PV Power Research Articles

Supporting strategy for investment evaluation of photovoltaic power generation engineering projects using multi-criteria decision analysis methods

This scientific study examines the evaluation of photovoltaic power generation projects through the application of multi-criteria decision analysis methods. Two groups of large-scale grid-connected PV power generation system projects with a nominal power of 50 MW and 500 MW respectively were analyzed and evaluated. These systems were designed to be installed in the same wider geographical area in northern Greece, but they were differentiated in the part of the PV circuit. Twelve systems were analyzed in which either monocrystalline silicon panels or poly-crystalline silicon panels or bifacial photovoltaic panels were to be installed. In these systems either central photovoltaic inverters or photovoltaic string inverters were considered for installation. The following criteria were used to evaluate the investment in these projects. These criteria were related to the profitability, the financial cost, the technical level, and the electrical energy production of the systems and these were the initial investment cost, the operation and maintenance cost, the levelized cost of electricity, the net present value, the internal rate of return, the capital recovery or payback period, the technical level of the photovoltaic circuit, the technical maturity of the photovoltaic circuit, and the annual electricity production. The evaluation of these criteria was initially conducted with fixed weighting coefficients followed by a sensitivity analysis of these weighting coefficients. The results of the evaluation using the PROMETHEE, AHP and TOPSIS multi-criteria decision analysis methods showed that the PV power generation systems which should be preferred are those with increased nominal power, where monocrystalline silicon technology panels are employed following the central inverter topology.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

RCPI controller-based multilevel multistring grid following inverter for large rooftop PV power plant application

RCPI controller-based multilevel multistring grid following inverter for large rooftop PV power plant application

Performance analysis of floating bifacial stand-alone photovoltaic module in tropical freshwater systems of Southern Asia: an experimental study

The optimization of floating bifacial solar panels (FBS PV) in tropical freshwater systems is explored by employing response surface methodology (RSM) and central composite design (CCD). Previous studies have yet to explore the long-term durability, environmental impact, economic viability, and performance of FBS PV systems under various climatic conditions. This study addresses this gap by focusing on panel height, water depth, and tilt angle to improve performance. The quadratic model reveals significant non-linear relationships impacting FBS PV power generation with freshwater cooling. Our models demonstrate high explanatory power, with R-squared values of 0.9831 for output power and 0.9900 for Bi-Facial gain. Experimental validation using conventional white surface (CWS) and proposed freshwater surface (PFS) indicates notable improvements in power generation, achieving a 4.34 to 4.86% gain in bifacial efficiency across various irradiation levels. Under 950 W/m2 irradiation, freshwater cooling achieves a 3.19% higher bifacial gain compared to CWS cooling. Panel temperature analysis shows consistent reductions with freshwater cooling, ranging from 1.43 to 2.72 °C, enhancing overall efficiency and longevity. This research highlights the potential of freshwater cooling in optimizing bifacial solar systems, offering actionable insights for sustainable energy solutions in tropical regions.

Short-term PV power prediction study based on EEMD-KPCA-LSTM

Abstract Short-term PV power prediction is important for PV power generation stability and power dispatch. In order to cope with the noise disturbances occurring in PV power generation, this paper proposes a prediction model with an improved algorithm. Firstly, the annual PV data are decomposed by using EEMD to obtain the eigenmode function components; KPCA is used to reduce the dimensionality of the original sequences brought by EEMD, eliminate redundancy, and divide the test set and training set; The LSTM neural network is used to model the multivariate feature sequences in dynamic time to achieve the prediction of PV power. The model compares the prediction model of a single algorithm, and the accuracy of the prediction is improved.

A new y-source high-gain buck-boost converter suitable for solar PV power generation system

A new y-source high-gain buck-boost converter suitable for solar PV power generation system

FIDVR Capability of Hybrid Grid-Forming PV Power Plants During Feeder Restoration

FIDVR Capability of Hybrid Grid-Forming PV Power Plants During Feeder Restoration

Economy and energy flexibility optimization of the photovoltaic heat pump system with thermal energy storage

Photovoltaic systems have been widely applied in building energy systems. However, with the sharp increase in demand of electricity in buildings during heating and cooling seasons, the uncertainty of photovoltaic generation further exacerbates the peak-valley difference in electricity use. To improve the economy and energy flexibility of buildings in hot summer and cold winter zones of China, a rule-based operation strategy was proposed for the photovoltaic heat pump with thermal energy storage system to optimize the size of the thermal energy storage system and system operation, with aims of minimizing the total annual cost of the system and maximizing the self-consumption rate of the photovoltaic generation. The effects of grid export power limits, grid import power limits, feed-in tariffs, and PV generation on the system operation optimization results were also investigated. The results indicated that by integrating the thermal energy storage system into the photovoltaic heat pump system, the self-consumption rate of the photovoltaic generation was reduced by 2.39 %, the total annual cost of the system was decreased by 6.61 %, and the payback period of the thermal energy storage system was 1.31 years. The optimum size of the thermal energy storage system and the self-consumption rate of the photovoltaic generation decreased with the increasing grid export power limits and grid import power limits. In contrast, higher grid export power limits, grid import power limits, feed-in tariffs, and PV generation all resulted in a lower total annual cost. This study provided a systematic design and operation optimization method for building energy systems.

Empowering the solar energy landscape: The techno-economic analysis of grid-connected PV power plants in Uganda

Solar PV power is still under-utilized despite the abundance of solar radiation in Uganda. There is need for empowering renewable energy landscape through unlocking the technical and economic feasibility of solar photovoltaic power. We analyzed data from 56 locations for the techno-economic and environmental assessment of photovoltaic power facilities in Uganda. This was based on weather data availability and accessibility to the national power grid. Analysis of the energy generation and different input factors was done using PVsyst 7.2. A three stage approach to losses was adopted: absorption of sunlight, conversion to DC and DC to AC conversion. Findings indicate that most of the countryside is suitable for construction of large scale grid-connected photovoltaic power facilities. Due to longer sunshine duration and stronger Global Horizontal Irradiance (GHI) which are associated with high energy yield, northern Uganda performed better than the rest of the country, making it a preferential siting for large scale grid-connected photovoltaic facilities. South western Uganda performed the poorest. After a thorough energy accounting and a list of all performance metrics, the viability of investing in grid-connected photovoltaic power facilities was assessed.

Performance Analysis of the Outdoor Concentrating Photovoltaic–Thermoelectric Coupling System Under Uniform Illumination

Concentrating photovoltaic–thermoelectric (CPV–TE) coupling system is an efficient solar‐to‐electric technology, but the nonuniform illumination and temperature caused by the concentrated light have a significant impact on the power generation performance of the cell. This work improves the intensity distribution through the self‐made birefringent prism, and further studies the effect of uniform or nonuniform illumination on the power generation performance of CPV–TE system under different light intensities and cooling conditions. The results show that the output power of PV cell at uniform illumination can achieve 14.74% higher than that at nonuniform illumination due to the decline of cell’ surface temperature difference. Meanwhile, the cooling condition can further enhance the output power of PV or TE cell, and weakens the impact of the illumination nonuniformity on the power generation performance of PV cell. Through the joint optimization of uniform illumination and 10 °C cooling condition, CPV–TE coupling system increases the output power by 15.83% at 10 kW m−2. TE device can further enhance the output power by 46.09 mW through the thermoelectric conversion. Surprisingly, the environmental cost from CPV–TE system reduces carbon dioxide emission of 26471.01¥ m−2 every day. The outdoor CPV–TE coupling system has a certain assistance for the practical development.

Modified Harris Hawks optimization for the 3E feasibility assessment of a hybrid renewable energy system

The off-grid Hybrid Renewable Energy Systems (HRES) demonstrate great potential to be sustainable and economically feasible options to meet the growing energy needs and counter the depletion of conventional energy sources. Therefore, it is crucial to optimize the size of HRES components to assess system cost and dependability. This paper presents the optimal sizing of HRES to provide a very cost-effective and efficient solution for supplying power to a rural region. This study develops a PV-Wind-Battery-DG system with an objective of 3E analysis which includes Energy, Economic, and Environmental CO2 emissions. Indispensable parameters like technical parameters (Loss of Power Supply Probability, Renewable factor, PV fraction, and Wind fraction) and social factor (Human Developing Index) are evaluated to show the proposed modified Harris Hawks Optimization (mHHO) algorithm’s merits over the existing algorithms. To achieve the objectives, the proposed mHHO algorithm uses nine distinct operators to obtain simultaneous optimization. Furthermore, the performance of mHHO is evaluated by using the CEC 2019 test suite and the most optimal mHHO is chosen for sizing and 3E analysis of HRES. The findings demonstrate that the mHHO has achieved optimized values for Cost of Energy (COE), Net Present Cost (NPC), and Annualized System Cost (ASC) with the lowest values being 0.14130 $/kWh, 1,649,900$, and 1,16,090$/year respectively. The reduction in COE value using the proposed mHHO approach is 0.49% in comparison with most of the other MH-algorithms. Additionally, the system primarily relies on renewable sources, with diesel usage accounting for only 0.03% of power generation. Overall, this study effectively addresses the challenge of performing a 3E analysis with mHHO algorithm which exhibits excellent convergence and is capable of producing high-quality outcomes in the design of HRES. The mHHO algorithm attains optimal economic efficiency while simultaneously minimizing the impact on the environment and maintaining a high human development index.

Performance prediction of ventilated building-integrated photovoltaic system with lightweight flexible crystalline silicon module based on experimental fitting method

The novel ventilated building-integrated photovoltaic system with lightweight flexible crystalline silicon modules (VL-BIPV) has a self-weight of only about 6 kg/m2, which helps to address weight-bearing challenges on low-capacity industrial building rooftops. However, the unique thermal dissipation features of the system pose challenges for the analysis of its photovoltaic performance and thermal processes. Therefore, a comparative experimental testing approach was employed, comparing the VL-BIPV system with a conventional rooftop photovoltaic system. Using the efficiency equations of the conventional photovoltaic system as a reference, the functional equations were fitted based on experimental results. Based on typical meteorological year data in Nanjing, annual power generation and PV cell temperature variations were evaluated, along with power generation and carbon reduction benefits over a 25-year lifetime. The results indicate that the VL-BIPV system achieves an annual power generation of 262.22 kWh/m2, a 6.52% increase compared to the conventional system. Additionally, the highest average and maximum cell temperatures in the VL-BIPV system during the year are 58.39 °C and 64.74 °C, respectively, both lower than the conventional system's peak at 68.34 °C. Over a 25-year lifespan, the VL-BIPV system reduces carbon emissions by 20.17 kgCO2/Wp, exceeding the conventional system's reduction by 1.25 kgCO2/Wp.

An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction

An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction

An IoT Enabled Energy Management System with Precise Forecasting and Load Optimization for PV Power Generation

An IoT Enabled Energy Management System with Precise Forecasting and Load Optimization for PV Power Generation

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.

Study on photovoltaic power prediction with TSO-LSTM-XGBoost coupled model accounting for weather factors

ABSTRACT The explicit prediction of PV power itself is of great significance to the scheduling and operation of the power grid. To ensure the stable operation of the power system, this paper proposes a coupled model for PV power prediction based on TSO-LSTM-XGBoost. This model considers the weather factor using the tuna swarm algorithm(TSO) to optimize the long and short-term memory network model(LSTM) to overcome the shortcomings of blindness and time-consuming in the process of randomly selecting the parameters of the LSTM model. At the same time, using the extreme gradient boosting model (XGBoost), the algorithm is improved and corrected for the large prediction error in cloudy and rainy weather, and the weighted error method is used to couple the model to obtain the final prediction results. Finally, the accuracy of the proposed model is verified by comparing the PV system and meteorological data of a certain region in Shenzhen, China. The results show that the proposed TSO-LSTM-XGBoost coupled model has a value of 71.98 for MAE in cloudy days and 82.09 for MAE in rainy days, and the prediction accuracy is better than PSO-LSTM and LSTM.

Evaluation and benchmarking of research-based microgrid systems using FWZIC-VIKOR approach for sustainable energy management

Microgrid (MG) is one of the technologies considered in the direction of providing green and sustainable energy resources for local communities. Ensuring the best performance of these MG technologies requires extensive research to provide the most efficient system. Research-based microgrids (RB-MGs) play a vital role in the development of green energy platforms, as microgrid applications vary according to different scenarios and locations. Selecting the best research-based microgrid ensures providing local communities and stakeholders with well-tested and examined MG systems. Assessing research-based microgrid systems (RB-MG) for sustainable green applications poses a challenging multi-attribute decision-making (MADM) problem. These complexities encompass the consideration of several evaluation criteria, the relative importance of these criteria, variations in data, and the inherent trade-offs and conflicts between these factors. Crisp and definite values to evaluate the research-based microgrids could not be found despite a comprehensive investigation. In this regard, appraisals and opinions of experts and professionals in providing sustainable energy with vast knowledge and experience in assessment, selection, installation, and operation were addressed as data. A novel decision-making model was developed to evaluate and select the most proper RB-MG system by processing these data. This study proposes an integrated MADM modelling approach using Fuzzy Weighted with Zero Inconsistency (FWZIC) method in conjunction with the Vlse-kriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method. The underlying process starts with constructing a decision matrix (evaluation criteria intersectioned with RB-MGs). Then, evaluation criteria are weighted using FWZIC, and the RB-MGs are ranked for each category using VIKOR. The results derived from FWZIC weights provide valuable insights. Key criteria such as 'installed power (KW)' and 'storage capacity (C3)' show notable values of 0.159 and 0.151, respectively, underscoring their importance in identifying optimal RB-MGs. These weights and alternatives were used to rank the highest RB-MG, which is LIER-CIRCE of the average PV power group, and Ormazabal from the 'Highest PV Power' group. Ormazabal obtained the lowest Qi (0.426). For the average PV power group, alternative number 7 (Atenea Centre) ranked as the best alternative with the lowest Qi among other RB-MGs in the same group (0.158107). Comparative assessments with various MCDM methods reveal strong correlations with TOPSIS and MABAC, but negative correlations with MAIRA. Additionally, there is a statistical difference in grouping by PV installed power (KW) or MPC.

Enhancing PV power generation via an adaptive neuro-fuzzy and fast terminal synergetic MPPT approach

Enhancing PV power generation via an adaptive neuro-fuzzy and fast terminal synergetic MPPT approach

Optimal integration of PV generators and D-STATCOMs into the electrical distribution system to reduce the annual investment and operational cost: A multiverse optimization algorithm and matrix power flow approach

In this work, we address the problem of optimally integrating photovoltaic distributed generators (PV) and distributed static compensators (D-STATCOMs) in distributed electrical systems (DES). Previous methods have struggled with the complexities of non-linear mathematical models and the integration of operating and investment costs while meeting the operational constraints of these devices. We propose a solution using a master-slave methodology that combines a discrete-continuous version of the multiverse optimization algorithm (MVO), with a matrix power flow method based on successive approximation (MAX). This approach aims to reduce annual costs by efficiently managing the grid’s operating costs and the investment and operational costs of PV and D-STATCOMs. Various researchers have suggested methodologies for solving the problem of optimal D-STATCOM integration in DES, focusing on minimizing investment and operating costs as the objective function. However, these studies often lack comparative methodologies and statistical analysis to validate the performance of their proposed approaches. Additionally, they do not consider the impact of variable power demand. We compared our methodology with other master-slave approaches that utilize different optimization algorithms, including the vortex search algorithm (VSA), crow search algorithm (CSA), a continuous genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). These comparisons were made using two test systems with 33 and 69 nodes, which incorporate variations in power demand and PV production from a region in Colombia. Our statistical analysis included evaluations of the best and average solutions, standard deviations, differences between the best and average solutions, and a dispersion analysis using box-and-whisker plots. The results demonstrate that MVO outperforms other methods, providing the best results with reduced processing times in both test systems.

A Three-Step Weather Data Approach in Solar Energy Prediction Using Machine Learning

Solar energy plays a critical part in lowering CO2 emissions and other greenhouse gases when integrated into the grid. Higher solar energy penetration is hindered by its intermittency leading to reliability issues. To forecast solar energy production, this study suggests a three-step forecasting method that selects weather variables with a moderate to strong positive correlation to solar radiation using Pearson correlation coefficient analysis. Low-level data fusion is used to combine weather inputs from a reliable local weather station and an on-site weather station, significantly improving the forecasting model’s accuracy regardless of the machine learning method used. Weather data was obtained from the Kisanhub Weather Station located in Cranfield University, UK and the meteorological station in Bedford, UK. In addition, PV power supply data was obtained from four solar plants. Using the Regression Learner app in MATLAB, the proposed architecture is tested on a utility scale solar plant (1 MW), showing a 6% and 13% prediction accuracy improvement when compared to solely using data from the on-site and local weather station respectively. It is further validated using data from three residential rooftop solar systems (8 kW, 10.5 kW and 15 kW), achieving root-mean square values of 0.0984, 0.0885, and 0.1425 respectively. The data was pre-processed using both rescaling and list-wise deletion methods. Training and testing data from the 1 MW solar plant was divided into 75% and 25% respectively, while 100% of the residential rooftop solar plants was used for validation.