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Renewable energy sources are not necessarily declining carbon sources. A hybrid generation network may be used in the power market to maximize the usage of renewable energy sources. The aim is to model hybrid power station and to determine the most effective system design through cost estimation. The power consumption of an agricultural device illustrates fertilizer house electricity needs and agricultural land irrigated pumps providing an average of 165.44 kWh / h of electricity per hour, an overall energy use of 7.12 kW and a total load of operation of 19.57 kW. The standard radiation intensity is 5.35 kWh per m2 per day, the luminosity amount is an around 0.534, and the maximum wind velocity is 4.60 m/s. Electricity generation is $323,659.25 and the gross electricity expense (Co2) is $1325 and reflects an annual monthly power consumption of 21,264 kWh (47.1%) and 12,756 kWh/yr (36.8%). [ABSTRACT FROM AUTHOR]

In order to maintain power system stability and process survivability for major system faults under a variety of system configuration topologies, the selection of proper governor system is essential. Islanded power systems present very distinct challenges, whereby the lack of utility interconnection hinders the system’s ability to recover from a loss of generation. In order to maintain power system stability in an islanded configuration, fast acting governor system in response to a specific event in the plant is the key factor governing plant survivability. In this paper an attempt has been made to study the performance of different excitation and Governor models of captive Generator sets for an Industry which supplies both process and utility load and exports to national grid. A new governor model is also developed to improve the transient stability performance captive generator system thus improve the reliability of power generation of captive power plant (CPP). The analysis was done using Electrical Transient Analysis Program (ETAP) Version 11 software and the simulation results are depicted in the graphs. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a comparison of results for optimization of captive power plant maintenance scheduling using genetic algorithm (GA) as well as hybrid GA/simulated annealing (SA) techniques. As utilities catered by captive power plants are very sensitive to power failure, therefore both deterministic and stochastic reliability objective functions have been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. The significant contribution of this paper is to incorporate stochastic feature of generating units and that of load using levelized risk method. Another significant contribution of this paper is to evaluate confidence interval for loss of load probability (LOLP) because some variations from optimum schedule are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are incorporated in terms of near-optimum schedules obtained from hybrid GA/SA technique during the final stages of convergence. Case studies corroborate that same optimum schedules are obtained using GA and hybrid GA/SA for respective deterministic and stochastic formulations. The comparison of results in terms of interval of confidence for LOLP indicates that levelized risk method adequately incorporates the stochastic nature of power system as compared with levelized reserve method. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power. [Copyright &y& Elsevier]

This paper presents a fuzzy Markov model to efficiently incorporate the influences of maintenance scheduling as well as aging of generating units on failure-repair cycle for computation of state probabilities. The proposed model, different from conventional models that are based on a probabilistic approach, employs a fuzzy set concept together with a probabilistic Markov model. This model incorporates fuzzy mean time to failure (FMTTF) and fuzzy mean time to repair (FMTTR) instead of crisp (expected) values for capturing the generating unit uncertainties more effectively through expert evaluation. The fuzzy state probabilities are computed from FMTTF and FMTTR values using fuzzy arithmetic operations for evaluation of the reliability index, fuzzy loss of load probability (FLOLP). Case studies for the maintenance scheduling of a captive power plant catering to an aluminum smelter have been formulated to demonstrate that fuzzy state probabilities as well as FLOLP provide a better explanation than the respective crisp counterparts. [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize maintenance scheduling of generating units of a captive thermal plant using intelligent optimization techniques such as genetic algorithm (GA) and simulated annealing (SA). A multi-class maintenance scheduling has been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. Such multi-class maintenance scheduling in terms of mathematical formulations necessitates a planning horizon of five years compared to the conventional one to two year horizon. As the utilities catered by captive power plants are very sensitive to power failure, the reliability of power supply has been evaluated by loss of load probability (LOLP) index. The significant contribution of this paper is to evaluate confidence intervals of LOLP instead of conventional point estimate of LOLP. A point-estimate of LOLP uses expected values and provides no information about the range of values over which LOLP may vary. Some variations from optimum schedules are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are considered in terms of near-optimum schedules obtained from SA during the final stages of convergence. LOLP evaluated from such sub-optimum schedules forms the basis to treat LOLP as a random variable for subsequent evaluation of confidence intervals. Case studies pertaining to a captive power plant for an aluminium smelter corroborate that the schedules obtained from two optimization techniques yield the same optimum schedule and thus the efficacy of the proposed algorithm is validated. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy model for reliability evaluation of captive power plant maintenance scheduling optimizing safety and reliability incorporating uncertain forced outage rate and load representation. Such a model is a combination of probabilistic fuzzy state (PROFUST) model and fuzzy load model. In PROFUST model, fuzzy numbers represent the failure and repair rates of generating units because it is inadequate from pragmatic prospective to represent them by crisp numbers. As the maintenance tends to be based on experience-based skills, therefore the fuzzy forced outage rate (fuzzy FOR) based on expert evaluation reflects the condition of operation and maintenance of thermal generating units more realistically compared to a constant failure and repair rate model yielding constant FOR value. The uncertainties due to load forecasting lead to fuzzy load model. Particularly the utilities catered by captive power plants are very sensitive to power failure and the reliability evaluation corroborates the effect of uncertainties through fuzzy loss of load probability (FLOLP) index. Case studies for the maintenance scheduling of a captive power plant catering to an aluminum smelter have been formulated based on both classical probabilistic as well as fuzzy model and comparisons of FLOLP demonstrate the efficacy of the proposed model. [Copyright &y& Elsevier]