Solar Photovoltaic Generation Research Articles

Model construction and electro-thermal-hydrogen co-generation performance for multi-section compound parabolic concentrator

Non-imaging solar concentrator has many applications in solar thermal conversion and photovoltaic power generation because of its performance characteristics such as no tracking, stable light concentration, compact structure and easy integration. For this purpose, this paper conducts a study on the non-imaging concentrator based on the optimized design of the concentrating surface structure for photovoltaic electric generation, thermal energy recovery and direct-drive hydrogen production. Firstly, a theoretical construction of a mathematical model of the solar Concentrating Photovoltaic and Photo-thermal (CPV/T) system with electricity-thermal-hydrogen co-generation and a numerical solution path for the Current and Voltage (I-V) characteristic curve is presented. Moreover, an integrated CPV/T system experimental setup is constructed outdoors and its power generation, heat collection and hydrogen production processes are measured. Finally, it is found that the average electrical efficiencies of the CPV/T system are 13.03 % and 13.29 % for theory and experiment, respectively. The theoretical and experimental average thermal efficiencies are 27.47 % and 27.24 %, respectively. The theoretical and experimental average hydrogen production rates based on Maximum Power Point Tracking (MPPT) control are 19.47 ml/min and 16.11 ml/min, respectively. The research results also show that the integrally constructed CPV/T system not only achieves effective conversion of solar energy into electrical and thermal energy, but also can directly convert electrical energy into clean, stable and easily stored hydrogen energy, which has potential application prospects.

The impact of photovoltaic power plants on surface energy budget based on an ecohydrological model

Solar photovoltaic (PV) generation has become the major type of solar energy utilization as the world's energy demand grows. However, the layout of large-scale public PV plants changes the original underlying conditions and has an impact on the local surface energy budget and distribution. Based on the ecohydrological model T&C, taking the fixed utility-scale PV plants as the typical object proposes a PV module, and the T&C-PV model is developed to simulate the impact of PV plants on the regional surface energy budget. Compared with the original T&C model, a substantially improvement of the simulation of energy flux in the PV plant paved underlying surface has been achieved in the T&C-PV model. This model is applied to PV power plants in Xinjiang Province, China, and the results show that the sensible heat flux increased by 12% and the latent heat flux decreased by 25%, with the percentage of transpiration decreasing from 90.1% to 74.5% and the percentage of evaporation increasing from 9.9% to 25.5% after the PV power plants are set up.

Efficient Integration of Photovoltaic Solar Generators in Monopolar DC Networks through a Convex Mixed-Integer Optimization Model

The problem regarding the optimal siting and sizing of photovoltaic (PV) generation units in electrical distribution networks with monopolar direct current (DC) operation technology was addressed in this research by proposing a two-stage convex optimization (TSCO) approach. In the first stage, the exact mixed-integer nonlinear programming (MINLP) formulation was relaxed via mixed-integer linear programming, defining the nodes where the PV generation units must be placed. In the second stage, the optimal power flow problem associated with PV sizing was solved by approximating the exact nonlinear component of the MINLP model into a second-order cone programming equivalent. The main contribution of this research is the use of two approximations to efficiently solve the studied problem, by taking advantage of convex optimization models. The numerical results in the monopolar DC version of the IEEE 33-bus grid demonstrate the effectiveness of the proposed approach when compared to multiple combinatorial optimization methods. Two evaluations were conducted, to confirm the efficiency of the proposed optimization model. The first evaluation considered the IEEE 33-bus grid without current limitations in all distribution branches, to later compare it to different metaheuristic approaches (discrete versions of the Chu and Beasley genetic algorithm, the vortex search algorithm, and the generalized normal distribution optimizer); the second simulation included the thermal current limits in the model’s optimization. The numerical results showed that when the maximum point power tracking was not regarded as a decision-making criterion, the expected annual investment and operating costs exhibited better performances, i.e., additional reductions of about USD 100,000 in the simulation cases compared to the scenarios involving maximum power point tracking.

Effect of Weather Dynamics to Energy Yield of Dual Axis Sun Tracker-Based Solar PV generation

Generally, solar PV Panel is constructed at fix tilted angle at different locations, such as: street light poles, rooftops, residential buildings, and canopy. The energy production from solar PV generation system is vary and influenced by site (location) and weather parameters. Solar PV generation analyzed in this research is Dual Axis Sun Tracker (DAST) Solar PV Generation. Dual axis sun tracker is a system controller to ensure solar PV panel following the sun path. Furthermore, energy production from DAST-based solar PV generation were measured and collected using energy meter as well as by using automatic weather station for the weather parameters. From these measrument data, it can be correlated the influence of weather parameters to the energy production from DAST PV generation system. The correlation between weather parameters and energy production are analyzed by statistical analysis using SPSS software and multiple linear regression models. From the results and analysis, it can be shown the amount of energy production from DAST-based solar PV Generation are influenced by weather parameters, namely: temperature, humidity, ultraviolet radiation, and solar radiation with significant level up to 98.2%.

Energy optimization of a food-energy-water microgrid living laboratory in Yukon, Canada

Like most northern settlements, Kluane Lake Research Station (KLRS) in Yukon Territory, Canada, is an islanded microgrid dependent on diesel generation and subject to high fuel costs. To reduce diesel costs, the station has a 48 kW solar photovoltaic (PV) array alongside a 27 kW/171 kWh lead-acid battery system to store solar energy for nighttime use, primarily during summer. However, substantial solar energy is often curtailed when the battery becomes full due to prior charging with diesel-generated electricity. The goal of this analysis is to determine how to best operate the diesel generator to maximize solar PV generation, and thus minimize diesel costs. On a monthly basis, solar PV plus batteries can meet 96% of load during June, but only 3% during December, and 67% year-round. This study also analyzes how demand-side management of new food and water infrastructure can aid this objective while providing a constant source of electricity, locally-grown food, and clean water. Findings demonstrate that optimizing the KLRS diesel generator, battery management, and solar energy conversion may reduce diesel generation by up to 100% during June, 31% during the field season (mid-April to early October), and approximately 31% year-round (due to limited solar PV generation during the winter), compared with past operational data.

A Novel on Energy Management Strategy with Maximum Exploitation of Renewables and EV Storage in Distribution Networks

Electric vehicles (EVs) are widely recognized for their environmentally friendly attributes and superior performance. They offer considerable potential for managing energy in low-voltage distribution networks through the use of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies. This article provides a detailed investigation into the management of energy in distribution networks using a combination of EVs, solar photovoltaic (PV), and diesel generators (DG). The water filling algorithm (WFA) is utilized to distribute the storage of EVs in each energy zone in an optimal manner, thereby achieving load flattening, minimizing energy costs, and reducing grid reliance. A multiobjective genetic algorithm (MOGA) is employed to solve a formulated multiobjective optimization problem for load flattening and voltage regulation, with optimal power transaction (OPT) serving as the decision variable. An adaptive neuro-fuzzy inference system (ANFIS) based EV ranking technique is employed to prioritize EVs based on their ability to provide the required services and determine optimal energy distribution (OED) for different scenarios. This study investigates the impact of OED in several scenarios and examines the influence of ANFIS prioritization on overall EV power availability and cost of charging (CoC). The findings of this study are crucial for developing effective energy management strategies that minimize energy costs and reduce grid reliance.

Sizing of Hybrid Power Systems for Off-Grid Applications Using Airborne Wind Energy

The majority of remote locations not connected to the main electricity grid rely on diesel generators to provide electrical power. High fuel transportation costs and significant carbon emissions have motivated the development and installation of hybrid power systems using renewable energy such these locations. Because wind and solar energy is intermittent, such sources are usually combined with energy storage for a more stable power supply. This paper presents a modelling and sizing framework for off-grid hybrid power systems using airborne wind energy, solar PV, batteries and diesel generators. The framework is based on hourly time-series data of wind resources from the ERA5 reanalysis dataset and solar resources from the National Solar Radiation Database maintained by NREL. The load data also include hourly time series generated using a combination of modelled and real-life data from the ENTSO-E platform maintained by the European Network of Transmission System Operators for Electricity. The backbone of the framework is a strategy for the sizing of hybrid power system components, which aims to minimise the levelised cost of electricity. A soft-wing ground-generation-based AWE system was modelled based on the specifications provided by Kitepower B.V. The power curve was computed by optimising the operation of the system using a quasi-steady model. The solar PV modules, battery systems and diesel generator models were based on the specifications from publicly available off-the-shelf solutions. The source code of the framework in the MATLAB environment was made available through a GitHub repository. For the representation of results, a hypothetical case study of an off-grid military training camp located in Marseille, France, was described. The results show that significant reductions in the cost of electricity were possible by shifting from purely diesel-based electricity generation to an hybrid power system comprising airborne wind energy, solar PV, batteries and diesel.

Research on Multiple Energy-Saving Strategies for Existing Coach Stations: A Case of the Xi’an Area, China

In the context of China’s dual-carbon goals, energy efficiency in public buildings has become a focal point of public concern. As large-scale public transportation buildings, the indoor thermal comfort and the current state of energy consumption of coach stations are increasingly being emphasized. This research used existing coach stations in the Xi’an region as the object; through on-site investigations and field tests of indoor thermal environments in winter and summer seasons, it was found that the coach stations had energy waste and high energy consumption; the enclosure structures had poor thermal performance; and the stations lacked effective energy-saving measures. Energy-saving transformation strategies were proposed from two aspects: enclosure structures and renewable energy utilization. Using DeST-C for energy consumption, the external walls, roofs, insulation materials, and glass materials were simulated, and nine different combinations of energy-saving schemes were simulated using orthogonal experiments. The optimal scheme was selected based on the comprehensive energy-saving rate and economic analysis results, which included using 80 mm XPS external insulation for the external walls, low-e hollow glass for the windows (low transmittance type), and an 80 mm PUR board for the roof insulation. The energy-saving rate of this scheme was 26.84%. The use of rooftop solar photovoltaic power generation and fresh air heat recovery devices can effectively reduce building energy consumption, and the investment payback period is less than 5 years. The research applications have practical significance for improving the indoor environment of existing coach stations and saving energy consumption.

Predictive Modeling of Photovoltaic Solar Power Generation

Photovoltaic solar power referred to as solar power using photovoltaic cells, is a renewable energy source. The solar cells' electricity may be utilized to power buildings, neighborhoods, and even entire cities. A stable and low-maintenance technology, photovoltaic solar power is an appealing alternative for generating energy since it emits no greenhouse gases and has no moving components. This paper aimed to provide a photovoltaic solar power generation forecasting model developed with machine learning approaches and historical data. In conclusion, this type of predictive model enables the evaluation of additional non-traditional sources of renewable energy, in this case, photovoltaic solar power, which facilitates the planning process for the diversification of the energy matrix. Random Forests obtain the highest performance, with this knowledge power systems operators may forecast outcomes more precisely, this is the main contribution of this work.

Probabilistic Assessment of Conservation Voltage Reduction Using Static Load Model Parameter in the Presence of Uncertainties

Higher penetration of solar PV and wind generation in distribution networks may change the conservation voltage reduction (CVR) capabilities. The uncertainties associated with the renewable generations and system loads are neglected in the deterministic CVR assessment. In this paper, a probabilistic framework for CVR assessment has been presented to assess the impact of uncertainties associated with renewable generations and system loads. A theoretical framework has been presented by establishing a mathematical relationship between the probability distribution of renewable generations (solar PV and wind) and the probability distribution of static exponential load model parameters. The simulation results have confirmed that the penetration of non-Gaussian solar PV and wind generation leads to non-Gaussian static exponential load model parameter distribution, which is validated by the normality tests (quantile-quantile plot, skewness, and kurtosis). Subsequently, the magnitude and probability distribution of the CVR capabilities of the network changes with the penetration of renewable generations, where the higher renewable penetration scenarios lead to higher CVR values and non-Gaussian (asymmetric) distribution.

확산모형을 이용한 주택용 상계 태양광 설비 보급량 예측

Most countries around the world are implementing carbon neutrality to respond to climate disasters. Solar photovoltaic (PV) plays an important role in carbon neutrality because it is easy to install and its cost is rapidly declining. Residential solar PV has the advantage of not requiring investment in transmission and distribution facilities and reducing the maximum load by lowering peak power demand. However, residential solar PV generation is variable and cannot be reliably predicted, causing uncertainty in the power system. Therefore, it is important to accurately forecast the size of residential solar PV capacity in order to prepare countermeasures that can enhance the stability of such power systems from a long-term perspective. This study forecasts the capacity of residential solar PV net metering in South Korea using diffusion models. The Gompertz model provided the best fit for the diffusion pattern of residential solar PV net metering in South Korea, outperforming the Bass and Logistic models. When the Gompertz model was used to predict the residential solar PV net metering capacity over the next 15 years, an increase from 2.1 GW (8.8% of total solar PV) in 2022 to 12.0 GW (15.4% of total solar PV) in 2036 was obtained. Therefore, this study suggests that it is necessary to consider the size and distribution of residential solar PV net metering in power system planning and operation, as it will play a major role in future power systems in South Korea.

On-site solar PV generation and use: Self-consumption and self-sufficiency

As energy storage systems are typically not installed with residential solar photovoltaic (PV) systems, any “excess” solar energy exceeding the house load remains unharvested or is exported to the grid. This paper introduces an approach towards a system design for improved PV self-consumption and self-sufficiency. As a result, a polyvalent heat pump, offering heating, cooling and domestic hot water, is considered alongside water storage tanks and batteries. Our method of system analysis begins with annual hourly thermal loads for heating and cooling a typical Australian house in Geelong, Victoria. These hourly heating and cooling loads are determined using Transient System Simulation (TRNSYS) software. The house’s annual hourly electricity consumption is analysed using smart meter data downloaded from the power supplier and PV generation data measured with a PV system controller. The results reveal that the proposed system could increase PV self-consumption and self-sufficiency to 41.96% and 86.34%, respectively, resulting in the annual imported energy being reduced by about 74%. The paper also provides sensitivity analyses for the hot and cold storage tank sizes, the coefficient of performance of the heat pump, solar PV and battery sizes. After establishing the limits of thermal storage size, a significant impact on self-efficiency can be realised through battery storage. This study demonstrates the feasibility of using a polyvalent heat pump together with water storage tanks and, ultimately, batteries to increase PV self-consumption and self-sufficiency. Future work will concentrate on determining a best-fit approach to system sizing embedded within the TRNSYS simulation tool.

Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review

The increasing demand for electricity and the need for environmentally friendly transportation systems has resulted in the proliferation of solar photovoltaic (PV) generators and electric vehicle (EV) charging within the low voltage (LV) distribution network. This high penetration of PV and EV charging can cause power quality challenges, hence the need for hosting capacity (HC) studies to estimate the maximum allowable connections. Although studies and reviews are abundant on the HC of PV and EV charging available in the literature, there is a lack of reviews on HC studies that cover both PV and EVs together. This paper fills this research gap by providing a detailed review of five commonly used methods for quantifying HC including deterministic, time series, stochastic, optimization, and streamlined methods. This paper comprehensively reviews the HC concept, methods, and tools, covering both PV and EV charging based on a survey of state-of-the-art literature published within the last five years (2017–2022). Voltage magnitude, thermal limit, and loading of lines, cables, and transformers are the main performance indices considered in most HC studies.

Charging scheduling and energy management for mobile chargers in a grid-interactive transportation system

Mobile charging refers to the mobility-on-demand battery packages that can provide flexible charging services and regulation capacities in a grid-interactive transportation system, especially for ridesharing fleets. In this study, we use real-world data to understand the impact of mobile charging in ridesharing operations, and propose a data-driven optimization framework to identify optimal integration scenarios for mobile chargers in urban transportation systems. A mixed-integer linear program is developed to determine how many mobile chargers are required to satisfy the charging demands of shared vehicles, and when and how much to store or discharge energy to the microgrid or to other shared vehicles vs. the time-of-use charging prices regimes during a day. The objective is to minimize the daily operating cost of mobile chargers. In the proposed optimization framework, we first analyze real-world datasets that include information on trips and charging demands of fleets sharing more than one thousand shared vehicles. We then investigate a microgrid power profile that consists of base load analysis and solar photovoltaic generation forecasting. Lastly, we implement our framework in a real-world case study to evaluate model performance. The results show that our approach can reduce the total costs by about 21 % compared to a non-scheduling greedy policy, and thus help stabilize the power profile by vehicle-to-grid implementation.

Key technologies for photovoltaic power generation

In the face of the increasingly serious energy and environmental problems in the world, it is imperative to develop renewable energy, including photovoltaic power generation. The fact that photovoltaics is still in their infancy suggests that they have a lot of potential. Wide-ranging potential for solar power generation opens up a lot of room for the advancement of photovoltaic technology and industrial growth. Solar energy is mainly used for photovoltaic power generation system (PV system). Its main components are solar cells, batteries, controllers and inverters. Solar cells and MPPT technology are the two main structure in PV system. The development of solar photovoltaic power generation is the premise of the development of photovoltaic technology, because he is an important element of photoelectric conversion, which is related to the energy conversion of the entire system. MPPT voltage is a very critical parameter in the design of photovoltaic power plants. In this article, advantages and disadvantages of four different types of solar cells and their improvement methods will be exponded, while the MPPT technology starts from the traditional algorithm and the intelligent algorithm, with the introduction of several different algorithms. The final prospect of the two key technologies is given at the end of this paper.

The European Solar Communication—Will It Pave the Road to Achieve 1 TW of Photovoltaic System Capacity in the European Union by 2030?

The urgency of a clean energy transition in the European Union is now not only driven by concerns about climate change, but also by using local renewable energy resources to reduce the dependency on foreign energy imports. Solar photovoltaic electricity generation is acknowledged as one of the pillars of this transition and various policy measures were implemented over the last two years to not only accelerate the deployment of solar photovoltaic electricity generation systems, but also to rebuild a competitive solar value chain in Europe to hedge against solar photovoltaic component supply chain disruptions. However, the current political ambitions still do not pay sufficient attention to the importance of solar photovoltaics as a major contributor to the necessary future renewable energy supply. According to the market trend between 2017 and 2022, the currently average EU installed capacity of 474.55 Wp/capita will not reach the necessary 2246 Wp/capita or 1 TWp target by 2030. At the moment, only seven member states have an installed capacity per capita higher than the average EU value.

Rapid mapping and spatial analysis on the distribution of photovoltaic power stations with Sentinel-1&2 images in Chinese coastal provinces

Photovoltaic (PV) solar energy generation attracts considerable attention to archive carbon neutrality goals worldwide. Geospatial data describing the PV system based on satellite images are critical for PV deployment. However, it remains challenging to obtain relative data in coastal zones due to frequent cloud cover. There have also been relatively few studies on spatial analysis of PV development in terms of solar resources and the energy demands. Therefore, this study took Chinese coastal provinces as example, and proposed a random forest classification method with information on morphology, optics, and SAR to address cloud pollution. The results obtained were analyzed with geographical and socioeconomic factors to determine its development status. The results have shown that (1) by October 2022, the total PV area in Chinese coastal provinces reached 837.3 km2, with an overall accuracy of 96.9 % and a Kappa coefficient of 0.91. Water-based PV represents 33.7 % which tended to be distributed near the shoreline. (2) Bounded by the Huai River, the PV density in the north was 9.0 km2 per 104 km2, which is more than twice that in the south. The development of water-based PV is a key reason for the high PV construction density in coastal areas. (3) PV distribution was slightly mismatched with solar resource and power demand, especially in Liaoning and Guangdong. Liaoning has relatively high potential for PV development. The study findings can play a key role in evaluation of PV power generation, development potential, and ecological environmental impacts.

A QUASI-Z-SOURCE Converter Using a Single-Stage Switched-Inductor Boost DC-DC Converter

Abstract: This paper introduces a new topology, yet simple and efficient, for a grid-connected wind-solar cogeneration system. A permanent magnet synchronous generator-based full-scale wind turbine is interconnected to the utility grid. The dc-link capacitor has been utilized to directly interface a photovoltaic solar generator. No dc/dc conversion stages are required, and hence, the hybrid system is simple and efficient. Moreover, the proposed topology features an independent maximum power point tracking for both the wind and the solar generators to maximize the extraction of renewable energy. This paper presents a quasiZ-source inverter (qZSI) that is a new topology derived from the traditional Z-source inverter (ZSI). The qZSI inherits all the advantages of the ZSI, which can realize buck/boost, inversion and power conditioning in a single stage with improved reliability. In addition, the proposed qZSI has the unique advantages of lower component ratings and constant dc current from the source. All of the boost control methods that have been developed for the ZSI can be used by the qZSI. One of the more commonly used techniques is the incremental conductance method. In this paper, an improved particle swarm optimization- (IPSO-) based MPPT technique for photovoltaic system operating under varying environmental conditions is proposed. Basically, an Artificial Neural Network (ANN) based approach is utilized to automatically detect the global maximum power point of the PV array by using a preselected number of power measurements of the PV system. The approach of linearly decreasing scheme for weighting factors and cognitive and social parameters is modified. The proposed control scheme can overcome deficiency and accelerate convergence of the IPSO-based MPPT algorithm. Detailed small-signal models for the system components are developed to characterize the overall stability. The influence of the utility-grid faults on the performance of the proposed system is also investigated. Nonlinear time-domain simulation results under different operating conditions are presented to validate the effectiveness of the proposed topology.

Satellite-derived solar radiation for intra-hour and intra-day applications: Biases and uncertainties by season and altitude

Regional to European scale intra-day forecasts of solar resources and photovoltaic (PV) power generation are of key relevance for operators of solar plants and power grids, as well as power traders, who seek to optimize revenues and maintain grid stability by matching power supply and demand. Our study addresses a key prerequisite for such forecasts: the accuracy of intra-hour and intra-day surface solar radiation (SSR) estimates of the HelioMont Meteosat product, a state of the art satellite product particularly suited for the Alpine region. A second satellite product, SARAH-2, as well as longer time scales, are also examined for comparison. The satellite SSR estimates are analyzed based on 136 ground stations across altitudes from 200 m to 3570 m in Switzerland in 2018. In peak daytime periods, the instantaneous satellite SSR deviates from the ground-measured SSR by a mean absolute deviation (MAD) of 110.4 and 99.6 W/m2 for SARAH-2 and HelioMont, respectively. For the daytime SSR, the instantaneous, hourly, and daily-mean MADs amount to 91.7, 81.1, 50.8, and 82.5, 66.7, 42.9 W/m2 for SARAH-2 and HelioMont, respectively. SARAH-2 drastically underestimates instantaneous SSR in winter at altitudes above 1000 m, possibly due to snow cover. HelioMont performs better in this respect, but the remaining substantial, spatially and temporally heterogeneous biases we identify pose a challenge with regard to the use of HelioMont for accurate intra-day PV estimates and forecasts.

Machine learning based renewable energy generation and energy consumption forecasting

ABSTRACT Renewable energy generation sources are the need of the hour to mitigate the challenges of fossil fuels, environmental impacts, large transmission and distribution losses, and variable consumption pattern. Renewable energy such as solar PV generation or wind generation is most unpredicted generation and depends upon weather constraints. A promising prediction of generation is a suitable solution for this problem. A machine learning-based conventional neural network algorithm is proposed to forecast the generation and calculate the errors in generation forecast. The generated energy is supplied to consumers, but the consumers consume electricity by their choice and an energy management model is required at the consumer end to supply the available predicted generation to develop a self-sustainable microgrid. The energy consumption at consumer end is essential to predict, and based on the predicted consumption at hourly bases, the cost model for the consumers is developed. The consumption profile is forecasted using long short-term memory as it is a good tool for time-series forecasting using available sequential load consumption data. The demand is forecasted and analyzed for selected load categories (i.e. fixed, non-shift-able, and shift-able loads). The evaluation matrix mean square error (MSE), mean absolute error, and root mean square error (RMSE) are used for the error calculations. The RMSE for solar and wind power generation forecasting have been 2.472 and 3.034, respectively. The RMSE for fixed, non-shiftable, and shiftable demand forecasting have been 0.027, 0.067, and 0.21, respectively. The results show that the proposed methods for predictions of solar, wind power generation and energy consumption forecast achieve better accuracy. The presented methodology can be implemented with energy storage for renewable energy production and demand management for energy from sustainable energy resources.