Investigation of Improved Distribution Load Allocation Using Expanded System Monitoring

Investigation of Improved Distribution Load Allocation Using Expanded System Monitoring Technical Brief — Distribution Operations and Planning; Power Delivery and Utilization Report Outline Introduction The remainder of this report has two parts: Importance of Improved Load Models 1. The first part analyzes various load allocation methods in detail. The System loads have a significant influence on system performance. accuracy of kWh load allocation is analyzed for kWh properties calcu- Consequently, accurate modeling of system loads with distribution plan- lated over different time periods. The first part of the report also ning tools is critical for effective system planning. Increasing levels of introduces a novel linear regression–based load allocation method and distributed energy resources (DER), however, also require planners to compares its performance with kWh load allocation. perform assessments beyond peak loading conditions. For example, host- 2. The second part provides additional insights on diversity factor by ing capacity is often calculated assuming off-peak loading conditions; comparing different diversity factor models to those estimated using inaccurate load models can result in under- or overestimating hosting AMI data. capacity. The application and value of advanced metering infrastructure (AMI) Load Allocation Methods with and other measurement data to improve system models are the focus of AMI Data this technical brief—in particular, how increased visibility from these new data sources could be used to improve load allocation techniques. This Background work is part of a continuing project, in EPRI’s Distribution Operations Load allocation is commonly a precursor to load flow analysis and other and Planning program, to advance methods for distribution load distribution planning assessments. Specifically, load allocation is a model- modeling. ing technique used to distribute or “allocate” the total forecasted power, typically at the feeder head, to each of the downstream loads. This was traditionally done in part because of limited customer measurement data Brief Background on the Analyzed Data Set as well as cost and inaccuracies associated with forecasting loads down- The analyses documented in this technical brief are based on a single stream of the substation. A more detailed introduction to load allocation feeder derived from the data set described in Reference [1]. The test and common load allocation methods can be found in Reference [1]. An feeder serves a total of 1898 customers off 614 service transformers. The implementation of load allocation in distribution planning software feeder load profile—the total of the active power of all loads on the OpenDSS is described in Reference [2]. Results for commonly used kWh feeder—over the measurement year is plotted in Figure 1. To emulate and load allocation methods using different measurement data are presented test the application of historical data to represent future loading condi- in this section along with a novel linear regression–based load allocation tions, the data surrounding the peak demand in July (7.75 MW) were method. often used in the analysis as the basis for load allocation models. These models were then tested against the measurement for August peak load Load allocations are performed at the service transformer level. As is cus- (7.33 MW). These two instances are indicated on the feeder’s annual load tomary for many utilities, assigning the demand of large industrial cus- profile shown in Figure 1. tomers is assumed to be done separately from the load allocations exam- ined here. Distribution losses, phase unbalance, and other aspects that may influence load allocations are not discussed but may be addressed in the future. Traditionally, feeder loads are allocated to represent expected peak conditions. Depending on the system, this may include both a winter and sum- mer peak. When representing non-peak conditions, some utilities may scale these allocations directly or may generate new allocation based on these conditions. Nevertheless, it is not well-understood how the allocated peak, minimum, or other system load conditions are best used to Figure 1. Measured annual load profile used in the analyses analyze other system load conditions. Load diversity varies over time, and 10217765 a load allocation from a single instance cannot be expected to perfectly represent feeder load diversity at another instance. To illustrate, transformer loads during a feeder’s July peak is compared to those for the August peak in Figure 2. Although an overall correlation exists across the loads during both system peaks, the magnitudes of each individual load may vary significantly. As a result, the same load allocation factors cannot perfectly represent both time instances. Recognizing this, the subsequent analyses examine how well peak load allocation can be used to represent other feeder load conditions. Figure 3. Distributions of feeder peak load allocation errors (allocated minus the measured kW). Each boxplot represents the distribution of 598 transformers. The median values are shown in a black line in the box: 90% of values are within the box, and 99% of the values are within the whiskers. Comparison of kWh Allocation Using Nonsequential Peak Values As discussed, the kWh properties of kWh allocation are typically calculated over the peak load month or over a year. Although using time periods shorter than a month appears to provide little if any benefit, the tem- Figure 2. Transformer load during the feeder peak load in July and a poral granularity of the AMI measurements allows allocation methods to high feeder load time in August. If the peak demand time and the high be based on a nonsequential set high feeder load times. This approach has demand time had the same load diversity, all the circles would be on the the potential to result in more accurate representation of the feeder load red line. diversity during feeder high-load times. Comparison of kWh Allocation Accuracy Using In this analysis, the kWh properties were calculated using 24 or 168 hours Sequential Measurements of the highest feeder load times during the peak month. The error results kWh allocations assume that customers with high energy demand con- from these cases are compared in Figure 4 along with those for the peak tribute more to the feeder load than customers with small energy demand. month-based allocation. As shown, the errors are similar between differ- Typically, monthly customer billing data—along with load surveys— ent allocation methods. This indicates that using nonsequential time were used as the basis for determine kWh load allocation models. The periods may not provide notable benefit compared to sequential time rollout of AMI provides the ability to examine customer peaks across the periods around the peak load. It should be noted that these findings are system as well as apply shorter time periods in the formulation of the based on one year of load data taken from one feeder. Different results kWh allocations. may be obtained if load pattern variations among years and/or feeders were considered. The distribution of calculated errors for kWh allocations using measurements representing different time periods (year, month, week, and day) around the peak are provided in Figure 3. For context, the errors com- puted when applying a kVA allocation method, which is based on transformer nameplate rating, are also provided for this feeder. Although the average error is zero for all allocation methods, the breadth of the error distribution varies considerably among the allocation methods. As was shown in Reference [1], the kVA allocation method does not perform as well as the kWh-based method does—the kVA allocation does not cap- ture the inherent diversity of loads and their relationship to the feeder peak. Reducing the time period of kWh allocation from a year to a month (or less) reduces the allocation errors noticeably by considering the sea- sonal variations in load diversity. However, comparing results based on Figure 4. Distributions of peak load allocation errors (allocated minus using a week or day of the peak load did not produce noticeable benefits measured kW). Each boxplot represents the distribution of 598 compared to month-based kWh allocations. This is somewhat surprising: transformers. The median values are shown in a black line in the box: 90% of values fall within the box, and 99% of the values fall within the one would expect shorter time periods to better represent the time-based whiskers. variations. EPRI Technical Brief 2 November 2018 10217765 Novel Linear Regression–Based Load Allocation Probabilistic load models would be necessary to fully account for the ran- Method domness of the individual loads but would require the application of A novel load allocation method that estimates a linear relationship probabilistic load flow methods to evaluate the system. However, because between each customer load and the feeder load was also developed in most planning studies focus on portions of the system that serve large this effort. This load allocation method is referred to here as linear regres- numbers of customers and where the degree of loading variations is not as sion (LR) load allocation. great, these methods are generally not necessary. To provide context, all load allocation methods are based on some Alternatively, when the focus of the planning assessment is on the feeder assumption of how the customer loads contribute to the feeder load with edges, diversity factors could be used to adjust the allocated loads in a the focus typically being on the feeder peak load (and sometimes mini- small localized area. It is important to note that diversity factors, which mum load). kVA allocation, for example, assumes that the kVA rating of are discussed later, cannot be simultaneously applied to all the feeder the transformer correlates with the load’s contribution to the total feeder loads at the same time—it would result in higher overall feeder demand.

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