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Off-shore wind energy in Europe plays a key role in the transition to renewable energy, and its usage is expected to increase in the next few decades. According to the working regimes of a wind turbine, wind energy production can be disrupted by extreme atmospheric events related to low wind speed below the cut-in wind speed and high wind speed above the cut-out wind speed. The purpose of this work is to estimate the behavior of extreme winds on the European panorama, over the period 1950–2020, in order to investigate the large-scale weather regimes related to them and their impact on off-shore wind energy availability. We detected significant changes in the frequency of high and low extreme wind events, proving that climate change or long-term internal climate variability have already affected the off-shore wind power output. Moreover, the analysis of weather regimes showed that high and low extreme wind events can occur simultaneously over Europe. Our results suggest the necessity to implement efficient European energy management policies, to minimize the deficit in wind power supply.

Due to the uncertainty of the accuracy of wind power forecasting, wind turbines cannot be accurately equated with dispatchable units in the preparation of a day-ahead dispatching plan for power grid. A robust optimization model for the uncertainty of wind power forecasting with a given confidence level is established. Based on the forecasting value of wind power and the divergence function of forecasting error, a robust evaluation method for the availability of wind power forecasting during given load peaks is established. A simulation example is established based on a power system in Northeast China and an IEEE 39-node model. The availability estimation parameters are used to calculate the equivalent value of wind power of the conventional unit to participate in the day-ahead dispatching plan. The simulation results show that the model can effectively handle the challenge of uncertainty of wind power forecasting, and enhance the consumption of wind power for the power system.

The recent increase in infiltration of distributed resources has challenged the traditional operation of power systems. Simultaneously, devastating effects of recent natural disasters have questioned the resilience of power infrastructure for an electricity dependent community. In this study, a solution has been presented in the form of a resilient smart grid network which utilises distributed energy resources (DERs) and machine learning (ML) algorithms to improve the power availability during disastrous events. In addition to power electronics with load categorisation features, the presented system utilises ML tools to use the information from neighbouring units and external sources to make complicated logical decisions directed towards providing power to critical loads at all times. Furthermore, the provided model encourages consideration of ML tools as a part of smart grid design process together with power electronics and controls, rather than as an additional feature.

Sugarcane is an important industrial crop of India after cotton, contributing around 6% of the agricultural GDP. A study was undertaken to analyse the sugarcane mechanization in the selected area and also to identify the injury-related operations in sugarcane cultivation. A direct interview with farmers was conducted to gather information using the designed schedule on mechanization level, adoption of equipment, injury and cultivation practices, etc. Seventy-two farmers from three villages of Erode District were considered for this study. The data were analysed statistically to infer the results. The analysis indicates that farmers cultivated sugarcane crops in 36.3 to 47.9% of their available land. An increasing trend of 30–35 kW tractors was observed with 76.3% of farmers. Out of 18 unit operations, five unit operations were completely mechanized, while irrigation was partially mechanized. The farms have enough power sources of 4.13 kW/ha, but most operations were performed manually, reflecting the overall mechanization indicator of 0.162. This indicates that strategies should focus on the penetration of the developed technologies in villages. In the injury analysis, sugarcane leaves were associated with 88% of the total injuries and 12% were tools related. The causation factor included leaf blades that caused cuts while moving in the fields, deep cuts due to pulling and plucking leaves from a cane and slippage of tool (spade), while hitting hard objects resulted in injuries. The injuries affect the productivity and efficiency of the workers. The study suggests cost-effective, simple interventions to prevent these injuries, like face shields, hand gloves and footwear. The suggestions of this study are implementable in developing nations to protect sugarcane workers who perform various activities manually.

In this paper, the performance analysis of a 30 MW wind power plant is performed. The farm consists of fifteen (T1-T15) G9 7/2000/GAMESA 2 MW grid-connected turbines. The farm is in operation mode installed 28 km south of Nouakchott city in Mauritania. The analyzed data are monitored from July 1st, 2015 (the first operation day of the power plant) to December 31st, 2019. The parameters of performance evaluation are power generation, capacity factor, machine availability, grid availability, and system availability. It is observed from data analysis, the wind farm supplies a total energy of 507.39 GWh to the power grid and have a high average capacity factor of 42.55%. T1 produces the highest amount of electrical energy among the other turbines with a total energy output of 35.46 GWh, an average capacity factor of 44.97%, and operating hours of 33,814 hours. While T12 produced the minimum amount of energy in this period, the difference in energy compared to T1 is 4.563 GWh. It is observed that the availability of the network is unstable and needs improvement, varying between 90.86% in 2016 and 93.16% in 2018. In the first year of operation, 97.06% of the turbines were available. However, the average availability of the wind farm is approximately 94% during the total study period.

India accounts for only about 2.4 % of the world's geographical area and 4 % of its water resources, but has to support about 17 % of the world's human population and 15 % of the livestock. Agriculture is an important sector of the Indian economy, accounting for 17% of the nation's GDP, about 11% of its exports, about half of the population still relies on agriculture as its principal source of income and it is a source of raw material for a large number of industries. Accelerating the growth of agriculture production is therefore necessary to meet the rising demand for food, but also to increase incomes of those dependent on agriculture to ensure inclusiveness. The productivity of farms depends greatly on the availability and judicious use of farm power by the farmers. Agricultural implements and machines enable the farmers to employ the power judiciously for production purposes. Agricultural machines increase productivity of land and labour by meeting timeliness of farm operations and increase work out-put per unit time. Besides its paramount contribution to the multiple cropping and diversification of agriculture, mechanization also enables efficient utilization of inputs such as seeds, fertilizers and irrigation water. Mechanization in agriculture enhances production and productivity of crops through timeliness, better management of inputs, improved quality of work and reduction of post-harvest losses. The study revealed that the domestic sale of tractor is the highest (45.41%) for the 41-50 hp range followed by 36 % for the 31-40 hp range during 2015-16. The demand of high power category tractors in India increased for using high capacity farm machines on custom hiring basis. The states of Uttar Pradesh, Rajasthan, Madhya Pradesh and Maharashtra together account for about 45.21% of the total tractors sale in India during 2015-16. The net area sown by a tractor was 487 ha during 1975-76 and reduced to 24 ha by 2015-16 that still lags behind the developed countries and even some of the developing countries of the world (e.g. China) in terms of the net area sown by a tractor. Farm power availability of India was 0.48 kW/ha in 1975-76 reached to 2.13 kW/ha in 2015-16, which is 4.44 times higher over a period of forty years. A positive direct correlation was found between available farm power, food grain productivity and cropping intensity for the country. With the increase in intensity of cropping, the turnaround time is drastically reduced and it is not possible to harvest and thresh the standing crop, on one hand, and prepare seed bed and do timely sowing operations of subsequent crop, on the other hand, in the limited time available, unless adequate farm power and matching implement is available.

This study established power-related efficiency measures from the perspective of reliability, namely, power system availability, expected power deficiency, accumulated power deficiency, instantaneous power capacity, and accumulated power capacity for a hybrid power system (HPS) in a generic smart grid. Methodologically, a power supply–demand stochastic model that simultaneously considers the inherently stochastic nature of power supply and demand was developed to quantify their interrelationship and characterize the dynamic behavior of an HPS in a continuous-time Markov chain. Preventive maintenance (PM) models were also constructed to determine the optimal PM strategy in alignment with specific scenarios that reflect the power performance requirements and resource limitations. A sensitivity analysis was conducted using the design of experiments (DOE) scheme that simulated climate change and revealed that extreme climate worsens power-related efficiency measures. This analysis provides further insight into the extent to which extreme climate conditions diminish the engineers and designers of smart grid systems’ power-related efficiency measures. The proposed approach will potentially contribute to sustainability and maintainability in the clean energy industry.

A large section of the Nepalese population is deprived of electricity coverage despite huge hydropower potential, particularly in rural areas. About 63 percent of Nepalese households lack access to electricity and depend on oil-based or renewable energy alternatives. The disparity in access is stark, with almost 90 percent of the urban population connected, but less than 30 percent of the rural population. Nepal has about 83,000 MW of economically exploitable resources, but only 650 MW have been developed so far. This study has been designated to organize an evaluation system that measures the impact of micro-hydro installations on rural livelihoods and to establish a monitoring system for Alternative Energy Promotion Center (APEC) to continually measure the results of the results of the renewable energy programs against the targets.

The algorithms relating to energy storage systems (ESSs) now play a central role in hybrid electric vehicle (HEV) applications; for example, the state of charge (SOC) of the battery system is often a critical factor in hybrid vehicle performance, fuel economy, and emissions. The voltage‐based SOC estimation method determines the SOC based on the measured or calculated open‐circuit voltage (OCV) of the battery. The accuracy of the SOC estimation not only affects the overall fuel economy, but also impacts the drivability and safety of the vehicle. This chapter introduces several SOC determination methods for HEV system applications. It discusses how battery temperature, cycle SOC depth, Ahr throughput, duty period, and the intensity of cycling affect the life of the battery in HEV/EV applications. The chapter also introduces a core temperature estimation algorithm for an air‐cooled, cylinder‐type HEV battery system. The algorithm inputs include measured cell surface temperature, current, voltage, and vehicle off time.

The first volume of Independent Evaluation Group (IEG) series (IEG 2009) examined World Bank experience with the promotion of the most important win-win (no regrets) energy policies, policies that combine domestic gains with global greenhouse gas (GHG) reductions. These included energy pricing reform and policies to promote energy efficiency. This second phase covers the entire World Bank Group (WBG), including the International Finance Corporation (IFC) and the Multilateral Investment Guarantee Agency (MIGA). It assesses of interventions, from technical assistance to financing to regulatory reform. This project-eye view of activities pertains to all the action areas of the Strategic Framework on Development and Climate Change (SFDCC). The third phase will look at the challenge of adaptation to climate change. The WBG's resources, human and financial, are small compared to the task at hand. The International Energy Agency estimates that developing and transition countries need $16 trillion of energy sector investments over 2008-30 under 'business as usual' operations, plus an additional $5 trillion to shift to an ambitiously low-carbon path. Much more is needed for sustainable land and forest management and for urban transport. So a prime focus of this evaluation is how the WBG can get the most leverage, the widest positive impact on both development and climate change mitigation, from its limited resources.