Optimal reliability planning for a composite electric power system based on Monte Carlo simulation using particle swarm optimization

Bakkiyaraj, R. Ashok; Kumarappan, N.

doi:10.1016/j.ijepes.2012.10.055

Cited by 62 publications

(25 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to strictly control carbon dioxide emissions and reduce greenhouse gas outputs, carbon emissions need to be lower than the maximum carbon emissions (CO 2max ): (19) •…”

Section: Inequality Constraintsmentioning

confidence: 99%

“…Liu W. et al [18] use the pseudo-sequential Monte Carlo simulation to model the uncertainty of PV outputs, and it also combines the intelligent state space reduction to evaluate the economic performance of a power system with PV installation. In addition, based on the state sampling non-sequential Monte Carlo simulation and the DC load flow-based load curtailment model, Bakkiyaraj R.A. et al simulated the random fault of the power system, and put forward a power system reliability evaluation method [19]. Also, by using the Monte Carlo method to simulate unbalanced voltage and then analyzing the effects of unbalanced voltage on PV hosting capacity, the hosting capacity of PV in different regions was shown in [20].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a joint operation model is proposed to decrease the high uncertainty of a wind power system by centralizing electric vehicle charging stations. In [18][19][20][21], the Monte Carlo method was successfully used to model the uncertainty of power systems, but this method has some limitations, such as for example, high complexity and low computation efficiency. In order to avoid the limitations of the Monte Carlo method, factors (excluding the uncertainty of renewable energy resource) that can affect energy storage system installation capacity are comprehensively considered when sizing hybrid energy storage systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales

Liu

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

Abstract:With the rapid development of industry, more fossil energy is consumed to generate electricity, which increases carbon emissions and aggravates the burden of environmental protection. To reduce carbon emissions, traditional centralized power generation networks are transforming into distributed renewable generation systems. However, the deployment of distributed generation systems can affect power system economy and stability. In this paper, under different time scales, system economy, stability, carbon emissions, and renewable energy fluctuation are comprehensively considered to optimize battery and super-capacitor installation capacity for an off-grid power system. After that, based on the genetic algorithm, this paper shows the optimal system operation strategy under the condition of the theoretical best energy storage capacity. Finally, the theoretical best capacity is tested under different renewable energy volatility rates. The simulation results show that by properly sizing the storage system's capacity, although the average daily costs of the system can increase by 10%, the system's carbon emissions also reduce by 42%. Additionally, the system peak valley gap reduces by 23.3%, and the renewable energy output's fluctuation range and system loss of load probability are successfully limited in an allowable range. Lastly, it has less influence on the theoretical best energy storage capacity if the renewable energy volatility rate can be limited to within 10%.

show abstract

“…In order to strictly control carbon dioxide emissions and reduce greenhouse gas outputs, carbon emissions need to be lower than the maximum carbon emissions (CO 2max ): (19) •…”

Section: Inequality Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales

Liu

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Sahoo et al [10] used particle swarm optimization (PSO) for multi objective planning of electrical distribution system accounting sectionalizing switches and tie lines. Bakkiyaraj and Kumarappan [11] used Monte Carlo simulation (MCS) along with PSO for reliability planning of power network in terms of forced outage rate (FOR) allocation. Damghani et al [12] used PSO for multi objective planning and redundancy allocation.…”

Section: List Of Symbolsmentioning

confidence: 99%

Reliability Optimization of Radial Distribution Systems Employing Differential Evolution and Bare Bones Particle Swarm Optimization

Kela¹,

Arya²

2014

J. Inst. Eng. India Ser. B

View full text Add to dashboard Cite

This paper describes a methodology for determination of optimum failure rate and repair time for each section of a radial distribution system. An objective function in terms of reliability indices and their target values is selected. These indices depend mainly on failure rate and repair time of a section present in a distribution network. A cost is associated with the modification of failure rate and repair time. Hence the objective function is optimized subject to failure rate and repair time of each section of the distribution network considering the total budget allocated to achieve the task. The problem has been solved using differential evolution and bare bones particle swarm optimization. The algorithm has been implemented on a sample radial distribution system. List of symbolsSAIFI System average interruption frequency index SAIDI System average interruption duration index CAIDI Customer average interruption duration index AENS Average energy not supplied k k , r k Failure rate and average repair time of k th distributor segment respectively L i Average load connected at i th load point k sys,i System failure rate at i th load point U sys,i System annual outage duration at i th load point N i Number of customers at load point i J Objective function k i,min and r i,min Reachable minimum values of failure rate and repair time of i th distributor segment k i,max and r i,max Maximum allowable failure rate and repair time of i th distributor segment SAIFI t, SAIDI t , CAIDI t and AENS t Target values of the respective indices CBUDGET The total budget available for preventive maintenance and corrective repair a K, b K Cost coefficients X ij 0 j th parameter of X i vector X j,min and X j,max Lower and upper bounds on variable X j rand j Random digit in the range [0, 1] r Scale factor X base (k) Base vector V ðkÞ i Mutant vector t i (k) Trial vector Cr Cross over rate or cross over probability j rand Random integer between [1,D] X ðkÞ i

show abstract

“…Prediction of reliability is extremely important for the distribution network and it is considered as a property of performance evaluation in distribution network [1]- [2]. It is very important to make a proper invest for power enterprise, which aims to gain a better profit.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Equipment Failure Rate Accounting for the Uncertainty

Tian

Wen

Wang

et al. 2015

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

A fuzzy model for failure rate with the consideration of the effects of uncertain factors in distribution reliability evaluation is presented. The possibility and credibility distribution analyzed on the basis of sample datum are used for quantifying effects of the uncertainty done to failure rate.Mathematically, the failure rate can be obtained in the interval integration. Moreover, aiming to make the calculating quantity of system reliability evaluation simple and easy, the fuzzy clustering analysis of equipment is adopted. The technique proposed has been implemented in an example distribution system for illustration and the results obtained have been compared with those obtained with average model.

show abstract

Optimal reliability planning for a composite electric power system based on Monte Carlo simulation using particle swarm optimization

Cited by 62 publications

References 20 publications

Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales

Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales

Reliability Optimization of Radial Distribution Systems Employing Differential Evolution and Bare Bones Particle Swarm Optimization

Modelling of Equipment Failure Rate Accounting for the Uncertainty

Contact Info

Product

Resources

About