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Electrical power demand is increasing cumulatively with the increase in population. Economic and environmental impacts restrict the expansion of conventional generation but prompting for intermittent wind sources due to its economic and environmental benefits. But the average capacity value of the wind power is not the same as that of the same rate...
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Decreasing the electric length of an antenna results in increasing its input reactance that becomes greater than its input resistance, resulting in a significant rise in its quality factor and in a drastic reduction of its potential operating bandwidth. For such small antennas, namely ESA for Electrically Small Antenna, passive matching is restrict...
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... 9,12 Therefore, it is mere practical to match the LOLE with that of a conventional unit of known outage rate, known as ECC. 9,12 The capacity value estimation approaches are mainly focusing on maintaining the adequacy level before and after integration of the solar capacity. Let us consider that the rated capacity of solar power, which is P SPVr (with available power P SPV ), is integrated to the conventional system (with available power A); the probability of not taking the load L is given by P(A + P SPV < L), and the reliability-based methods available for capacity value estimation are explained as below. ...
... To match the earlier index, load is to be gradually raised. 9,11,12 The steps to find ELCC of the solar generation are as follows: ...
... Again, it determines the lower and upper limit of the discriminator key for the two new subfiles. 9,26 If the node under inspection is a terminal node, the stored data in the bucket are investigated. A priority queue is always maintained during search with a list of the adjacent records so far encountered and their distance to the query record. ...
The estimation of capacity values of unconventional sources like solar and wind is quite difficult due to their intermittent nature. Mainly reliability‐based methods are generally followed to calculate the capacity values. Intermittent solar power cannot be clubbed directly in the process of reliability calculation. Solar power is generally taken as negative load in adequacy assessment and subtracted from the load on hourly basis. Random solar radiation creates redundant solar power generation. Bypassing multiple estimations of capacity values for the redundant solar power, an approach is made to construct a database considering the incremental added capacities with their resulting loss of load expectation. The database is portrayed in a multidimensional k‐d tree to locate queried expected loss of load of solar power effectively using nearest neighbor search algorithm. The algorithm is providing the best match record as well as the associated information like the capacity values stored along with that record. A case study of 75 MW of conventional with 15 MW solar powers is considered for the validation of the proposed methodology. Capacity values corresponding to each reliability‐based methods resulted from the database seem to be satisfactory in evaluating the capacity values of solar power. The applicability of the proposed method is extended through implementing on the IEEE RTS system.
... To discover the wind turbines' optimal number that must be introduced in the wind plants under production of particular power, an optimization problem was planned by minimizing the total cost. Based on reliability assessment of the system, few meanings of capacity value of wind power were shown by Nayak et al. 26 At wind, speed of hourly step is changing so the power the estimation of capacity value is not simple. In this manner, in a single database, another methodology was created to amass all the data like the wind power sequential value, reliability indices, and relating capacity values. ...
Reliability evaluation of power‐generating systems gives a mechanism to guarantee proper system operations in the face of different uncertainties including equipment failures. It is regularly not attainable to identify all possible failure states to figure the reliability indices because of the large number of system states engaged with system operations. Therefore, a hybrid optimization technique is required to analyse the reliability of the power system. This paper proposes a hybrid optimization technique to evaluate the reliability of a power system for the generation expansion planning incorporating wind energy source. The proposed hybrid methodology is the joined execution of both ant lion optimization algorithm (ALO) and lightning search algorithm (LSA), and it is named as ANLSA. ALO searching behavior is enhanced by LSA. Through the inherent convergence mechanisms, ANLSA search the meaningful system states. The most probable failure states contribute reliability indices of power generating system including mean down time (MDT), loss of load probability (LOLP), loss of load expectation (LOLE), loss of load frequency (LOLF), and expected demand not supplied (EDNS). Furthermore, ANLSA is utilized to assess the reliability of system under normal condition, integration of wind farm with capacity of 150 MW, and considering spinning reserve requirement (SRR). By then, the proposed work is actualized in MATLAB/Simulink platform and it is demonstrated on IEEE reliability test system (IEEE RTS‐79). Furthermore, the statistical analysis of proposed and existing techniques such as Monte Carlo simulation (MCS) and discrete convolution (DC) is considered. The comparison results demonstrate that proposed approach confirms its ability for evaluating the power system reliability.
Today, the increase of renewable energy in the overall power generation mix has become a necessity for achieving environmental sustainability. It has provided a tectonic shift in the procuring, transmission, and distribution of power. This will enhance the share of renewable energy from 28% to nearly double in merely 15‐20 years. This change has catapulted into the power generation mix due to the evolution of new technologies like low‐power grid connectivity or off‐grid distributed generation systems, alternative charging modes for electric vehicles favorable to the conventional automobile technology. New advances in wind, solar, and other renewable resources permit them to be utilized over a broader range of conditions. Researchers are rethinking the traditional designs, operations, and planning from technical, economic, environmental, and socio‐political viewpoints for remote power distribution infrastructure grid connection. The transition from considerable power generating systems to low‐power distribution arrangements has brought new energy management challenges, power quality, and system reliability. The chapter addresses these issues. It starts with a methodology of implementing a hybrid energy system/integrated renewable energy system. Then it focuses on the challenges during grid connection of integrated renewable energy systems or hybrid energy systems and how to address them.
Insights from geohash coding algorithms introduce significant opportunities for various spatial applications. However, these algorithms require massive storage, complex bit manipulation, and extensive code modification when scaled to higher dimensions. In this article, we have developed a two‐bit geohash coding algorithm that divides the search space into four equal partitions where each partition is assigned a two‐bit label as 00, 01, 10, and 11, which helps to uniquely identify a chosen data point and the two neighbors on its either side, taken along a particular dimension. This salient feature of the algorithm simplifies the generation of geohash code for the neighboring grid cells. In addition, it achieves efficient memory utilization by storing the geohash values of the training points as integers. Demonstrated by experiments for climate data assimilation, model‐to‐observation space mapping with a geohash code length of 24 bits for Lat‐Lon extent of India has shown favorable results with an accuracy of 85%. Performance and scalability evaluation of the proposed algorithm, optimized for multicore and many‐core processors has shown significant speedups outperforming a tree‐based approach. This algorithm provides a foundation for new spatial statistical methods that can be used for pattern discovery and detection in spatial big data.
