Photovoltaic Generation Research Articles

Optimal Coordination of Hybrid AC/DC Microgrids based on IEEE-12 Bus System: An Analytical Approach

Recently, the microgrid's design and implementation techniques have placed a significant emphasis on alternating current (AC) systems. Due to their many benefits, DC microgrids are becoming more and more popular than AC microgrids. The benefits include the elimination of frequency modulation and the requirement for synchronization. DC microgrids are better suited for distributed energy resources (DERs) and DC loads. When it comes to hybrid microgrids, the options are practical and reasonably priced. Graphical depictions of the voltage profiles, wind generation, and total PV generation show how the system operates and performs. The removal of power converters between AC, DC systems is the cause of this. This article suggests an analytical approach for the best possible coordination between AC, DC microgrids using the IEEE 12 bus system.

Dataset for Machine Learning: Explicit All-Sky Image Features to Enhance Solar Irradiance Prediction

Prediction of solar irradiance is crucial for photovoltaic energy generation, as it helps mitigate intermittencies caused by atmospheric fluctuations such as clouds, wind, and temperature. Numerous studies have applied machine learning and deep learning techniques from artificial intelligence to address this challenge. Based on the recently proposed Hybrid Prediction Method (HPM), this paper presents an original and comprehensive dataset with nine attributes extracted from all-sky images developed using image processing techniques. This dataset and analysis of its attributes offer new avenues for research into solar irradiance forecasting. To ensure reproducibility, the data processing workflow and the standardized dataset have been meticulously detailed and made available to the scientific community to promote further research into prediction methods for photovoltaic energy generation.

Module‐Based Supercapacitors: Potential Energy Storage Solutions for Large‐Scale Photovoltaic Systems

Intermittency is an inherent characteristic of photovoltaic (PV) power generation and results in high ramp rates of the generated power. This article explores the feasibility of integrating supercapacitors at the PV module level, aiming to reduce the power fluctuations of PV systems and control the power ramp rate into the power grid. First, an equivalent circuit model of a single‐phase grid‐connected PV system based on module‐based supercapacitors is proposed, and a power ramp rate control scheme is established. Then, experimental setups for a single‐phase grid‐connected PV system based on module‐based supercapacitors are implemented, and the computational model is verified through experiments. Finally, using the verified computational model and the proposed control scheme, the module‐based supercapacitor sizes for different PV system sizes (PV module, rooftop, small system, large system) that meet specific ramp rate requirements under different ramp rate limits (5, 10, 15% min−1) are compared. Case studies show that large‐scale PV systems with geographical smoothing effects help to reduce the size of module‐based supercapacitors per normalized power of installed PV, providing the possibility for the application of modular supercapacitors as potential energy storage solutions to improve power ramp rate performance in large‐scale PV systems.

Development of Novel Ternary Alloy of CSiSn for Low-Cost Next Generation Photovoltaics

The growing need for highly efficient solar cells calls for significant advancements in the development of a new material system to meet the needs of low-cost and low-cost production. Current multi-junction solar cell alternatives are more costly to produce. This research showcases the development and analysis of a novel ternary material system, The CSiSn (CST) alloy, which shows potential for creating low-cost material with high absorption in of solar spectrum. In this work, the following aspects regarding the CST material are investigated: 1) Material modeling using density functional theory, 2) electronic band structure estimations, 3) low-temperature material growth technique via Plasma Enhanced Chemical Vapor Deposition (PE-CVD) system; 4) advanced material and optical characterizations of CST alloys.

Modeling method of photovoltaic power generation grid connection based on particle swarm optimization neural network

Aiming at the complex structure, numerous equipment, intricate control and protection logic, as well as the existence of numerous unmodeled dynamics and black-box device models in photovoltaic (PV) grid-connected systems, a modeling method based on Particle Swarm Optimization Neural Network (PSO-NN) is proposed to address the inability of pure mechanism models to accurately simulate their operational dynamics. Utilizing the differences in active power response waveforms under Voltage-Frequency (Vf) control, Power-Reactive Power (PQ) control, and Droop control as criteria for control strategy identification, a PSO-NN model is constructed for PV grid-connected systems, with inputs comprising temperature, humidity, light intensity, voltage, and frequency disturbances, and outputs being active and reactive power. To validate the model's effectiveness, a PV grid-connected system model is built in a self-developed simulation software and connected to an IEEE 14-bus distribution network for simulation verification. The results demonstrate that the proposed PV grid-connected model can effectively identify the types of Vf control, PQ control, and Droop control strategies, and accurately reflect the dynamic response characteristics of active and reactive power under various voltage and frequency disturbances.

A Bayesian Approach for Modeling and Forecasting Solar Photovoltaic Power Generation.

In this paper, we propose a Bayesian approach to estimate the curve of a function f(·) that models the solar power generated at k moments per day for n days and to forecast the curve for the (n+1)th day by using the history of recorded values. We assume that f(·) is an unknown function and adopt a Bayesian model with a Gaussian-process prior on the vector of values f(t)=f(1),…, f(k). An advantage of this approach is that we may estimate the curves of f(·) and fn+1(·) as "smooth functions" obtained by interpolating between the points generated from a k-variate normal distribution with appropriate mean vector and covariance matrix. Since the joint posterior distribution for the parameters of interest does not have a known mathematical form, we describe how to implement a Gibbs sampling algorithm to obtain estimates for the parameters. The good performance of the proposed approach is illustrated using two simulation studies and an application to a real dataset. As performance measures, we calculate the absolute percentage error, the mean absolute percentage error (MAPE), and the root-mean-square error (RMSE). In all simulated cases and in the application to real-world data, the MAPE and RMSE values were all near 0, indicating the very good performance of the proposed approach.

Coordinated frequency control strategy for modern power system considering engagement willingness

The large scale integration of renewable energy sources (RES) is posing unprecedented challenges to the frequency stability of modern power systems, with system inertia being the primary concern. Under such a landscape, this paper introduces a coordinated control strategy aiming at enhancing frequency stability by swiftly adjusting the power output of flexible resources to mitigate power imbalances and further dampen frequency fluctuations. Firstly, a communication-free center of inertia (COI) frequency estimation method is proposed. An equivalent inertia estimation method tailored for modern power systems is then developed by exploiting the correlation between synchronous inertia and active power fluctuations. By integrating the COI frequency variation rate with the estimated equivalent inertia, the imbalance power is calculated and compensated through the proposed coordinated control strategy. In this work, two typical flexible resources including photovoltaic (PV) generation and electric vehicles (EVs) are modeled and integrated into the testing system in PSCAD/EMTDC. Considering the contradiction between maximizing generation profit and reducing power output to mitigate frequency variation, the Corporate Social Responsibility (CSR) of renewable Generation Companies (GENCOs) is modeled to sketch their willingness of engaging into frequency support. Moreover, the frequency modulation potential of EVs in both commercial and residential areas are quantified for practical implementation. The simulations results successfully validate the effectiveness of the proposed control strategy.

Characterisation and simulated evaluation of electricity management strategies for Prosumers, Prosumagers, and other end-users

ABSTRACT Distributed photovoltaic generation has been raised as a dependable electricity substitute for implementing a decarbonised energy transition. The dynamics of PV systems with battery storage have led to new electricity end-user classifications that select their electricity systems and resources wisely, i.e. Prosumers and Prosumagers. This work presents a technical and economic assessment to analyse the effect of decision-making independent end-users and electricity management strategies through a novel time-discrete deterministic simulation model. The end-user's choice of battery storage technology and their approach to electricity management emerge as pivotal factors influencing their strategic decisions. Adopting a prosumer strategy, such as storing and selling surplus self-production, reduces the Levelized Cost of Energy (LCOE), benefiting the end-user economically. Notably, the most effective battery storage technology may only sometimes be the one that is most readily available in the market. These findings enhance our understanding of novel end-user types and underscore the practical implications of their evolutionary shift towards independence.

Increasing photovoltaic self-consumption for objects using domestic hot water systems

The technique of estimating the expected decrease in electricity consumption from the grid and using PV energy for the taken load schedule based on archival data for 5 years is refined. With full self-consumption (SC), the reduction of consumption from the grid can be increased by 9.5%–30.7% for a year according to the rated PV power. Consumption should increase when PV generation exceeds a certain value. A discrete time control of the power of an electric storage boiler (ESB) is proposed based on the deviation of the storage battery (SB) state of charge from a given schedule with a heating concentration during hours of high PV generation. In the considered application, it is possible to increase SC by up to 21%. Reducing the load in the evening allows us to use SB energy to reduce consumption from the grid at night. The possibility of complete photovoltaic SC when the ESB is used with an air conditioner is substantiated. Limitations for air conditioner energy consumption according to PV generation are determined. The system’s 24h model of energy processes is supplemented with a thermal model. The standard use of ESB with water temperature maintenance was also considered for comparison. ESB power control allows you to reduce daily energy consumption from the grid by 1.7–2 times. When combining an adjustable ESB with an air conditioner, it is possible to reduce consumption from the grid by 1.466–1.558 times at minimum and increase consumption from the grid by 2–5% at maximum air conditioner consumption.

Impact of electric circuit configurations on power generation in a photovoltaic and thermoelectric generator hybrid system

Energy harvesting using a thermoelectric generator (TEG) leverages the Seebeck effect to generate electricity from temperature differences. As the demand for electrical energy rises, especially in zero-energy buildings and building conversions, there is an increasing focus on recovering untapped energy sources within buildings. This study aims to analyze the thermal characteristics of a hybrid energy harvester that integrates a TEG with phase change materials (PCM), with the objective of recovering and generating power from wasted sunlight and heat within a building envelope. Additionally, we experimentally investigated the circuit configuration of the TEG to optimize energy harvesting. The experimental results highlighted distinct power generation patterns depending on the TEG's location. To achieve maximum power output, TEGs exhibiting similar power generation patterns should be configured in series to ensure optimal performance. Implementing the maximum power circuit configuration resulted in a 70 % increase in power generation compared to a non-optimized configuration. Moreover, configuring an equal number of TEGs in series yielded a 94.4 % increase in power generation compared to a scenario where all TEGs were connected in series on a single panel.

Coordinated control algorithm of hydrogen production-battery based hybrid energy storage system for suppressing fluctuation of PV power

Photovoltaic (PV) power generation has issues of volatility and intermittency. Currently, PV plants are generally equipped with 10% rated capacity lithium-ion (Li) battery energy storage systems in China, who often fail to suppress fluctuation in the output power of PV plants effectively and meet the grid-connected standard. The hybrid energy storage system (HESS) combining with hydrogen production and Li battery system can produce hydrogen by water electrolysis during the peak period of PV power generation, effectively improving PV utilization efficiency, while smoothing PV power fluctuation and improving grid connection electricity quality. Firstly, models of the solid oxide electrolysis cell (SOEC) and alkaline electrolysis cell (AEC) systems for hydrogen production, and Li battery energy storage system are established, and the transient response characteristics of each system are analyzed. Secondly, an adaptive wavelet packet decomposition (AWPD) method for PV power signal decomposition is proposed based on the wavelet packet decomposition (WPD) method. Thirdly, a capacity configuration method for HESS are proposed based on the APWD method. Fourthly, a coordinated control strategy for HESS is proposed with the transient response characteristics of different energy storage systems and the state of charge for Li battery system. Finally, the proposed method is validated through simulation experiments based on the actual power data of the PV plant. The results show that the developed methods can effectively utilize partial PV power generation to produce hydrogen, improve PV utilization, and the combined output power of PV plant and HESS can fulfill the grid-connected standard.

Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data

Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability.

Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors

Efficient sizing and economic analysis of community battery systems is crucial for enhancing energy efficiency and sustainability in rooftop PV panel-rich communities. This paper proposes a comprehensive model that integrates key technical and economic factors to optimize the size and operation of the prosumer-owned battery, maximizing the financial returns over the life span of the battery. Sensitivity and uncertainty analyses were also conducted on a number of factors that are constantly changing over the years such as per-unit cost of the battery and interest rate. Monte Carlo simulations were utilized to replicate the unpredictable PV generations and the volatility of house load demands. The developed model is evaluated under three scenarios: a shared community battery for all houses, individual batteries for each house, and a combined system with an additional large load. Particle Swarm Optimization (PSO) is utilized to maximize the formulated objective function subject to the considered constraints. The findings indicate that integrating community batteries offered a substantial economic advantage compared to individual home batteries. The additional revenue stream of incorporating larger consumers looking to reduce their carbon footprint (e.g., commercial) returned a further augmented net present value (NPV). The influence of different tariff structures was also assessed and it was found that critical peak pricing (CPP) was the most prolific. The outcomes offer valuable insights for policymakers and stakeholders in the energy sector to facilitate a more sustainable future.

Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy

Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy

Day-ahead dynamic operating envelopes using stochastic unbalanced optimal power flow

Driven by the energy transition, distribution networks are dealing with increasing uptake of distributed energy resources, including solar photovoltaic generation. Residential rooftop solar allows customers to minimize exposure to price increases in the market, which has led to high penetration of PV in regions with high amounts of solar hours such as Australia. Eventually, this leads to congestion in the network, either due to voltage rise, or due to overcurrent in lines or transformers. Distribution utilities are now moving on from static export limits for customers in congested networks, to dynamic limits that are based on the state of the network. It is considered thoughtful to give customers advance warning, e.g. day-ahead, of the moments and degrees of export limitation, despite uncertainty surrounding the future state of the network. Therefore, in this paper, we consider the application of general polynomial chaos expansion based stochastic unbalanced optimal power flow to the day-ahead determination of dynamic export limits. We perform a numerical study on a European style low voltage feeder and illustrate the impact of fairness principles on chance-constrained stochastic nonlinear optimization without the need of sampling, linearizing the power flow equations, or applying relaxations. Case studies show the necessity of considering unbalanced study of distribution system. It was observed that equality measures reduce the overall output of the system in an attempt to achieve equal relative injection, while alpha fairness with a higher value of alpha is a compromise between the efficiency and fairness in DOEs.

Air Pollution and Solar Photovoltaic Power Generation: Evidence from South Korea

Solar energy, with its declining costs and enhanced efficiency, is a viable alternative to traditional fossil fuels. However, its effectiveness is compromised by atmospheric and meteorological conditions, particularly air pollution, which reduces solar radiation and panel efficiency. This study estimates the impact of air pollution on solar photovoltaic (PV) power generation in South Korea, a rapidly industrializing nation with high levels of air pollution and a growing focus on renewable energy. Using hourly power generation data from 2006 to 2013 and addressing potential endogeneity of PM10 with an instrumental variable approach, we find that a 10 mg/m3 increase in PM10 reduces solar power generation by 2.17 MWh, resulting in an estimated annual economic loss of approximately USD 2.2 million during the study period. These findings highlight the urgent need to mitigate air pollution to enhance solar power efficiency and maximize the social benefits of renewable energy.

Iterative sizing methodology for photovoltaic plants coupled with battery energy storage systems to ensure smooth power output and power availability

Photovoltaic (PV) solar energy is a fundamental technology that will help transition from a fossil fuel–based energy mix to a future with high shares of renewable energy. To do so, PV plants coupled with energy storage systems can accumulate excess power and dispatch it when PV generation changes, performing PV smoothing. While coupling PV plants with battery energy storage systems (BESS) offers a solution, current methodologies often need to thoroughly describe the interplay between BESS energy capacity, power rating, and the long–term impacts of battery degradation. This paper addresses this gap by proposing a four–step methodology that optimizes BESS sizing for PV plants, accounting for both cycling and calendar aging effects on system performance and the economic implications of battery replacements. We use a model that considers the degradation of PV modules and two Li–ion battery types (LiFePO4, LFP, and LiNiMnCoO2, NMC). A 16.3 MW PV plant is simulated along with the BESS to test the methodology. The results indicate that an LFP–based BESS of 2.5 MWh with a rated power of 1.25 MW ensures a stable output power with variation below 10% of the rated PV plant 98 % of the time. In contrast, an NMC–based BESS, while effective in reducing non–compliance, incurs higher costs due to more frequent battery replacements, making it less economically viable. The methodology and results presented in this paper provide valuable insights for designing cost–effective and reliable energy storage solutions in PV plants, ensuring compliance with set power availability.

Optimal scheduling of a multi-energy complementary system simultaneously considering the trading of carbon emission and green certificate

This paper uses a multi-energy complementary system composed of thermal, wind, photovoltaic power generation, and electric energy storage units to participate in four market mechanisms and construct the optimal scheduling model of these trading mechanisms. These four mechanisms are carbon emission trading, green certificate trading, these two trading that are not joint, and joint. Under the latter two mechanisms, the excess carbon emissions of the system can be reduced to 9258t, and the utilization rate of renewable energy can reach 100 %. However, the operating costs under the fourth mechanism are 9695.80 $ lower than the third one because 5947 pieces of green certificates are converted into carbon emission rights. When the carbon price rises, the cost of the fourth mechanism increases slower than that of the third mechanism. The conversion volume of green certificates increases from 5947 to 10947, and the cost difference between the two increases to 67558.33 $. When green certificate prices rise, the cost under the fourth trading mechanism decreases faster. As that cost steadily declines, the carbon emission rights are converted into 907 green certificates, and the cost difference between this and the third mechanism increases to 4695.52 $.

Short-term photovoltaic forecasting model with parallel multi-channel optimization based on improved dung beetle algorithm

Accurate prediction of photovoltaic(PV) generation plays a vital role in power dispatching and is one of the effective ways to ensure the safe operation of power grid. In response to this issue, this paper improves the Rhino beetle optimization algorithm (LSDBO) using Logistic chaos mapping and sine function strategies an optimizes the PCL-MHA model (running CNN-MHA and LSTM-MHA models in parallel, PCL; Multi-Head-Attention, MHA) to enhance predictive accuracy. Various experiments were conducted using historical data from the Alice Springs PV system in Australia. PV component technologies comprise monocrystalline silicon and polycrystalline silicon, with array fixed on the ground. Through experiments, the proposed model in this paper achieved the best results in 16 metrics under different weather conditions, with average values of 98.43 % for R2, 2.69 % for MSE, 7.8 % for MAE, and 15.09 % for RMSE. Compared to other models, an average improvement of 2.41 %, 6.6 %, 7.77 %, and 11.21 % in these metrics.

An Active Distribution Network Voltage Optimization Method Based on Source-Network-Load-Storage Coordination and Interaction

In response to global energy, environment, and climate concerns, distributed photovoltaic (PV) power generation has seen rapid growth. However, the intermittent and uncertain nature of PVs can cause voltage fluctuations in distribution systems, threatening their stability. To address this challenge, this paper proposes an active distribution network voltage optimization method, of which the main contribution is the development of a comprehensive voltage optimization strategy that integrates day-ahead prediction and real-time adjustment, significantly enhancing the stability and efficiency of distribution networks with high PV penetration. In the day-ahead prediction stage, the forecast scenarios of load and PV output guide network reconfiguration for improved voltage distribution. In the real-time operation stage, flexible regulation of PV and energy storage systems is used to adjust power outputs, further optimizing voltage quality. Simulations on the IEEE 33-bus system show that the method effectively improves voltage distribution, enhances renewable energy consumption, and ensures the safe, economic operation of the distribution system.