Characterization of power quality performance at network buses using unified power quality index

Power Quality performance is a critical issue for both contemporary and future distribution networks operation and planning. DSOs started to pay significant attention to the levels of the PQ provided in their networks for many reasons, such as, the current deregulated electricity markets, increased DG penetration, regulatory frameworks and the increased numbers of sensitive loads to voltage variations. Although main PQ phenomena are well defined in international standards, there is no standard way to evaluate the PQ as a whole for a site or a network. This paper presents a methodology to comprehensively evaluate the PQ for a network considering both the suggested planned levels set by the utility and the expected different customers’ PQ requirements. The suggested index is calculated using numerical consolidation of several separate indices. The considered phenomena in this paper, to demonstrate the methodology, are voltage harmonics, unbalance and flicker. The results are presented in a 295-bus Generic Distribution Network. INTRODUCTION The PQ performance can be considered an issue driven mainly by the customers’ requirements. Utilities are trying to minimize their cost while respecting different customers’ needs for certain level of PQ. The Council of European Energy Regulators (CEER) considers the assessment of the impact on customers as the main reason for monitoring and regulating PQ, “The ultimate aim of voltage quality regulation is to ensure that the functioning of equipment is not impacted by voltage disturbances coming from the network” as it is stated in [1]. Almost all main PQ phenomena are well defined in international standards. Indices for evaluating, methodologies for calculation and measuring the phenomena and other characterizing parameters are usually defined in these standards. For example, harmonic performance planning and compatibility levels are described in IEC 61000-3-6 and IEEE 519, the voltage unbalance calculations and emission limits are described in EN 50160 and IEC 61000-3-13, also the voltage flicker planning and compatibility levels can be found in IEC 61000-3-7 and IEC 61000-2-12. Nevertheless, there is no standard way to describe the overall PQ performance for a site or a network, furthermore, how to characterize the PQ performance, represent the results and quantifying the disturbances impact are still undergoing research areas. Recent publications discuss unified PQ for bus characterization. Methods like Analytic hierarchy process (AHP), numerical consolidation and fuzzy logic were adopted. The methodology suggested in [2] for overall PQ evaluation compares the ideal waveform to the recorded waveform of the voltage and the RMS error is calculated and compared. The methodology is capable of capturing event-type disturbances like voltage sag and continuous-type disturbances like harmonics. Number of references also suggested the application of fuzzy systems for unified PQ evaluation [3-6]. The fuzzy system is an efficient tool when it comes to considering many uncertainties involved in the PQ evaluation and also the different weights (importance) of the different PQ phenomena in the overall evaluation. Also, a number of references adopted the AHP model for unified PQ evaluation [7-9]. The AHP model can be used to assign different priorities to different phenomena, for example based on the expected cost of each phenomenon/disturbance, the overall expected cost of PQ at the buses can be determined. This is a useful index especially for the network reinforcement planning purposes. References [10, 11] proposed a framework for unified PQ evaluation. The suggested index is calculated using multi-stage framework in which the recorded data for different PQ phenomena are compressed in each stage to give a single index describing the PQ. The compression can be performed either for time or space data, i.e. for data from long term measurements and then for the data from different sites and feeders. The unified index proposed is based on normalization and numerical consolidation, the normalization was based on the thresholds of the considered phenomena, and then the exceeding phenomena scores are consolidated in one index. The index is unity or below in case of no exceedances for all considered phenomena. Further improvement of the index proposed in [11] was presented in [12] where the concept of the ‘distance’ between performance levels and thresholds (PQ reserve) is suggested for the evaluation. The proposed methodology compares number of phenomena reserves, using equation (1) below where r represents PQ reserve, m represents the actual level of performance and g represents the adopted threshold. r = 1 − m g ⁄ (1) The index is calculated by taking the minimum reserve (in case of no exceedances) or the summation of the exceeding phenomena reserves (negative reserves) as the index to describe the site performance. Building on the work presented in [11][12] an index based on the normalization and numerical consolidation of different PQ phenomena reserves is proposed in this paper. The methodology proposed here takes into account the perspectives of the utilities regarding the suggested accepted levels (planning levels) and the customers’ perspectives regarding the variable required levels of performances (compatibility levels). By comparing the planning levels with spatially probabilistically varying 23rd International Conference on Electricity Distribution Lyon, 15-18 June 2015 > ACCEPTED VERSION OF THE PAPER < Paper 0860 CIRED 2015 2/5 compatibility levels, using heat maps, the areas of expected inadequate PQ can be pinpointed. The methodology is demonstrated on a case study of generic distribution network (295-bus) considering voltage harmonics, unbalance and flicker as the phenomena of interest. METHODOLOGY Framework The general framework of the proposed methodology is shown in Fig. 1. The overall PQ index is calculated in three steps: PQ measurements/simulation: in this step the different PQ phenomena are analysed separately, by using the indices proposed in the standards. For each phenomenon, the indices are averaged over short periods (e.g. 10-min averages), then for a longer study period (e.g. a week) the 95th percentiles are taken as the measures of the phenomena, as suggested in the standards. PQ Evaluations and Bus Sensitivities: in this step the separate phenomena performances are compared to predefined thresholds, either from the standards or based on customers’ requirements. Then, the different phenomena are weighted for the overall evaluation based on the utility’s planning levels. The planning levels can be suggested based on the importance of the bus or the bus dominant equipment type, e.g. for induction motor dominant buses the most weighted phenomenon will be unbalance (customers’ size). Or based on expected customers’ financial losses (customers’ sensitivity). Or it can be suggested based on expected disturbances (increase of DG or EV). Overall PQ evaluation: in this step the different phenomena performances are combined, considering different weights, to come up with a single index for the PQ performance. Different techniques are proposed for combining the indices, as summarized in the previous section. In this paper, the index is calculated using numerical consolidation. PQ Measurements/Simulations