A multi-state model for the reliability assessment of a distributed generation system via universal generating function

Abstract

International audienceThe current and future developments of electric power systems are pushing the boundaries of reliability assessment to consider distribution networks with renewable generators. Given the stochastic features of these elements, most modeling approaches rely on Monte Carlo simulation. The computational costs associated to the simulation approach force to treating mostly small-sized systems, i.e. with a limited number of lumped components of a given renewable technology (e.g. wind or solar, etc.) whose behavior is described by a binary state, working or failed. In this paper, we propose an analytical multi-state modeling approach for the reliability assessment of distributed generation (DG). The approach allows looking to a number of diverse energy generation technologies distributed on the system. Multiple states are used to describe the randomness in the generation units, due to the stochastic nature of the generation sources and of the mechanical degradation/failure behavior of the generation systems. The universal generating function (UGF) technique is used for the individual component multi-state modeling. A multiplication-type composition operator is introduced to combine the UGFs for the mechanical degradation and renewable generation source states into the UGF of the renewable generator power output. The overall multi-state DG system UGF is then constructed and classical reliability indices (e.g. loss of load expectation (LOLE), expected energy not supplied (EENS)) are computed from the DG system generation and load UGFs. An application of the model is shown on a DG system adapted from the IEEE 34 nodes distribution test feeder

Optimal planning of stand-alone microgrids incorporating reliability

“…Genetic algorithm [25] is applied to solve the optimization problem (13), which is no longer illustrated here for simplicity.…”

“…A reliability index is usually treated as either an objective [3] or a constraint [2], [4], [7], which can be evaluated using analytical methods [7]- [8], or Monte Carlo simulation [9], [10]. To consider the impacts of the volatility of power outputs of the distributed generators on the system reliability, multistate models are developed in [11], [12]- [13]. Each state, generated by splitting the probability distributions of RESs, stands for a special power level with corresponding occurrence probability.…”

Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model

“…Note that for the Hybrid Basic Events of the inverters, variable failure rates are updated during the simulation of an iteration in order to consider the variation of the aging variable. From lines (4)(5)(6)(7)(8)(9)(10)(11), the other variables required for the Monte Carlo simulation are initialized and line (12) sets the name of the Simulink model (which corresponds with the .slx file) that will be called at the beginning of the simulation (in our code it is named 'hybrid_pair_1'). With line (13) the variables initialized with the Matlab script are passed to the Simulink environment and line (14) starts the simulation.…”

“…InitDP(); # initialize the parameters used in the Simulink deterministic block In particular, the script stops the Simulink simulation (line 2), updates the global variables and the estimator of the Matlab workspace (with the method UpdateGlobalVariables()) using the information generated in the Simulink environment (line 3) and verify if the accuracy required by the Monte Carlo setting is reached (line 4). If the variable 'completed' is not True (=1), lines (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) resets the simulation parameters to prepare for a next iteration. Before calling the built-in method of Simulink 'set_param' (line 17) to update the Simulink workspace for a new iteration, an important setting has to be performed (lines [8][9][10][11][12].…”

“…The availability of a system is defined as the probability of a system to operate satisfactorily at a given point in time under stated operation conditions [9] and it can be computed for any type of industrial system comprised of different components through quantitative stochastic modelling methods The models typically adopted are shown in Table 2. They can be divided in three different groups: static, dynamic and hybrid-dynamic models [10,11]. Static or Boolean models are the simplest models as they can be solved directly with combinatorial logic.…”