Reliability Evaluation of Bangladesh Power System Using Cumulant Method

Topics: Electric power transmission, Electricity distribution, Failure Pages: 15 (3172 words) Published: August 26, 2013
Reliability Evaluation of Bangladesh Power System Using Cumulant Method Nahid-Al-Masood1, M. N. Sahadat2, S.R. Deeba
Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka, Bangladesh E-mail: 1 nahid@eee.buet.ac.bd, 2sahil0406@gmail.com

S.Ahmed1, G.A.K.Biswas, A.U.Elahi, N.M.Zakaria
Department of Electrical and Electronic Engineering BRAC University Dhaka, Bangladesh E-mail: 1shahriar.me@gmail.com weather effects is presented in [6] where the DC-OPF approach is used to determine minimal cut sets (MCS) up to a preset order and then MCSM is used to calculate reliability indices. The appropriate incorporation and presentation of the implications of uncertainty are widely recognized as fundamental components in the analyses of complex systems [7].There are two fundamentally different forms of uncertainty in power system reliability assessment [7, 8]. Aleatory and epistemic uncertainties are considered in power system reliability evaluation in [9] where aleatory uncertainty arises because the study system can potentially behave in many different ways. A method for incorporating the failures due to aging in power system reliability evaluation is presented in [10]. It includes the development of a calculation approach with two possible probability distribution models for unavailability of aging failures and implementation in reliability evaluation. Adverse weather such as hurricanes can have significant impact on power system reliability [11, 12]. One of the challenges of incorporating weather effects in power system reliability evaluation is to assess how adverse weather affects the reliability parameters of system components. A fuzzy inference system (FIS) built by using fuzzy clustering method is combined with the regional weather model to solve the preceding problem is illustrated in [13]. A new computationally efficient methodology for calculating the reliability indices of a bulk power system using the state enumeration approach is depicted in [14]. The approach utilizes topological analysis to determine the contribution of each system state to the frequency and duration indices at both the system and the bus level. Common cause outage is also considered in power system reliability evaluation [15]. Power system reliability evaluation and quality assessment using fuzzy logic and genetic algorithm are depicted in [16] and [17], respectively. Cumulant method, a very fast computational technique is used to evaluate the reliability of BPS in this paper. Reliability index ‘LOLP’ is assessed for this intention. LOLP gives the probability that the available generation capacity will be insufficient to meet the daily peak loads. The simulation results show that the LOLP of BPS is 2.06%.

Abstract—In recent years, power systems are frequently operating under highly stressed and unpredictable conditions because of not only the market-oriented reform of power systems but also the integration of various renewable energy sources. The uncertain factors resulting from the market constraints and the inherent randomness of renewable energy set higher requirements on the reliability of electric networks. This paper presents the reliability assessment of Bangladesh Power System (BPS). Reliability index, loss of load probability (LOLP) of BPS is evaluated using cumulant method. The rationale for using the cumulant method is to take advantage of its computational efficiency. BPS has sixty one generators and a total installed capacity of 5275 MW. The maximum demand of BPS is about 5000 MW. The relevant data of the generators and hourly load profiles are collected from the National Load Dispatch Center (NLDC) of Bangladesh and reliability index ‘LOLP’ is assessed. The simulation results show that the LOLP of BPS is 2.06%. Keywords—cumulant method, convolution, LOLP, forced outage rate, Gram-Charlier series

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INTRODUCTION

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