Energy,exergy,economic,environmental,advanced exergy and exergoeconomic (extended exergy) analysis of hybrid wind-solar power plant

Abstract

Aiming to net-zero emissions, hybrid power generation through renewable means has gained substantial attention across the globe. Considering the stochastic nature of renewable energy resources, a comprehensive performance assessment is a must prior to project development. Present work is a novel multidimensional 6E analysis (energy, exergy, economic, environmental, advanced exergy, and exergoeconomic) to evaluate the performance of hybrid wind-solar energy systems. The analysis is performed using long-tern (41 years) high-resolution ERA5 reanalysis resource data and the mathematical modeling by means of MATLAB R2018a computation software. The long-term data facilitates reliable and precise predictions of resource availability, power generation, and system performance during the lifespan of the project. The performance of HWSES in terms of capacity factor and exergy efficiency is computed to be 9.6–35.5% and 4.7–10.4% respectively, whereas the extended exergy efficiency lies in the range of 3.39–5.79%. Hybridizing wind power projects with solar power enhances the overall system capacity factor, exergy efficiency, and extended exergy efficiency by 3.46%, 5.12%, and 2.87% respectively. Hence, the hybridization leads to superior year-round system performance with smaller power fluctuations than the standalone systems. Further, wind, solar and hybrid systems would annually reduce the Specific Emission Reduction of 1128 tone/kW, 1685 tone/kW, and 1407tone/kW respectively. The present research will be helpful to the policy-makers and the project developers in the project feasibility study of hybrid energy systems.

Keywords

Exergy advanced exergy exergoeconomic wind energy solar energy hybrid energy

Necessity and status of hybrid energy

There has been a significant rise in renewable energy (RE) based power generation due to the apparent disadvantages of conventional power generation systems (depletion of fossil fuels and environmental pollution etc.). India achieved the power generation of 114 TWh in 2019–20 through wind and solar energy sources (64.64 TWh and 50.10 TWh from wind and solar power, which is 4.7% and 3.6% of total power generation respectively).^1,2 The political agreement of the COP21 (conference of Parties) summit³ and the national target of 175 GW by the year 2022⁴ are the key driving forces for RE deployment in India. Wind and solar energy sources are stochastic by nature and exhibit fluctuations as a function of time.⁵ To rectify such fluctuations and harness, both these energy sources are integrated and developed as hybrid wind-solar energy systems (HWSES) across the globe.^6,7 HWSES is an efficient solution for reducing the power generation fluctuations, smoothing the aggregate output power, and reducing the overall cost of the system by utilizing the same infrastructure).^8,9 The salient features of HWSES across the globe are tabulated in Table 1. Therefore, the performance of the hybrid system has to be assessed exhaustively to ensure the suitability and reliability of the project.^10,11 The hybrid systems include wind integrated ammonia and power generation system,¹² wind and solar-thermal integrated hydrogen generation system,¹³ wind-solar and osmotic power system,¹⁴ and hybrid fuel cell-PV-based electricity and H₂ generation system.¹⁵ Based on the comparative performance evaluation, it can be deduced that the wind-solar PV systems show better performance in terms of energy and exergy than that of wind-solar thermal systems.

Table 1.

Hybrid wind-solar power plants across the globe (only mw level plants are included).

Country	Type	Company	Capacity (MW)				Status
Country	Type	Company	Total	Wind	Solar	Battery	Status
India³²	Wind-solar	SB Energy, Adani Green Energy and ReNew Power	2400	No info	No info	No info	Auction completed
India³³	Wind-solar	Premier Mills	2.2	2	0.2	0	Operational since 2007
India	Wind-solar	Hero Future Energies / Siemens Gamesa	78.8	50	28.8	0	Operational
India	Wind-solar-storage	Solar Energy Corporation	No info	No info	No info	No info	Approved
India	Wind-solar-storage	L&FS Energy Development Company Limited – GE	51	16	25	10	Contracted
China	Wind-solar-storage	Luneng Haixi	No Info	400	250	No Info (100 MWh)	Approved
United States	Wind-solar-storage	GE	2.5	2	0.5	0	Operational
United States	Wind-solar-storage	NextEra Energy Resource & Portland general Electric Co.	380	300	50	30 (120 MWh)	Contracted
United Kingdom	Wind-solar	Vattenfall	8.98	4	4.95	0	Operational since 2017
Netherlands	Wind-solar-storage	Vattenfall	65	21	32	12	Under construction (commissioned in 2020)
Spain	Wind-solar	Acciona energia	3.1232	2	1.1232	0	Operational since 2019
Spain	Wind-solar-storage	Siemens Gamesa	1.674	1	0.245	0.429 (0.5 MWh)	Operational since 2016
Portugal	Wind-solar-storage	Younicos	12	5	1	6 (3.2 MWh)	Operational since 2016
Australia	Wind-solar	Apa Group	147.5	130	17.5	0	Under development
Greece	Wind-solar	Vestas / Terna Energy	25	24	1	0	Operational since 2012
Greece	Wind-solar-storage	Aiolian land	3.3	1	0.86	1.44 (7.2 MWh)	Under licensing (commissioned in 2018)
Greece	Wind-solar-storage	Research consortium	1.24	1	0.16	0.8 (2.4 MWh)	Under licensing (commissioned in 2017)
Greece	Wind-solar-storage	Aiolian land	1.821	1	0.101	0.72 (3.6 MWh)	Under development
Brazil	Wind-solar	Iberdrola	27	22	5	0	Operational since 2014
Brazil	Wind-solar	Enel Green Power	91	80	11	0	Operational since2015
Chile	Wind-solar-storage	Enel	0.455	0	0.205	0.25 (0.8 MWh)	Operational

To evaluate the overall performance of a hybrid energy system, multidimensional assessment which includes thermodynamic,¹⁶ economic,^17,18 and environmental¹⁹ analyzes are essential in the decision-making process. The thermodynamic analysis consists of combined energy and exergy analyzes.²⁰ The conventional energy analysis is conducted based on the first law of thermodynamics using conservation of mass and energy, whereas the exergy analysis is conducted based on the second law of thermodynamics to pinpoint the location of losses.²¹ The economic analysis is performed in terms of overall system and power generation costs considering financial parameters (tax, discount rate, annual escalation, etc.).²² The environmental analysis presents the amount of reduction in CO₂ emission by employing power generation through renewable energy sources.²³ These multidimensional analyzes used for evaluating the performance of energy systems namely, energy, exergy, economic and environmental analyzes are conjointly termed as 4E analyzes.^24,25 Such analyzes have been performed on conventional energy systems like gas turbine and solar-thermal systems as well.^26–28 In addition to the conventional 4E analyzes, the advanced exergy and exergoeconomic (extended exergy) analyzes are important to gain further insight into system performance¹⁶ and these are conjointly referred as 6E analyzes. The advanced exergy analysis is important for additional understanding of the type (endogenous and exogenous) and regime (avoidable and unavoidable) of exergy destruction (ExD), unlike conventional exergy analysis. The exergoeconomic analysis incorporates the economic inputs to the systems along with the exergy inputs, which gives a broader overview of the system's performance.

The hybrid systems that include wind integrated ammonia and power generation system,¹² wind and solar-thermal integrated hydrogen generation system,¹³ wind-solar and osmotic power system,¹⁴ and hybrid fuel cell-PV-based electricity and H₂ generation system¹⁵ are summarized in Table 2. Based on the comparative performance evaluation, it can be deduced that the wind-solar PV systems show better performance in terms of energy and exergy than that of wind-solar thermal systems.

Table 2.

Literature survey on different types of analyzes conducted on standalone and hybrid wind and solar energy systems for different regions using diverse meteorological data.

Author	Region / Location	Renewable energy system	Type of Analysis				Additional Tool or technique used	Resource data used	Outcomes of the study
Author	Region / Location	Renewable energy system	Energy	Exergy	Economic	Environmental	Additional Tool or technique used	Resource data used	Outcomes of the study
²³	Two point locations (Tangier and Dakhla, cities of Morocco)	Wind turbine	Yes	Yes	Yes	Yes	TRNSYS for air density and specific humidity calculation	Hourly data of one year (Typical metrological year (TMY) of the synthetic weather data series)	Sustainability index (SI) of 1.62 and 1.74 at study locations
¹⁶	Two point locations (Tehran and Manjil, cities of Iran)	Wind turbine	Yes	Conventional, extended and advanced	Yes	No	-	Monthly averages of one-year data recorded by meteorological stations	Windy areas have higher energy efficiency (88.7%) and lower exergy efficiency (58.9%)
³⁴	Single point location (Turkey)	Solar PV + thermal	Yes	Yes	Yes	Yes	-	Experimentally measures data using solarimeter	Mean thermal and electrical efficiencies of 43.7% and 15.0% respectively
²⁹	Region (30 cities of Iran)	Simulation-based grid-connected PV system	No	Conventional and extended	No	No	PVsyst software (for simulation) and Meteonorm Software (for resource data)	Monthly mean data of January, April, July, and October months of a single year obtained from Meteonorm software	Exergy and extended exergy efficiencies of 13.6-14.8% and 9.5-10.5% respectively
³⁵	Point location, (Hakkari, Turkey)	Solar energy	Yes	Yes	No	No	Gaussian Process Regression (GPR) modeling and sensitivity analysis	Monthly mean meteorological data of 10 years (2002-2011)	Correlation of determination (R²) of 0.97 for estimation of total solar radiation by GPR model
¹²	Single point location (Ontario, Canada)	Wind and solar integrated with ammonia and power production system	Yes	Yes	No	No	Engineering Equation Solver (EES), System Advisor Model (SAM), Eureqa and Aspen Plus software	No actual resource data used. The model simulated using SAM software	Max. overall energy and exergy efficiencies of 75.8% and 73.6% respectively
¹⁴	-	Wind, solar and osmotic power system	Yes	Yes	No	No	EES	No resource data input. Model simulated data used for the study	overall energy and exergy efficiencies of 73.3% and 30.6% respectively
¹⁵	Point location (Tehran, Iran)	Hybrid fuel cell-PV for electricity and H₂ generation	Yes	Conventional and advanced	Yes	No	MATLAB	Monthly average of one-year data (calculated using the theoretical model)	Overall energy and exergy efficiencies of 20.4% and 21.8% respectively
¹⁷	Point location (Iran)	Hybrid wind-solar-gas generator system with battery storage	No	No	Yes	Yes	HOMER simulation tool	Hourly mean solar radiation data and 3-h interval wind data	Reduction in CO₂ emissions by 19% due to RE systems, leading to lower penalty costs
¹⁸	Point location (Brazil)	Hybrid wind-solar	Yes	No	Yes	No	Monte-Carlo Simulation (MCS)	MERRA reanalysis data (wind) (1979-2018) and HelioClim-1 reanalysis model (solar radiation) (1997-2018)	HWSES reduces the probability of profitability (98% at the standalone wind and 0% at 40:60 wind-solar proportion)
¹⁹	25 point locations (Republic of Chad)	Grid-wind-solar power generation system with battery storage	No	No	Yes	Yes	HOMER simulation tool	20-year annual average wind and solar data (HOMER connected NASA database)	Lowest observed CO₂ emission for Grid-wind-solar and wind-solar is 1109 and 63.1 kg/year
³⁰	Point location (Universidad del Magdalena, Colombia)	Grid-wind-solar-diesel generator system with battery storage	No	No	Yes	Yes	HOMER simulation tool	Monthly mean wind and solar data of one year (HOMER connected NASA database)	The grid-connected hybrid system give payback in 4 years and 7 months
¹³	Single point location (Toronto, Canada)	Wind, solar-thermal integrated hydrogen generation system	Yes	Yes	No	No	Aspen Plus 9.0 and EES	No resource data input. Model simulated data used for the study	overall energy and exergy efficiencies of 49.0% and 48.2% respectively
³⁶	Point location (Bushehr, Iran)	Hybrid wind-solar energy system with hydrogen storage	Yes	Yes	Yes	No	-	Daily mean data of 2016 (calculated using a theoretical model)	Energy and exergy efficiencies of 12% and 16% for PV, and 32% and 25% for wind turbine system respectively
³⁷	-	Hybrid wind- solar-gas turbine heating- cooling-power system	Yes	Yes	No	No	-	No resource data input i.e. parametric study	The exergy efficiency of 45% and 12.6% for wind turbine and solar-PV respectively

Recent research studies reported on standalone wind, solar and hybrid energy systems with multidimensional analyzes have been summarized in Table 2. There are very limited studies reported on standalone wind and solar photovoltaic (PV) systems using 4E analyzes. Allouhi²³ conducted the 4E analyzes using the TRNSYS software tool for a horizontal axis wind turbine (HAWT) of 800 kW rated capacity located in two cities of Morocco and evaluated the Sustainability Index (SI) ranging from 1.62 to 1.74. Ehyaei et al.¹⁶ performed advanced and extended exergy analyzes in addition to the conventional energy, exergy, and economic analyzes for a 10 kW HAWT using monthly mean measured wind data of one year for two cities of Iran. The study concluded that windy regions have observed higher energy efficiency (88.7%) and lower exergy efficiency (58.9%). In a study on standalone SES (grid-connected PV system), it was presented that the framework of exergy analysis can be applied to other standalone and hybrid systems.²⁹

There are a few studies conducted which include only energy, economic and environmental analyzes for wind-solar-gas generator system,¹⁷ wind-solar energy system,¹⁸ grid-wind-solar power system,¹⁹ and grid-wind-solar-diesel energy system.³⁰ Jahangiri et al.¹⁹ simulated standalone and grid-connected wind-solar energy systems using twenty-year average resource data and found that the lowest possible CO₂ emission reduction for the prescribed energy systems are 63.1 and 1109 kg/year respectively. Oliveros-Cano et al.³⁰ utilized monthly mean data of one year for grid-connected wind-solar-diesel energy systems and reported a payback period of 4 years and 7 months. Saheli et al.¹⁷ analyzed the economic and environmental performance of wind-solar-gas generator systems and concluded that the addition of RE systems (wind-solar) to the gas generator system reduces CO₂ emission by 19.0%.

Research gaps, objectives and scope

From the literature survey, it is observed that a multidimensional analysis has not been applied to analyze and evaluate the performance of a grid connected HWSES. Till date, the extended exergy analysis has only been conducted on an overall basis (single efficiency for the entire duration of the study) rather than the instantaneous basis for standalone as well as hybrid systems.¹⁶ The overall basis merely indicates a single value of exergy efficiency for the entire life span of the project, whereas the instantaneous presentation shows the variation of performance parameters with time on the annual and monthly time scale, which is desirable for thorough analysis. Moreover, the recent studies^16,23 have used the wind resource data for a very short duration (single year only) which may not be reliable while designing a long-term renewable energy (RE) power plant having an operational life for at least two decades. Moreover, as per the literature survey, the integrated multidimensional 6E analysis consisting of advanced exergy and exergoeconomic analyzes have not been performed on instantaneous basis till date for HWSES. Furthermore, the impact of government policies on the economics of the power projects which leads to lower system costs³¹ has not been evaluated so far for the hybrid energy system.

In order to bridge the research gap, the objective of the present study is to apply instantaneous multidimensional 6E analysis (energy, exergy, economic, environmental, advance exergy and exergoeconomic (extended exergy)) on a 5.6 MW HWSES having equal wind and solar installation capacity (2.8 MW each for wind turbine and solar PV) (Figure 1). Long-term meteorological data (ERA5 reanalysis data with 0.5° × 0.5° spatial and 6 h temporal resolution) of wind and solar energy resources of the last 41 years (1979–2019) are used for the study. Further, the addition feature of this study is that economic analysis is performed based on the existing government policy benefits while computing the overall system cost Such long-term multidimensional analysis will be beneficial in assessing the overall impact of 6E factors on the performance of HWSES (Figure 2). Such comprehensive and holistic approach used to assess hybrid energy systems is the main novelty and the development of hybrid system modeling and the corresponding performance evaluation have been reported for the first time.

The authors were inspired to explore this research because it is relevant in a few ways. These findings will bolster theoretical support for the use of hybrid systems for electricity generation across the world. This specific study for India is original in terms of an exhaustive performance evaluation. The methods utilized in this study can be universally adopted to analyze other locations throughout the world, allowing for comparison and comprehension of HWSES system performance across various climatic and geographical zones. Other areas and climates can be benefitted from the defined technological principles and decision-making framework. This study offers an indication of the progress that how widely HWSES will be commercialized and implemented in the near future. As a consequence, energy experts and scientific readers throughout the world will be benefitted from the findings of this research. The methodology and results of the present study will be advantageous to the project developers and investors to ensure the successful deployment and operation of the RE project throughout its lifespan.

Mathematical model

The mathematical model used for the [resent study has been depicted in Figure 3 and the corresponding formulation is tabulated in Table 3. The study has been conducted for a location situated at the coast of Gujarat, India (20.75° N, 71.25°E), which has been identified as a suitable location for the establishment of a standalone wind power project by the National Institute of Wind Energy (NIWE).³⁸ The resource data of wind and solar energy resources for the last 41 years duration (1979–2019) is taken from the ERA5 reanalysis dataset.

Energy assessment (1e)

The extrapolated WS (u in m/s) is utilized to calculate the wind power output (P_W in kW) using the expression developed by polynomial curve fitting of power curve given by the manufacturer of selected wind turbine SUZLON-S128 (system data given in Table 4 and formulation in Table 3). Further, photovoltaic (PV) power generation is dependent on a number of variables and may be described as shown in Table 3. The power generated through photovoltaic (PV) depends on multiple parameters and can be modeled from the data given in Table 5 and formulatins given in Table 3. The solar irradiation at standard test conditions (H_STC), which is 1000 W/m².³⁹ P_S,r is the rated power of the PV system (W). c₁ and c₂ are the derating coefficient and power temperature coefficient of PV panels respectively. T_S and T_STC are the temperatures of the PV panel in working and standard test conditions (K). T_STC is taken as 298 K. The expression for T_S can be given in Table 3.³⁹ T_s,TETC and T_a,TETC are the temperature of PV panel and ambient temperature in temperature estimation test conditions and taken as 320 K and 293 K respectively.³⁹ H_TETC is solar radiation at the temperature estimation test conditions which is 800 W/m².³⁹ Finally, the total energy intake and outflow for the system are used to calculate the energy efficiency and CF of HWSES.

Table 3.

Mathematical modeling for energy and exergy analyzes.

Analysis	Parameter	Wind	Solar PV	Hybrid wind-solar PV
1E – Energy	Power	$P_{W} = {\begin{matrix} 0 & (u < u_{c i}) \\ - 496.9 \cdot u^{5} + 11493 \cdot u^{4} - 94363 \cdot u^{3} + 372336 \cdot u^{2} - 539386 \cdot u + 3 & (u_{c i} \leq u \leq u_{r}) \\ P_{W, r} & (u_{r} \leq u \leq u_{c o}) \\ 0 & (u > u_{c o}) \end{matrix}$	$\begin{aligned} P_{S} & = \frac{H_{s}}{H_{S T C}} \cdot P_{S, r} \cdot c_{1} \cdot [1 + c_{2} (T_{s} - T_{S T C})] \\ T s & = T_{a} + H_{s} \cdot \frac{T_{s, T E T C} - T_{a, T E T C}}{H_{T E T C}} \end{aligned}$	$P_{H} = P_{W} + P_{S}$
	Capacity Factor	$C F_{W} = \frac{P_{W}}{P_{W, r}}$	$C F_{S} = \frac{P_{S}}{P_{S, r}}$	$C F_{H} = \frac{P_{W} + P_{S}}{P_{W, r} + P_{S, r}}$
	Energy Efficiency	$η_{W} = C_{P} = \frac{P_{w i n d}}{P_{k i n e t i c}} = \frac{P_{W}}{\frac{1}{2} ρ A_{T} u^{3}}$	$η_{S} = \frac{P_{S}}{H_{S} \times A_{P V}}$	$η_{H} = \frac{P_{W} + P_{S}}{P_{k i n e t i c} + (H_{S} \cdot A_{P V})}$
2E - Exergy	Specific Exergies	$e_{} = e_{K} + e_{P} + e_{P h} + e_{C h}$	-	-
		$e_{K i} = \frac{1}{2} u^{2}$ and $e_{K o} = \frac{1}{2} (a \cdot u)^{2}$
		$e_{P h} = (c_{p a} + ω \cdot c_{p v}) \cdot T_{0} [\frac{T}{T_{0}} - 1 - \ln (\frac{T}{T_{0}})] + (1 + 1.6078 \cdot ω) \cdot R T_{0} \ln (\frac{P}{P_{0}})$
		$e_{C h} = R T_{0} [(1 + 1.6078 \cdot ω) \cdot \ln (\frac{1 + 1.6078 \cdot ω_{0}}{1 + 1.6078 \cdot ω_{}})] + 1.6078 \cdot ω \cdot \ln (\frac{ω}{ω_{0}})$
	Net Exergy Input	${\dot{E}}_{N i, W} = \dot{m} \cdot [(e_{K i} - e_{K o}) + (e_{P h i} - e_{P h o}) + (e_{C h i} - e_{C h o})]$	${\dot{E}}_{N i, S} = [1 - \frac{4}{3} \cdot (\frac{T_{a}}{T_{S u n}}) + \frac{1}{3} \cdot {(\frac{T_{a}}{T_{S u n}})}^{4}] \cdot H_{S} A_{P V}$	${\dot{E}}_{N i} = {\dot{E}}_{N i, W} + {\dot{E}}_{N i, S}$
	Exergy Destruction (ExD) Rate	${\dot{E}}_{D, W} = {\dot{E}}_{N i, W} - P_{W} = \dot{m} \cdot [(e_{K i} - e_{K o}) + (e_{P h i} - e_{P h o}) + (e_{C h i} - e_{C h o})] - P_{W}$	${\dot{E}}_{D, S} = {\dot{E}}_{N i, S} - P_{S}$	${\dot{E}}_{D} = {\dot{E}}_{D, W} + {\dot{E}}_{D, S}$
	Exergy Efficiency	$ψ_{W} = \frac{P_{S}}{{\dot{E}}_{N i, W}}$	$ψ_{S} = \frac{P_{S}}{{\dot{E}}_{N i, S}}$	$ψ_{H} = \frac{P_{W} + P_{S}}{{\dot{E}}_{N i, W} + {\dot{E}}_{N i, S}}$
3E - Advanced Exergy	Exergy Destruction	${\dot{E}}_{D} = {\dot{E}}_{D, A V, E N} + {\dot{E}}_{D, U N, E N} + {\dot{E}}_{D, A V, E X} + {\dot{E}}_{D, U N, E X}$ ${\dot{E}}_{D, A V, E X} + {\dot{E}}_{D, U N, E X} = 0$
3E - Advanced Exergy	Exergy component	${\dot{E}}_{D w, A V, E N} = {\dot{m}}_{a} [(e_{K i} - e_{K o}) + (e_{P h i} - e_{P h o}) + (e_{C h i} - e_{C h o})] - P_{W, r}$	${\dot{E}}_{D, S, A V, E N} = {\dot{E}}_{D, S} + P_{S} - P_{S, r} = {\dot{E}}_{N i, S} - P_{S, r}$	${\dot{E}}_{D, H, A V, E N} = {\dot{E}}_{D, W, A V, E N} + {\dot{E}}_{D, S, A V, E N}$
4E - Exergo-economic (Extended Exergy)	Efficiency	$ψ_{E, W} = \frac{P_{W}}{{({\dot{E}}_{N i} + E {\dot{E}}_{C} + E {\dot{E}}_{O} + E {\dot{E}}_{I} + E {\dot{E}}_{L})}_{W}}$	$ψ_{E, P V} = \frac{P_{S}}{{({\dot{E}}_{N i} + E {\dot{E}}_{C} + E {\dot{E}}_{O} + E {\dot{E}}_{I} + E {\dot{E}}_{L})}_{S}}$	$ψ_{E} = ψ_{E, W} + ψ_{E, P V}$
4E - Exergo-economic (Extended Exergy)	Extended exergies	${\dot{E}}_{N i} + E {\dot{E}}_{C} + E {\dot{E}}_{O} + E {\dot{E}}_{I} + E {\dot{E}}_{L} - {\dot{E}}_{g e n} - {\dot{E}}_{D} = 0$ $E E_{C} + E E_{O} + E E_{I} = (C_{C} + C_{0} + C_{I}) \cdot \frac{365 \cdot H D I \cdot e x_{s u r v} \cdot N_{h}}{H D I_{0} \cdot S}$ $E E_{L} = L \cdot \frac{365 \cdot H D I \cdot e x_{s u r v} \cdot N_{h}}{H D I_{0} \cdot N_{w h}}$
5E – Economic	LCOE	$L C O E = \frac{C - t r \sum_{t = 1}^{Y} \frac{D_{t}}{{(1 + r)}^{t}} + (1 - t r) \sum_{t = 1}^{Y} \frac{O_{t}}{{(1 + r)}^{t}} - (1 - t r) \frac{C_{Y}}{{(1 + r)}^{Y}}}{8760 \times (1 - t r) \times \sum_{t = 1}^{T} \frac{C F_{t} \times (1 - x_{t})}{{(1 + r)}^{t}}}$
6E - Environmental	SER	$S E R = \frac{(C I_{n} - C I_{}) E_{g} \sum_{t = 1}^{T} (1 - x_{})}{P_{r}}$

Exergy assessment (2e)

The main aim of the exergy analysis or second law analysis (based on the second law of thermodynamics) is to evaluate the amount of exergy destruction (ExD) in each component and to determine the exergy efficiency of the system. A system can be optimized employing exergy analysis.⁴⁰ This knowledge further assists in categorizing system components according to the maximum ExD. Overall exergy of a system is the sum of kinetic, potential, physics, and chemical exergies. The expression for the net exergy input rate and, ExD rate are given in Table 3. The exergy efficiency of a wind turbine is the ratio of exergy generation to the net exergy input to the system. The expression for the exergy efficiency is similar to energy efficiency, except for the use of available exergy instead of energy input..²³ The ExD in the solar energy system can be given as the difference of exergy available through the solar radiation and the power generated through the solar energy system.³⁷ The expression for the exergy efficiencies are tabulated in Table 3.³⁷

Advanced exergy assessment (3e)

The exergy analysis emphasizes the total quantity of ExD, whereas the advanced exergy analysis bifurcates ExD into two fractions, endogenous (EN) and exogenous (EX) ExD. The fraction implying the irreversibility of system components themselves is termed as endogenous ExD, whereas the fraction refers to the ineffectiveness of system components affecting the given component is termed as exogenous ExD. Due to the partition of ExD, the perception of the effect of each component feature gets stronger, and the component interaction gets comprehensible (Figure 4).¹⁵ Thus, the amount of ExD can be estimated for the component itself as well as how much it relates to other components in the system.

Figure 1.

Schematic diagram of hybrid wind-solar energy system.

Figure 2.

Categories of multidimensional 6E analysis.

Figure 3.

Flow diagram of the methodology adopted for the 6E analysis.

Figure 4.

Bifurcation of exergy destruction in advanced exergy analysis.

The endogenous and exogenous ExDs are further sub-divided into two parts as avoidable (AV) and unavoidable (UN). The part prevailing due to the manufacturing processes and constraints cannot be avoided is termed as unavoidable or inevitable ExD. The remnant portion of the ExD can be eliminated or at least minimized through performance enhancement and is termed as avoidable ExD. This classification is beneficial for determining the extent to which the ExD can be reduced using diminishing the avoidable part of ExD. Further, the scope for the advancement of the system components can also be identified. Hence, the ExD is segregated into four parts as, avoidable endogenous (AV-EN), unavoidable endogenous (UN-EN), avoidable exogenous (AV-EX), and unavoidable exogenous (UN-EX) ExD.

Figure 5.

Month-wise average values of (a) wind speed, (b) surface incoming shortwave flux, and ambient temperature over the years (1979-2019).

Table 4.

Specifications of the wind turbines manufactured by Suzlon energy ltd.

	Parameter	SUZLON-S128 wind turbine
Operational data	Rated power (MW)	2.8
	Wind class	IEC S
	Cut-in wind speed (m/s)	3
	Rated wind speed (m/s)	9.5 (with turbulence intensity according to IEC61400)
	Cut-out wind speed (m/s)	30 (3sec. average), 20 (10 min. average)
Rotor	Diameter (m)	128
	Swept area (m²)	13070
	Blade	Make - Suzlon SB63
Generator	Frequency (Hz)	50/60
Generator	Type	Slip ring asynchronous
Tower	Hub height (m)	105
Tower	Type	Steel Tubular / Hybrid Lattice / Hybrid Concrete

Table 5.

Specifications of thin-film solar PV system .

Parameter	Value
Rated power (P_s,r)	2.8 MW
Surface area of PV panel (A_PV)	16,473 m²
Solar irradiation at standard test conditions (H_STC)	1000 W/m²
Derating coefficient (c₁)	93%
Power temperature coefficient (c₂)	−0.5%
Temperature of PV panel in working and standard test conditions (T_STC)	298 K
Temperature of PV panel in temperature estimation test conditions (T_s,TETC)	320 K
Ambient temperature in temperature estimation test conditions (T_a,TETC)	293 K
Solar irradiation in temperature estimation test conditions (H_TETC)	800 W/m²

As the wind energy system i.e. wind turbine is an independent device and there are no other components that are affecting the performance of the system, the exogenous part of the ExD is zero. The avoidable part of the endogenous ExD can be calculated by subtracting the rated power of the wind turbine (P_W,r) from the net exergy input to the system ( ${\dot{E}}_{N i}$ ). The unavoidable endogenous destruction can be computed by subtracting the avoidable part from the total exergy destruction.

For solar PV energy systems, the ExD due to the solar radiation is inevitable; however, the ExD owing to the material used for manufacturing and the performance characteristics of PV modules (e.g. efficiency reduction with temperature increment) can be enhanced. The ExD in the solar energy system can be represented as the sum of avoidable and unavoidable endogenous ExD. The avoidable and unavoidable endogenous ExD for HWSES are tabulated in Table 3.

Exergoeconomic (extended exergy) assessment (4e)

The exergy and advanced exergy analysis deal with exergy efficiencies merely based on the ExD process to enhance the power generation. The energy systems can be further optimized using exergoeconomic analysis, which consists of the exergy data and its corresponding economic values. The exergoeconomic analysis includes Exergy Life Cycle Analysis (ELCA) based on Cumulative Exergy Consumption (CEXC) approach.¹⁶ The ELCA was first employed in 1970 and incorporates all the exergy streams from natural resource and material extraction to the final product including the exergy flows during the manufacturing and operational period. This is a thermo-economic approach encompassing all the dynamic parameters which include the non-thermal parameters along with thermal parameters.¹⁶ The thermo-economic method converts all the costs incorporated to exergy flow as given in Table 3. The values of the parameters used for the analysis are given in Table 6.

Table 6.

Exergoeconomic parameters .

Parameter	Value
Human development index of study region (HDI)	0.647⁴¹
Human development index of primitive society (HDI₀)	0.055⁴²
Exergy consumption for survival (ex_surv)	1.05 × 10⁷ J/person-day¹⁶
Number of the inhabitant of the country (N_h)	138 × 10⁷ for India
National monetary amount of wages and salaries (S)	$2.66 × 10¹⁵/year (₹1.8634 × 10¹⁷/year) in 2020 in India⁴³
Number of annual labor work hours (L)	2496 h/year for India⁴⁴
Total working hours in a year (N_wh)	54539487345 h/year²⁹

Economic assessment (5e)

The economic analysis of an energy system is generally represented in terms of Net Present Value (NPV) or Levelized Cost of Electricity (LCOE). NPV indicates the net value of the energy project at the end of the project lifetime, whereas the LCOE is the cost of energy generation per unit of power generation.⁴⁵ Both NPV and LCOE are based on the net cash flow approach; therefore, any one of these parameters is sufficient for the representation of the system economics. The expression of LCOE remains the same for any kind of power generation system as given in Table 3.⁴⁵ The particulars used for the LCOE evaluation are tabulated in Table 7. Indian government provides Accelerated depreciation (AD) policy to RE projects, which allows the developers to depreciate 40.0% of the loan amount during the first year of operation.²² Benefiting the AD policy reduces the interest expenditure and hence project LCOE.

Environmental assessment (6e)

The environmental analysis of renewable energy systems focuses on the CO₂ emission reduction due to the power generation through renewable energy sources instead of conventional methods. The amount of CO₂ emission reduction per unit of installed capacity (tones of CO₂/kW_(installed)) is termed as the Specific Emission Reduction (SER) and given in Table 3.²³ CI stands for the carbon intensity of power generation (tones of CO₂/kWh), which is the net amount of carbon emission occurring during various phases of the energy system starting from manufacturing, transportation, installation, operation and maintenance to recycling and × is the annual degradation factor for the energy system.

Results and discussion

Resource assessment

Wind and solar resource data is obtained from the ERA5 reanalysis dataset for the last 41 years (1979–2019) for the location 20.75° N, 71.25° E in Gujarat state. WS data obtained from the ERA5 database for 10 m height is extrapolated to 105 m hub height. To understand intra-annual periodic fluctuations in resource availability, the month-wise mean values of WS, SISF, and ambient temperature over the study period are presented in Figure 5. All three parameters i.e. WS, SISF, and ambient temperature go through two-peak cycles; however, the months of achieving peak and valley are diverse. The WS variation cycle indicates valleys (WS below 2 m/s) in February and October while having the peak in the month of July. The mean WS during the months from October to March lies below the cut-in speed of wind turbine i.e. 3 m/s implied to no power generation. For the rest of the months, the WS is above the cut-in speed reaching a peak of 7.23 m/s in July. The monthly variation in SISF availability indicates the lowest values around 185 W/m² during the monsoon months of July and August while touching the peaks in April (303 W/m²) and October (232 W/m²). The mean ambient temperature remain above 280 K between March an October months for which the WS stays above 3 m/s value. Further, Figure 6 depicts the variations in wind and solar energy resources availability and atmospheric parameters over the study period of 41 years. The extrapolated monthly mean WS is observed to reach up to 9.23 m/s with an overall monthly mean of 3.86 m/s. SISF and ambient temperature indicate ranges of 139–312 W/m2 and 294–303 K over the study period respectively. The mean atmospheric pressure at hub height is observed to be around 1.012 × 105 Pa with minimal variations. The values of specific humidity vary from 0.0029 to 0.0224.

Figure 6.

Time series of monthly mean meteorological data of (a) wind speed, (b) surface incoming shortwave flux and ambient temperature, and (c) atmospheric pressure and specific humidity over the years (1979-2019).

To evaluate the frequencies of resource availability, the histograms of the resource data are developed using the monthly average values of the entire period of the study. The histograms assist in understanding the most occurring values over the divided timespan. The histogram distributions of key input variables are depicted in Figure 7 for (a) WS, (b) SISF, and (c) ambient temperature. Monthly mean WS values remain below 3 m/s (cut-in speed of wind turbine) value for around 44.8% of the instances, leading to lower productivity of WES. On the contrary, the SES will be generating power for all the instances as per the magnitude of SISF without touching the zero production mark.

Figure 7.

Histogram of the monthly mean of meteorological parameters (a) wind speed, (b) surface incoming shortwave flux, and (c) ambient temperature.

Energetic assessment (1e)

The wind power generation (P_W) through wind turbine is calculated using the ERA5 wind resource data as input to the power curve of selected SUZLON-S128 (2.8 MW) wind turbine. Similarly, the power generated by 2.8 MW capacity SES (P_S) is computed, followed by computation of CF and energy efficiencies. The variation of energy efficiency and CF of WES with WS depicts that the values of these parameters remain zero up to the WS of 3 m/s due to no power generation below cut-in speed (Figure 8). The scatter points of energy efficiency concerning WS values trace parabolic curve, indicating the higher rate of increment at near-cut-in speeds and damped increment at higher WS. However, the scatter points of CF trace the curve similar to the power curve of the selected wind turbine.

Figure 8.

(a) Energy efficiency and (b) capacity factor of WES w.r.t. WS variations.

The variation in solar power generation (P_S) due to the input variables SISF and ambient temperature is shown in Figure 9. P_S indicates linear relation with SISF with the slope nearly equal to unity, which means that increment in SISF directly proportional to solar power generation. However, there are multiple values of P_S for the same input of SISF, which is due to the influence of ambient temperature. Moreover, the ambient temperature does not indicate any clear relation with P_S (Figure 9 (b)). Since there are multiple values of SISF available, the narrow range of P_S will exist.

Figure 9.

Solar power generation in 2.8 MW SES over different values of (a) SISF and (b) ambient temperature.

The histograms for the energy analysis are shown in Figure 10 i.e. (a) CF for WES and (b) CF of SES. The WS below 3 m/s does not contribute to power generation (due to being the cut-in speed of wind turbine) and leads to zero CF (for 44.8% of the instances). However, higher values of CF are observed for the rest of the instances due to higher WS values with the peak CF value of 95.4%. The CF of SES does not fall below 12.0% at any instance (i.e. zero occurrences) and reaches a healthy peak CF value around 28.0% (i.e. highest occurring value).

Figure 10.

Histogram monthly mean of (a) capacity factor of WES, (b) capacity factor of SES.

The month-wise scenario of instantaneous power generated by WES, SES, and HWSES over the study period of 41 years (1979–2019) is presented in Figure 11. As the amount of power generation is directly dependent on the resource availability, the power generated by WES and SES traces a similar curve as of WS and SISF respectively (refer Figure 5). The month-wise wind power through WES goes through significant changes varying from almost zero power to 1.54 MW. Here, it can be noted that the wind power values lie below 0.1 MW for six months of the year (Oct. to March), but achieves the peak value of 1.54 MW in July. Whereas, the solar power through 2.8 MW SES lies between 0.45 and 0.74 MW indicating consistent power generation throughout the year. Hybridization of WES and SES presents the variation range of 0.53–1.99 MW, gaining benefits from both (does not fall to zero value and achieves a noticeable peak). Hence, HWSES enhances the monthly power availability and continuously generates power. Moreover, as elaborated earlier, the highest power value in WES is observed to be in July, whereas in the same month SES shows the lowest power values of the year. Hence, wind and solar power generation are complementary to each other.

Figure 11.

Month-wise mean instantaneous power generation of WES (blue), SES (green), and HWSES (red) over last 41 years (1979-2019).

Exergy assessment (2e)

The values of exergy efficiency and exergy destruction in WES and SES w.r.t. WS and SISF respectively are depicted in Figure 12. The ExD of WES exhibits a directly proportional relation with WS; hence the higher value of WS leads to higher ExD (Figure 12 (a)). The exergy efficiency of WES also represents a proportional trend with WS while starting from cut-in speed and attaining an instantaneous peak value of 8.64.0% (Figure 12 (b)). Here, it should be noted that the rate of increment in ExD is lower at higher WS, specifically near rated speed. Such a phenomenon leads to a steep increment in exergy efficiency at WS values above 6 m/s. The ExD for SES (1.7–4.1 MW) depicts linearly proportional relation with SISF (140–315 W/m²) (Figure 12 (c)). The scatter plot presenting the variation in exergy efficiency of SES with respect to SISF exhibits an inversely proportional relation (Figure 12 (d)). The variation of 180 K (from 124 K to 320 K) in SISF leads to the alteration in exergy efficiency by 1.2% only; hence the rate of variation is insignificant. Hence, the variation in SISF does not have a significant impact on the exergy efficiency of SES. Moreover, the irregularities in the scatter points are due to the impact of ambient temperature (of fourth power).

Figure 12.

(a) Exergy destruction and (b) exergy efficiency of WES w.r.t. WS, (c) exergy destruction and (d) exergy efficiency of SES w.r.t. SSIF.

Figure 13 shows the histogram distributions of exergy efficiency of WES and SES. The exergy efficiencies of WES depict similar distribution to the histogram of CF presented earlier in Figure 10. The monthly mean exergy efficiencies of WES lies below 1.0% for 50.9% of the instances respectively and do not surpass the value of 10.0% over the study period of 41 years. The exergy efficiency of SES exhibits a variation range of 15.6–16.7%.

Figure 13.

Histograms monthly mean values of (a) exergy efficiency of WES, (b) exergy efficiency of SES.

Advanced exergy assessment (3e)

The advanced exergy analysis helps in breaking down the ExD into avoidable and unavoidable regimes. Avoidable ExD indicates that part of total ExD that can be eliminated by better resource availability (higher values with fewer fluctuations) and enhanced performance with the same system (enhances plant design). On the contrary, it is not possible to diminish the unavoidable ExD by the existing system, which indicates the necessity of technological advancements. Figure 14 shows 41 years (1979–2019) time series of avoidable and unavoidable endogenous ExD for WES and SES. The avoidable and unavoidable endogenous ExD goes through inverse periodic variations (crest of the avoidable part while having trough of unavoidable part and vice versa) in both WES and SES. Avoidable ExD in WES indicates a higher range of fluctuations from zero to around 30 MW, whereas the unavoidable ExD approximately varies over a narrower range of zero to 2.5 MW. Moreover, the unavoidable ExD in WES is 91.7% lower than the avoidable part at the mentioned extreme high values. The total exergy input in SES varies between 2–5 MW, out of which, 2–4 MW goes unproductive in the form of ExD and the rest contributes to the power generation. Avoidable ExD in SES indicates significantly lower fluctuations than the WES varying from zero to around 2 MW, whereas the unavoidable ExD approximately varies in an, even more, narrowed range of 2–2.4 MW. The mean value of the avoidable ExD part is 63.9% lower than the avoidable ExD of SES. The significance of this comparative scenario is discussed in the following discussion on month-wise depictions.

Figure 14.

Advance exergy analysis outcomes in terms of avoidable, unavoidable, and total endogenous exergy destruction for (a) WES and (b) SES over the years (1979-2019).

The month-wise comparative scenario of avoidable and unavoidable ExD regimes along with total ExD is presented in Figure 15 for WES, SES, and HWSES. The avoidable ExD for WES and SES (and hence, the HWSES as well) follows a similar month-wise trend as the resource availability (refer Figure 15(a) and Figure 5(a)). Moreover, the avoidable ExD of SES (0.01–1.84 MW) is insignificant compared to WES (zero to 21.89 MW). Hence, the hybridization of WES by integrating SES would not contribute to the overall avoidable ExD. The magnitude of unavoidable ExD is about one-tenth of the avoidable fraction. Moreover, it does not change significantly over the months for both WES (1.26–2.8 MW) and SES (2.06–2.34 MW); and hence for HWSES (3.61–5.07 MW) as well (Figure 15 (b)). Moreover, the total ExD is the algebraic sum of avoidable and unavoidable endogenous ExD and is depicted in Figure 15 (c). The peak values of total ExD observed for WES and SES are 23.39 MW and 3.91 MW, whereas the same for HWSES is 26.47 MW. The hybridization of WES by adding SES would merely add around 14.32% to the total ExD of the system. Hence, hybridization leads to higher exergy efficiency supporting the conclusions derived from Figure 15.

Figure 15.

Advance exergy analysis outcomes in terms of month-wise (a) avoidable and (b) unavoidable and (c) total endogenous exergy destruction for WES (blue), SES (green), and HWSES (red) over the last 41 years (1979-2019).

Furthermore, the comparison of avoidable and unavoidable components of the same system gives insight into the scope of performance enhancement. The avoidable ExD of WES remains higher (>9.1 MW) than the unavoidable ExD for the months (April-Sept.), which are the windy months having WS greater than 3 m/s. This indicates that the resource is available in excess during these months and the system is unable to convert the available exergy into power generation. Hence, there is significant scope for the reduction of ExD in WES and increment in power generation. This can be achieved by larger nameplate capacity (as an individual wind turbine or cumulative of multiple wind turbines), larger rotor diameter (128 m in the present case), and the lower-rated WS (as 9.5 m/s in the present case). On the contrary, the SES exhibits higher unavoidable ExD (2.08–2.34 MW) than the avoidable component (0.01–1.84 MW) throughout the year. This implies that the ExD due to system characteristics is significantly higher than the same due to excess resource availability. Hence, the unavoidable ExD component of SES can only be reduced through technological advancements in future.

Exergoeconomic (extended exergy) assessment (4e)

The exergoeconomic analysis incorporates the exergy input associated with economic parameters along with the conventional exergy. The financial inflow i.e. capital investment, O&M cost, insurance cost, and labor cost are converted into exergetic forms using the methodology presented earlier and used for the computation of extended exergy efficiency. The comparative share of different exergy forms as total exergy input to the system for WES and SES is depicted in Figure 16. The conventional exergy comprises 74.0% and 60.0% of total exergy input to WES and SES respectively. These parts are incorporated during conventional exergy analysis whereas the rest of the fractions are considered in extended exergy analysis. Capital and O&M costs jointly contribute around 24.0% and 35.0%, whereas the labor costs constitute 2.0% and 5.0% of the total extended exergy input for WES and SES respectively. The extended exergy efficiency is calculated considering the total extended exergy input consisting of the above-mentioned exergy inputs. The histogram distributions of monthly mean extended exergy efficiency of WES and SES are presented in Figure 17. The extended exergy efficiencies of WES depict similar distribution when compared to CF and exergy efficiency presented earlier in Figure 10 and Figure 13 respectively. The monthly mean extended exergy efficiencies of WES remain below 1% for 54.5% of the instances and do not surpass the value of 10% over the study period of 41 years. The monthly mean extended exergy efficiencies of SES exhibit a variation range of 7.5–10.5%.

Figure 16.

Share of different types of extended exergy inputs to the WES and SES (EE_C, EE_O are EE_l are extended exergy of capital cost, operating cost and labor cost. E_in is conventional exergy input).

Figure 17.

Histograms monthly mean values of (a) extended exergy efficiency of WES and (b) extended exergy efficiency of SES.

Economic assessment (5e)

As explained earlier, the economic analysis of HWSES along with WES and SES has been performed, with and without the consideration of AD policy; and the outcomes are depicted in Figure 18. Here, a higher LCOE of $0.0697/kWh (₹4.88/kWh) is obtained for WES which is due to its higher capital cost Hybridization of wind-solar projects reduces LCOE to $0.0578/kWh (₹4.05/kWh). The provision of AD policy reduces the project LCOE by around 4.0%. Further, it should be noted that the determined LCOE is for the project lifespan of 20 years, and planning the project for a longer duration e.g. 25 years or longer will reduce overall project LCOE.²² Moreover, extending the project at the end of the prior duration with necessary advancements and/or repowering would further reduce the LCOE.

Figure 18.

LCOE of WES, SES, and HWSES with and without the consideration of accelerated depreciation (ad) policy.

Environmental assessment (6e)

The environmental assessment of a power generation system is performed in terms of carbon intensity and emission reduction intensity. The overall carbon intensity of electricity generation in India is 87.3 gCO₂/MJ.⁴⁶ Wind and solar power are being considered to be greenhouse gas emission-free sources of electricity.⁴⁷ Hence, every MJ capacity addition from RE source will reduce the overall carbon intensity of the country. The SER values for RE power systems are 0.947 tone CO₂/MWh,⁴⁸ which means every MWh power generation by wind and solar power systems contributes to the reduction of 0.947 tone CO₂ emission. In terms of the capacity of considered systems, the annual mean SER values for WES, SES, and HWSES are 1128 tone/kW, 1685 tone/kW, and 1407tone/kW respectively. As of now, around 2.19 MT CO₂ emissions have been reduced by 123 solar PV projects in India.⁴⁹ Wind and solar power generation are projected to replace anthropogenic emissions of 93022 tones CO₂/year approximately and reduce the reliance on fossil fuels by replacing 95.145 GWh/year amount of electricity generation of RE sources in India.⁴⁷ In addition to the environmental analysis, the exergo-environmental analysis is required for the evaluation of the adverse effect of ExD on the environment. However, in the RE based energy systems there are no harmful impacts on the environment and therefore exergo-environmental analysis is not necessary for the present study. Here, the study has been conducted on a point location; however, the same model can be universally applied across the globe for any kind of thermal or power generation systems with minor fundamental modifications. Moreover, the selection of machined and technology can also be explored further for detailed conception of the hybrid wind-solar energy systems.

Overall system performance

The time series (1979–2019) of CF and system efficiencies (energy, exergy, and extended exergy) is shown in Figure 19 to compare WES and SES with HWSES. For WES, the energy efficiency ranges from zero to 50.0%, whereas the exergy and extended exergy efficiencies do not surpass 10% following a similar cycle of variation. For SES, the CF values observe intense periodic variations ranging from 13.0% to 27.0%, whereas the exergy and extended exergy efficiencies remain around 16.0% and 7.5–11.0% respectively while going through minimal cyclic variations. The mean CF of 5.6 MW HWSES is 17.3% whereas the same for WES and SES (2.8 MW each) are 13.8% and 20.7% respectively (Figure 19 (a)). This indicates that the addition of SES to WES and vice versa enhances the system CF by 3.5%. A similar comparative scenario is observed in terms of energy efficiency as well (Figure 19 (b)). However, in terms of exergy efficiency, the gap in the mean values for HWSES (6.98%) and SES (16.15%) is larger than the same with WES (1.86%) (Figure 19 (c)). A similar scenario is observed for extended exergy efficiency as well with mean values of 4.40%, 1.5%, and 9.6% for HWSES, WES, and SES respectively. Hence, the values of different system efficiencies for the HWSES system lies between the same range of WES and SES with a narrower range of variation leading to more reliable and less uncertain system performance. Moreover, hybridization increases the overall system capacity while utilizing the same land and other transmission resources.

Figure 19.

Time series of efficiencies of WES (blue), SES (green), and HWSES (red) over last 41 years (1979-2019) namely: (a) capacity factor, (b) energy efficiency, (c) exergy efficiency, and (d) extended exergy efficiency.

For understanding the comparative intra-annual scenario, the month-wise averages (over 41 years (1979–2019)) of CF and system efficiencies (energy, exergy, and extended exergy) are presented and compared in Figure 20 for WES, SES, and HWSES. The time-series and histogram depictions of the resource data and the computed system parameters have been presented in supplementary materials. This indicates the impact of seasonal meteorological changes on these parameters. For WES, the six months of summer and monsoon seasons (April to September) can be observed as productive periods having peak values in July with the CF equal to 54.81% (Figure 20 (a)). Unlike WES, the SES attains higher CF values above 15% all around the year, with a maximum value of 26.3% in April. The SES exhibits CF in the range of 16.22–26.30% throughout the year. However, the CF of HWSES does not fall below 9.6% throughout the year attaining the healthy peak of 35.51% in July.

Figure 20.

Month-wise efficiencies of WES (blue), SES (green), and HWSES (red) over last 41 years (1979-2019): (a) capacity factor, (b) energy efficiency, (c) exergy efficiency, and (d) extended exergy efficiency.

Moreover, the outcomes of the present study matches with the same of prior studies (kindly refer to supplementary material for the tabulated result comparisons). This profoundly indicates the benefits of hybridization of wind and solar power projects for the generation of power through sustainable sources with lower uncertainty and fewer fluctuations over the time period. The monthly variation of CF indicated that the HWSES is more preferable for year-round performance. SES exhibits the lowest CF values in July, whereas the WES reaches to peak in the same month. This implies that the wind and solar systems complement each other. The energy efficiency of WES follows a similar trend (as of CF) while achieving stable values over 45% from May to August, whereas the WES does not indicate any significant alterations being around 15%. This leads to more stable energy efficiency for HWSES starting from the bottom value of 15% to the attaining peak around 32%. The performance of the hybrid system is observed to be superior to the individual systems in terms of exergy and extended exergy efficiency having the variation range of 4.74–10.40% and 3.39–5.79% respectively (Figure 20 (c) and (d)).

The total system capacity factor, exergy efficiency, and extended exergy efficiency all improve by 3.46%, 5.12%, and 2.87%, respectively, when wind power projects are combined with solar power (Figure 21). As a result, hybridization provides better year-round system performance with lower power fluctuations than solo systems.

Figure 21.

Mean system efficiencies for standalone and hybrid system.

Table 7.

Economics of WES and SES .

Parameter	Value
Initial capital expenditure or CAPEX (₹/kW)	₹70000/kW ($1000/kW) (WES) ₹ 40,000/kW ($571.43/kW) (SES)
Tax rate (in %/year)	33.0%
Loan period (years)	10
Operating expenditure or OPEX (% of CAPEX/year)	2.0%
Operating expenditure or OPEX escalation rate (%/year)	5.0%
Terminal value of plant (% of the capital cost)	10.0%
Degradation factor for technology (%/year)	99.5%
Discount rate (%/year)	11.5%
Lifetime (years)	20

Uncertainty analysis

Uncertainty refers to the domain of estimated parameter values. Parameter errors are classified into two types: systematic errors and random errors. Random errors can be avoided by keeping the experimental process and operating parameters intact; nevertheless, systematic errors are unavoidable and cannot be avoided.⁵⁰ Random error arises when experiments are conducted under consistent settings, yet systematic errors stay unchanged. The major categories of uncertainty correspond to two types of errors: internal uncertainty (Type A) and external/standard uncertainty (Type B).⁵¹ Type B uncertainty is associated with systematic errors and can be calculated using data from calibration test reports or instrument manuals, whereas Type A uncertainty is associated with random errors and can be assessed using statistical analysis. The standard uncertainty (Type B) of instruments can be calculated using the following expression^52–55,

Uncertainty = \frac{Accuracy}{\sqrt{3}}

The standard uncertainty of the measuring instruments (Type B) is shown in Table 8. The uncertainties in the wind turbine power and the temperature of solar PV panel are found to be 0.58 kW and 0.058 °C respectively.

Table 8.

Standard uncertainty of instruments (type B or external).

Sr. No.	Instrument	Range	Accuracy	Standard Uncertainty (Type B)
1	Wind turbine power	0 to 2.8 MW	± 10 W	± 5.774 W
2	Solar PV temperature	−40 to 85 °C	± 0.1 °C	± 0.058 °C
3	Solar power	0 to 2.8 MW	± 1 W	± 0.577 W

Comparison with literature

Moreover, the results of the present study are observed to be in similar ranges concerning the same obtained during the past studies on WES and SES (refer Table 9). Hence, it can be stated that the presented mathematical model is reliable to be applied to individual and hybrid wind and solar energy systems. Here the study has been conducted on a point location; however, the same model can be universally applied across the globe for any kind of thermal or power generation systems with minor fundamental modifications.

Table 9.

Comparison of present results with literature.

Authors	Energy source	System capacity	Type of analysis
Authors	Energy source	System capacity	Energy analysis (CF)	Exergy analysis (ψ)	Advanced exergy analysis (Exergy destruction)	Economic analysis (LCOE)	Exergoeconomic analysis (extended exergy efficiency)	Environmental analysis (SER)
Present research	Wind	2.8 MW	0.14-54.81% (13.88% mean)	0.11-6.03% (1.86% mean)	11.21 MW ( ${\dot{E}}_{D}$ ), 8.80 MW ( ${\dot{E}}_{D, A V}$ ), 2.41 MW ( ${\dot{E}}_{D, U N}$ )	₹4.88/kWh	0.05-5.21% (1.53% mean)	1128 tone/kW
	Solar	2.8 MW	20.74%	16.15%	3.02 MW ( ${\dot{E}}_{D}$ ), 0.80 MW ( ${\dot{E}}_{D, A V}$ ), 2.22 MW ( ${\dot{E}}_{D, U N}$ )	₹4.05/kWh	9.64%	1685 tone/kW
	Hybrid wind-solar	5.6 MW	17.27%	6.98%	14.23 MW ( ${\dot{E}}_{D}$ ), 9.6 MW ( ${\dot{E}}_{D, A V}$ ), 4.63 MW ( ${\dot{E}}_{D, U N}$ )	₹4.54/kWh	4.40%	1407 tone/kW
Allouhi²³	Wind	0.8 MW	22.38%	38.27%	-	20.95 c$/kWh	-	23.31 tone/kW
Ehyaei et al.¹⁶	Wind	10 kW	3.33% 1.08%	9.07% 1.74%	22.27 kW ( ${\dot{E}}_{D}$ ), 13.25 kW ( ${\dot{E}}_{D, A V}$ ), 9.02 kW ( ${\dot{E}}_{D, U N}$ )	0.23 $/kWh 0.73 $/kWh	1.24% 0.60%	-
Arslan et al.³⁴	Solar	1 kW	13.49%	15.03%	-	-	-	1.98 kg/hour
Aghbashlo et al.²⁹	Solar	500 kW	18.33%	13.6-14.8%	-	-	9.46-10.5%	-
Shaygan et al.¹⁵	Solar	5.5 kW	20.4%	8.2%	37.7 kW ( ${\dot{E}}_{D}$ ), 1.8 kW ( ${\dot{E}}_{D, A V}$ ), 35.8 kW ( ${\dot{E}}_{D, U N}$ )	0.127 $/kWh	-	-

Conclusion

The multidimensional 6E analysis consisting of energy, exergy, economic, and environmental aspects with advanced exergy and exergoeconomic analysis is conducted on the individual and hybrid power plants. The performance characteristics of energy systems are studied in terms of parametric variation (for WS and SISF fluctuations) by means of meteorological data from ERA5 reanalysis dataset spanning 41 years (1979–2019).The following conclusions are drawn from the present study.

CF of HWSES does not fall below 9.58% throughout the year attaining a healthy peak of 35.51% in July, which is a healthy and proficient range for long term operation as compared to standalone systems. The CF and exergy efficiency of HWSES are found to be 9.6–35.5% and 4.7–10.4%, respectively, while the extended exergy efficiency is determined to be 3.39–5.79%. The system capacity factor, exergy efficiency and extended exergy efficiency improve by 3.46%, 5.12%, and 2.87%, respectively, when wind power projects are hybridized with solar power. As a result, hybridization provides better year-round system performance with lower power fluctuations than solo systems.

The avoidable and unavoidable endogenous ExD in HWSES follows the same variation path through the different months as in WES, however, with shorter variation range due to less fluctuating ExD of SES. The avoidable and unavoidable endogenous ExD show inverse trends with time i.e. higher values of the avoidable regime while having lower values of the unavoidable regime and vice versa. The avoidable and unavoidable ExD in WES reaches up to 30 MW and 2.5 MW respectively throughout the study period. The ExD in SES indicates direct proportional relation with SISF with a variation range of 1.7–4.1 MW. For SES, the avoidable ExD remains below 2 MW, whereas the unavoidable part exhibits the range of 2–2.4 MW.

LCOE computed with the provision of AD policy for WES, SES and HWSES are $0.0697/kWh (₹4.88/kWh), $0.0578 /kWh (₹4.05/kWh), and $0.0648/kWh (₹4.54/kWh) respectively. The LCOE can further be lowered by extending the project duration with or without technical upgrade and repowering. Power generation through RE sources reduces 0.947 tone CO₂ emission per MWh power generation. The addition of every MJ of electricity generated though RE systems reduces the overall carbon emission intensity (which is currently at 87.3 gCO₂/MJ levels for India).

The study profoundly designates the benefits of hybridization of wind and solar power projects for the generation of power through sustainable sources with lower uncertainty and fewer fluctuations over the entire project life span. This will provide basic guidelines to the project developers in decision-making process for the successful deployment and operation of new and existing HWSES.

Future work

Present study encompasses the system performance in terms of overall system efficiencies and CF. However, integration of hybrid power plants to the central grid necessitate simultaneity assessment of the energy resources (i.e. complementarity and synergy) for meeting base load and peak load requirements, and the site selection is ought to be executed accordingly.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Hardik K. Jani

Garlapati Nagababu

Hardik K. Jani has been working as a Research Associate for Suzlon Energy Limited since May 2018 and pursuing PhD at Pandit Deendayal Energy University, Gandhinagar, India, since July 2019. He has completed his master's degree, ME Mechanical (Energy Engineering), from Government Engineering College, Valsad, India. He has 5 years of research experience in the field of renewable energy. He has also worked as an associate researcher in the Vivekananda International Foundation (VIF) for assisting the VIF Task Force for NPCIL Project on ‘India’s Energy Transition in a Carbon-Constrained World: The Role of Nuclear Power’.

Surendra Singh Kachhwaha completed his BE degree in Mechanical Engineering from M. B. M. Engineering College, Jodhpur. He did his MTech in Heat Power from Indian Institute of Technology, BHU Varanasi and PhD in Evaporative Cooling from IIT Delhi. He has taught various thermal engineering courses at undergraduate and post-graduate levels and guided 30 MTech dissertation and 5 PhDs. Presently he is working as Chair Professor Suzlon in the Mechanical Engineering Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar, India. He has been a co-author of 60 technical publications in reputed national and international journals and more than 70 publications in national/international conferences.

Garlapati Nagababu has been working as an assistant professor in the mechanical engineering department, PDPU, Gandhinagar, since 2013. He completed his PhD in the field of offshore wind energy and MTech from IIT Guwahati in machine design specialization. Nagababu has a teaching experience of more than seven years in the field of mechanical engineering at UG and PG levels. His research mainly focuses on offshore renewable energy resource assessment, development of small-scale wind turbine and hybrid energy system. He has published a number of research articles in highly reputed journals and international conferences in the field of energy. He is currently working on different projects funded by ISRO and ORSP.

Alok Das is at present working as Vice President – Business Development in Suzlon Energy Ltd in the field of Renewable Energy and pursuing PhD programme in Pandit Deendayal Energy University, Gandhinagar, India. He has completed his graduate studies leading to MTech (Thermal Engineering) from IIT, Kanpur, and MBA from the University of Kolkata. He has 27 years’ experience in the field of engineering, research & development, green field generation, distribution & transmission of conventional power and non-conventional energy / renewable energy sectors.

MA Ehyaei is an associate professor of Pardis Branch, Islamic Azad University. He received his PhD in mechanical engineering from the Sharif University of Technology in 2007 (First rank: GPA 19.13/20) and his postdoc from Ontario University in 2010. He published 70 ISI journal papers on energy, exergy and renewable energy field up to now. For 92% of his publications he did a mathematical modeling and computer simulation. His research interests include renewable energy exergy analysis; dispersed power generation; advanced and extended exergy analyses; fuel cell; and thermal cycle.

Nomenclature

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