal analysis. It is pointed out that the PEC-interfaced renewable energy generators based on VSG control technology could change the oscillation mode of the original system. The influence of VSG's main parameters and capacity on system dynamics is analyzed. Finally, a synchrophasor-based inter-area oscillation suppressed comprehensive control strategy is proposed to suppress the LFO of active power on the tie line, and the superiority of the strategy is verified by DIgSILENT /PowerFactory. [ABSTRACT FROM AUTHOR]

The intermittent and stochastic nature of solar energy generation systems, climate change, and the inefficiency of modern power systems due to zero inertia have created many challenges for on-grid operators. Solar forecasting systems based on machine learning algorithms are an emerging and effective solution that uses Big Data related to weather phenomena. However, the predictive ability of these algorithms is hampered by the sporadic nature of solar energy generation. In this article, a robust hybrid machine learning system that utilizes multiple linear regression (MLR) and a Pearson correlation coefficient (PCC) was tested on solar power plant sites of varying capacities in Germany (100–8500 kW). The volume of Big Data features can be reduced by focusing on the features that significantly improve the reliability of the mid-term forecasting system. In this way, drastic fluctuations in the prediction of photovoltaic (PV) power generation can be avoided. The results of our approach are evaluated regarding real-world data using the extreme gradient boosting (XGBoost) with feature engineering, and principal component analysis (PCA), in order to forecast PV energy, rank, and track the importance of feature engineering for different PV capacities. Furthermore, we found that the need for selectivity and reduction of performance error was supported by ridge regression. In addition, the proposed novel XGBoost forecast system decreased the root-mean-square error (RMSE) and mean absolute error (MAE) by 30% and 18%, respectively, compared to the Autoencoder and long short-term memory (LSTM) for same datasets. Furthermore, the CoD determination coefficient ($R^{2}$) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA). [ABSTRACT FROM AUTHOR]

As the penetration of photovoltaic (PV) solar generation has increased, a significant number of residential and commercial solar PV systems have been installed without meters. The majority of these systems, however, are also not monitored by power system operators. Therefore, the uncertainty of the net load owing to this “invisible” solar power generation has raised additional challenges for power system operation. To reduce the impact of this, we propose a novel method to estimate the total solar power generation in a large region from a small representative subset. The proposed method is capable of capturing all relevant information that could assist in the identification of the representative subsets. Moreover, different optimization algorithms are utilized and evaluated to select the optimal number of clusters and representative subsets. As a case study, the power generation of 166 PV sites in Taiwan was collected and analyzed. The proposed method demonstrates a significant improvement in estimating aggregated power generation compared to other existing methods. [ABSTRACT FROM AUTHOR]

The integration of distributed photovoltaic and new energy vehicles could further aggravate the over-voltage, over-load, and three-phase voltage unbalance in distribution networks. Multistation integrated system (MSIS) can resolve the aforementioned power quality issues. In this article, a new topology for a MSIS is proposed, which uses soft open point as the key equipment of the system and adopts the hierarchical power supply mode for a data center, electric vehicle charging pile, and other function stations. Based on the topology of the MSIS and smooth switching of the converter control mode, a coordinated control strategy is proposed to control the working mode of each function station and further optimize the system operation conditions. The small signal model of the MSIS is derived, which can provide theoretical support for the stable operation of the system. The simulation results of two different application scenarios verify that the proposed topology of the MSIS and coordinated control strategy effectively improve the voltage level of the distribution network, enhance the reliability of sensitive load power supply, and ensure safe and reliable operation of the MSIS. Finally, the fusion and integration functions of the MSIS are tested and verified. The on-site demonstration indicated a mutual win–win situation for the power supply company, energy supplier, equipment provider and users. [ABSTRACT FROM AUTHOR]

A photovoltaic (PV) energy harvesting system with 2-Dimensional Fast Maximum Power Point Tracking (2-D FMPPT), based on the hill climbing (HC) MPPT technique is proposed. The 3-step MPPT modes for the energy harvesting system are employed to obtain KVOC that is independent of environmental effects such as irradiance and radiated temperature. The system consists of a dynamic peak transducer power sensor (DPTPS), Coarse (1st) FMPPT mode, Ton Increment/Decrement Mode, Fine (2nd) mode, MPPT logics, and a controller. The system can handle a voltage range of 0.5 V to 2.4 V and a power range of 1.4 mW to 119 mW. A peak MPPT efficiency of 99.4% and over 96.1% MPPT efficiency in 1 k to 50 k illuminance are achieved. The proposed system is fabricated on 0.18- $\mu m$ CMOS process and occupies an active area of 1.4 mm2. [ABSTRACT FROM AUTHOR]

The design and operation of modern energy systems are heavily influenced by time-dependent and uncertain parameters, e.g., renewable electricity generation, load-demand, and electricity prices. These are typically represented by a set of discrete realizations known as scenarios. A popular scenario generation approach uses deep generative models (DGM) that allow scenario generation without prior assumptions about the data distribution. However, the validation of generated scenarios is difficult, and a comprehensive discussion about appropriate validation methods is currently lacking. To start this discussion, we provide a critical assessment of the currently used validation methods in the energy scenario generation literature. In particular, we assess validation methods based on probability density, auto-correlation, and power spectral density. Furthermore, we propose using the multifractal detrended fluctuation analysis (MFDFA) as an additional validation method for non-trivial features like peaks, bursts, and plateaus. As representative examples, we train generative adversarial networks (GANs), Wasserstein GANs (WGANs), and variational autoencoders (VAEs) on two renewable power generation time series (photovoltaic and wind from Germany in 2013 to 2015) and an intra-day electricity price time series form the European Energy Exchange in 2017 to 2019. We apply the four validation methods to both the historical and the generated data and discuss the interpretation of validation results as well as common mistakes, pitfalls, and limitations of the validation methods. Our assessment shows that no single method sufficiently characterizes a scenario but ideally validation should include multiple methods and be interpreted carefully in the context of scenarios over short time periods.

The power output of the solar panels follows a power-voltage (P-V) characteristic containing only one Global Maximum Point (GMP) in the normal conditions. However, under Partial Shading Conditions (PSC), the unbalanced irradiance in the panels creates Local Maximum Points (LMP) in the P-V curve. Standard control techniques for Maximum Power Point Tracking (MPPT) can not properly locate the GMP, stagnating in LMPs and generating losses in the energy harvesting. Specific techniques to locate the GMP are presented in the literature. However, the condition to restart the GMP is not widely discussed. The main challenges of global search algorithms are related to the restarting conditions. Avoiding unnecessary searching and providing an assertive GMP restarting condition is crucial for PV systems operation. In every GMP search, the solar inverters oscillate the power exchanged with the grid, causing frequency and voltage variations depending on the size of the PV plant. This paper proposes a novel technique that uses a centralized controller to identify the shaded inverters, creating flags that locally start the GMP searching. The solution minimizes the number of times the search is performed by providing an assertive GMP restarting condition, saving energy, and avoiding unnecessary output power oscillation. The proposed control technique was evaluated using the data of a real 150-kW solar farm containing five inverters with two MPPT trackers each.

The output power from a solar power generation system (SPGS) changes significantly because of environmental factors, which affects the stability and reliability of a power distribution system. This study proposes a SPGS with the power smoothing function. The proposed SPGS consists of a solar cell array, a battery set, a dual-input buck-boost DC-AC inverter (DIBBDAI) and a boost power converter (BPC). The DIBBDAI combines the functions of voltage boost, voltage buck and DC-AC power conversion. The BPC acts as a battery charger between the solar cell array and the battery set. For the proposed SPGS, the DC power that is provided by the solar cell array or the battery set is converted into AC power through only one power stage. The solar cell array also charges the battery set through only one power stage. This increases the power conversion efficiency for the solar cell array, the battery set and the utility. The battery set is charged/discharged when the output power of the solar cell array changes drastically, in order to smooth the output power from the SPGS. In addition, the DIBBDAI can suppress the leakage current that is induced by the parasitic capacitance of the solar cell array. The proposed power conversion interface increases power efficiency, smooths power fluctuation and decreases leakage current for a SPGS. A hardware prototype is completed to verify the performance of the proposed SPGS.

It is the year 2045 in the carbon-neutral southwestern United States. While summer heatwaves have increased in frequency and intensity because of climate change, the region’s abundant solar generation produces tremendous amounts of low-cost energy on hot summer days. This energy not only serves daytime loads but it also charges an enormous fleet of batteries that, aided by wind and geothermal power, discharge overnight to effectively eliminate summertime electric reliability concerns. [ABSTRACT FROM AUTHOR]

This paper demonstrates the potential of inverter-based loads to support grid reliability during power system transients thereby enabling reliable integration of renewable energy in power systems. Such loads are referred to in this paper as grid-supportive loads (GSLs). A new GSL model is developed that simulates the transient response capabilities that can be programmed in electronic loads. The model’s design enables it to be easily integrated in widely used commercial power system transient analysis software. Theoretical expressions are derived that explain the workings of the GSL model. The performance, numerical stability, and impact of the GSL model is validated on 9-bus and 2000-bus synthetic power system models using generator tripping and bus fault disturbances. Results on the 2000 bus system show that in the absence of frequency support from wind/solar generation resources, just 20% of loads with grid-supportive capabilities can improve frequency response by up to 2000 MW/0.1 Hz and reduce deviation in frequency at nadir by up to 60% compared to the situation when GSLs are absent. Power system reliability also improves under fault events. It is further shown that GSLs can aid in integrating more renewable generation without degrading the overall transient response of the power system. [ABSTRACT FROM AUTHOR]

The capacity for renewable energy generation, particularly solar and wind, tends to grow over the years. However, locating regions favorable to the development of solar and wind generation is not trivial and is subject to errors due to the social, environmental, economic, political and administrative aspects of the regions. Therefore, this article proposes an algorithm based on digital image processing, capable of performing a variable weight multi-parameter evaluation and quantifying how favorable a given territorial region is for the implantation of wind or solar plants. Maps of the region under analysis are taken as input information. The region taken as a case study is the Brazilian state of Pernambuco, due to the access to data related to the evaluated characteristics: solar radiation, solar insolation, precipitation, air humidity, temperature, electrogeography, demographic density, environmental protection areas, slope, hydrography, wind speed, urban spot and the presence of aerodromes. It was possible to list for each type generation (wind or solar, exclusively), the 15 most favorable regions for the implementation plants. Furthermore, the proposed technique presents a practical indifference to the territorial extension of the evaluated region, which can comprehend a micro-region or an entire continent, requiring only that sufficiently detailed maps of the evaluated characteristics be provided. [ABSTRACT FROM AUTHOR]

The Interconnections Seam Study examines the potential economic value of increasing electricity transfer between the Eastern and Western Interconnections using high-voltage direct-current (HVDC) transmission and cost-optimizing both generation and transmission resources across the United States, proposing, assessing, justifying, and illustrating a major infrastructure change involving two of the world's largest power grids. The study conducted a multi-model analysis that used co-optimized generation and transmission expansion planning and production cost modeling. Four transmission designs under eight scenarios were developed and studied to estimate costs and potential benefits. The results show benefit-to-cost ratios that reach as high as 2.5, indicating significant value to increasing the transmission capacity between the interconnections under the cases considered, realized through sharing generation resources and flexibility across regions. [ABSTRACT FROM AUTHOR]

In recent years, with the rise of environmental awareness worldwide, the number of solar power plants has significantly increased. However, the maintenance of solar power plants is not an easy job, especially the detection of malfunctioning photovoltaic (PV) cells in large-scale or remote power plants. Therefore, finding these cells and replacing them in time before severe events occur is increasingly important. In this paper, we propose a hybrid scheme with three embedded learning methods to enhance the detection of malfunctioning PV modules with validated efficiencies. For the first method, we combine the improved gamma correction function (preprocess) with a convolutional neural network (CNN). Infrared (IR) thermographic images of solar modules are used to train the abovementioned improved algorithm. For the second method, we train a CNN model using the IR temperatures of PV modules with the preprocessing of a threshold function. A compression procedure is then designed to cut the time-consuming preprocesses. The third method is to replace the CNN with the eXtreme Gradient Boosting (XGBoost) algorithm and the selected temperature statistics. The experimental results show that all three methods can be implemented with high detection accuracy and low time consumption, and furthermore, the hybrid scheme provides an even better accuracy.

This article presents a novel simplified method to implement the finite set - model predictive control technique for photovoltaic generation systems connected to the ac network. This method maintains the advantages of the conventional finite set - model predictive control, such as fast response, simple implementation, and easy understanding; but it also eliminates the use of a cost function and hence the weighting factors, instead, it finds the optimal operating state directly from the model and the discrete number of valid states of the converter. Although the proposed algorithm does not compute a cost function, it is able to select the inverter state that minimizes the tracking error by using a hexagonal convergence region. The main advantage of this technique is to reduce the computational cost in 43% of the algorithm that selects the best state, presenting a simple and complete algorithm without compromising the predictive control performance. The proposed algorithm properly operates under various conditions such as changes in the network frequency and changes in the system parameters.

Immunization of mission-critical facilities such as hospitals and first responders against power outages is crucial for the operators due to their significant value of the lost load, affecting citizens' lives. This paper proposes a novel evaluating framework which enables facility operators to efficiently size and optimally dispatch their behind-the-meter energy storage systems (BTM-ESS) for resiliency purposes during grid emergencies. The proposed framework, formulated as a mixed integer linear programming model, aids facility operators to quantify the impacts of various BTM-ESSs on resilience enhancement where the Avoided Loss of Load (ALOL) is incorporated as the resilience indicator. BTM-ESS is assumed to be operated in both standalone and coupled with solar photovoltaic (PV) as an onside backup generation which is a viable energy solution for more prolonged power outages. The proposed model is developed on a probabilistic energy procurement model, aiming to minimize the facility's total operation cost. The uncertainty of power outages is characterized by a set of a large number of scenarios generated by the brute-force enumeration method. Additionally, to analyze the impacts of facilities' behaviors on the BTM-ESS evaluation procedure, a set of 24 facilities from different end use sectors with various functionalities are simulated by employing our in-house-developed building simulator, which is a physics-based simulation tool. Finally, the practicality of the proposed evaluating framework is investigated through two case studies where both short and long-duration grid outages are examined based on the historical outage data adopted from New Jersey, USA. The simulation results reveal that a BTM-ESS with 4 hours discharge duration that is sized at rated power equal to 50% or more of the facility's peak load generates sufficient resilience benefits for most of the 24 representative facilities in case of short-duration power outages.

Eighteen percent of the world’s population do not have access to electricity, impeding economic, social, and human development. The electricity access challenge can be attributed to the significant investment gap needed to finance new power projects, requiring new and innovative financing options. Independent Power Projects, funded, built, owned, and operated by the private sector and constituted via a special purpose vehicle—a legal entity whose sole purpose is implementing a power project—have become one of the fastest-growing sources of investment in the electricity sector. The limitations of traditional finance sources, including high credit, liquidity, margining, third-party, legal, and process risks, means that funds for these projects are expensive and raised only after a long and arduous process. The present article addresses these challenges by describing a novel decentralized autonomous organization, a blockchain-agnostic special purpose vehicle underpinned by a trio of autonomous mechanisms—mobilization, collateralization, and settlement. These mechanisms enable seamless finance mobilization for the special purpose vehicle from a location-independent crowd, revenue collection from the electricity buyer in a risk-mitigated manner, and disbursal of eventual project revenues to investors. [ABSTRACT FROM AUTHOR]

The increasing penetration of solar generation into power grids has promoted the need for accurate and reliable short-term solar irradiance forecasting. This article introduces a novel multibranch attentive gated recurrent residual network (ResAttGRU) consisting of multiple branches of residual networks, gated recurrent units (GRUs), and the attention mechanism. The proposed multibranch ResAttGRU is capable of modeling data at various resolutions, extracting hierarchical features, and capturing short- and long-term dependencies. Moreover, this network also presents a strong multitimescale representative within the proposed architecture, while GRUs can exploit temporal information at less computational cost than the popular long short-term memory (LSTM). The novelty of the proposed architecture is to employ multiple convolutional-based branches with different filter lengths to learn multitimescale features jointly, accelerate the learning process, and reduce overfitting by leveraging shared representations as the auxiliary information. This study also compares the multibranch ResAttGRU networks with state-of-the-art deep learning methods using 18 years of NSRDB data at 12 solar sites. Finally, the proposed multibranch ResAttGRU requires 7.1% fewer parameters than multibranch residual LSTM while achieving similar average RMSE, MAE, and R-squared values. [ABSTRACT FROM AUTHOR]

With the increase of the component number, the power stress distribution among differential power processing (DPP) converters, control implementation, system cost, and reliability become the most challenging issues for a practical photovoltaic (PV) DPP system. This article introduces an improved power-rating balance (IPRB) control for the PV-to-bus based DPP architecture that ensures each PV submodule operate at its true maximum power point (MPP) while achieving more balanced power stress distribution and higher reliability. Specifically, a submodule-level finite-state-machine-based MPP tracking is implemented to guarantee always maximum power yield, whereas a string-level power-rating balancing (PRB) control is adopted to balance the unit-maximum proceeded power by DPP converters based on the built power flow model with respect to the string current. A comprehensive comparison of advanced control strategies for PV-to-bus DPP architectures, including least power point tracking, voltage equalization (VE) based PRB control, and the proposed IPRB, has been carried out with the mission-profile-based reliability assessment under different partial shading scenarios. Component-failure-rate-based reliability analysis shows that the PV-to-bus DPP architecture with the proposed IPRB control can significantly improve the system reliability. Main simulation and experimental evaluations are carried out to verify the effectiveness of the proposed control. [ABSTRACT FROM AUTHOR]

Accurate estimation of residential solar photovoltaic (PV) generation is crucial for the power distribution and demand response program implementation. Currently, most distributed PVs are installed behind-the-meters (BTMs), and are thus invisible to the utilities. The existing methods separate the BTM solar generation from the available net load in a centralized manner assuming that all data are accessible to utilities. However, this can cause privacy issues, since the data are owned by different utilities and they may be unwilling to share their data. To this end, a novel method is proposed for disaggregating community-level BTM solar generation using a federated learning-based Bayesian neural network (FL-BNN), which can preserve the privacy of utilities. Specifically, a Bayesian neural network (BNN) is designed as the probabilistic energy disaggregation model with the ability to capture uncertainties. The BNN training process is extended into a decentralized manner based on the federated learning framework. To enable the model customized for each community, the layers of BNN are categorized into shallow and deep layers, and a layerwise parameter aggregation strategy is proposed to update the model. Both community-specific features and community-invariant features can be learned. The effectiveness of the proposed method is validated on a publicly available dataset. [ABSTRACT FROM AUTHOR]

The power budget of a CubeSat is difficult to estimate when its orientation while in orbit is unknown. If the attitude control system is absent, when there was a short-term system failure or it completely failed, the satellite usually begins to rotate; in this case, the axis of its own rotation can be any. The amount of available electrical energy in free-orientation mode determines how quickly it can restore its functionality and whether it can at all. Thus, the available energy can be obtained using a statistical estimate. This article provides power budget estimates in free-orientation mode for popular CubeSat designs. To estimate the energy, one needs to substitute the parameters of the solar cells used (at the maximum power point), as well as the fill factor of the solar array panels. [ABSTRACT FROM AUTHOR]

The present work discusses the green-mediated fabrication of SnO2/Ag/Au nanocomposites using the extract of Corallo Carpus epigaeus rhizome. XRD pattern confirmed the tetragonal rutile structure of pure SnO2 and face-centered cubic phases of Ag systems. Field emission scanning electron microscopy (FESEM) and TEM images revealed the shape and average size of the particles. The surface plasmon resonance (SPR) effect was investigated using UV–Vis spectroscopy. Photoluminescence studies were carried out to detect the surface defects and electron–hole pair recombination processes. The size effect of nanoscale crystallites was studied by Raman spectroscopy. It revealed that the reduction in grain size of the particles broadened the intensity peaks due to the increase in oxygen vacancies. Dye sensitized solar cell (DSSC) ${I}-{V}$ characteristics exposed that the combination of SnO2/Ag/Au nanocomposites exhibited an improved efficiency (11.37%) than its pure form (7.68%). Supercapacitor studies involved cyclic voltammetry and galvanostatic charge–discharge analysis. Ag/Au composited with SnO2 nanomaterials attained an enhanced capacitance value of 483.76 F/g at 10 mV/s. Electrochemical impedance studies were also conducted to analyze the electrochemical behavior of the nanomaterials. [ABSTRACT FROM AUTHOR]

Flexible DC distribution networks have been widely developed because of their advantages of high efficiency, low cost, and easy access to renewable energy. However, as distribution networks have more ends than the two-terminal transmission line, it is more challenging to protect them and locate their faults. Therefore, this paper proposes a modified T-source circuit breaker to protect against DC faults. Based on the proposed circuit breaker, an integral fault location method using double-terminal asynchronous data is proposed. The accuracy of the algorithm is not affected by the fault resistance. There are no differential terms in the algorithm, which greatly reduces the sensitivity to noise. The integral algorithm can remain steady when the voltage and current are zero in oscillation and attenuation periods. This constitutes the main contribution of this paper. Finally, it is verified by simulation and experiment that the proposed circuit breaker can isolate the power source within a short time in the typical DC distribution network. Under a noise of 30 dB for simulation, the location error of the proposed algorithm is <0.4% when the fault resistance is 0.5 Ω–500 Ω. [ABSTRACT FROM AUTHOR]

We propose a two-stage probabilistic solar power (SP) forecasting algorithm to utilize the solar irradiation (SI) observations measured from different locations. In the first stage, we predict the SI based on the numerical weather prediction (NWP) after interpolating SI observations. Since the SI on the target location is not measured, we interpolate it using the spatio-temporal Kriging technique based on the SI observed from nearby weather stations. In the second stage, we forecast the SP based on the SI predictions after training the SI and SP observations. The model is trained by observations, but it forecasts based on predictions. Furthermore, in the two-stage model, forecasting errors can propagate across stages. We overcome these problems by using probabilistic forecasting. We design distributions of SI predictions through the probabilistic graphical model. Then, we extract SI scenarios from the distributions and predict SP scenarios based on these SI scenarios. We also group the NWP with respect to its prediction time, and we subdivide these groups as subgroups with respect to weather conditions. Furthermore, we propose a changeable ensemble model, where we have different weights for each weather condition. We verify our algorithm based on the data from the Korea power exchange renewable energy forecasting competition 2019. We finished the competition in 2nd place among a few hundred participants. [ABSTRACT FROM AUTHOR]

Renewable energy has gained significant attention of researchers in the last years, mainly due to the importance of using unlimited energy sources to supply homes, industries, cities and countries. In this context, this document focuses on the solar injection by employing a neutral point clamped (NPC) topology together with utilization of a maximum power point tracking (MPPT) and space vector modulation (SVM) techniques. Model predictive control (MPC) is employed to manage the currents and track their references. The proposed algorithms do not employ a cost function to decide which voltage to apply resulting in a spread frequency spectrum, and instead, a concentrated SVM spectrum is imposed. Notwithstanding, the DC link capacitors voltage balance is ensured and the computational burden is notably reduced as compared to traditional Finite Set Model Predictive Control (FS-MPC). Nevertheless, the consistent results are a consequence of the critical analysis that shows the feasibility of the proposal and guarantees the good performance of the entire system in simulated and experimental platforms.

To improve the thermoelectric conversion efficiency of solar thermoelectric power, a concentration solar thermoelectric generator (CTEG) unit based on concentrating and storing energy is designed. A Fresnel lens is used to concentrate thermal energy, and a phase change material (PCM) is used to store thermal energy to increase the temperature difference between the hot and cold ends of thermoelectric (TE) sheets. The heat stored in the PCM container will help to generate continuous solar energy at night and improve the thermal power conversion efficiency of the TEGs. The energy conversion equilibrium equation is established for the CTEG unit. By numerical calculation, we conclude that the absorption rate of the coating surface is reduced by 0.1 and the maximum thermoelectric conversion efficiency is reduced by approximately 0.4%. By selecting different heights of the heat exchanger, the unit supplies 1.025 v, 0.4204 v and 0.299 v open circuit voltages on average for approximately 30, 44 and 53 minutes, respectively. Therefore, the height of the heat exchanger affects the rate of heat energy absorption and release. The reversible operation of the CTEG-PCM unit is conducive to the day and night operation of solar power generation and is more suitable to solving the self-supplying power problem for wireless temperature sensors in forests.

A photovoltaic–thermoelectric (PV–TE) hybrid system can be used for efficient thermal energy utilization from the generated waste heat in PV devices. In this article, an efficient PV–TE hybrid system with intelligent power-track switching technique and thermal management based on energy conversion is proposed. To make the output power of PV–TE system stable and normalized, an incorporated stable voltage circuit is designed based on energy conversion. In addition, a control-and-monitoring strategy is launched in the system to realize the normal collecting for the output power of PV–TE system. Finally, a battery protection circuit is performed to ensure that the energy converted by the entire system is effectively stored. The experimental results show that more electrical energy about 84 034 J was obtained with our energy harvesting system than that of a single photovoltaic (PV) cell. Besides, the thermal gradient of PV cells is indirectly reduced the operation of the whole system, which is automatically monitored due to the proposed intelligent power-track switching technique. [ABSTRACT FROM AUTHOR]

For an optimal operation of a three-phase single-stage grid-tied solar photovoltaic (PV) system, the steepest descent Laplacian regression (SDLR) based adaptive control technique is proposed in this brief. In this topology, the local loads are considered on CPI (Common Point of Interface). Therefore, the objectives of SDLR based control technique, are harmonics mitigation and power quality improvement of the grid currents. Moreover, an additional feature of DSTATCOM (Distribution Static Compensator) is also included in the control scheme. During the night, or when solar PV power generation is zero, then the voltage source converter and capacitor of DC-link are operated as DSTATCOM, which provides reactive power support to the grid. An effectiveness of SDLR based control is validated through experimentation. Here during testing, different types of adverse conditions are considered, such as solar insolation variation, unbalanced load condition, unbalances in grid voltages, etc. [ABSTRACT FROM AUTHOR]

This study is interested in the application of demand response (DR) to building energy management via heating, ventilation and air conditioning (HVAC) controls. The main objective of this study is to develop an optimization model to appropriately control HVAC operations to minimize cost in response to time-varying electricity prices and intermittent solar power generation. The main idea is to adjust electricity consumption associated with HVAC operations while ensuring the thermal comfort of occupants. In particular, we propose a two-stage stochastic program formulated to find an optimal HVAC operations schedule that comprises proactive and reactive controls to minimize energy cost against the stochastic nature in electricity prices, solar power generation, and weather conditions. Specifically, we introduce a chance constraint that can be appropriately integrated with the proposed optimization model to ensure thermal comfort at the desired level. To validate and evaluate the proposed approach, numerical experiments are designed to compare the proposed approach with the benchmark control policies designed to simulate the practice of the general purpose thermostat and the algorithm proposed by relevant study with various parameter settings. The numerical experiments show that the proposed approach results in significant cost savings while capturing optimal HVAC operations. [ABSTRACT FROM AUTHOR]

This paper proposes a combination of phasor particle swarm optimization (PPSO) and a gravitational search algorithm, namely a hybrid PPSOGSA algorithm, for optimal power flow (OPF) in power systems with an integrated wind turbine (WT) and solar photovoltaic (PV) generators. The OPF formulation includes the forecasted active power generation of WT and PV as dependent variables, whereas the voltage magnitude at WT and PV buses is considered as control (decision) variables. Forecasting the output power of WT and PV generators is based on the real-time measurements and the probabilistic models of wind speed and solar irradiance. The proposed OPF approach and the solution method are verified on the IEEE 30-bus test system. The robustness and efficiency of the proposed PPSOGSA algorithm in solving the OPF problem are evaluated by comparing with 20 well-established metaheuristic optimization methods under the same system data, control variables, and constraints. The statistical features of the OPF results are estimated by using the Monte Carlo method.

The time to market of maximum power point tracking circuits for solar cells depends on the time of transient simulations. But, the traditional tools use a nonlinear resistor model for the solar cells. The question is if it is possible to use another nonlinear relationship to improve the convergence features. This paper presents a new method for modeling a solar cell, considering temperature and irradiation changes and an improved convergence performance. In this model, the solar cell's current-voltage (I-V) characteristics for three irradiation and two temperature conditions are required, showing a feasible procedure. The proposed model is fitted to three commercial solar cells exhibiting a maximum average error of 2.51 percent. It is tested in a transient analysis converging to the expected values in a maximum power point tracking circuit.

We study the scheduling of large-scale electric vehicle (EV) charging in a power distribution network under random renewable generation and electricity prices. The problem is formulated as a stochastic dynamic program with unknown state transition probability. To mitigate the curse of dimensionality, we establish the nodal multi-target (NMT) characterization of the optimal scheduling policy: all EVs with the same deadline at the same bus should be charged to approach a single target of remaining energy demand. We prove that the NMT characterization is optimal under arbitrarily random system dynamics. To adaptively learn the dynamics of system uncertainty, we propose a model-free soft-actor-critic (SAC) based method to determine the target levels for the characterized NMT policy. The proposed SAC + NMT approach significantly outperforms existing deep reinforcement learning methods (in our numerical experiments on the IEEE 37-node test feeder), as the established NMT characterization sharply reduces the dimensionality of neural network outputs without loss of optimality. [ABSTRACT FROM AUTHOR]

A solar photovoltaic (PV)—battery energy storage (BES) involved the microgrid (MG) system is presented in this article. It contains a bidirectional dc–dc converter (BDDC), which is responsible for extracting the maximum power output from the PV array by regulating the dc-link voltage to the maximum power point (MPP) voltage of the PV array. Whenever the PV array is not delivering any power, then the control of the BDDC is automatically shifted to regulate the dc-link voltage to a constant dc voltage. Thus, the BDDC regulates the dc-link voltage to the desired voltage in the presence of BES in the MG. Whenever the BES is disconnected from the MG, then the voltage source converter (VSC) regulates the dc-link voltage to the desired value. Thus, it enhances the reliability of the system. The neutral current compensation is performed by the zig-zag transformer, which is connected at the load end. The VSC performs different functions such as mitigation of harmonics and reactive power, balancing of grid currents, and the regulation of the dc-link voltage in the absence of BES. The system behavior is analyzed under different operating conditions in the simulation platform and in the laboratory conditions on a prototype. [ABSTRACT FROM AUTHOR]

This article deals with a ride-through strategy for a three-phase grid-tied solar photovoltaic array system. Unlike the conventional control algorithms, this control algorithm does not tradeoff with power quality melioration features while permitting ride-through dynamics of the solar energy conversion system (SECS). As per the revised IEEE-1547 standard, the distributed energy resources have to provide a full range of services even under voltage disturbances caused by faults like existing power plants. In this article, the grid-connected solar power generation system follows the grid code even under ride-through operation with the presence of reactive local load, unlike the traditional algorithms. In order to map future challenges, the low-voltage ride-through features of three-phase SECS at symmetrical/asymmetrical faults, are explored here. This control strategy is capable to operate SECS at various modes. To operate the voltage source converter within a safe limit, a constraint is incorporated on the active power of SECS to avoid overcurrent and energy aggregation in the dc-link capacitor, which reduces the life-time of the dc-link capacitor. Simulated results demonstrate the effectiveness of the control strategy for various operating conditions. The comparative performances of the presented control strategy are analyzed to demonstrate the effectiveness under the presence of distorted grid voltages and non-Gaussian noises in sensed measurements. Test results manifest the dynamics of SECS at various operating conditions. [ABSTRACT FROM AUTHOR]

To exploit the massive solar energy available in the region, photovoltaic plants have been built in the mountain areas in Southwest China, coexisting with many small cascaded run-of-the-river hydropower plants, interconnected by short-distance transmission lines. Network constraints inside these systems are often negligible. However, due to the volatile nature of solar power and the weak connection to the external power grid, without proper coordination, solar power curtailment and water spillage are inevitable. To address this issue, this paper proposes an ultra-short-term stochastic generation control method for cascaded hydropower to mitigate solar power volatility. Two technical challenges are specifically addressed: to tackle the time-varying stochastic volatility of solar power, the Itô process model is introduced; to characterize the spatial-temporal hydraulic coupling of the cascaded hydropower plants and river operation constraints, a state-space dynamic river model derived from shallow water equations is included. Followingly, a stochastic control (SC) approach for hydropower generation based on stochastic programming is proposed, where hydropower generation commands are parameterized as affine functions of measured solar power, enabling quick response to solar power volatility. Simulation of a real-life cascaded hydro-solar system and a modified system verifies the proposed control method. [ABSTRACT FROM AUTHOR]

An analytic model for calculating the instantaneous and average orbital power available to a satellite in a circular low Earth orbit is presented. By accounting for the effects of angle of solar incidence and eclipsing, for both stabilized and tumbling satellites, this model allows for fast, simple, accurate calculations of available power without the use of complex, often expensive, numerical simulation software. In addition, this method provides the value of maximum and minimum solar power available. [ABSTRACT FROM AUTHOR]

Integration of renewable distributed generation (DG) units into distribution networks is gaining widespread popularity. However, uncertainties in generation availability associated with renewable DG units pose a major challenge. These uncertainties should be properly addressed to ensure acceptable system performance and improve customer-side reliability. In this article, the reliability assessment of distribution systems embedded with renewable DG sources has been carried out giving emphasis to system uncertainties and optimal restoration strategies. The uncertainties associated with the power output from renewable resources, time-varying load demand, stochastic prediction errors, and random fault events have been accounted for in the restoration optimization formulation for reliability evaluation. In this article, a parameter-free particle swarm optimization (PSO) technique is applied to address the complexity involved in the formulation. Moreover, a problem-specific encoding scheme is also proposed in conjunction with PSO to ensure optimality. [ABSTRACT FROM AUTHOR]

The rate of change taking place in the energy industry is truly mind-boggling. There was a time, a few decades ago, when the electric system was viewed as a technological backwater, and universities were dropping power engineering programs left and right. The industry’s problems had all been solved, or so the story went. Today, there is a shortage of trained power system engineers capable of tackling the problems of tomorrow, and schools cannot add new programs fast enough because they cannot find enough trained faculty. What happened in the intervening time? [ABSTRACT FROM AUTHOR]

In this article, an operational framework is proposed for peer-to-peer (P2P) energy trading between a group of electric vehicles (EVs) within a charging station and a business entity equipped with solar generation that will significantly improve the benefit of all parties compared to having sole agreements with the utility. A dynamic pricing mechanism for the EVs is developed based on the price of the stored energy that not only increases owners’ profit but also promotes the contribution of charging stations in P2P energy markets. To evaluate the proposed framework, the performance of the system under P2P energy sharing is compared with peer-to-grid (P2G) energy trading. The results show a 23.24% reduction in total cost of prosumers and an improvement of 10% in self-consumption of Photovoltaics (PV) generation as well as 100% participation willingness of prosumers. The results also suggest that considering the availability of solar energy during daylight hours and the possibility to trade the stored energy in EVs outside of home, P2P energy transactions under the proposed control framework will bring significant economic benefits for EV owners. [ABSTRACT FROM AUTHOR]

A good solar power photovoltaic generation forecast depends on good quality time series data from measurements of Global Horizontal Irradiance and Solar Power Generation. However, measurement system failures and errors in data handling can corrupt data records with gaps and outliers that undermine forecasting accuracy. Therefore, it is important that the fitting of solar energy prediction models must be preceded by a data analysis in order to detect and correct measurement errors. Given that Global Horizontal Irradiance and Solar Power Generation are correlated variables, this paper aims to present the main characteristics of an offline approach developed for the joint treatment of hourly data values of both variables in a photovoltaic plant. In the proposed methodology, the measurements of Global Horizontal Irradiance and Solar Power Generation are analyzed by using reanalysis data and statistical and data mining techniques for the correction of outliers and the filling of data gaps. The application of the approach is illustrated by the analysis of measurements from a real solar PV system. [ABSTRACT FROM AUTHOR]

Inverter-based solar generation can cause load rejection overvoltage when the connected distribution circuit is islanded from the utility grid. The load rejection overvoltage presents electric stress on utility equipment, metering devices, and customer equipment. This article investigates the load rejection overvoltage mechanism of utility-scale solar generation based on measurements collected by utility protective relays. The impact of solar inverter induced overvoltage on arresters and customer equipment is analyzed. [ABSTRACT FROM AUTHOR]

Rooftop solar photovoltaic (PV) power generator is a widely used distributed energy resource (DER) in distribution systems. Currently, the majority of PVs are installed behind-the-meter (BTM), where only customers’ net demand is recorded by smart meters. Disaggregating BTM PV generation from net demand is critical to utilities for enhancing grid-edge observability. In this paper, a data-driven approach is proposed for BTM PV generation disaggregation using solar and demand exemplars. First, a data clustering procedure is developed to construct a library of candidate load/solar exemplars. To handle the volatility of BTM resources, a novel game-theoretic learning process is proposed to adaptively generate optimal composite exemplars using the constructed library of candidate exemplars, through repeated evaluation of disaggregation residuals. Finally, the composite native demand and solar exemplars are employed to disaggregate solar generation from net demand using a semi-supervised source separator. The proposed methodology has been verified using real smart meter data and feeder models. [ABSTRACT FROM AUTHOR]

This article elucidates a real-time energy management strategy for a smart residential apartment building having nonidentical occupants at the dwelling units (DUs). The aim of the present article is to design a distributed energy management algorithm, which can optimize the real-time demand of the entire building against abruptly updated rooftop solar generation and real-time price (RTP) of energy. The proposed energy management strategy differentiates among the DUs by considering a new parameter named load criticality level, which is defined as the value imposed by the DU residents to their power consumption. The optimization portfolio is developed as a novel bilevel, stochastic, multiobjective optimization problem where the maximization of utility of the consumed power is considered simultaneously with the cost minimization. To this end, a virtual energy trading platform is designed in this article between central building management system and the DUs, where they interact with each other by following the directives of single-leader multifollower Stackelberg game. The solution strategy is proposed as a Lyapunov optimization, which needs only the current values of the uncertain parameters, such as load variation, renewable generation, and energy price, and do not require any knowledge about their probabilistic variation, to eliminate the complexities regarding time average stochastic equations. Strenuous simulation on real-time data of four DUs, it is proved that the proposed framework can track the abrupt change in RTP and solar generation efficiently. Comparing with two benchmark methods viz. centralized process and greedy algorithm, the superiority of the designed energy management portfolio is established. [ABSTRACT FROM AUTHOR]

The alarming situation of global warming leads to the full adoption of the renewable energy-based transportation system. However, their sustainable deployment at a mass level has been a challenging task. This article presents the design aspects and practical implementation of the modern solar-assisted level-2 electric vehicle charging station which is controlled by a Type-1 vehicle connector. The designed model is developed in MATLAB/Simulink environment, the circuit operation is examined and its methodological model is derived to study the parametric design features. Furthermore, the complete hardware setup has developed to test the performance of the power factor correction under the steady-state condition with respect to variation in load for the input of 3 kW, 230 Vrms at 1-phase, 50 Hz rated, and to produce a 48 V buck converter dc output. The 6.4 kW solar photovoltaic (PV) charging station, installed at the Centre of Advanced Research in Electrified Transportation building parking area in Aligarh Muslim University campus, selected as a case site. Moreover, the controller circuit is simulated in PROTEUS software and a prototype model has been tested in the lab. The study is performed on a 10 kWh lithium-ion battery pack on a bright sunny day at standard test condition of the solar panel. [ABSTRACT FROM AUTHOR]

The relative share of renewable energy, specifically the solar photovoltaic (PV), is increasing exponentially in the world electric energy sector. This is a cumulative result of reduction in the cost of solar panels, improvement in the panel efficiency, and advancement in the associated power electronics. Among different types of PV plants, installation of small-scale rooftop PV is growing rapidly due to direct end-user benefits and lucrative governmental incentives. There are various standards developed in regards to grid integration of PVs and other distributed generations (DGs). Different power converter topologies are developed to interface the PV panel with the utility grid. To keep up with the stringent regulations imposed by the standards, various control strategies and grid synchronization methods have been developed. This review article amalgamates and summarizes all of the aforementioned aspects of a grid-integrated PV system including various standards, power stage architectures, grid synchronization methods, operation under extreme events, and control methodologies, pertaining to small-scale PV plants. This article will help freshman researchers to gain some familiarity with the topic and introduce them to some of the key issues encountered in this field. [ABSTRACT FROM AUTHOR]

A computationally proficient real-time energy management method with stochastic optimization is presented for a residential photovoltaic (PV)-storage hybrid system comprised of a solar PV generation and a battery energy storage (BES). Existing offline energy management approaches for day-ahead scheduling of BES suffer from energy loss in real time due to the stochastic nature of load and solar generation. On the other hand, typical online algorithms do not offer optimal solutions for minimizing electricity purchase costs to the owners. To overcome these limitations, we propose an integrated energy management framework consisting of an offline optimization model concurrent with a real-time rule-based controller. The optimization is performed in receding horizon with load and solar generation forecast profiles using deep learning-based long short term memory method in rolling horizon to reduce the daily electricity purchase costs. The optimization model is formulated as a multistage stochastic program where we use the stochastic dual dynamic programming algorithm in the receding horizon to update the optimal set point for BES dispatch at a fixed interval. To prevent loss of energy during optimal solution update intervals, we introduce a rule-based controller underneath the optimization layer in finer time resolution at the power electronics converter control level. The proposed framework is evaluated using a real-time controller-hardware-in-the-loop test platform in an OPAL-RT simulator. The proposed real-time method is effective in reducing the net electricity purchase cost compared to other existing energy management methods. [ABSTRACT FROM AUTHOR]

The Chinese Government has issued numerous regulations that significantly affect the number of photovoltaic (PV) installations in the country and the subsidies for their use. This article summarizes the internal and external environment of China's PV industry and describes its future trends and prospects and also discusses a proposed rate-making process and renewable energy supervision. [ABSTRACT FROM AUTHOR]

The uptake of behind-the-meter distributed energy resources in low-voltage distribution networks has progressed to a point where phenomena including overvoltage and reverse power flow are emerging. The limited nature of supervision and control on these networks require novel tools for operation and planning in this environment. In this paper, we propose a methodology for synthesizing stochastic demand and generation profiles for unobserved customers with rooftop PV, called prosumers. The proposed model bridges the gap between the limited available empirical data, and the large amount of high-quality, stochastic demand and generation data required for probabilistic analysis. The approach employs clustering analysis and a Dirichlet-categorical hierarchical model of the features of unobserved prosumers. Based on the data of clusters of prosumers, Markov chain models of demand and generation profiles are constructed from empirical data, and synthetic demand profiles are subsequently sampled from these. The sampled traces are cross-validated and show a good statistical fit to the observed data. Two case studies are considered to confirm the validity of the proposed methodology. The first studies the impact of behavioral differences on the synthetic demand profiles, while the second looks at the impact of varying solar generation penetration on demand profiles. [ABSTRACT FROM AUTHOR]