Robust loss coefficients: application to power systems with solar and wind energy

This work deals with assessing the reliability of wind energy sources incorporated into a microgrid system to increase grid stability. As the share of power generated from wind energy continues to increase due to environmental concerns and to reduce carbon footprints, the case study proposed is designing an effective wind energy system for a smart building environment to reduce the burden on the grid. Incorporating wind energy systems into grid-connected buildings increases the reliability of the microgrid system for smart building environments. Applying smart techniques with integrated automation in the built environment can play a key role in decreasing peak demand for load development in smart cities. This can also enhance the overall efficiency of power grids, reduce waste, and enable the effective use of energy resources. Furthermore, researchers are finding new ways to collect energy from renewable sources and power-built environments due to dwindling fossil fuel resources and increased environmental awareness. The analysis of wind-connected power systems has been initiated to satisfy the increasing demand for reliable supply systems by consumers. This has prompted the use of wind energy systems with controlled techniques to enhance the reliability of power systems and reduce carbon footprints generated by fossil fuel-based power systems. Therefore, the main purpose of this work is to understand the economic prospects of wind energy sources for smart building environments, reliability analysis of wind energy sources-based power systems, reduce carbon footprints, and balance power demand through wind energy systems. The Progressive Wind Model Taylor-Sequence Proliferation (PWMTSP) and Innovative Six Parameters Based Progressive Beta Creative Model (ISPBPBCM) approaches are proposed to understand the reliability of wind energy-based power systems in smart building environments. The work also provides an economic analysis of the proposed low-cost wind power system with different capacities for residential consumers in selected geographic location. Eight cases are considered for the proposed effective wind energy system for smart building environments ranging from 2 MW to 32 MW. Hence, the economic analysis of the proposed systems is discussed for residential users. All eight cases are eco-friendly and reduce carbon emissions as these generated units from wind energy sources need not be purchased from fossil fuel-based plants, which increases the reliability of the proposed system. The expected energy not supplied (EENS) in the planned work increases as wind power capacity increases. Loss of load expectation (LOLE), loss of load probability (LOLP), and failure rate all decrease with the installation of more wind power systems. Moreover, it can be seen that mean time to repair (MTTR) improves when wind energy capacity rises incrementally from 2 MW to 32 MW. The proposed method is anticipated to be adaptable to various types of wind energy systems where dependability is a crucial component of system design and scalable to large wind integrated power systems.

Assessing complementarity of wind and solar resources for energy production in Italy. A Monte Carlo approach

Nowadays, the increasing energy demand, development of smart grids, and the combination of different energy systems have led to the complexity of power systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of solar and wind energies have made electricity networks operate in more complex and uncertain conditions. Therefore, analysis of traditional power and energy systems requires physical modeling and extensive numerical computation. To analyze these systems’ behavior, advanced metering and condition monitoring devices and systems are utilized, which generate huge amounts of data. Assessment of these data is approximately impossible by conventional methods and requires powerful data mining procedures. Machine learning, deep learning, and a variety of regression, classification, and clustering algorithms are powerful tools to use in these issues. These procedures can be utilized for load/demand forecasting, demand response evaluation, defect/fault detection in electrical equipment, power system analysis and control, cybersecurity, and renewable energy generation prediction. Understanding the structure and functioning of each learning method is therefore one of the most important issues in the application of them to solve power system problems. In this chapter, we will introduce and discuss selected methods of data mining based on their learning, structure, formulation, mode of operation, and application in power systems. Literature on machine learning and deep learning procedures, train and test process of networked methods, and, finally, applications of each procedure are presented in this chapter.

A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria

With the rapid development of wind power generation technologies, increasing wind power has been integrated into power systems. In order to study the impact of wind power uncertainties on power systems, efficient wind speed modeling and simulation methods are demanded. In this paper, the stochastic characteristics and spatially correlation of wind speeds of wind sites are presented. Then, three common wind speed simulation methods to generate spatially correlated wind speeds are compared, including Cholesky decomposition, Nataf transformation and Copula method. Further, the historical wind speeds of two wind sites in Netherland are used, and the performances of the three simulation methods are analyzed by some numerical tests. This paper provides guidance for the choosing of wind speed simulation methods in power system analysis.

Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation: A case study in East and West Malaysia

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.

The article highlights the feasibility of using dual-current systems in ship electrical power systems in conditions of using renewable energy sources. The charts of energy resources of solar and wind energy in Russia have been presented and analyzed. The feasibility of using wind energy in the northern and eastern regions and solar energy in the southern regions has been discussed. Alternative sources on dry-cargo ships, tankers and bulk barges may well provide propulsion energy. Operation of traditional energy sources together with the alternative ones in a combined ship electric power system is provided by the system of dual current type with built-in DC network. The test results of the pilot plant which allows parallel operation of different generators in a dual current system have been described. The experiments included the parallel operation of one generator; load distribution between parallel-running synchronous generators, asynchronous generators, between synchronous and asynchronous generators; load transfer and decommissioning of one of the generators. Parallel operation was carried out on DC network. The load transfer of the synchronous generator can be carried out by changing excitation current, and asynchronous generator - by changing frequency of rotation of the drive motor. It has been found out that the DC network allows: to connect various sources for parallel operation in terms of power, statism, and type of current; to regulate loading by changing the moment on a shaft of the driving engine; to fulfil easier switching to the “built-in” DC network of renewable energy sources.

An integrated development environment based situational awareness for operational reliability evaluation in wind energy systems incorporating uncertainties

The wind power generation is increasing in many countries as a result of decreasing technology costs, active government policies for renewable energy sources, environmental concerns, etc.. This paper investigates the impact of wind power generation on ATC (available transmission capacity) calculation. In order to determine the maximum incremental MW transfer possible between two parts of a power system without violating any specified limits, ATCs are calculated. When calculating ATC values, it is necessary to assume production and consumption pattern in power system. Production of wind power depends on the wind speed, which is a random variable and it is impossible to forecast exactly the production of wind power that is needed for the ATCs calculation. In order to investigate influence of the stochastic wind power production on the ATCs value, computer model of Croatian electric power system is made in Power World Simulator. ATCs are calculated for southern part of Croatian power system in which besides wind power, hydro power plants are only type of power generation. Available wind speed measurements are used as input data for wind power production. The results of the ATC calculation for different scenario of wind power production and location in the Southern Croatian power system are presented and discussed in the paper.

The imminent exhaustion of fossil energy resources and the increasing demand for energy were the motives for those reasonable in world to put into practice an energy policy based on rational use of energy; and on exploitation of new and renewable energy sources. After 1980, as the supply of conventional energy has not been able to follow the tremendous increase of the production demand in rural areas of the world, a renewed interest for the application of solar and wind energy has shown in many places. Therefore, the researchers and engineers began to pay more attention to wind and solar energy utilisation in rural areas. Because the wind energy resource in many rural areas is sufficient for attractive application of wind pumps, and as fuel is insufficient, the wind pumps will be spread on a rather large-scale in the near future. Wind is a form of renewable energy, which is always in a non-steady state due to the wide temporal and spatial variations of wind velocity. The need for the provision of new data stations in order to enable a complete and reliable assessment of the overall wind power potential of the country is to be identified and specific locations suggested. This article presents the background and ideas of the development of the concept as well as the main results and experience gained during ongoing projects up to now. In the world, various designs of wind machines for water pumping have been developed and some designs are presently manufactured commercially. Results suggest that wind power would be more profitably used for local and small-scale applications especially for remote rural areas. It is concluded that many parts of the world is enjoyed with abundant wind and solar energy resources.

There is a growing need for an environmentally friendly source of energy that can replace the conventional fossil fuel energy. This is because the effects of global warming is becoming very obvious, as evidenced by the severe flooding that occurred in the U.S. in 20171. Two notable solutions to this dilemma are wind and solar energy. Solar powered devices derive their energy from the sun, hence, the amount of energy is severely limited during the cold months of the year when solar intensity is typically low. Wind energy, on the other hand is most prevalent during this cold months when the wind speed is typically higher. The aim of this research is therefore to conduct a comprehensive assessment of wind energy potential in Michigan City, Indiana, United States. This information will allow homeowners and investors with interest in alternative energy to make critical decisions in this regard. The study was conducted using wind speed data collected over a five-year period from 2012 to 2016. In this work, we have also determined the best method for evaluating the Weibull parameters (shape and scale factors) for wind data analysis. The site average wind speed ranged from 4m/s to 9m/s with a peak in the winter months and minimum in the summer months. The wind speed with the maximum energy at the hub height varied between 5.84 m/s in August 2016 to 12.79 m/s in October 2012 with annual average speeds between 8.85 and 9.35 m/s and a five-year average of 9.13 m/s. The prevailing wind speed was within the range of 4–8m/s and strongest on the Southern part of the site especially in the South Southeastern direction. Consequently, siting a wind turbine on the Southern part of the City would generate more energy than on any other direction. Among the turbines analyzed, ITALTECH 250 will yield the maximize energy production with a capacity factor of 0.385 and average annual energy production of 840 MWh/yr. The results presented in this work proves the great potential for investments in wind energy in Michigan City.

With increasing heating demand and installed capacity of wind farms, combined heat and power (CHP) systems with inflexible generation are confronted with a major challenge for the efficient utilization of the wind power. Distributed heat sources (DHSs), such as electric boilers and heat storage, offer an effective way to enhance wind power penetration in a combined heat and power system by reducing the electric-heat coupling of the CHP unit. In this paper, an assessment approach for the wind power penetration of a CHP system with DHSs is proposed, and a two-stage multiple types of DHSs optimal configuration strategy to realize the minimum construction cost at a certain permissible wind power curtailment rate is established. After that, the proposed strategy is applied to a case study of a practical CHP system with in-depth analysis and detailed discussion, which reveals the effectiveness of the proposed strategy and indicates its promising applications for DHS configuration.

The Landscape Influence on the Wind Energy Distribution in Height on the Latvian Coast of the Baltic Sea

The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm, the power-law process was used to simulate the wind speeds at a hub height of 60 m. The Weibull and Rayleigh distributions were chosen to express the wind speeds at two different heights. The parameters in the model were estimated via the least square (LS) method and the maximum likelihood estimation (MLE) method, respectively. An adjusted MLE approach was also presented for parameter estimation. The main indices of wind energy characteristics were calculated based on observational wind speed data. A case study based on the data of Hexi area, Gansu Province of China was given. The results show that MLE method generally outperforms LS method for parameter estimation, and Weibull distribution is more appropriate to describe the wind speed at the hub height.