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Studying a renewable energy integrated power system's features is essential, especially for deregulated systems. The unpredictability of renewable sources is the main barrier to integrating renewable energy-producing units with the current electrical grid. Due to its unpredictable nature, integrating wind power into an existing power system requires significant consideration. In a deregulated electricity market, this paper examines the implications of wind farm (WF) integration with CAES on electric losses, voltage profile, generation costs, and system economics. Comparative research was done to determine the impact of wind farm integration on regulated and deregulated environments. Four randomly chosen locations in India were chosen for this investigation, together with real-time information on each location's real wind speed (RWS) and predicted wind speed (PWS). Surplus charge rates and deficit charge rates were created to assess the imbalance cost arising from the discrepancy between predicted and real wind speeds to calculate the system economics. When the effect of imbalance cost is considered, the daily system profit shows a variation of about 1.9% for the locations under study. Customers are always seeking electricity that is dependable, affordable, and efficient due to the reorganization of the power system. As a result, the system security limit could be exceeded or the system might function dangerously. The final section of this paper presents an economic risk analysis using heuristic algorithms such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO). It also discusses how the CAES is used to correct the deviation of WF integration in the real-time electricity market. Economic risk analysis tools include value-at-risk (VaR) and conditional value-at-risk (CVaR). The entire piece of work was validated using a modified IEEE 30-bus test system. This works shows that with a three-fold increase in wind generation, the risk coefficient values improves by 1%. [ABSTRACT FROM AUTHOR]

Power generation using wind has been extensively utilised, with substantial capacity add-on worldwide, during recent decades. The wind power energy sector is growing, and has turned into a great source of renewable power production. In the past decades of the 21st century, the capacity of installed wind energy has almost doubled every three years. This review paper presents the crucial facets and advancement strategies that were approved and adopted by the Government of India for intensifying the country's own power safety, by the appropriate use of existing power sources. From India's viewpoint, wind energy is not only utilized for power production but also to provide power in a more economical way. The particulars of India's total energy production, contributions of numerous renewable sources and their demand are also encompassed in this paper. After an exhaustive review of the literature, detailed facts have been identified about the present position of wind energy, with an emphasis on government achievements, targets, initiatives, and various strategic advances in the wind power sector. Wind power potential is discussed, which can assist renewable power companies to select efficient and productive locations. All analyses carried out in this paper will be incredibly valuable to future renewable energy investors and researchers. The current scenario of wind power production in India is also paralleled with that of other globally prominent countries. [ABSTRACT FROM AUTHOR]

It is important to understand the features of an integrated renewable energy power system, especially for deregulated systems. The greatest obstacle to assimilating renewable energy generators with the existing electrical system is their unpredictability. Because wind energy is inconsistent, incorporating it into an established power system necessitates more planning. The effects of wind farm (WF) incorporation with fuel cells and a unified power flow controller (UPFC) on electric losses, voltage profile, generating price, and the economics of the system in a deregulated power market are examined in this paper. An impact analysis of integrating wind farms into controlled and uncontrolled situations is conducted. At two randomly selected locations in India, the real-time statistics of the actual wind speed (AWS) and forecasted wind speed (FWS) were merged for this study. The surplus charge rate and deficit charge rate are intended to evaluate the imbalance cost which is arising from the difference between anticipated and true wind speeds to determine the economics of the system. Customers are always trying to find electricity that is reliable, inexpensive, and efficient due to the reconfiguration of the power system. As a consequence, the security limitations of the system may be surpassed or might function beyond the safety limit, which is undesirable. In the last section, heuristic algorithms, such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth-flame optimization algorithms (MFO), are employed to analyze economic risk. In the real-time energy market, it also covers how the fuel cells and UPFC are utilized to rectify the WF integration's deviation. Economic risk evaluation approaches include value-at-risk (VaR) and conditional value-at-risk (CVaR). A modified IEEE 30-bus test system is used throughout the whole project. [ABSTRACT FROM AUTHOR]

With the negative climate impact of fossil fuel power generation and the requirement of global policy to shift towards a green mix of energy production, the investment in renewable energy is an opportunity in developing countries. However, poor economy associated with limited income, funds availability, and regulations governing project funding and development are key factors that challenge investors in the energy sector. Given the various power generation resources, including renewables, it is necessary to evaluate the possible power generation investment options from an economic perspective. To realize this objective, solar PV, wind and diesel power generations are economically compared, considering the incremental rate of return and incremental benefit to cost ratio techniques. The alternative investment options of distributed generation technologies are evaluated for Maharashtra, India under different depreciation methods, and the effect of the latter on selecting the best investment candidate is investigated. The paper also conducts sensitivity analysis to examine the impact of capital cost, operation and maintenance cost, and fuel cost variations on the selection decision considering a comparison of the different general projects' cash flow structures discussed in the literature. The economic aspects of selecting a project among possible alternatives for an investment in the power sector are analyzed, and the presented review provides comprehensive comparisons with respect to the literature approaches. The results reveal that, in the benchmark case study, the PV project is rejected and disregarded from further comparisons with other candidate projects since its equity internal rate of return (10.25%) is less than the minimum accepted rate of return, leaving the selection between wind and diesel energy projects. The study reveals that the incremental rates of return under such a comparison are 37.88%, 45.94% and 37.50% when MACRS, declining balance and straight line depreciations techniques are applied, respectively. Thus, the wind energy project is the favored option in this case. For the economic assessment of other case studies, the application of both sensitivity analysis on the capital cost and operation and maintenance cost and literature approaches to structure the projects reveal that wind energy for Maharashtra, India is a more attractive and feasible option compared to other distribution generation projects, while diesel is only considered to be a good option when its fuel cost is reduced by 5%. Finally, the paper highlights policy implications that can influence the decision to move towards investment in distributed generation technologies as a future research direction. [ABSTRACT FROM AUTHOR]

Higher penetration of variable renewable energy sources into the grid brings down the plant load factor of thermal power plants. However, during sudden changes in load, the thermal power plants support the grid, though at higher ramping rates and with inefficient operation. Hence, further renewable additions must be backed by battery energy storage systems to limit the ramping rate of a thermal power plant and to avoid deploying diesel generators. In this paper, battery-integrated renewable energy systems that include floating solar, bifacial rooftop, and wind energy systems are evaluated for a designated smart city in India to reduce ramping support by a thermal power plant. Two variants of adaptive-local-attractor-based quantum-behaved particle swarm optimization (ALA-QPSO) are applied for optimal sizing of battery-integrated and hybrid renewable energy sources to minimize the levelized cost of energy (LCoE), battery life cycle loss (LCL), and loss of power supply probability (LPSP). The obtained results are then compared with four variants of differential evolution. The results show that out of 427 MW of the energy potential, an optimal set of hybrid renewable energy sources containing 274 MW of rooftop PV, 99 MW of floating PV, and 60 MW of wind energy systems supported by 131 MWh of batteries results in an LPSP of 0.005%, an LCoE of 0.077 USD/kW, and an LCL of 0.0087. A sensitivity analysis of the results obtained through ALA-QPSO is performed to assess the impact of damage to batteries and unplanned load appreciation, and it is found that the optimal set results in more energy sustainability. [ABSTRACT FROM AUTHOR]

Solar and wind power systems have been prime solutions to the challenges centered on reliable power supply, sustainability, and energy costs for several years. However, there are still various challenges in these renewable industries, especially regarding limited peak periods. Solar–wind hybrid technology introduced to mitigate these setbacks has significant drawbacks and suffers from low adoption rates in many geographies. Hence, it is essential to investigate the challenges faced with these technologies and analyze the viable solutions proposed. This work examined solar–wind hybrid plants' economic and technical opportunities and challenges. In the present work, the pressing challenges solar–wind hybrids face were detailed through extensive case studies, the case study of enabling policies in India, and overproduction in Germany. Presently, the principal challenges of solar–wind hybrids are overproduction, enabling policies, and electricity storage. This review highlights specific, viable, proposed solutions to these problems. As already recorded in the literature, it was discovered that academic research in this space focuses majorly on the techno-economic and seemingly theoretical aspects of these hybrid systems. In contrast, reports and publications from original equipment manufacturers (OEMs) and engineering, procurement, and construction engineers (EPCs) are more rounded, featuring real-life application and implementation. [ABSTRACT FROM AUTHOR]

In this paper the multiverse optimization (MVO) was used for estimating Weibull parameters. These parameters were further used to analyze the wind data available at a particular location in the Tirumala region in India. An effort had been made to study the wind potential in this region (13°41′30.4″ N 79°21′34.4″ E) using the Weibull parameters. The wind data had been measured at this site for a period of six years from January 2012 to December 2017. The analysis was performed at two different hub heights of 10 m and 65 m. The frequency distribution of wind speed, wind direction and mean wind speeds were calculated for this region. To compare the performance of the MVO, gray wolf optimizer (GWO), moth flame optimization (MFO), particle swarm optimization (PSO) and other numerical methods were considered. From this study, the performance had been analyzed and the best results were obtained by using the MVO with an error less than one. Along with the Weibull frequency distribution for the selected region, wind direction and wind speed were also provided. From the analysis, wind speed from 2 m/s to 10 m/s was present in sector 260–280° and wind from 0–4 m/s were present in sector 170–180° of the Tirumala region in India. [ABSTRACT FROM AUTHOR]

This study focuses on the life cycle assessment (LCA) of an onshore wind farm in India. The study is conducted on 10 Vestas 1.65 MW wind turbines situated in the Karnataka state of India. Following the ISO 14044 standard, SimaPro LCA software is used to model the process. The functional unit is chosen as 1 MWh sent out electricity. The results of the life cycle-based emissions of wind farm are compared with those of the coal power plant. The global warming potential is found to be 11.3 g CO2-eq/MWh for wind power, which is 98.8% lower than that for the coal power plant. A comparison of data available in SimaPro LCA software was carried out with data in GaBi software. There is a small difference between the two databases. This may be due to different boundary and inclusion of input items. Steel, aluminium, and concrete contributed 86%, 84%, 84% and 85% of total CO2, NOx, SO2 and PM2.5 emissions, respectively. Recycling the materials of a wind turbine at the end of its life can reduce the environmental impact. Higher capacity factors can increase the electricity generation with reduced environmental impact. A 22% increase in capacity factor can reduce environmental impact by 19%. In addition, the increase in the life of wind turbines reduces the environmental impact, as a wind turbine only has a few moving parts and requires minimum regular maintenance. [ABSTRACT FROM AUTHOR]

This study evaluated a potential transition of India's power sector to 100% wind and solar energy sources. Applying a macro-energy IDEEA (Indian Zero Carbon Energy Pathways) model to 32 regions and 114 locations of potential installation of wind energy and 60 locations of solar energy, we evaluated a 100% renewable power system in India as a concept. We considered 153 scenarios with varying sets of generating and balancing technologies to evaluate each intermittent energy source separately and their complementarity. Our analysis confirms the potential technical feasibility and long-term reliability of a 100% renewable system for India, even with solar and wind energy only. Such a dual energy source system can potentially deliver fivefold the annual demand of 2019. The robust, reliable supply can be achieved in the long term, as verified by 41 years of weather data. The required expansion of energy storage and the grid will depend on the wind and solar energy structure and the types of generating technologies. Solar energy mostly requires intraday balancing that can be achieved through storage or demand-side flexibility. Wind energy is more seasonal and spatially scattered, and benefits from the long-distance grid expansion for balancing. The complementarity of the two resources on a spatial scale reduces requirements for energy storage. The demand-side flexibility is the key in developing low-cost supply with minimum curtailments. This can be potentially achieved with the proposed two-level electricity market where electricity prices reflect variability of the supply. A modelled experiment with price signals demonstrates how balancing capacity depends on the price levels of guaranteed and flexible types of loads, and therefore, can be defined by the market. [ABSTRACT FROM AUTHOR]

Wind power variations at two heights (the surface level and turbine hub level) were investigated at 20 locations in the shelf seas of India using hourly fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate (ERA5) data covering the last 40 years (1979 to 2018). The interannual and seasonal variability in wind power was studied. The wind power density, the exceedance probability of power density and the exploitable wind resources were examined. In the Indian shelf seas, the annual mean wind power density at 10 m above mean sea level varies from 82 to 353 W/m2. Wind power density at 110.8 m is 20% to 40% higher than at 10 m above mean sea level. The study shows that the shelf seas have an abundance of wind power, with wind speeds over 3 m/s during 90% of the time at locations 1 to 3, 12 and 13, with a high occurrence of exploitable wind energy above 0.7 × 103 kWh/m2. Among the locations studied, the most power-rich area was location 12, where during ~62% of the time power was greater than 200 W/m2. A significant change (~10–35%) in inter-annual wind power density was detected at a few locations, and these variations were associated with Indian summer monsoon and El Niño–Southern Oscillation events. Trend analysis suggests a decreasing trend in the annual mean wind power density for most of the locations in the Indian shelf seas over the last 40 years. Wind power has considerable directional distribution, and at different locations the annual wind power from the dominant direction is 10% to 79% of the total available power from all directions. [ABSTRACT FROM AUTHOR]

In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package 'decomposedPSF' which is discussed in the Appendix. [ABSTRACT FROM AUTHOR]