Generation Reliability Assessment Research Articles

Reliability assessment of generation capacity in modern power systems via analytical methodologies

With the recent transition to a low-carbon electrical power system (EPS), the large-scale utilization of renewable energy resources in electrical power generation introduces a substantial amount of uncertainty on the generation side of the EPS. This uncertainty, along with the inherent uncertainty of electricity demand, makes assessing generation reliability a very computationally intensive process. To enhance the computation efficiency of EPS generation reliability assessment, it is crucial to have an efficient probabilistic model of available generation capacities that strikes a balance between improved computational performance and model accuracy. In this paper, various probabilistic models are proposed to characterize the variability and uncertainty of conventional and renewable power generations (photovoltaic and wind). On the basis of these models, an analytical formulation of probabilistic reliability indices (RIs) is implemented. The computation time and accurate RIs values found using the Monte Carlo simulation method serve as the basis for reporting solving time improvements with corresponding losses in the accuracy of the RIs for different analytical methodologies. The results of multiple case studies of an EPS are presented, considering various combinations of conventional and renewable generation capacity, levels of renewable power penetration, and system reliability levels. The results indicate the practical implementation of analytical assessment methodologies compared to the simulation method in terms of accuracy and computational effort. This study is of immediate relevance and potential importance to operational reliability and generation expansion planning studies in EPSs.

The Effects of Forced Outages Rates and Load Uncertainty on The Generation Reliability Evaluation.(Dept.E)

In analysis of generation reliability assessment in power systems, the effect of both forced outages, and uncertainties in load forecasting must be taken into consideration. In this paper, the analysis has been evolved from loss-of-load and loss-of-energy probability techniques. The student-t-distribution method is used to calculate the uncertainty of the forecast peak demand at specified intervals of confidence. The effects of the numbers and the forced outage rates of the generating units have been studied.

Power system generation reliability assessment based on multi-agents technique

Power system generation reliability assessment based on multi-agents technique

Impact of Wind Generation in the Generation Reliability Assessment

Due to its intermittent behavior wind speed supposes a WTG generating model of more than two states. Uncertainty in wind power implies a non-constant power output; hence, there are a risk of loss of capacity and loads. This paper proposes a multistate model of wind generating unit, to include the effect of wind variability besides conventional generators in HL1 reliability evaluation. Through non-sequential Monte Carlo simulations it is shown that system performance decreases as a result of wind power inclusion, and reliability indices validate it. Markov process is used to design the multistate model of wind generating unit. Historical data wind velocity for sixteen past years is gathered and used for modeling, and as case study a demand data in north of Colombia is selected.

Generation reliability assessment in power markets using Monte Carlo simulation and soft computing

Deregulation policy has caused some changes in the concepts of power systems reliability assessment and enhancement. In the present research, generation reliability is considered, and a method for its assessment is proposed using intelligent systems. Also, due to the stochastic behavior of power market and generators’ forced outages, Monte Carlo Simulation is used for reliability evaluation. Generation reliability merely focuses on the interaction between generation complex and load. Therefore, in this research, based on the type of market and concentration, reserve margin and various future times, a Neuro-Fuzzy system is proposed for evaluation of generation reliability, which is valid and usable for all kinds of power pool markets. Finally, the proposed method is assessed on IEEE-Reliability Test System with satisfactory results. The results further showed that if market becomes more concentrated or price elasticity of demand increases, reliability will improve.