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• Original Research Article
• Open access
• Published: 17 August 2020

Wind turbine performance analysis for energy cost minimization

• Yassine Charabi   ORCID: orcid.org/0000-0003-2054-688X 1 &
• Sabah Abdul-Wahab 2  

Renewables: Wind, Water, and Solar volume  7 , Article number:  5 ( 2020 ) Cite this article

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A Correction to this article was published on 17 January 2021

This article has been updated

The use of wind energy worldwide has overgrown in recent years to reduce greenhouse gas emissions. Wind power is free, but the installation and maintenance of wind turbines remain very costly. The size of the installation of the wind turbine is not only determined by wind statistics at a given location, but also by turbine infrastructure and maintenance costs. The payback time of the turbine is dependent on turbine energy costs. This study estimates the wind power generation capacity of Northern and Southern Oman and discusses the selection of the most economical, efficient and reliable wind turbines in Oman. HOMER Pro Software was used in this paper to evaluate the wind energy data in the north and south of Oman and to provide well-informed guidance on the most suitable turbines for the power needs of each area. Six different standard wind turbines were measured and compared in terms of the cost of energy and performance. The simulation analysis reveals that the DW54 turbine is the best possible turbine to generate electricity in northern Oman at $0.119/kW. Due to the difference in the wind regime between the north and the south of Oman, the simulation showed that the Hummer H25.0–200 kW turbine is the best option for south Oman with power generation at $0.070/kW. The northern wind turbine plant can efficiently contribute to decarbonization of the energy sector in Oman, with a potential reduction of CO 2 emission approximately 19,000 tons/year in comparison to natural gas and 28,000 tons/year in comparison to diesel. In the Southern Power Plant, carbon emissions are reduced by 18,000 and 12,000 tons/year compared to diesel and natural gas.

Introduction

The rise in global temperature and severe climate change worldwide has increased environmental concerns. Nowadays, more than 90% of the world’s electricity comes from fossil fuels (World-Bank 2015 ), and that energy production plays a vital role in global warming. Any changes in this field can have a significant impact on the environment. Numerous researchers, therefore, have attempted to change or alleviate the negative impacts of global warming, with much of this effort coming from the energy sector (Ghodsi et al. 2019 ; Khare et al. 2016 ; Sahu et al. 2018 ). In comparison to fossil fuels, the impact of renewable energy sources on the environment is negligible. These sources, for example, have no direct CO 2 or NOx emissions. From solar panels to wind turbine generators, a wide range of devices can convert ambient energy into a more useful form, like electricity (Charabi et al. 2019 ). Among these devices, wind turbines are some of the most popular and accessible methods of converting ambient energy to electricity (Yang et al. 2018 ). However, wind energy, like most other sources of renewable energy, has high capital costs, but during the past decade, this trend has changed tremendously. Statics show that the cost of wind production has dropped enormously in recent years, from two million dollars per M.W. to one million in the last decade (Moné 2017 ). This achievement has made it possible to see wind power plants with increasing frequency in both developed and developing countries (Sahu 2018 ).

As a Middle Eastern, oil-dependent country, Oman has started in a new direction on its path of development. The country is trying to change its electricity production industry from one that is entirely oil-based to one that is more reliant on sustainable “greener” energy sources (Abdul-Wahab et al. 2019a ; Al-Suleiman et al. 2019 ). The main two options for this plan are solar and wind energy. Although Oman’s sunny weather provides a unique opportunity for solar energy generation, the country’s wind power potential must not be neglected. As of this article’s writing, Oman has no industrial wind power stations, and the country’s wind turbines are mainly used for research purposes. However, this situation is changing, beginning with developing an understanding of the country’s wind power potential. An incorrect estimation of wind energy needs or the use of low-performance equipment not only reduces the benefits of the project, but also might lead to economic disaster (Dolatabadi et al. 2017 ).

Over the last decade, considerable information on wind resource mapping across Oman has been accumulated to stimulate the deployment of wind power (Al-Yayai and Charabi 2015 ; Al-Yahyai et al. 2012 , 2013 ; Charabi et al. 2011 ; Al Yahyai el al. 2010). Despite the availability of wind mapping information, the deployment of wind energy across Oman is still lagging due to the lack of accurate information on turbine energy cost. Without access to sound information on the cost of wind power technology, it is difficult for decision-makers, if not impossible; to evaluate which wind turbine technologies will most fit their national circumstances. The fast growth and cost reductions in the installed wind energy technologies mean that even data aged one or 2 years will substantially overestimate the cost of power from wind energy technologies. There is also a significant amount of perceived knowledge about the cost and performance of wind power generation technologies that are not accurate or is misleading. Significant knowledge of the cost and performance of wind generation technologies is also viewed that is not right or misleading. This paper fills a significant information gap because there is a lack of precise, comparable, and the latest data on the costs and performance of wind turbines in Oman.

Studies on the viability and economic potential of wind energy have recently spread worldwide.

Kumar and Gaddada ( 2015 ) have explored the outputs of four statistical methods to evaluate Weibull parameters for wind energy applications in four selected sites, located in northern Ethiopia. Gaddada and Kodicherla ( 2016 ) have evaluated wind power capacity and wind energy cost estimates for electricity generation systems in eight selected locations in Tigray (Ethiopia). Kodicherla et al. ( 2017 ) explored the potential of wind energy and developed an economic assessment of the water pumping system in various wind power conversion systems. In three selected Fiji Island stations, Kodicherla et al. ( 2018 ) have investigated the potential of wind power-assisted wind hydrogen production using different types of turbines. The literature also reflects different foci around wind turbines. Many researchers have worked on defining the shape and structure of wind turbines and their effects on aerodynamics (Cai 2019 ; Nema et al. 2009 ; Akpinar and Akpinar 2006 ). Others have tried to improve the performance of current turbines by optimizing placement and hub height (Abdul-Wahab et al. 2019b ; Elkinton et al. 2008 ).

Despite these efforts, the stochastic nature of wind speed makes wind energy generation difficult for some places (Padrón et al. 2019 ). A deep understanding of the specifications of each wind turbine and complete statistical data on wind velocity in any given location can begin to address this problem. These data must be processed and matched to a potential turbine to give a realistic and feasible answer to the suitability of any given piece of wind power equipment. In this paper, HOMER Pro software (HOMER Energy L.L.C., Boulder, Colorado, U.S.A.) was used to analyze wind data for the north and south of Oman and make a well-informed recommendation on the most suitable turbines for each region’s power needs. HOMER Pro software can combine data associated with wind regime, the specifications of wind turbines, and the power demands of consumers to estimate the cost of producing energy using different generators.

In this study, the researchers tried to estimate the potential for wind energy production in Oman’s north and south and suggest the feasibility of using wind turbines in the country. To this end, the performance of six different popular wind turbines was calculated and compared. By considering the performance and cost of energy (C.O.E.), suggestions on the best possible turbines for the north and south of Oman are provided.

Study areas

As has been mentioned previously, two locations were selected for the wind power plants. The northern site was located in Al Batinah North Governorate (24° 42′ 23″ N 56° 28′ 48″ E). The southern site was Mirbat, Dhofar Governorate (16° 58′ 22″ N 54° 42′ 56″ E) (Fig.  1 ). Both plants are located in rural areas with low populations and, therefore, low power demands. Population, power consumption per capita and power consumption patterns change power demands in an area. Demand also changes daily, hourly, and even in the summer and winter. The last reported data from Oman show that each Omani annually consumes around 6550 kWh on average (S.A.O.C 2017 ). Based on this information and the population of the area, the size of the wind power plant is considered at 10 MW. This size can cover current electricity consumption and any possible future growth. Even with a highly accurate prediction, real conditions can have unexpected variations. In order to consider this variation, the monthly 2% day-to-day random variability and 2% time-to-time step of random variability was considered. Figure  2 shows the power consumption patterns in Oman’s households. As can be seen in the figure, April to October is Oman’s summer season and has high electricity demand, while in wintertime, November to March, the power demand decreases significantly. The high demand for energy by cooling systems in the long summer of Oman is the main reason for this trend.

The location of the wind farms in north and south of Oman

Monthly average of power demand (MW)

HOMER software

Wind turbine performance analysis.

A realistic estimation of power production requires accurate statistical data on wind velocity for an extended period, like a year or more, if possible. The accuracy of the output results entirely depends on the accuracy of this information. Wind velocity is usually measured on an hourly basis. Due to the high number of measurements in a calendar year, however, further processing for such an extended period would be time-consuming and difficult. Therefore, when making calculations based on such large data sets, the average wind velocity is usually used to reduce the processing load. Although using the monthly average seems practical, such a simple average can be misleading. For instance, by using a wind velocity of 0 m/s for 50% of the time and using a velocity of 6 m/s for the rest of the time, the simple average of the wind velocity would be 3 m/s.

Considering a wind turbine with a maximum output power of 3 m/s, the output performance would be wrongly calculated at 100% all day long. Such a system would have 100% output at 50% of the time at best. In order to address such miscalculations, in this research, the two-parameter Weibull distribution was used (Wang et al. 2018 ). In this method, both wind velocity and its probability over time are considered, and the distribution of the wind velocity is used for the following calculations (Moein et al. 2018 ). The probability density (f) and cumulative distribution (F) of the wind based on Weibull distribution are:

where c is the Weibull scale (m/s), and k is the Weibull shape factor.

The different wind turbines on the market have very different specifications. Considering and analyzing all of these turbines in this paper is not possible. Six of the most popular turbines on the market were selected and analyzed in order to make the article descriptive, rational, and practical. In some countries, other brands and models of turbines might be more popular, but the present approach can be used in those countries, too. In making this comparison, the C.O.E. production for each turbine must be calculated and compared carefully. Moreover, the whole system of a wind power plant consisting of one or more turbines must be able to handle the load demand of consumers with no or limited access to the main power line, for such a scenario where there is no access to the power grid, the power generation system has to be equipped with a sufficiently sized battery bank or a fossil fuel generator to cover non-windy hours or days. In order to simplify the problem and eliminate the calculation of fossil fuel generators, the system under consideration was conceptualized as having up to a 10% deficiency in a limited number of days. In real conditions, this amount of energy can be obtained from the main power lines (if accessible) or local generators. However, in this article, further calculations based on these generators were not considered.

Wind speed calculations represent the first phase of the HOMER Pro simulation. The wind velocity was measured and recorded every hour for 1 year. The system measured wind speed at a 10-m height above the sea level, which is the standard height for the measurement. Table  1 shows a sample of the measurements from the northern site for 1 week. For the calculation of the velocity at a different height (based on the height of each wind turbine), the measured values must be modified as in Eq. ( 3 ):

where \(V_{\text{Turbine}}\) and \(V\) show the wind velocity at the turbine and standard anemometer height, \(Z_{\text{Turbine}}\) and \(Z_{\text{anm}}\) are the height of the turbine and the anemometer (m) and \(Z_{0}\) is the surface roughness (m). Surface roughness characterizes the roughness of the field around the turbine. In this project, based on the local properties of the site location, \(Z_{0}\) was considered 0.03 m, which indicates a smooth field with some crops and no trees or buildings in the surrounding area (Homer-Energy 2016 ).

By combining the Weibull equation and Eq. ( 3 ), the average wind velocity can be written as:

And the output power in a wind turbine can be written in the form:

where \(\tau\) is the time, \(C_{p}\) is the turbine’s nominal capacity, and \(f_{v}\) is the wind velocity distribution.

The producers also provide the power curve of each turbine by testing different wind velocities. The power curve shows the real output power of the system in different ranges of wind velocity. Figure  3 shows the power curves of the six selected turbines with data extracted from the producers’ datasheet for the following turbine models:

The power curves of the selected turbines

GE 1.5 SLE (GE Power, Schenectady, New York, USA).

Enercon E44 (Enercon, Aurich, Germany).

Enercon E53 (Enercon, Aurich, Germany).

FD21-100 (Enercon, Aurich, Germany).

EWT DW54 (Emergya Wind Turbines Pvt. Ltd., Amersfoort, The Netherlands);

Hummer H25.0–200 kW (Anhui Hummer Dynamo Co., Ltd., Hefei, Anhui, People’s Republic of China).

Economic analysis

In project planning, economic analysis is the most critical factor in decision-making. In this study, an economic analysis was the only indicator considered to show the feasibility of wind projects. Economic feasibility incorporates long-term performance, pointing to the best possible option among the wind turbines. In order to make an accurate estimation of economic feasibility, the total cost of the project must be calculated, including the capital cost (initial cost of the construction and devices), replacement cost as necessary, and maintenance costs. Operation costs should also be considered for the whole project. However, due to the low cost of operation in wind turbines, the operation cost can be considered part of maintenance costs. By accurately estimating these costs, the price of power generation per kW can be estimated. This price is a suitable indicator for choosing the best possible turbine for a wind power plant. In this research, the cost of energy (C.O.E.) per kW was the distinguishing feature considered among the turbines studied. HOMER sensitivity and optimization algorithms were used to select the best wind turbine (Pahlavan et al. 2018 ; Vahdatpour et al. 2017 ). The equations of the method of optimal system measuring, which has a minimum amount of total net present cost (N.P.C.), are as follows:

where C ann,total , C.R.F. i and R proj are the total annual cost, cost recovery factor, real interest rate and lifetime of the project, respectively.

All costs and incomes are evaluated at a constant interest rate over the year. The actual interest rate resulting from inflation is calculated and the effect of the change in interest rate on final N.P.C. is applied to purpose of influencing inflation in calculations. The cost recovery factor (C.R.F.), which indicates the cost recovery over the N  years, is calculated as follows:

Software is able to calculate the annual interest rate through the following equation:

Also, the cost of per kW of energy during the lifetime of the project is obtained by software from the following equation:

In the above equation, E Load served is the real electric load in the hybrid system by unit kW/year.

Table  2 shows all costs associated with the selected turbines and which include:

The Capital cost is the initial purchase price,

The Replacement cost is the cost of replacing the generator at the end of its lifetime, the O&M cost is the annual cost of operating and maintaining the generator.

No energy battery storage system storage was taken into consideration for the current simulation focusing on the selection of the best wind turbine, and an annual interest rate of 6% was taken into account.

Results and discussion

Comparison between the proposed wind turbines.

Implementing big data associated with turbine measurements and specifications is difficult. HOMER Pro helps analyze this data and simulate plans for 20 years. The results of the simulation for each turbine are presented in Table  3 .

The main findings from the turbines simulation were as follows:

G.E. Energy 1.5 SLE This turbine is designed and manufactured by G.E. Power, a subsidiary of the General Electric Energy Company, and is a 1500-kW-rated power producer. This model has the highest power output among the selected turbines. It has a three-blade rotor with a 77-m diameter and 85-m hub height. The cut-in wind velocity for this model is 3 m/s, and the cut-off speed is 25 m/s. Cut-in and cut-off velocities can have a significant impact on the performance of the turbine. A turbine with a lower cut-off speed has the advantage of generating power in lower wind speed locations, like the north of Oman. The results of the simulation show that the C.O.E. for this turbine is USD$0.171 for each kW of energy in the north and USD$0.089 in the south. This cost contains the USD$1.75 million dollar maintenance cost for 20 years of operation and a capital cost of USD$3.38 million.

Enercon E44 This turbine, produced in Germany, has the second-highest power output of those considered, with a 900-kW-rated generator, 55-m hub height, and 44-m blade size. This Enercon production has a minimum cut-off wind velocity of 3 m/s, and a 28 m/s maximum cut-off. The HOMER Pro results showed that, by considering the capital cost of USD$2.34m and a maintenance cost of around USD$1 million, the C.O.E. would be USD$0.303 for each kW of energy in the north and USD$0.135/kW in the south.

Enercon E53 This turbine has a 53-m rotor diameter and 800 kW power production potential. Due to the lower power output, this model has lower capital and maintenance costs. Considering all of the costs of the turbine, the system would be able to generate power at USD$0.163/kW and USD$0.088/kW in the north and south, respectively.

FD21 - 100 This Enercon model uses GHREPOWER production with 100-kW output power. The lower output power makes it suitable for smaller wind power plants. FD21-100 has a 3–25 m/s range of working speed, and its highest possible hub height is 42 m. The HOMER Pro software simulation for this turbine showed that the C.O.E. would reach up to USD$0.290 per kW in the north and USD$0.144 kW in the south. In comparison to other turbines, this model has the highest cost of power generation for both locations.

DW54 This turbine is a 500-kW generator designed and produced by Energy Wind Technology (E.W.T.) in Amersfoort, The Netherlands. It has a 54-m rotor diameter and a working velocity between 3 and 10 m/s. With a USD$1.2 million capital cost and USD$750,000 maintenance cost over 20 years, the power generation cost would be USD$0.119/kW. This cost is the lowest possible for generating power in the north of Oman. However, the simulation showed that, due to differences in the wind regime in the north and south, this model is not the best possible option for the south. Each kW of energy produced in the south would cost USD$0.071. However, with its C.O.E., this model is the second best possible turbine for Oman’s north.

Hummer H25.0 – 200   K.W. This model is a 200-kW-rated wind turbine produced by the Anhui Hummer Dynamo Company of Hefei, China. In comparison to other analyzed turbines, this model has a lower cut-in wind velocity by 2.5 m/s and a smaller blade size (12 m). The simulation showed that while the capital cost of the turbine could be as low as USD$300,000, this model’s C.O.E. is not the best for all situations. In the north, power production would cost USD$0.132/kW. While this price is not the best possible option for the north, the results for the south are different. The simulation showed that the turbine would have the best possible results in the south among the selected models, generating power at USD$0.070/kW.

Considering the above-mentioned findings, the DW54 turbine is the best possible turbine for the north of Oman. On the other hand, the Hummer H25.0–200 KW turbine is the best option for Oman’s south. These models can generate electricity at the lowest possible cost. Figure  4 shows the graph of energy production cost for each turbine in the northern and southern sites.

Cost of electricity for different turbines

Advantages of provided wind turbines over natural gas and diesel generators

The current power plants in Oman mostly use natural gas for electricity production. On the other hand, for off-grid consumers (some rural regions), the diesel generators are the primary source of electricity. It is clear that fossil fuel generators emit pollutant gases into the atmosphere and have negative impacts on the environment. In short, the diesel generator’s gas emission is calculated using the same energy production as the best wind turbines. For comparison, the unmet electrical load of wind turbines is considered (Fig.  5 ). Table  4 shows the emitted pollutant gases over one year of use. As it can be seen in Table  3 , the wind turbine power plant in the north can stop the CO 2 emission approximately 19,000 ton/year in comparison to natural gas and 28,000 ton/year in comparison to diesel. In the southern power plant, the reduced gas emission in comparison to diesel and natural gas are 18,000 and 12,000 ton/year, respectively.

Unmet electrical loads for different turbines

In this study, the feasibility of using wind energy as a source of power production was calculated by collecting and analyzing hourly data on wind regimes over a 1-year period. HOMER Pro software was used to calculate the C.O.E. production of six different wind turbines, in order to select the most suitable wind turbine for two distinct locations in the north and south of Oman. The study’s main findings can be summarized as follows:

DW54 turbine produced by Energy Wind Technology in Amersfoort, The Netherlands, would have the best performance for Oman’s northern regions and can generate the cheapest possible energy from wind at $0.119/kW.

H25.0–200 kW turbine manufactured by the Anhui Hummer Dynamo Company of Hefeit, China, gives the best C.O.E. production for the southern regions of Oman and the lowest possible wind energy can be produced at $0.70/KW.

The difference of the wind regime between the northern and southern parts of Oman and the power curves of the turbines are the main reasons for the selection of two different wind turbines form different manufacturers.

The northern wind turbine plant is estimated to decrease CO 2 emissions by around 19,000 tons per year, compared to natural gas, while diesel emissions by around by 28,000 tons per year.

The southern wind turbines have a potential carbon emission reduction of about 18,000 and 12,000 tons per year compared to diesel and natural gas.

The application of the turbine selection using the HOMER Model described in this paper determined that the H25.0–200 kW turbine selected for the southern parts of Oman has a C.O.E. that is 58.8% lower than the DW54 turbine that was selected for the northern parts of the country. The application of the method followed in this research by developers during the planning stage could significantly improve the financial performance of their investment. Similarly, such techniques could be added to tools such as WAsP to improve decision-making during the initial planning stage.

Availability of data and materials

Data are openly available with HOMER software. HOMER uses the monthly average wind speeds, plus four parameters (Weibull k, 1-h autocorrelation factor, Diurnal pattern strength and Hour of peak wind speed) to synthesize wind data for simulation.

Change history

17 january 2021.

An amendment to this paper has been published and can be accessed via the original article.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful suggestions and careful reading of the manuscript.

The authors received no specific funding for this work.

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Yassine Charabi

Department of Mechanical and Industrial Engineering, Sultan Qaboos University, Muscat, Oman

Sabah Abdul-Wahab

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The first and the second author contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yassine Charabi .

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Charabi, Y., Abdul-Wahab, S. Wind turbine performance analysis for energy cost minimization. Renewables 7 , 5 (2020). https://doi.org/10.1186/s40807-020-00062-7

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DOI : https://doi.org/10.1186/s40807-020-00062-7

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Assessing the eco-environmental aspects of fossil fuels-based units substitution of Point Aconi thermal power plant by green-based energies: a case study of Canada

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• Published: 04 June 2024

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• Nima Amiri 1 ,
• Mohammad Shaterabadi 2 ,
• Lazhar Ben-Brahim 2 &
• Mehdi Ahmadi Jirdehi 3  

Canada possesses significant potential in harnessing renewable energy from its vast and diverse geography, which can generate clean electricity. This paper presents a model that replaces fossil fuels used in a proposed thermal power plant in Point Aconi, Nova Scotia, with photovoltaic and wind turbine units based on the region’s climate conditions. The research results are based on evaluating multiple thermal power plants worldwide and examining various wind turbines and PV panels from different companies to ensure accuracy. The chosen units that best suit the location’s geographical and biological conditions, transmission, and operation costs demonstrate that the power plant currently consumes approximately 47 tons of coal and petroleum coke per hour. Replacing these materials with the proposed green units makes it possible to reduce environmental pollution by eliminating almost 165 tons of CO 2 and other pollutants per hour while increasing the plant’s efficiency and independence from fossil fuel price variations. The presented structure’s ROI is approximately 20 years, which is reasonable compared to the economic and environmental benefits of utilizing such a structure and converting the thermal power plant to green units.

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1 Introduction

1.1 general background of the research study.

Modern life has led to a continuous increase in electricity demand [ 1 , 2 ]. As a result of the observed increase, there has been a corresponding escalation in environmental pollution emissions and climate change, largely attributed to the impact of greenhouse gas emissions [ 3 , 4 , 5 ]. Thermal power plants play a critical role in powering our societies. However, their reliance on fossil fuels, such as coal, petroleum, and natural gas, has released pollutants into the environment. Despite these concerns, thermal power plants remain essential for meeting the increasing demand for electricity, thanks to their significant output capacity. The various types of thermal power plants include coal, petroleum, nuclear, geothermal, waste incineration plants, and natural gas power plants [ 6 , 7 ]. The thermal power plant operates by heating water within a boiler, resulting in the evaporation of said water. This evaporation induces turbine blades’ movement through high-pressure steam [ 8 , 9 ]. The Rankine cycle is a well-established thermal power plant process that comprises four essential components: a boiler, turbine, condenser, and pump. This process is widely used in the energy industry due to its simplicity and efficiency in generating power. The system works by converting heat energy into mechanical energy, which can then be used to generate electricity. Each component plays a vital role in the overall process, and the system’s efficiency depends on the proper functioning of all parts.

Overall, the Rankine cycle remains a critical process in the energy industry, and its continued use ensures reliable and sustainable power generation [ 10 , 11 ]. The boiler plays a critical role in providing superheated steam to the turbine stage through heat input. Recent research endeavors have explored various aspects of the boiler section, including an in-depth analysis of mathematical models and simulations. Several recent studies have also presented comprehensive surveys of these models and simulations to enhance our understanding of this crucial component [ 12 , 13 , 14 ]. Recent investigations have led to attempts to improve the performance and efficiency of boilers. This has been achieved by ensuring that the superheated steam maintains constant pressure and temperature. As a result, the boiler and power plant’s overall safety and efficiency have significantly improved [ 15 , 16 , 17 ]. It is worth noting that the conventional design and construction of thermal power plants have traditionally centered around the first law of thermodynamics, with a key focus on energy conservation. However, in recent times, the discovery of the exergy criterion, which is grounded in the second law of thermodynamics, has gained widespread acceptance in the design and enhancement of thermal power plant units, with an emphasis on energy quality [ 18 , 19 ].

During the early part of the nineteenth century, discourse surrounding climate change emerged following the discovery of the inherent greenhouse effect. As humanity’s comprehension of the various aspects and ramifications of global warming on the planet’s ecological system expanded, apprehension regarding the unregulated consumption of fossil fuels grew [ 20 , 21 , 22 ]. The burning of coal has been identified as a primary source of carbon dioxide (CO 2 ), sulfur dioxide (SO 2 ), nitrogen oxides (NOx), mercury, and heavy metals such as fly ash and bottom ash. These harmful substances have prompted international agreements and guidelines to reduce greenhouse gas emissions. In order to achieve this goal, it is imperative to decrease or eliminate the use of thermal power plants, which are significant contributors to environmental pollution. A recent report by Carbon Brief indicates that approximately 80 countries across the globe rely on coal power plants, which generate nearly 40% of the world’s total electricity output [ 23 , 24 , 25 ]. Canada currently runs a fleet of 16 coal-fired power plants that generate approximately 10,000 MW of electricity [ 26 ]. The following sections review the latest research studies to declare the most significant findings and work in this field. Moreover, the main key points are presented to help the presented paper’s novelty for understanding future readers.

1.2 Survey on the recent research studies

Several recent studies have addressed the issues of assessing the life span of thermal power plants. Steinmann et al. determined the uncertainty of CO 2 and CH 4 emissions for 364 United States (US) coal power plants via Monte Carlo simulation. As a result, they could implement a framework for separating variability and uncertainty in the CO 2 trace of coal-fired power plants. It helps to reduce the uncertainty in the range of non-predicted emissions [ 27 ]. Ren et al. used previous studies and experiments on coal-fired power plants to determine the spatial and temporal distribution of SO 3 emissions. Also, the effect of dust collectors and desulfurization systems on the amount of SO 3 emissions was investigated [ 28 ].

Moreover, various methods were implemented to reduce the pollution of thermal power plants, including the combined cycle power plant, which involved some gas turbines and steam turbines. The exhaust gases of the steam turbines, which can reach temperatures of up to 600 °C, were used as a heat or preheat source requirement for the steam turbine boiler [ 29 ]. The growth of industrial societies, besides the growing need for energy on the one hand, and the limited and unequal distribution of fossil fuels (oil, gas, coal) energy worldwide on the other hand, along with the environmental concerns of excessive consumption of fossil fuels, has led governments to think about alternative energies. The most important features of green energies are purity, availability, and renewability [ 30 ]. Therefore, using alternative energies, such as energy generated by wind, solar, biomass, geothermal, and water (hydropower or waves), is at the forefront of government planning. According to the International Energy Agency (IEA), the contribution of renewable energies in providing diverse countries’ needs for electricity by 2018 has been about 11% of total energy consumption [ 31 ]. Although renewable energies seem to have a long way to go in competing with other energies, many countries are planning to expand the use of these resources shortly, and therefore, they have invested dramatically in technical research and expert planning in this area.

The utilization of wind turbines as a source of green energy has been on the rise, with an annual increase of over 25%. Wind power is highly accessible and widely available; however, it only contributes a small fraction of the global energy supply [ 32 , 33 ]. Wind energy is commonly harnessed through towering turbines that can reach heights of up to 600 feet, each equipped with three lengthy blades [ 34 ]. Improvement and development of wind turbines are continuing, and researchers have investigated this field [ 35 ]. Mishnaevsky et al. considered applications of composite materials like natural composites, hybrid, and nano-engineered composites with their modeling and testing approaches in the manufacturing of wind turbines in a review paper [ 36 ]. Hansen analyzed the shape mode of the particular turbine and found differences in the blades’ backward and forward whirling modes [ 37 ]. Azadeh et al. developed a computer program for optimizing wind turbine blades according to several criteria [ 38 ]. Chiu et al. quantified and investigated the impact of aerodynamic performance, structural performance, and reduced aerodynamic loads of biplane wind turbine blades.

Moreover, optimal mass and mass-driving constraints in realistic loads were considered [ 39 ]. Kusiak and Song presented a model to capture the maximum wind energy of wind farms based on the wind distribution function. The model considered wake losses based on wind turbine locations and wind direction [ 40 ]. Charhouni et al. optimized the wind farm using the genetic algorithm (GA) for different wind turbine types [ 41 ]. Rumsey and Paquette investigated structural health monitoring and NDT system setups for monitoring wind turbine structures from a fatigue test of a carbon-epoxy and glass–epoxy wind turbine blade [ 42 ]. Feng et al. examined gearbox failure modes and detection methods of incipient gearbox failures by the supervisory control and data acquisition (SCADA) and condition monitoring system (CMS) [ 43 ]. Kusiak and Verma used the power, rotor, and blade pitch curves to monitor the wind farm’s performance [ 44 ]. Liu et al. utilized decision trees to predict the wind turbine voltage output based on online information and show prediction results by lights to help operators in wind farms understand intuitive risk levels [ 45 ].

Solar energy is a prevalent form of renewable energy that is widely accessible and found in most regions worldwide. Several technologies are utilized to convert solar irradiation into usable energy, with one such technology being solar cells or photovoltaic cells. These cells are responsible for converting sunlight into electrical energy and are predominantly composed of silicon. The efficiency and cost of these cells vary depending on the type—non-crystalline, polycrystalline, or crystalline. Assembled in a specific configuration, panels comprise a collection of cells. Manufacturers have developed panels with varying attributes and qualities, such as nominal maximum power, module efficiency, and nominal module operating temperature, to suit various applications, including residential, commercial, and utility-scale photovoltaics [ 46 ]. Kabir et al. discussed the advantages and disadvantages of solar technology and analyzed the future of solar energy according to the regulation policy frameworks. Results depict that research projects should decrease solar usage costs and improve efficiency and competitiveness with conventional resources and other renewable energies [ 47 ]. Vorobiev et al. investigated semiconductor materials at high temperatures with two different options and different band-gap quantities that could be suitable and efficient [ 48 ]. Peng and Lee reviewed silicon nanowires (SNW) for photovoltaics and assessed recent developments in SNW to obtain an SNW with high performance and reasonable cost [ 49 ]. Zhu et al. studied the potential applications of various carbon materials in photovoltaic cells (PVCs), such as silicon-based solar cells, organic solar cells, and dye-sensitized solar cells. Results show that carbon utilization in PVCs still needs more investigation to enhance efficiency [ 50 ]. Skoplaki and Palyvos surveyed the effective and appropriate performance temperature of silicon-based photovoltaic panels and provided an appropriate tabulation that helps the modeling and design process according to the solar energy’s potential [ 51 ]. Ghiassi-Farrokhfal et al. assessed allocating a specific budget to solar panels and storage to maximize expected income. Their results show that allocating the budget for storage reduces the funding for solar panels, and storage devices are incomplete.

Furthermore, they present a power commitment approach for problem optimization [ 52 ]. Mallor et al. presented a new method to detect outliers or faults in PV solar farms. Data from this method were created based on two stages: the first stage, by outlier detection methods and functional principal component analysis, and in the second stage, parametric methods and three nonparametric methods [ 53 ]. Zhou et al. analyzed hybrid solar–wind energy systems’ simulation, control, and optimization. This study evaluated new research methods and developments by considering the performance of combined solar and wind systems [ 54 ]. Yang et al. optimized the annual cost and losses of hybrid solar–wind systems by five decision variables: the PV’s module number, module slope angle, wind turbine number, wind turbine installation height, and battery capacity [ 55 ].

1.3 The main research hypothesis and critical points

This paper investigates a techno-economic analysis and modeling for substituting the inlet fossil fuels of a thermal power plant by combining solar and wind power plants (hybrid solar–wind) according to Nova Scotia (Canada) geographical and biological conditions. The case study of this research is Point Aconi’s thermal power plant, with a capacity of about 171 MW. Many thermal power plants worldwide are examined, and the one with the best conditions and the ability to exert and reach the assumed aims is selected. Therefore, the output power generated by renewable resources (instead of coal and petroleum coke) is applied and delivered to electrical boilers designed and considered concerning the requirements and consumption. Many factors, such as solar irradiance and wind speed of the site in different months of the year, the possibility of wind turbines shut down due to bird migration, flicker, and noise issues, as well as land issues for construction wind and solar farms, participate in the proposed study. Furthermore, various turbines and panels of several companies with different characteristics are checked and evaluated to show precision and select the best concerning the critical factors for the presented research study.

According to the above, the main key points and novelties of the paper are proposed as follows:

Using RERs as the input sector of thermal power plants instead of coal and petro coke.

Planning has been done by considering the geographical and environmental conditions of Nova Scotia.

Various turbines and panels of several companies with different characteristics have been examined.

Environmental pollution and expansion planning costs have decreased.

The return on investment has been reasonably acceptable compared to the amount of pollution reduction.

The penetration of clean energies to overcome climate change has grown.

1.4 The arrangement of the presented paper

The rest of the paper is organized as follows: Point Aconi’s power plant details and the site’s regional climate and characteristics are explained in parts 2 and 3, respectively. Section  4 expresses the equations, equipment selection, and proposed model of the renewable-thermal power plant. Section  5 dedicates to the numerical results, discussion, and diverse analyses, and eventually, in Sect.  6 , the conclusion is brought.

2 Point Aconi power plant specifications

Nova Scotia is a thin peninsula on Canada’s southeast coast, and its maximum distance from the sea is 34 miles. The Point Aconi Generating Station (shown in Fig.  1 ), which burns a mix of coal and petroleum coke, is placed on the shores of the Cabot Strait at the northeastern tip of Boularderie Island (Latitude: 46.32, Longitude: − 60.32). PAGS started work four years after construction in January 1990, and it is the largest generating unit in Nova Scotia and Cape Breton province. PAGS has one 171 MW unit (nameplate capacity) and uses a circulating fluidized bed (CFB), which Finland’s Ahlstrom Pyropower drew to reduce emissions of NO x and SO 2 [ 56 ]. PAGS’s boiler was the world’s largest CFB plant and the first in North America [ 57 ]. PAGS burns 22 tons of bituminous coal and 25 tons of petroleum coke per hour, and the drum-type reheat of the CFB boiler has been designed to provide 1,207,150 lb/hr of steam (maximum continuous rating) at 1800 psi and 1000 F [ 58 ]. Also, it accounts for approximately 15% of total coal power plants in the province and emits 1,188,377 tons of CO 2 (roughly 17% of the total emission of Nova Scotia), according to the 2017 annual report [ 59 ].

Location and general outlook of Point Aconi’s thermal power plant

3 Regional climate

3.1 temperature and climate conditions.

According to meteoblue’s latest 30-year data, January and February have the lowest temperatures of the year; in return, June and August have the highest temperatures. The average annual temperature variation is shown in Fig.  2 [ 60 ].

The average annual temperature variation

According to Solar Atlas’s Normal irradiation data, the average vertical radiation of about 7 months a year is approximately the same, but the difference is in the number of effective daily hours. It should be noted that hours with an average irradiation of less than 80 Wh/m 2 have been ignored when designing solar power plants for the proposed study. The average direct normal irradiation and the number of days’ efficient hours in different months are shown in Fig.  3 [ 61 ].

Average direct normal radiation and day’s efficient radiation hours of different months

3.2 Wind conditions

Based on the Global Wind Atlas’s information, wind speed variation over the month each day changes between 6.3 and 10.5 m/s. The change rate for each month is almost the same, as shown in Fig.  4 [ 62 ]. Moreover, hourly wind speed data in 2019 show that wind speeds are higher at higher altitudes and at night, which helps to generate more power.

Average hourly wind speed for each month

3.3 Bird migration

Despite the development in utilizing wind energy, the side effects of wind turbines should be noted. In other words, one of the issues in the planning that should be included in the design of wind farms concerns the birds’ migration and the period of the wind turbine’s blackout. According to bird life data, there are nine major routes of intercontinental bird migrations. The closest to the Point Aconi site is the Atlantic American Flyway, which is almost the appropriate distance from the study area [ 63 ], as illustrated in Fig.  5 . Special attention has been paid to details to obtain valid results. In other words, it can generate continuous power throughout the year and does not require the compulsory shutdown of wind turbines.

Nine major flyways of migratory birds

4 Main equipment selection

According to the climate and local potentials, panels and wind turbines are selected to provide the required power for the inlet fuel, substituting the thermal power plant. Figure  6 shows the proposed model of the combined power plant. Critical characteristics and features of the selected panels and turbines are shown in Table  1 .

The assumed model for the renewable-based thermal power plant

The panel’s output power depends on various parameters such as ambient temperature, cell temperature, humidity, and irradiation level. Equations ( 1 ) and ( 2 ) are utilized to calculate the output power of PV panels [ 30 , 64 ].

\({P}_{\text{PV}.\text{STC}}, {G}_{T,\text{STC}}, \gamma , {N}_{\text{PV}s}, {N}_{\text{PV}p}, {T}_{\text{amp}}\) , and NOCT are maximum test power in STC (standard test conditions), solar radiation in STC (standard test conditions) (kW/m 2 ), power-temperature coefficient, the numeral of series cells in the PV module, the numeral of parallel cells in PV module, environmental temperature (°C), and normal operating cell temperature (°C), respectively. The wind turbine output power is calculated based on Vestas’s standard chart, as illustrated in Fig.  7 .

Annual energy production of wind turbines based on average wind speed

5 Numerical results evaluation and discussion

This section is divided into various subsections in which numerical results evaluation and many analyzing approaches related to the research study are explained.

5.1 The numerical results analyzing of the proposed study

Given that one of the purposes of this paper is to replace the fossil fuels needed by thermal power plants; initially, the amount of energy input of PAGS has been calculated by using heating values of 22 tons of bituminous coal and 25 tons of petroleum coke per hour, as are shown in Table  2 .

According to the total inlet power calculated based on Table  2 , two optimal combined solar and wind power plants with a minimal actual output power of 405 MW have been designed. According to Table  3 , the output power of the panels for three temperature ranges of the mean daily maximum and the mean daily minimum temperature based on Eqs. ( 1 ) and ( 2 ) have been computed and expressed. Figure  8 shows that May, June, and April have had the most monthly energy production because of more daily hours with more effective radiation levels.

Panel’s monthly energy production (kWh)

As shown in Fig.  9 and according to the 2019 temperature data of the Point Aconi region, the probability of mean daily minimum temperature occurrence has been much higher than the probability of mean daily maximum temperature. Therefore, the proposed model design has been considered based on the worst-case scenario to ensure that the appropriate design of the solar power plant has been done. In other words, other scenarios could generate more energy than expected.

Probability of most temperature scenarios occurrence

The amount of energy produced has been calculated according to the turbine selection (Table  2 ) and wind characteristics, which are shown in Fig.  10 . Calculations have illustrated that wind energy generation is higher in cold seasons and at night, which can compensate for the shortage of energy production by solar power plants during these seasons. Figure  11 depicts that the probability of appropriate wind conditions and speeds exceeding 7.5 (m/s) during the day was about 16% in August. However, based on Fig.  10 , the minimum wind speed was about 6.3 (m/s) during the year, and in August, the performance of solar energy generation was acceptable, which compensated for the shortage of wind energy generation.

Monthly energy production (GWh) based on average wind speed

Probability of most wind speed occurrence (more than 7.5 m/s) during the day

After determining the environmental information and selecting the main and proper equipment, the optimal dimensions of the combined power plant, the total cost, and the fuel-substituting effects of the power plant by renewable energies on environmental pollution reduction have been analyzed. The cost function of the new hybrid-thermal power plant has been defined by (3).

where \({\lambda }_{1}\) and \({\lambda }_{2}\) are the total installation cost of solar and wind energy, respectively, and \({W}_{\text{name} \text{plate}.S}\) and \({W}_{\text{name} \text{plate}.W}\) are the designed nameplate capacity of solar and wind power plants. The price variations of bituminous coal and petroleum coke are shown in Fig.  12 . Analysis of long-time variations in the price of petroleum coke and coal fuels has demonstrated that these constantly change according to global conditions; however, these do not fall from their overall average price. The total installation costs have been based on reported data from the International Renewable Energy Agency (IRENA), which have been considered for determining the total cost in this paper (Table  4 ). As shown in Table  4 , the reserve margin for increasing the system’s overall reliability has been considered in the design of the proposed renewable-thermal power plant. The reserve margin has been assumed to be about 10% (405 + 40 MW).

Coal and petroleum coke price variations in recent years [ 65 , 66 ]

According to Fig.  12 , coal and petroleum coke combustion costs are 60 $/ton and 220 $/ton, respectively. This means that the total cost could be around 60 million dollars per year for the consumption of fossil fuels for the PAGS. In other words, over a period of about 20 years, it can compensate for the construction cost of its renewable power plants just by removing fossil fuels. Moreover, eliminating or reducing the fossil fuels needed by thermal power plants (especially coal-fired plants) can help to reduce greenhouse gas emissions and advance the Paris Agreement in 2016. Furthermore, Fig.  13 shows the results and positive impacts of substituting inlet fuels (with renewable resources) to reduce GHG emissions of the proposed PAGS.

The thermal power plant’s total emission of various pollutants [ 67 ]

Regarding the efficiency of the proposed model and based on all explanations and evaluations, the total efficiency of renewable energy resources has always been high. At least during the last decade, moral progress has been made. In other words, the technologies utilized all these years ensure good yield (wind and solar). In return, fossil fuel-based units have not been very efficient. The critical and significant problem that should not be ignored is their emissions and their consequences (lung and cardiac issues, the destruction of construction structures over time due to acid rainfall, global warming, climate change, and much worse problems). Some of the reasons that fossil fuels are still used have been their lower price, the lack of investors’ attention (for investing in RERs), and the insignificance of environmental issues in governments’ thoughts and public beliefs.

In conclusion, the technologies utilized for RERs during these years have made significant progress, and therefore, the efficiency of RERs, especially the common ones (solar, wind), has been acceptable; however, it can always be better. Furthermore, when each wind, solar, and electric boiler’s efficiency has been proper, their combination can also be appropriate and more efficient than fossil fuel-based units. In other words, an efficient system can be achieved if the planners choose the most efficient technology for each of them, considering the geographical, environmental, and economic conditions.

5.2 Life cycle assessment explanation and analysis (LCA)

The life cycle assessment (LCA) is a technique used to assess the environmental impact of a model, product, or service throughout its entire life cycle. It looks at the whole system, from the initial raw material extraction and manufacturing to the use, repair, maintenance, final disposal, or recycling. LCA is a holistic approach that quantifies the environmental impact of a model, product, or service. The LCA process is divided into four key steps:

Goal and Scope Definition: This step involves defining the purpose and boundaries of the assessment. This includes selecting the functional unit, the product system, the life cycle stages, and the impact categories to be included in the assessment.

Inventory Analysis: This step involves collecting data about the product system, such as the energy and raw materials consumed, the air, water, and solid waste generated, and the emissions released into the environment.

Impact Assessment: This step includes assessing the environmental impact of the product system. This involves quantifying emissions and resource use, as well as the evaluation of effects on human health and the environment.

Interpretation: This step involves interpreting the results of the assessment. This includes evaluating the environmental impact and identifying opportunities for improving the environmental performance of the product system.

As mentioned, life cycle thinking is an important concept in sustainability. It encourages considering the entire life cycle of a system or model, from raw material extraction to disposal, when assessing its environmental and social impacts; this allows for a better understanding of the potential impacts of a proposed model or service and making informed decisions that are environmentally and socially responsible. Life cycle thinking helps to consider the full scope of impacts associated with actions and to develop strategies that reduce the environmental and social impacts of the models and services. The potential benefits of conducting an LCA include identifying opportunities for reducing environmental impacts, reporting model design decisions, improving environmental performance, and increasing public awareness of environmental issues. LCA can also support environmental policies and regulations and provide evidence for environmental claims. Therefore, by looking at the proposed model and numerical results, based on the mentioned explanation, it can be understood that the impacts of exerting such a system positively affect the environmental aspects; therefore, it has avoided emitting about 165 tons of CO 2 and other pollutants. Figure  14 demonstrates the LCA’s different steps and their relations.

The different steps of LCA and the relations between them

5.3 The return on investment explanations and analysis (ROI)

Return on investment (ROI) measures the profit or loss generated from an investment. It is calculated by taking the total net income from the investment and dividing it by the total cost of the investment (Fig.  15 ). The resulting figure is expressed as a percentage, which is the ROI. ROI is a valuable metric for assessing the profitability and efficiency of an investment. It can be used to compare investments and determine the potential return on a proposed investment. According to the numerical results, the cost of burning coal and petroleum coke per year for the PAGS has been estimated to be around 60 million dollars. Over the period of 20 years, this cost could be used to offset the construction costs of renewable power plants by substituting fossil fuels with renewable energy sources. The higher the return on investment, the more profitable the investment.

The formulation of the return on investment (ROI)

5.4 The energy analysis and definition

Energy analysis is understanding how energy is used in a system (proposed model), typically focusing on energy flow into, through, and out of the system. The analysis involves understanding the sources of energy, how energy is utilized, and how energy is wasted. The analysis results can be used to identify improvement opportunities and assess the system’s environmental impact. Energy analysis is an important tool for reducing operational costs and promoting sustainability. Since the presented model has utilized RERs and electric boilers for its purposes, and the operating and maintenance costs have been lower, it could demonstrate positive results if the energy analysis is performed. In other words, as the whole system’s efficiency has enhanced compared to the conventional unit, the energy analysis results can also be satisfying by considering this type of modeling.

5.5 General pros and cons of this approach

Using electric boilers supplied by renewable energy resources in a thermal power plant in Nova Scotia, Canada, instead of burning coal and oil, has several advantages and disadvantages. From a positive point of view,

Environmental benefits: By using renewable energy sources to power electric boilers, the thermal power plant can significantly reduce its carbon footprint and greenhouse gas emissions. This can help combat climate change and improve air quality in the region.

Energy efficiency: Electric boilers are generally more efficient than traditional coal or oil-fired boilers, leading to lower energy consumption and operating costs.

Renewable energy integration: By incorporating renewable energy sources into the power generation process, the thermal power plant can contribute to the growth of the clean energy sector and support sustainable development.

Energy security: Using electric boilers powered by renewable energy sources can enhance energy security by reducing dependence on fossil fuels that are subject to price fluctuations and supply disruptions. On the other side, it also has limitations and constraints that could impact this type of planning; in other words,

Cost implications: The initial investment required to install electric boilers and renewable energy infrastructure may be higher compared to traditional coal or oil-fired boilers. This could potentially increase electricity prices for consumers.

Reliability concerns: Renewable energy sources such as wind and solar power are intermittent, which could lead to fluctuations in electricity generation and potential reliability issues for the thermal power plant.

Grid integration challenges: Integrating a large amount of renewable energy into the grid can pose technical challenges related to grid stability, voltage control, and frequency regulation.

Limited capacity: The capacity of electric boilers powered by renewable energy sources may be limited compared to traditional coal or oil-fired boilers, which could impact the overall output of the thermal power plant.

Briefly, while using electric boilers supplied by renewable energy resources in a thermal power plant in Nova Scotia has several environmental and economic benefits, there are also challenges related to cost, reliability, grid integration, and capacity limitations that need to be carefully considered before implementing this approach. Policymakers and stakeholders need to weigh these pros and cons carefully when making decisions about transitioning toward cleaner energy solutions in the region.

5.6 Inertia in the power system by this approach

In the context of a thermal power plant transitioning from burning coal and oil to using electric boilers powered by renewable energy resources in Nova Scotia, Canada, it is essential to understand the concept of inertia in the power system. Inertia in a power system refers to its ability to maintain stable operation in the face of disturbances, such as sudden changes in load or generation. Traditional thermal power plants, like those using coal and oil, inherently possess a significant amount of inertia due to the rotating mass of turbines and generators. This inertia helps stabilize the system by resisting sudden changes in frequency caused by fluctuations in supply or demand. However, when transitioning to electric boilers powered by renewable energy sources like wind or solar, the inertia of the system can be affected. A decrease in system inertia can impact the stability of the power grid, as it may become more susceptible to frequency deviations caused by sudden changes in generation or load. Without sufficient inertia, the grid may experience frequency fluctuations that can lead to instability or even blackouts if not properly managed. In order to address this challenge, grid operators must implement measures to enhance the inertia of the system. This can include integrating energy storage systems or utilizing advanced control algorithms to mimic the stabilizing effects of inertia. Moreover, maintaining a diverse mix of generation sources, including those with high levels of inertia, can help ensure grid stability during the transition to renewable energy. Overall, while the transition to electric boilers powered by renewable energy resources offers significant environmental benefits, it is crucial to consider the impact on system inertia and implement appropriate measures to maintain grid stability.

5.7 Power system infrastructure and expansion planning

Transitioning to electric boilers supplied by renewable energy resources would require significant upgrades to the existing power system infrastructure. Firstly, the transmission and distribution network would need to be expanded and upgraded to accommodate the increased demand for electricity from the electric boilers. This may involve building new transmission lines, substations, and distribution networks to ensure that the electricity generated from renewable sources can be efficiently delivered to the thermal power plant. In terms of handling a large number of solar panels and wind turbines, the power system would need to be equipped with advanced grid management technologies such as smart meters, energy storage systems, and demand response programs. These technologies can help balance the intermittent nature of renewable energy sources and ensure a reliable supply of electricity to the thermal power plant. The impact on the whole power system would be significant but ultimately positive. By transitioning to electric boilers supplied by renewable energy resources, the thermal power plant would reduce its carbon emissions and reliance on fossil fuels. This would help Nova Scotia meet its climate goals and contribute to a more sustainable energy future.

6 Conclusions

In this paper, we have presented a new approach to using local renewable energy to substitute the inlet fossil fuels of thermal power plants. The case study is Point Aconi’s power plant in Canada, which has a capacity of 171 MW, and its consumption fuel is bituminous coal and petroleum coke. PAGS was the first CFB thermal power plant in the region and North America, accounting for about 15% of the region’s coal-fired power plant. The renewable-thermal power plant that we designed based on renewable sources’ local potential and the combination of wind and solar power plants to increase the model’s reliability, which helped compensate for each power plant’s shortage at specific times. The renewable-thermal power plant reduced fossil fuel emissions; therefore, about 1.45 million tons of environmental pollutants can be removed yearly (eliminating almost 165 tons of CO 2 and other pollutants per hour). Also, over nearly 20 years, it could compensate for the construction cost of its renewable power plants. In other words, the return on investment was reasonably acceptable compared to the amount of pollution reduction. The plan presented in this study prevents the closure of newly established thermal power plants due to laws and prohibitions on the continued release of environmental pollutants. The superiority of renewable-thermal power plants over biomass power plants is to release lower pollution and the lack of harmful chemicals due to burning various materials and preventing deforestation. Due to the use of electric boilers in the renewable-thermal power plant, the boiler’s efficiency could be much higher than that of the standard boiler, which was close to 98%. Electric heating could be steadier than combustion, which caused the production of better quality steam and reduced the moisture content of the steam; as a result, lower maintenance costs were achieved, and less damage was done to the turbine’s blades.

Abbreviations

Condition monitoring system

Carbon dioxide

Circulating fluidized bed

Distributed energy resources

Distributed generation

Genetic algorithm

Greenhouse GAS

International Renewable Energy Agency

International Energy Agency

Life cycle assessment

Normal operating cell temperature

Nitrogen oxide

Polyvinyl chloride

Point Aconi Generating Station

Photovoltaic

Return on investment

Renewable energy resources

Standard test condition

Sulfur dioxide

Silicon nanowires

Supervisory control and data acquisition

Wind turbine

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Wind power and solar photovoltaics found to have higher energy returns than fossil fuels

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A key issue in net energy analysis is the omission of the effects of end-use efficiencies on the energy returns of technologies. Now, an analysis shows that these effects strongly favour the energy returns of wind power and solar photovoltaics, which are found to be higher than those of fossil fuels.

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Raugei, M. & Leccisi, E. A comprehensive assessment of the energy performance of electricity generation technologies deployed in the United Kingdom. Energy Policy 90 , 46–59 (2020). This article presents a net energy analysis of electricity generation technologies.

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