List of Figures
1.4. Power Distribution Reliability
1.4.3. Statistical Analysis
Statistical analysis allows monitoring system performance from a reliability point of view; it also provides information that can help validate predictive analysis as well as identify areas where reliability needs to be improved. Statistical approaches require historical data of all service interruptions experienced by customers over a defined evaluation period. System reliability will be quantified by means of the customer interruption indices; the main indices are calculated according to the following equations [1.9].
System average interruption frequency index:
T n i i N N SAIFI
∑
= = 1 ( 1.7 ) TTR TTF TTR Up Down TimeChapter 1: Introduction
21 System average interruption duration index:
T n i i i N H N SAIDI
∑
= ⋅ = 1 ( 1.8 )Customer average interruption duration index:
SAIFI SAIDI N H N CAIDI n i i n i i i = ⋅ =
∑
∑
= = 1 1 ( 1.9 )Where k is the number of interruptions, Ni is the number of customer interrupted by a fault, NT is the total number of customers in the system, and Hi is the duration of interruption to customers interrupted by a fault.
The need of much detailed data for calculation of reliability indices may represent an important drawback for certain utilities as they do not possess the necessary facilities to keep record of every interruption experienced in their systems or lack an application that allows a prompt and easy access to it, posing an obstacle for an accurate evaluation of the system reliability.
1.5. Accomplishments
The work in this Thesis has been oriented at developing a procedure for the optimum allocation of distributed generation and another procedure for reliability evaluation of distributed systems; both procedures are based on the Monte Carlo method.
The procedure for optimum allocation of distributed generation has been developed to determine the quasi-optimum rated power and location of one or more generation units when the objective is to achieve the maximum energy loss reduction; it is capable of evaluating any system regardless of its topology or model used for load representation. Energy system losses are calculated by simulating the system for the specified evaluation period; the procedure can cope with different evaluation periods, ranging from one year to up to 10 years or more. The general Monte Carlo procedure was refined in order to reduce the number of necessary executions; the new methods introduced as “Refined Monte Carlo” and “Divide and Conquer” were tested and proved to cause a reduction in total simulation times without loss in the results accuracy.
The reliability evaluation of distribution systems is carried out by simulating the effects of element failure in the continuity of service. Failed elements and repair times are randomly generated to replicate the stochastic nature of system failure. The procedure has been developed to cope with system reconfiguration processes and the presence of distributed generation in the system. The system under evaluation is simulated during consecutive runs (varying the number and characteristics of failed elements) in order to obtain the probability density functions of reliability indices.
Analysis of Power Distribution Systems Using a Multicore Environment
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Due to their Monte Carlo nature both developed methods are time consuming and require a large number of runs/samples to obtain accurate results. As a result it was necessary to introduce new techniques in order to reduce total simulation times; parallel computing was the tool chosen to achieve this goal. Thanks to the application of parallel computing it was possible to execute both methods in affordable times without any loss in accuracy. Additionally, these methods require information regarding the load and generation behavior over the evaluation period; therefore, three algorithms were implemented in order to obtain node load profiles, and solar and wind generation curves. These three algorithms allow the user to generate the necessary information without having to rely on external tools (e.g. HOMER [1.33]).
As a result of the research work carried out for this Thesis, several technical papers have been submitted to different conferences and journals. The complete list of accepted and submitted papers is as follows:
1. J.A. Martinez and G. Guerra, “Optimum placement of distributed generation in three-phase distribution systems with time varying load using a Monte Carlo approach,” IEEE PES General Meeting, San Diego, July 2012.
2. J.A. Martinez and G. Guerra, “A Monte Carlo approach for distribution reliability assessment considering time varying loads and system reconfiguration,” IEEE PES General Meeting, Vancouver, July 2013.
3. J.A. Martinez and G. Guerra, “A Parallel Monte Carlo method for optimum allocation of distributed generation,” IEEE Trans. on Power Systems, vol. 29, no. 6, pp. 2926-2933, November 2014.
4. G. Guerra and J.A. Martinez, “A Monte Carlo method for optimum placement of photovoltaic generation using a multicore computing environment,” IEEE PES
General Meeting, National Harbor, USA, July 2014.
5. J.A. Martinez and G. Guerra, “A Parallel Monte Carlo approach for distribution reliability assessment”, IET Gener., Transm. Distrib., vol. 8, no. 11, pp. 1810- 1819, November 2014.
6. J.A. Martinez-Velasco and G. Guerra, “Analysis of large distribution networks with distributed energy resources”, Ingeniare, vol. 23, no. 4, pp. 594-608, October 2015.
7. G. Guerra, J.A. Corea-Araujo, J.A. Martinez, and F. Gonzalez-Molina, “Generation of bifurcation diagrams for ferroresonance characterization using parallel computing,” EEUG Conf., Grenoble (France), September 2015.
8. G. Guerra and J.A. Martinez, “Optimum allocation of distributed generation in multi-feeder systems using long term evaluation and assuming voltage- dependent loads,” Sustainable Energy, Grids and Networks, vol. 5, pp. 13-26, March 2016.
9. J.A. Martinez and G. Guerra, “Reliability Assessment of Distribution Systems with Distributed Generation Using a Power Flow Simulator and a Parallel Monte Carlo Approach,” Submitted for publication in Sustainable Energy, Grids and
Networks.
10. J.A. Martinez-Velasco and G. Guerra, “Allocation of Distributed Generation for Maximum Reduction of Energy Losses in Distribution Systems,” Chapter 12 of
Energy Management of Distributed Generation Systems, InTech, In editing
Chapter 1: Introduction
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