Solar Energy Generation System Research Articles

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING DROOP CONTROL TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using the droop control technique. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing an efficient energy management system for a small-scale hybrid wind-solar-battery-based microgrid to extract maximum profit from electricity generation. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

Reliability Evaluation of Multi-State Solar Energy Generating System with Inverters Considering Common Cause Failures

To ensure the efficient functioning of solar energy generation systems, it is crucial to have dependable designs and regular maintenance. However, when these systems or their components operate at multiple working levels, optimizing reliability becomes a complex task for models and analyses. In the context of reliability modeling in solar energy generation systems, researchers often assume that random variables follow an exponential distribution (binary-state representation) as a simplification, although this may not always hold true for real-world engineering systems. In the present paper, a multi-state solar energy generating system with inverters in series configuration is investigated, in which unreliable by-pass changeover switches, common cause failures (CCFs), and multiple repairman vacations are also considered. Furthermore, the arrivals of CCFs and the repair processes of the failed system due to CCFs are governed by different Markovian arrival processes (MAPs), and the lifetimes and repair times of inverters and by-pass changeover switches and the repairman vacation time in the system have different phase-type (PH) distributions. Therefore, the behavior of the system is represented using a Markov process methodology, and reliability measures for the proposed system are derived utilizing aggregated stochastic process theory. Finally, a numerical example and a comparison analysis are presented to demonstrate the findings.

An artificial gorilla troops optimizer for stochastic unit commitment problem solution incorporating solar, wind, and load uncertainties.

The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.

Energy potential assessment and techno–economic analysis of micro hydro–photovoltaic hybrid system in Goda Warke village, Ethiopia

Abstract Rural Ethiopia has significant untapped potential for hydro and solar energy generation systems. However, challenges arise from seasonal variations and unfavourable topographic positions of flowing rivers, hindering the efficient exploitation of these resources. Despite the country’s abundance in hydro and solar energy resources, >75% of the population still lack access to electricity from the national grid. This work deals with energy resource potential assessment and techno–economic analysis of micro hydro–photovoltaic (PV) hybrid systems, considered in the case study of Goda Warke village, located in the Yaya Gulele district. A novel framework is proposed that utilizes the Natural Resource Soil Conservation Service curve number method to assess the energy potential of micro-hydro energy in ungauged basins, specifically at the exit point of the Girar River basin catchment. The average monthly flow rate in the basin is 0.975 m3/s, while the area exhibits a solar radiation potential of 5.39 kWh/m2/day. Energy policy promotes expanding access to modern energy sources and utilization of indigenous energy resources. Simulation results indicate that the hydro/PV/diesel generator (DG)/battery and hydro/PV/battery systems are the most optimal choices based on net present cost, with the inclusion of a DG for economic comparison. Micro-hydro energy covers most of the electric load in the area, achieving a capacity factor of 47.5%. The cost of energy and net present cost were found to be sensitive to variables such as the price of diesel fuel, pipe head loss, and the growth of the village load. The optimized system demonstrated a hydro energy potential of 1405.37 MWh/year and a PV energy output of 274.04 MWh/year, resulting in a levelized cost of energy of 0.0057 and 0.049 $/kWh for the hydro and PV components, respectively.

Enhance Power Quality of Solar PV Energy Generation System with Integrated DG and Grid

This paper examines a multifunctional DS (Distributed Sparse) control technique for a single-stage PV system (SPEGS). When solar irradiation fluctuates, this SPEGS compensates for the nonlinear demand at common connections. SPEGS can multitask. It connects solar PV to the three-phase grid. Reduced harmonics to increase three-phase grid stability. SPEGS includes PV panels, an inverter of voltage sources, a nonlinear load, a three-phase grid, and a DC capacitance are all involved. In the absence of sun irradiation, DSTATCOM (Distribution Static Compensator) may be used (Voltage Source Converter). P&O (Perturb and Observe) maximises PV power extraction. The P&O scheme's tracking capabilities and efficiency under shifting environmental circumstances are also evaluated. Using DS control, it's possible to estimate the reference grid currents' basic component.

Net green energy potential of solar photovoltaic and wind energy generation systems

“Green energy” is the energy that can be produced while sustaining ecosystem services. Maintenance of the services provided by the ecosystem requires energy, termed the ecosystem maintenance energy (ESME). Including ESME costs in energy accounting enables assessment of an energy plant's ability to produce energy over and above inputs and EMSE costs over the lifetime of the plant. In this work, an assessment of the potential of two renewable energy plants wind and solar photovoltaic to produce “green energy” is undertaken, those were chosen due to their likely dominance of the future energy market. The assessment is done using a methodology that unifies environmental impact assessment, ESME and energy accounting into a single metric termed the green energy return on investment (EROIg). The paper also extends ESME calculation by incorporating ecosystem impacts that were previously unaccounted-for such as: biodiversity loss; delayed ecosystem maintenance; carbon capture by photosynthesis; ozone depletion and formation and ionizing radiation. The solar and wind energy plants considered were sized to produce an equivalent amount of annual energy and were placed at a theoretical location where the capacity factor (CF) is equivalent to the average global utility scale CF for each resource. The study found that onshore wind energy has a greater potential in generating green energy with an EROIg of 17.19. Removal of impacts related to biodiversity loss, which is included here for the first time, is found to play a critical role in plant assessment from both biodiversity and CO2 capture by photosynthesis perspective. Plants needing and providing biodiversity offsets are shown to provide an overall 18%–25% improvement in EROIg. This improvement is triggered by the corresponding CO2 capture by photosynthesis. Some design options were evaluated to determine the sensitivity of the resulting EROIg to the plant design and to the temporal element of the investigation. Those include the addition of battery storage and the delay in action on ecosystem maintenance from a 20-year to a 100-year time horizon which were found to reduce the resultant EROIg by >40% and >87% respectively.

Optimal storage for solar energy self-sufficiency

We determine the energy storage needed to achieve self sufficiency to a given reliability as a function of excess capacity in a combined solar-energy generation and storage system. Based on 40 years of solar-energy data for the St. Louis region, we formulate a statistical model that we use to generate synthetic insolation data over millions of years. We use these data to monitor the energy depletion in the storage system near the winter solstice. From this information, we develop explicit formulas for the required storage and the nature of cost-optimized system configurations as a function of reliability and the excess generation capacity. Minimizing the cost of the combined generation and storage system gives the optimal mix of these two constituents. For an annual failure rate of less than 3%, it is sufficient to have a solar generation capacity that slightly exceeds the daily electrical load at the winter solstice, together with a few days of storage.

Retrospection and investigation of ANN-based MPPT technique in comparison with soft computing-based MPPT techniques for PV solar and wind energy generation system

Retrospection and investigation of ANN-based MPPT technique in comparison with soft computing-based MPPT techniques for PV solar and wind energy generation system

Software-based solar energy potential assessment for an industrial facility: a case study from Aegean Region of Turkey

Solar energy is considered as one of the clean and sustainable energy sources. As known, big portion of the energy demand in industrial processes are generally met by fossil energy sources. Utilizing renewable and sustainable energy technologies in industrial facilities can decrease negative environmental effects. In this study, applicability of a rooftop solar energy generation system in an industrial building has been analyzed. In the simulation process, PvSOL software has been employed. Considering available areas on the roof of the facility, solar electrical energy generation system with installed power of 573.75 kWp was planned and simulated. Moreover, economic analysis results of the proposed rooftop solar energy system were presented within the scope of this research. According to the economic analysis outcomes, payback period of the proposed rooftop energy system was achieved less than 4 years.

Dynamic Forecasting of Solar Energy Microgrid Systems Using Feature Engineering

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 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> ) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA).

Planning an on-grid solar energy generation system for the Dwimatama pier at Tanjung Emas port

Global warming is the gradual increase in the temperature of the earth’s atmosphere caused by increasing levels of pollutant gases such as carbon dioxide. Natural characteristics are expected to change as the earth’s surface temperature rises. By 2025, Indonesia’s renewable energy use is expected to reach 23%. (Ministry of Energy and Mineral Resources, 2021). Existing critical sectors such as transportation and freight forwarding are being examined. For instance, vessels can reduce their carbon footprint by connecting to shore-connection facilities powering renewable energy sources in ports. The case study is the Dwimatama pier in Semarang’s Tanjung Eams port. The city of Semarang receives approximately six hours of sunlight per day. The solar power plant could serve as a source of electrical energy in this region. The results of this study indicate that to generate electrical energy from 1 MWp of solar radiation, a panel area of approximately 1 hectare is required for a solar panel with a 20% efficiency.

Impact of on-grid solar energy generation system on low voltage ride through capability

This paper represented a control strategy for photovoltaic (PV) system, this control strategy was referred to as a low voltage ride through (LVRT), it had been achieved by using three phase (3-PH) (PV) grid-connected system, where this paper discussed the way to achieve maximum output active power from the solar system, while the solar system remained connected to the grid with voltage decreasing controller techniques and this was valid until certain amount of voltage decreasing which was clarified in the results. The main goal of low voltage ride through depended on injecting reactive power to the grid, amount of the injected reactive power depended on regulations of the grid code using the control of the inverter and the strategy depended on the grid voltage drop amount. MATLAB simulation had been used to achieve what was mentioned above, which led to present various cases of achieving maximum output active power with grid voltage drops by using conventional proportional integral (PI) control of the inverter. Finally, another control method, which was proportional integral genetic algorithm (PI-GA), had been used to improve value of the generated output active power.

Feasibility assessment of adopting distributed solar photovoltaics and phase change materials in multifamily residential buildings

• Feasibility study to assess adopting PVs and BioPCMs in multifamily residential designs. • Energy-efficient VRF-HVAC systems are considered in multifamily residential designs. • Simulation-based assessment is conducted with LCCA of multifamily residential designs. • Effects of annual site energy use intensity and availability are investigated and discussed. • 35–50% Of annual energy needs can be satisfied, with lifecycle cost reductions of 6–9%. Given the growing environmental concerns due to climate change, the combination of renewable energy systems and feasible design optimization strategies of buildings, has been considered as a promising integrated solution to promote sustainable residential infrastructure and maintain carbon emission reductions. As part of promoting sustainable residential infrastructure development projects involving low-energy buildings, this study evaluates the feasibility of applying distributed solar photovoltaic (PV) systems and Bio-phase change materials (BioPCMs) with energy-efficient variable refrigerant flow (VRF) systems in multifamily residential buildings. To this end, this study conducts lifecycle cost analysis (LCCA) of low-energy multifamily residential building (LEMRB) design with energy-efficient VRF type systems, distributed solar energy generation systems (PVs), and BioPCMs. To enable LEMRB design, Korean medium-sized multifamily residential buildings on the national level, are selected as a case study. The results demonstrate that the application of both solar PV systems and BioPCMs to medium-sized multifamily residential buildings significantly help to reduce 35.03–49.78% of the total annual energy needs of each medium-sized multifamily residential building in various Korean climatic locations. Accordingly, the lifecycle cost (LCC) of low-energy medium-sized multifamily residential buildings showed significantly feasible in all Korean climate locations, as the discounted payback (DPB) period of investment costs is ranged from 5 to 8 years. However, further systematic studies to optimize the efficiency of solar energy systems and phase change materials are required. These include investigating more residential design alternatives and considering the effects of uncertainty and degradation on LEMRB lifecycle performance for achieving more sustainably and cost-effectively low-energy multifamily residential buildings in the future.

Analysis of the Economic Viability of the Installation of a Photovoltaic System at Jorge de Abreu Regional Hospital in Sinop-MT

Brazilian consumers have the possibility to produce their own energy using renewable sources or qualified cogeneration and even provide the surplus for the distribution network of their locality, as determined by the publication of Normative Resolution 482/2012 by the National Electric Energy Agency (ANEEL). In view of this, the use of photovoltaic systems for electricity generation has grown surprisingly, reaching the mark of 8 gigawatts of installed power on Brazilian soil in the year 2021 [1]. In Brazil, public agencies represent 0.1% with 4.7MW installed [1], and in the state of Mato Grosso 0.38% [14]. Thus, aiming at the rise of electricity generation in public buildings, due to the possibility of reducing costs with the electric bill, the present work deals with the dimensioning, budget quotation and economic feasibility analysis regarding the installation of a grid-connected photovoltaic power generation system to meet the energy demands of the Jorge de Abreu Regional Hospital in the city of Sinop, Mato Grosso state, Brazil. Whose, after carrying out the necessary research based on the techniques present in the economic sciences, as Minimum Attractiveness Rate (MRA), Net Present Value (VLP), Internal Rate of Return (IRR) and calculation of Payback. The present work considered a grid-connected solar energy generation system of 585 kW to supply the hospital's average monthly consumption of 93.829 kWh/month. The investment analysis showed that the project for the implementation of the photovoltaic system will have its NPV viable from the 4th year of installation and the Payback (return on investment), observing the monetary corrections, of approximately 4,29 years, thus configuring the system as economic financially.

Analysis of the Economic Viability of the Installation of a Photovoltaic System at Jorge de Abreu Regional Hospital in Sinop-MT

Brazilian consumers have the possibility to produce their own energy using renewable sources or qualified cogeneration and even provide the surplus for the distribution network of their locality, as determined by the publication of Normative Resolution 482/2012 by the National Electric Energy Agency (ANEEL). In view of this, the use of photovoltaic systems for electricity generation has grown surprisingly, reaching the mark of 8 gigawatts of installed power on Brazilian soil in the year 2021 [1]. In Brazil, public agencies represent 0.1% with 4.7MW installed [1], and in the state of Mato Grosso 0.38% [14]. Thus, aiming at the rise of electricity generation in public buildings, due to the possibility of reducing costs with the electric bill, the present work deals with the dimensioning, budget quotation and economic feasibility analysis regarding the installation of a grid-connected photovoltaic power generation system to meet the energy demands of the Jorge de Abreu Regional Hospital in the city of Sinop, Mato Grosso state, Brazil. Whose, after carrying out the necessary research based on the techniques present in the economic sciences, as Minimum Attractiveness Rate (MRA), Net Present Value (VLP), Internal Rate of Return (IRR) and calculation of Payback. The present work considered a grid-connected solar energy generation system of 585 kW to supply the hospital's average monthly consumption of 93.829 kWh/month. The investment analysis showed that the project for the implementation of the photovoltaic system will have its NPV viable from the 4th year of installation and the Payback (return on investment), observing the monetary corrections, of approximately 4,29 years, thus configuring the system as economic financially.

Recent Improvements of the PV Solar Energy Generation Performance

PV solar energy is the upcoming king of the energy source in the world, which is the fastest growing, most available, sustainable, clean, and environmentally friendly renewable energy. The essential characteristic of PV solar energy is generating the maximum power at mid-day. At the same time, the energy demand is high during the daytime. Due to this, PV solar energy replaces the conventional energy demand at peak periods. The sun is the source of PV solar energy, and it changed into electricity directly by using solar cells, which are made from semiconductor materials called silicon. Therefore, PV solar energy plays a crucial role in providing usable energy, and as well as reducing carbon dioxide emissions. However, the solar energy generation systems not achieved the desired efficiency yet, because of many unsolved problems like weather conditions, losses, materials made by and so on. The aims of this paper is to review the current literature on the improvement of the PV solar energy generation system's overall performance. First, to figure out the existing challenges, like environmental factors and natural phenomena that affect the PV solar modules efficiency. Then it presents the techniques that are used to enhance the PV solar modules overall performance. Finally, to propose the best ways and techniques to improve the PV modules efficiency and suggest to further studies.

Energy efficiency project: analysis of the installation of photovoltaic panels at the Curitiba City Hall

Brazil stands out for the use of renewable energy sources in its energy matrix. Even so, it suffers from the risks of collapse and depletion of resources, as occurred in the beginning of the 21st century. In view of this, there is a need to promote growth in the use of other less polluting energy sources, such as photovoltaic solar energy. Some factors contribute to the growth in the use of this technology, such as: scarce rains, constant increases in electricity tariffs, tax exemption and expansion of credit lines that guarantee resources for the best use of abundant solar radiation. In this context, this study aimed to analyze the feasibility of implementing the solar energy generation system, from the installation of photovoltaic panels, in the Curitiba (Brazil) City Hall, seeking to encourage the expansion of access to photovoltaic energy, as a way to reduce spending on electricity, preserving the environment and maximizing the municipality's energy efficiency. This is an exploratory study, developed based on data obtained from secondary sources, which provided a new approach to the subject and which enabled the significant achievement of positive results, including the financial one.

Characterization of photovoltaic solar energy systems in a Colombian region

Objective: To establish a baseline about the solar photovoltaic energy systems installed in the department of Caldas, Colombia. Methodology: The solar photovoltaic installations were identified from secondary sources of information, followed by field visits to collect information on the characteristics of these systems. An evaluation instrument was designed consisting of 6 categories which was subsequently evaluated by expert peers. It was applied accompanied by interviews with owners of the identified systems. Results: 41 solar photovoltaic systems were identified and installed in 11 municipalities of Caldas. Data was collected from 28 installations with different characteristics. 50% of the solar photovoltaic systems are in the Central-South region of Caldas. 71% of the systems are mainly installed in the urban area; 64% correspond to small-scale isolated solar photovoltaic energy generation systems, with a maximum power of less than 600W. These installations have become a viable energy alternative for the region and are being used especially for lighting and the operation of household appliances in residences, hotels, restaurants, and educational institutions. Conclusions: Although regulations on solar energy installations had not been established, these systems have already been implemented in the department in a successful, but moderate, manner.

Remote sun tracing and solar energy generation system

This paper aims to generate the most electricity on a solar board. A hemisphere suntracing-module composed of small solar boards is established. According to the electrical voltages of the small solar boards, the coordinates (angles) of the solar board with maximal electrical voltage will be recognized by the server port. With this, the server port will send the information to the client ports (the solar energy generation stations) via the internet. The client ports that wirelessly (with Wi-Fi module) connect to the individual microcontroller will direct the microcontroller to trigger two step motors to rotate to the related coordinates. All the main solar boards will be simultaneously adjusted to the best direction to generate the maximal solar electricity. The generated electricity will be stored in the related storage battery. For every time interval, the new coordinate will be recognized by the sun tracing module and then forwarded to the solar energy generation stations. The time interval can be predetermined on the server port.

Ambient temperature and solar irradiance forecasting prediction horizon sensitivity analysis

Selecting the correct weather forecasting technique is a crucial task when planning an efficient solar energy generation system. Estimating accurate solar photovoltaic systems power output depends on the correct modeling of solar irradiance and ambient temperature, evidencing the need for a framework to select the correct technique to forecast these parameters. This paper presents a review of the forecasting methods to predict solar irradiance and ambient temperature, considering the sensitivity to the forecasting horizon. The methodology includes estimating an interval for ambient temperature and solar irradiance by using the Mean Absolute Error as the percentage of variation in these parameters. To provide context, the study considers best-case and worst-case scenarios for four cities, estimating the power output for a sample array and analyzing the differences between the cases. The power output estimation of the PV array varied between 36% and 50% (on average) for the short-term prediction, and 54% to 95% for the long-term. The changes in the location produced an average variation of 43% in terms of power production, and up to 187% in economic value (USD) for the short term, and 44.5% and 187% for the long term. The results suggest a marked sensitivity to the variation in the forecasting horizon and significance with regards to location selection (considering the changes in solar irradiation and the cost of electricity).