Electricity Generation Plants Research Articles

Simultaneous costs minimizing in electricity and gas micro-grids with the presence of distributed generation

Distributed generation can actively participate in the day-ahead markets, real-time power balance, and wholesale gas markets to achieve various goals, such as supplying gas to various electric power generation plants. A multi-objective network with two types of loads is considered in this paper. The reason for the simultaneous optimization of these two networks is that these two energy carriers are dependent on each other and gas is needed to produce electricity, so this issue can be addressed with a multi-objective function. The simulation carried out in this article is coded in GAMS software as a mixed integer linear programming (MILP). The efficiency of gas turbines and fuel cells in this article is dependent on their working point, and considering the exact model of these resources and the relationships related to the calculation of their fuel consumption is non-linear. On the other hand, a binary variable has been used to show the charging and discharging state of the storage and the on-and-off state of the gas turbines. Therefore, the problem considered in this article is a MILP problem. The results of this article are the proper planning of charging and discharging of the energy storage system with the proper planning of the power generation of different energy sources considering the network loads in two optimized and non-optimized scenarios.

Microgravity analysis of periodic oscillations of heat and mass transfer of Darcy-Forchheimer nanofluid along radiating stretching surface with Joule heating effects

Reduced gravity impact on mixed convection flow plays a significant role for designing of electric-generating plants in space, unwanted effects of free convection, thermodynamic stability, space devices, surface tension and movement of nanoparticles. The novelty of present work is to find the impact of reduced gravity, Joule heating and thermal radiations on Darcy Forchheimer magnetized flow of nanofluid along the stretching porous sheet. The variable gravity is assumed as temperature dependent with maximum density and maximum density. The governing model is converted into convenient model to find physical thermo parameters. The primitive steady, real and imaginary equations are formed by using stokes and primitive transformations. To make programming algorithm in FORTRAN Lahey-90/95, the primitively terms are deduced in each equation. For tabular and numerical findings of steady velocity, temperature and concentration, the implicit form of finite difference approach is applied with Gaussian elimination method. The fluctuating skin friction, fluctuating heat transfer and fluctuating mass transfer are displayed by using steady outcomes in main formula. It is found that the magnitude of fluid velocity enhances as magnetic force, reduced gravity and Darcy Forchheimer parameter enhances. It is concluded that temperature distribution decreases as magnetic force enhances. It is noted that oscillating frequency in skin friction and heat transfer enhances as Schmidt number enhances. It is found that the maximum fluctuating layer in heat and mass transfer enhances as Prandtl number enhances.

Promotion of CO2 assimilation by stopping NP elimination is best method to stop global warming

Stop the electricity to stop the elimination of nitrogen and phosphorous. Stop the addition of ammonia at exit gas of electricity generation plant. Abandon the law about bon fire. Throw radioactive substance to deep sea. If these four items are done global warming will stop.

Estimation of landfill gas emissions at the solid waste disposal site of low-population regions with LandGEM and tabasaran–rettenberger mathematical models

ABSTRACT In developing countries, solid waste most commonly ultimately ends up is landfill. Landfill sites release significant amounts of air pollutants, such as methane (CH4) and carbon dioxide. It is thus important to reduce, control, and recycle these gases. In this study, we aimed to calculate gas emissions and energy production from landfill sites serving low-population areas. Future landfill gas production was estimated using LandGEM and Tabasaran – Rettenberger models, based on population and waste volume predictions. In both models, total amount of landfill gas and CH4 produced reached their highest values in 2035. According to the LandGEM model, this will amount to 11,230 and 3,179 Mg/year, respectively, while the Tabasaran – Rettenberger model predicted 14,986 and 4,014 Mg/year, respectively. The volume of landfill gas calculated using the Tabasaran – Rettenberger model was 3.730 million m3/year for 2021, while this was 2.544 million m3/year using the LandGEM model. In 2021, 84007 Mg of waste was disposed of at the landfill site. According to data from the electricity generation plant at the site, the electrical energy generated in 2021 was 5,309 kWh. Comparing the results from these two models with actual landfill data, the Tabasaran – Rettenberger model came closest to the estimate. Therefore, the Tabasaran – Rettenberger model can be used to estimate the potential for gas production at landfill sites in cities with similar waste components.

Numerical Study for the Design of a Thermal Energy Storage System with Multiple Tunnels Based on Phase Change Material: Case Study Mining in Chile (Thermal Storage in Off-Grid Industrial Applications)

This paper presents a numerical model for thermal energy storage systems’ design, development, and feasibility. The energy storage was composed of a tank that stores phase change material (AlSi12) and internal pipes with heat transfer fluid (Cerrolow 117), coupled to a power block to dispatch electrical energy on a small scale for off-grid industrial applications. Subsequently, the evolution of the temperature in charge/discharge cycles, temperature degradation, and storage efficiency was determined with the appropriate magnitudes and behavior through the resolution of a numerical model. In addition, for the proposed electric power generation plant for an off-grid pumping system in the mining industry of Chile, a numerical model was developed using the finite volumes method to simulate the thermocline performance. As a result, the temperature history reflects stable thermal behavior, low degradation, and high efficiency of approximately 92%, with a storage time increasing up to 13 [h] and 384.8 [kWh] capacity. Also, implementation was feasible on a small scale due to its compact, modular, and economically competitive characteristics in a concentrated solar power plant. Finally, the proposed design was proven to be an accurate and reliable alternative for small-scale off-grid mining applications.

Summary Paper: An Initial Heliostat Supply Chain Analysis

This paper summarizes the prior analysis, key findings, and recommendations from the published report titled “Initial Heliostat Supply Chain Analysis” [1]. Globally, the growing demand for concentrating solar power (CSP) technologies, primarily for electricity generation plants has been met with supply chains primarily composed of plentiful commodity materials such as steel, aluminum, and glass. Often the commodity materials can be sourced in the domestic market where power plant will be constructed. Although specialty components are required for CSP solar fields —including mirror panels used for heliostat applications—these specialty components constitute about 30-50% of total system installed costs [2]. Only a few companies and countries, including the United States, have developed the capacity to supply such specialty components. The U.S. heliostat supply chain at present is comprised of few companies (e.g., CSP developers), component suppliers, and is its infancy.
 By 2035, with current and aggressive solar photovoltaic (PV) capacity expansions, there is the potential for 500,000–1,500,000 direct and indirect jobs in the areas of manufacturing, installation and development, and operations and maintenance (O&M) [3]. Utilizing the CSP capacity estimations the recent NREL report [4], the construction of 39 gigawatts (GW) of CSP (assuming mainly power tower) in the U.S. could lead to approximately 195,000 manufacturing, construction, and O&M jobs. This does not include the longer-term jobs and economic impact (e.g., taxes from plant operations staff) from operating the plants once constructed. It is recommended that further CSP component and system supply chain analysis and modelling be undertaken.

Atmospheric SO2 pollutant prediction using mutual information based TCNN-GRU model for flue gas desulfurization process

Over the past several years, sulfur dioxide (SO2) has raised growing concern in China owing to its adverse impact on atmosphere and human respiratory system. The major contributor to SO2 emissions is flue gas generated by fossil-fired electricity-generating plants, and as a consequence diverse flue gas desulphurization (FGD) techniques are installed to abate SO2 emissions. However, the FGD is a dynamic process with serious nonlinearity and large time delay, making the FGD process modeling problem a formidable one. In our research study, a novel hybrid deep learning model with temporal convolution neural network (TCNN), gated recurrent unit (GRU) and mutual information (MI) technique is proposed to predict SO2 emissions in an FGD process. Among those technique, MI is applied to select variables that are best suited for SO2 emission prediction, while TCNN and GRU are innovatively integrated to capture dynamics of SO2 emission in the FGD process. A real FGD system in a power plant with a coal-fired unit of 1000 MW is used as a study case for SO2 emission prediction. Experimental results show that the proposed approach offers satisfactory performance in predicting SO2 emissions for the FGD process, and outperforms other contrastive predictive methods in terms of different performance indicators.

Ultrasonic measurement of temperature distributions in extreme environments: Electrical power plants testing in utility-scale steam generators

Thermal heterogeneities within energy conversion and storage, material processing, nuclear processes, aerospace, and military applications are often inaccessible to characterization by insertion sensors. When sensor deployment is possible, conventional pointwise temperature probes quickly degrade when inserted into harsh environments typical of such processes. We developed spatially-resolved ultrasonic thermometry to noninvasively measure the spatial distributions of thermal properties in such applications, even when sizable thermal gradients are present. Our method divides the path of ultrasonic propagation into segments bound by echogenic features, which create echoes in pulse-echo mode, encoding the information about interior temperature distributions. We use the acquired ultrasonic responses to estimate the internal temperature distributions by solving an inverse problem or concatenating segmental estimates. This work describes the implementation and industrial testing of the developed method at a coal-fired electrical power generation plant. We inserted an echogenically segmented Inconel 625 waveguide into the combustion zone of the utility-scale boiler and continuously acquired ultrasonic data while keeping sensitive components away from the damaging combustion environment. The accuracy of the time-dependent temperature distributions reconstructed from the ultrasonic measurements was comparable to that of thermocouples. The resiliency of ultrasonic thermometry to harsh combustion conditions was far superior to conventional insertion sensors. The measurements obtained during plant operation captured daily steam generation cycles in response to changing customer demand and intermittent contributions of renewable power sources to the power grid. These measurements have revealed new insights into the relationship between the dynamic power generation load and the conditions inside the steam generator. The successful industrial testing of spatially-resolved ultrasonic thermometry in solids indicates that the developed technology has matured to become an attractive alternative to conventional sensing in solving challenging problems of long-term thermal characterizations in extreme environments.

Hydro-power generation forecast in South Africa based on Machine Learning (ML) models

With the advancement of technology and the ever-growing need for electronics, electricity has become an indispensable aspect of modern life. Developing or underdeveloped nations must overcome a number of obstacles to balance the supply and demand for electricity. The difference between supply and demand for electricity has a significant impact on economic growth. Energy forecasting will be crucial in helping policymakers recognize unanticipated changes in the demand for electricity under certain circumstances in a timely manner. Energy constitutes one of the most critical components in a country's growth. To achieve this, machine learning (ML) model was created for hydropower plant electricity generation predictions. In this study, the forecasting model examined the meteorological variables in South Africa. The data included how the South African Weather Service categorized different years from 2001 to 2019 in the southern hemisphere based on climatological and social factors. The simulation allows for an experimental paradigm for the established model to explore the behaviour and performance of the components effective for prospective saving variables related to the design and applications of forecasting hydropower generation in South Africa. In addition, a variety of time-series-based models was used and supervised machine learning techniques to predict the output of energy. The precipitation model achieved regression fitting results of the highest accuracy with training R-value = 0.99976), validation (R-value = 0.99991), and testing (R-value = 0.99931). In addendum, variation in future precipitation and how hydropower plants will generate power in order to adapt to climate change were also addressed in this research. With a mean square error (MSE) of just 1.7262, our suggested model can forecast the production of hydro-electricity generation. The machine learning models created for hydroelectricity generation will minimize operating costs and maximize the energy output of hydropower generation. The suggested remedy is predicated on hydropower plant energy generation forecasts for the future, which can help decision-makers make more informed choices. Given the local weather, the suggested research project will aid in reducing the widening gap between energy output and demand.

Electricity demand forecasting based on feature extraction and optimized backpropagation neural network

As the global population is growing at a high rate, so is the electricity demand also increasing at a faster rate. This exerts pressure on electricity-generating plants and maintenance engineers because of the variability in demand. Avoiding disruption in the supply to meet demand requires forecasting what the future of demand will look like to be able to plan adequately towards it. This study, therefore, develops a new forecasting model using feature extraction (FE) where statistical information of the hourly demand data is extracted which serves as input variables for Backpropagation neural network (BPNN) optimized by particle swarm optimization (PSO) for electricity demand forecasting in Ghana. The model known as FE-PSO-BPNN is compared to other seven models such as Radial Basis Function (RBFNN), Random Forest (RF), Gradient Boosting Machine (GBM), Multivariate Adaptive Regression Splines (MARS), BPNN, and PSO-RBFNN where FE selects the input variables for all models. Electricity demand data from Ghana Grid Company from the period including 1st September 2018 to 30th November 2019 is used for the testing of the model's performance. Evaluation criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Scatter Index (SI) were used. The proposed model is more powerful in forecasting electricity demand than the others as it has RMSE (0.5344), MAE (3.3845), MAPE (0.1773), and SI (0.0003). The model is expected to be a better option for electricity sector managers when considering demand forecasting.

An Estimation Method for Bias Error of Measurements by Utilizing Process Data, An Incidence Matrix and a Reference Instrument for Data Validation and Reconciliation

Abstract With further applications of AI, IoT, and digital twin technology to plant operation and maintenance, it is becoming increasingly important to ensure data reliability. Data validation and reconciliation (DVR) represents one promising technique to ensure data reliability by minimizing the uncertainty of measurements based on statistics. DVR has been widely applied to nuclear power electrical generation plants in Europe and the U.S. in recent years. The most important input for DVR analysis is measurement uncertainty. In Japan, performance management of nuclear power plants is often done by measuring condensate flowrate. While the uncertainty of other flowmeters is handled by the JIS standard, the condensate flowmeter is specially calibrated every few cycles. This leads to reduction of effectiveness of DVR analysis due to variations in measurement uncertainty management. To overcome this issue, we propose an estimation method for measurement uncertainty by utilizing process data, an incidence matrix between sensors, and a reference instrument. The conventional method proposed in the previous study only treats the random error. The proposed method quantitatively estimates not only random error but also bias error by considering the uncertainty of the reference instrument. Using several benchmark problems, we found that the proposed method was applicable to various flow conditions, including physically fluctuating flow such as that observed in the feedwater flow in nuclear power plants. We anticipate that the proposed method will promote use of DVR analysis in nuclear power plants in Japan.

Integrating Cyber Resilience Strategies for OT Data Acquisition Devices

Operational Technology (OT), Industrial Control Systems (ICS), and Supervisory Control and Data Acquisition (SCADA) devices have been around in some form since the 1960s and they control specific functions in many critical industries such as electrical power generation, oil refineries, and water treatment plants. Since most are integrated with IT-based protocols (e.g., TCP/IP), there has been an explosion of OT control systems to provide meaningful information to businesses. While OT and IT speak a common language, this has exposed OT to much more Cyber Attacks that were once applied only to IT-based systems. Data Acquisition Devices (DAS), such as those manufactured by Moxa, now have ethernet ports to provide this link between both networks. The combination of new ethernet-capable OT devices, and those using upgraded converters (e.g., Moxa) for older OT devices, has primarily increased the attack surface. This study proposes a secure layered architecture that can be deployed to limit security threats for ethernet-capable (DAS) devices. The current state of many organizations is the lack of visibility of their OT assets and a knowledge gap on how to secure them. Keywords—Control systems, Cyber attacks, Cybersecurity resilience, Operational technology

Process design and simulation study of an electricity generation plant utilizing low-grade wasted thermal energy using aspen Hysys software

An analysis of the operational parameters of a small-scale electrical generation facility that uses the thermal energy contained in the flue gases from rubbish waste incineration is the goal of this study. To assess this system, the organic Rankine cycle (ORC) thermodynamic system was used. Since the organic fluid has a lower boiling point than water and can be evaporated with less thermal energy, it was chosen as a heat transfer medium instead of water. Aspen Hysys was utilized as a simulation tool, while R11 was used as the working fluid. To maximize the amount of available electrical output power, the plant's operational temperature, working fluid flow rate, and pressure are all maximized. According to the simulation's findings, flue gas may generate electric power between the ranges of 3.12 – 29.71 kW at working pressures between 2.5 and 3.5 bar and working fluid flow rates between 3.600 and 7200 kg/h when the temperature is between 50 and 95 oC. The system reaches a thermal efficiency of about 8.30 at 350 kPa of working fluid pressure.

Do the Dam Project—Evaluating floating solar photovoltaic and energy storage at Inanda Dam within eThekwini Municipality, South Africa

South Africa’s electricity generation plant portfolio includes several aged units, resulting in frequent breakdowns, electricity shortages and load shedding. This study evaluates the feasibility of generating electricity at the Inanda Dam located within eThekwini Municipality of South Africa by installing a floating photovoltaic (FPV) system. The Inanda Dam provides water for many local communities, and load shedding affects pumping operations and disrupts the continuity of water supply. Therefore, the study further evaluates the plausibility of coupling energy storage/alternate generation with the FPV system to provide enough electricity for pumping operations during an outage. In addition, this study evaluates the financial benefits to the municipality resulting from the project implementation. The Hybrid Optimization Model for Multiple Energy Resources (HOMER) software tool was utilised to conduct techno-economic analysis and optimise the size of system components to meet the load requirements while minimising the overall net present costs (NPC). Without limiting the dam usage space for FPV, the optimal FPV system size was 10 MWp to maintain the least NPC. However, with the generation system size larger than the water pumping demand, the excess solar energy generated can be exported back into the grid resulting in the municipality gaining $1 015 473 in the first year of operation. Incorporating battery energy storage or a diesel generator can mitigate load shedding at the pumping site. However, battery energy storage requires significantly more capital upfront and produces a nil financial investment return compared to the diesel generator option, making it the least preferred option to implement.

Modeling and techno-economic analysis of a hybrid sugarcane plant fed by vinasse biogas and bagasse surplus for electricity generation

The growing demand for electricity from renewable energy sources has motivated sugarcane producers to improve the power generation capacity of their plants. In this regard, the present work aims to contribute to the energy recovery process from two sugarcane byproducts: bagasse and vinasse. A novel hybrid combined cycle is devised to use the bagasse surplus generated during the sugarcane season to produce high enthalpy steam for a dedicated condensing turbine. A gas turbine, fed by vinasse biogas vinasse, is used to provide thermal energy to the steam bottoming cycle. Concomitantly, steam for the sugar and ethanol is produced by the existing sugarcane cogeneration system. This approach allows to increase the plant's electricity generation. From a prescribed milling capacity, a conventional sugarcane plant is modeled and validated at steady-state regime. Then, a hybrid plant, designed to consume the bagasse surplus and the vinasse biogas, is presented, and compared to the conventional plant. During the off-season, in the absence of vinasse and biogas, an off-design option is proposed to keep the system running full-time. Results showed that the hybrid plant electricity generation (kWhel) is 7.2–41.5% higher than the conventional plant. The economic feasibility of retrofitting a typical sugarcane plant (live steam parameters at 68 bar/480 °C, for example), by means of a hybrid combined cycle, points to an internal rate of return of around 22.3% and payback period of 5 years. A parametric analysis was carried out showing that, for a process steam consumption below 540kg of steam per tons of sugarcane milled, there will always be leftover bagasse of the season, regardless of the conventional plant steam profile. The production of vinasse required for the hybrid system ranges from 310 to 375 L by tons of sugarcane milled (TC), values within the range of existing sugarcane plants (156–910 L/TC). In addition, the plant operating time can be extended in the off-season, from 200 to 330 days per year, with electrical efficiency achieving around of 27% in this period. In conclusion, the proposed system moves towards the advancement of clean and sustainable electricity generation in the sugarcane industry.

Modelling of Electric Power Generation Plant Based on Gas Turbines with Agricultural Biomass Fuel

To ensure the survival of society, an enormous amount of energy is required to sustain the economic and social development of communities. In addition, there is a pressing need to achieve significant reductions in climate change and the associated costs of implementing systems based on traditional energy sources, as well as addressing the issue of providing electricity to isolated areas. In rural environments, there is an alternative energy source with enormous potential, agricultural biomass, which can produce electrical and thermal energy and can progressively help to reduce dependence on fossil fuels. The purpose of this work is to present a dynamic simulation model of a power generation plant that uses the Joule Brayton thermodynamic cycle, based on a gas turbine which is fueled by residual agricultural biomass; the cycle converts mechanical energy to electrical energy. The problem is approached through the characterization of the biomass, mathematical models of the plant components, and simulation of the system behavior in different scenarios. The simulations are processed in Matlab/Simulink, which allows the model to be verified, validating the equilibrium relationship between generation and load demand.

Design of lightweight concrete with olive biomass bottom ash for use in buildings

The ash generated during the combustion of biomass in electricity generation plants is a waste that has increased considerably in recent years, and whose management constitutes an environmental problem. In this regard, the recovery of biomass bottom ash as a partial replacement of natural materials for use in different civil engineering applications, as well as for the manufacture of construction materials, has been the subject of numerous studies that have shown its technical feasibility. However, for its application in the development of new sustainable materials, with thermal insulation properties, it is necessary to expand our existing knowledge of it. In this study, the use of bottom ash from original and processed biomass bottom ash in the manufacture of lightweight concrete, as a replacement for sand (15%–25%) and expanded clay (25%–35%), has been evaluated. In addition, after subjecting the ash to a grinding process, it was also incorporated into the concrete by replacing cement. The physical, mechanical, thermal and durability properties were evaluated according to regulations and the results showed that the thermal treatment applied to biomass bottom ash improved the mechanical performance of lightweight concrete. Likewise, thermal conductivity was reduced by up to 43%, which allows these concretes to be used as insulating materials in buildings. Therefore, this study shows the possibility of recovering biomass bottom ash in the manufacture of lightweight concrete for use in construction.

New Homes, New Challenges: The Experience of Resettled Maasai Pastoralists of RAPland Village, Olkaria Kenya

Grand development projects sometimes result in the forced relocation of people. The relocation is usually stressful in terms of marginalization and social disarticulation, and more so for pastoral communities. The RAPland village at Olkaria, Kenya, was created to accommodate 155 households that were displaced by the development of a geothermal electricity generation plant. The study aimed to explain how relocation constrained resources for extensive pastoralism and how pastoralists cope through adaptation of old pastoralists' knowledge, attitudes, and perceptions and the creation of new strategies for resilient livelihoods using the case of the RAPland Community. Household surveys, key informant interviews, focus group discussions, and field observation were used to gather required information. It documented information on land, pasture, and water availability and access. It also assessed livestock production challenges before and after the resettlement and adopted coping strategies. Statistical package for social scientists (version 21) software was used in analysing the data after the screening and cleaning were done using Microsoft Excel (2019). There was a general perception that pastures and water availability were significantly (P<0.05) affected by the relocation. Besides, herders and their herds travelled longer distances, from a mean of 3.2 ±0.29 to 8.1 ±0.41 kilometres to access pastures. Before the relocation, the highest-ranking constraints were drought, livestock diseases, pasture inadequacy, and wildlife predation. After the relocation, access to water, poor pastures, grazing terrain (gulleys), wildlife predation, drought, and livestock diseases became the significant constraints affecting livestock productivity. In conclusion, the reduction in pastures and water access affected livestock productivity in spite of attempts at coping with encountered livestock challenges, the community's dependence on pastoral livestock is threatened, thus, their food and

Energy, exergy, economic and environmental analysis of a regasification system integrating simple ORC and LNG open power cycle in floating storage regasification units

A thermodynamic, economic and environmental analysis of a regasification system including a simple Organic Rankine Cycle (ORC) and an Open Organic Rankine Cycle (OC) to utilise the liquefied natural gas (LNG) cold energy is carried out in the present paper. The proposed system, called ORC-OC, uses ambient seawater as heat source (open loop) and is implemented on board a Floating Storage Regasification Unit (FSRU) in order to reduce the greenhouse gas (GHG) emissions associated with the electricity generation plant, i.e., dual fuel (DF) engines. Regarding the ORC working fluids analysed, an ethane/propane zeotropic mixture is applied. The ORC-OC is compared with the simple ORC architecture, giving the first one better energy (lower specific energy consumption), exergy (higher exergy efficiency) and environmental (lower CO2e emissions) results. When compared to the regasification systems installed on board, the ORC-OC system reduces the specific energy consumption by 86.99 % and increases the exergy efficiency by 17.82 % with respect to the most efficient conventional system installed on FSRUs (direct seawater regasification system), leading to a reduction of CO2e emissions of more than 80 %. In addition, the ORC-OC system is more cost-effective than conventional regasification systems when the LNG price is above 6,508 USD/MMBtu.

Bir Çatı Üstü Fotovoltaik Sistem için SAM ve PVsyst Simülasyonlarının Karşılaştırmalı Analizi

Turkiye is a country rich in renewable energy resources, one of which is its tremendous potential, especially in solar energy. Investments in photovoltaic systems are increasing day by day in order to evaluate this potential The simulations of these systems, which will be established while making investment plans, are very important in terms of investment return times. In this study, information and data were obtained from a working rooftop photovoltaic system and simulations were carried out using PVsyst and system advisor model (SAM) software. The simulation data was compared with the power plant's electricity generation data using statistical analysis methods of deviation, Root Mean Square Error (RMSD), and Mean Absolute Deviation (MAD). When all these analysis methods were evaluated, it was seen that the error and deviation values of the SAM software were lower on an annual basis. On the other hand, it has been determined that the error and deviation values of the PVsyst software are lower on a monthly basis.