Performance Evaluation of Solar PV Power Plants in Taiwan Using Data Envelopment Analysis

Performance study on a grid connected 20 kWp solar photovoltaic installation in an industry in Tiruchirappalli (India)

The peculiarity of the operation of solar photovoltaic power plants is the dependence of the generation power on weather conditions, which leads to the maximum production of electrical energy at noon hours of the day. Due to a decrease in electricity consumption, insufficient unloading capacity of pumped storage power plants in the integrated energy system of Ukraine and the specifics of electricity production at solar photovoltaic power plants, dispatching restrictions on the level of generation power are already taking place. To transfer volumes of electrical energy in the world, electrical energy storage systems are used, which operate based on lithium-ion storage batteries. Such systems have a number of advantages over other battery energy systems, which allows their implementation in almost any power generation facility. With the help of energy storage systems, it is possible to make a profit through the purchase of electric energy during a period of low prices and its release during a period of high prices, allowing consumers to save money on its payment. In this paper, we simulate the use of a battery energy storage system for storing electrical energy to transfer excess electrical energy from a solar photovoltaic power plant. To conduct a study and identify excess capacity of a solar photovoltaic power plant, the daily schedule of electrical load is equalized to the capacity of a separate power plant Because of the study, the optimal time for charging and discharging the battery was determined, from which it can be seen that the need to transfer excess electricity to a solar photovoltaic power plant occurs at lunchtime, and their discharge at the peak is the graph of the electrical load of the power system. The aggregate operation of a solar power plant with a total installed capacity of photovoltaic power at the level of 10 MW (DC) and a battery energy storage system for accumulating electric energy with a capacity of 3.75 MWh was simulated. For the study day, the required capacity of a battery system for accumulating electric energy at the level of 1.58 MW was determined. Using the methodology of the levelized cost of electricity and storage, a technical and economic assessment of the transfer of excess capacity of a solar photovoltaic power plant using a battery system for storing electrical energy was carried out. When calculating the cost of storage, the cost of the transferred electrical energy from the solar power plant was taken into account. From the results of technical and economic calculations, it can be seen that, in terms of the cost of equipment, as of 2020, the cost of supplying excess electrical energy from the battery energy storage system is growing when compared with the supply from a solar photovoltaic power plant. Taking into account some forecast assumptions, the cost of electricity supply from the battery energy storage system was calculated for the mode of transferring excess capacity of a solar photovoltaic power plant for 2025 and 2030 years. Keywords: modeling, power system, load demand curve, solar photovoltaic power plant, electric energy storage system, cost

This paper studies the economics of carbon-neutral synthetic fuel production from renewable electricity in remote areas where high-quality renewable resources are abundant. To this end, a graph-based optimisation modelling framework directly applicable to the strategic planning of remote renewable energy supply chains is proposed. More precisely, a hypergraph abstraction of planning problems is introduced, wherein nodes can be viewed as optimisation subproblems with their own parameters, variables, constraints and local objective. Nodes typically represent a subsystem such as a technology, a plant or a process. Hyperedges, on the other hand, express the connectivity between subsystems. The framework is leveraged to study the economics of carbon-neutral synthetic methane production from solar and wind energy in North Africa and its delivery to Northwestern European markets. The full supply chain is modelled in an integrated fashion, which makes it possible to accurately capture the interaction between various technologies on an hourly time scale. Results suggest that the cost of synthetic methane production and delivery would be slightly under 150 €/MWh (higher heating value) by 2030 for a system supplying 10 TWh annually and relying on a combination of solar photovoltaic and wind power plants, assuming a uniform weighted average cost of capital of 7%. A comprehensive sensitivity analysis is also carried out in order to assess the impact of various techno-economic parameters and assumptions on synthetic methane cost, including the availability of wind power plants, the investment costs of electrolysis, methanation and direct air capture plants, their operational flexibility, the energy consumption of direct air capture plants, and financing costs. The most expensive configuration (around 200 €/MWh) relies on solar photovoltaic power plants alone, while the cheapest configuration (around 88 €/MWh) makes use of a combination of solar PV and wind power plants and is obtained when financing costs are set to zero.

The objective is to analysis the cooling system in PV module development considering the know-how in developed and developing in the world, in order to determine the best system for UFVa prototype. In terms of methodology is to pondering the constructive aspects of operation, maintenance, and if possible to restrictions on the type of area for installation and principal the designed for that local specific longitude and latitude weather conditions. Also determine three main types of cooling systems. As results, we have the PVT collector-liquid and/or air ventilated PV with heat recovery. In this project the type of coolant used is the PVT-liquid. This water is redirected to the coolers installed in the photovoltaic modules of the plant to main line, and subsequently returns to the process without impacting the operation of the dam. The choice of this type of technology was based on an analysis of the region in which the plant will be installed, noting that the climate, availability of coolant, ambient temperature and solar radiation index, indicated for the use of the PVT-liquid system. With these results we conclude that the three types for constructing the pre-prototype is coolers of the coil (two variations) and multiple-channel.

Choosing sites for solar photovoltaic (PV) power plants in developing countries like India is a crucial task while considering multiple conflicting factors and sub-factors simultaneously. Multi-criteria decision-making (MCDM) is an optimisation method that provides a framework for handling such situations in an intuitionistic fuzzy environment. The complexity and uncertainty associated with the site selection model are dealt with professionally. The Criteria Importance Through Intercriteria Correlation (CRITIC) method is applied to determine the relative importance of the criteria, identifying airflow speed as the most influential factor, followed by humidity ratio, level of dust haze, availability of labour and resources, and ecological effects. This shows that airflow speed plays an important role in the power plant’s efficiency and performance. The Vlse Kriterijumska Optimizacija I Kompromisno Rešenje (VIKOR) method is then used to prioritise the alternatives as potential locations for setting up a solar PV power plant in India. A new de-i-fuzzification method based on the relative difference between two real numbers is also proposed. Sensitivity analyses and comparative studies are conducted to assess the robustness and effectiveness of the framework. Overall, the results demonstrate that the proposed framework is useful and effective for optimising site selection for solar power plants in India.

Solar power plants can be photovoltaic and concentrated thermal. Photovoltaic solar power plants are much more widespread and with much higher total installed power. The paper presents the good and bad effects on the environment of solar photovoltaic power plants. Good impacts are less air pollution due to reduced use of fossil fuels, reduced impact on global warming due to reduced carbon dioxide generation due to reducing the use of fossil fuels and less water used, due to avoiding its use in thermal power plants. The main negative impacts are the loss or change of habitat conditions for plants and animals, the use of hazardous materials (chemicals) in the production of photovoltaic panels and possible soil erosion. Other bad impacts are the creation of carbon dioxide during production, transportation, installation, maintenance, dismantling and recycling and the consumption of a large amount of electricity in the production of photovoltaic panels, which are offset by the subsequent use of solar photovoltaic power plants in the sense that electricity is produced without the creation of carbon dioxide. Additional bad impacts are at the place of electricity production of solar photovoltaic power plants in terms of possible changes in the local microclimate by increasing the temperature and changing the air flow.

This paper describes technical details regarding the construction and economic viability of renewable energy sources, with particular reference to the techno-economic analysis of solar PV plants in the Republic of Serbia. The potentials of solar energy are described, as well as a chronological overview of various Regulations for privileged producers of electricity from renewable energy sources and various constructions of photovoltaic power plants in the Republic of Serbia. In terms of monitoring development trends and changing feed-in-tariffs for privileged producers of electricity from renewable energy sources, a techno-economic analysis of the development and cost-effectiveness of investments in renewable energy sources was carried out, specifically in solar photovoltaic power plants in the Republic of Serbia.

Levelized Electricity Cost of Five Solar Photovoltaic Plants of Different Capacities

PurposeThis paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning.Design/methodology/approachIn this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model.FindingsThe LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability.Originality/valueThe proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

A systematic literature review on the economic performance of solar thermal power plants including integrated solar combined cycle (ISCC) plants was conducted. A number of solar thermal technologies like parabolic trough (PT), solar tower (ST), linear Fresnel reflector (LFR) and solar dish (SD) were evaluated. The evaluation revealed that solar tower plants typically had the highest capital costs, followed by parabolic-trough and linear Fresnel plants. The results of the studies showed that at smaller capacities of 10–50 MW, PT plants achieved lower LCOE than ST plants, while at larger capacities of 100 MW and above, ST tend to have lower LCOE than PT. There was limited comparative studies on the economic performance of LFR and SD plants. Hence, future studies should focus on the economic impact of different solar thermal technologies including LFR and SD of various capacities using homogeneous modelling conditions. The economic performance of direct steam generation (DSG)-ISCC plants was compared to ISCC, combined cycle gas turbine (CCGT) and conventional solar thermal plants and the results showed that DSG-ISCC plants achieved the lowest LCOE values. Studies also showed that in general, hybrid plants achieved lower LCOE than standalone solar thermal plants. LCOE and capital costs were the dominant financial metrics used in the literature, with very few studies using total life cycle cost, revenues, payback time and internal rate of return. Future studies should include these metrics in order to provide a comprehensive financial assessment of solar thermal power plants, enabling their economic performance to be compared with other renewable and non-renewable energy systems.

Study on the Performance of a solar chimney power plant chlef, Algeria region was presented in this work. The solar chimney is used in hot areas such as Chlef with high intensity of solar radiation. In this work, the solar chimney power plant (collector, chimney and turbine) is modeled theoretically, and the global solar radiation, the air temperature was measured during the period of 01/01/2015 to 1/06/2016. The model is employed in simulation to predict the electrical power produced by the solar chimney plant using the meteorological data of Chlef region. The effect of the geometrical parameters on the performances of the solar chimney power plant was studied. The influence of chimney height on electrical power produced is simulated in order to have the optimum SCPP to respond to the electricity demand in Chlef region. The performance characteristics of solar chimneys indicate that the dimensions of the chimney (height of the chimney, diameter of the collector, height of the collector and the diameter of the chimney) and the climatic condition (air temperature and solar radiation) are important parameters in the production of electrical energy.

A newly constructed 10MW solar photovoltaic power plant in Qinghai province China is examined for the latest cost benefits evaluation in this research, the RETScreen analysis software tool is used for the energy production estimation, financial feasibility analysis and environmental benefit analysis. Initial results show high solar PV potential in Qinghai province China, 20GWh power electricity generation can reduce more than 18,000 tons of CO2 emissions annually. Although current LCOE of PV power is still double than fossil fuel electricity, but the investment of solar PV is motivated by governmental subsidy policy, the payback of the solar PV project became attractive under the new market environment, the IRR could reach more than 20%, with further material cost decrease, efficiency improvement and continues subsidy policy, the solar PV power will be a trend of renewable energy in China. The GHG emissions reduction is another driving reason to encourage solar PV power development in China.

Environmental impact of renewable energy source based electrical power plants: Solar, wind, hydroelectric, biomass, geothermal, tidal, ocean, and osmotic

HELIAKI is a FORTRAN computer program which simulates the optical/thermal performance of a central receiver solar thermal power plant for the dynamic conversion of solar-generated heat to electricity. The solar power plant which this program simulates consists of a field of individual sun tracking mirror units, or heliostats, redirecting sunlight into a cavity, called the receiver, mounted atop a tower. The program calculates the power retained by that cavity receiver at any point in time or the energy into the receiver over a year's time using a Monte Carlo ray trace technique to solve the multiple integral equations. An artist's concept of this plant is shown.

This paper deals with the problem of determining the optimal capacity of concentrated solar power (CSP) plants, especially in the context of hybrid solar power plants. This work presents an innovative analytical approach to optimizing the capacity of concentrated solar plants. The proposed method is based on the use of additional non-dimensional parameters, in particular, the design factor and the solar multiple factor. This paper presents a mathematical optimization model that focuses on the capacity of concentrated solar power plants where thermal storage plays a key role in the energy source. The analytical approach provides a more complete understanding of the design process for hybrid power plants. In addition, the use of additional factors and the combination of the proposed method with existing numerical methods allows for more refined optimization, which allows for the more accurate selection of the capacity for specific geographical conditions. Importantly, the proposed method significantly increases the speed of computation compared to that of traditional numerical methods. Finally, the authors present the results of the analysis of the proposed system of equations for calculating the levelized cost of electricity (LCOE) for hybrid solar power plants. The nonlinearity of the LCOE on the main calculation parameters is shown.