Implementation of the WECC Composite Load Model for Utilities using the Component-Based Modeling Approach Anish Gaikwad, Member Penn Markham, Member Grid Operations & Planning Electric Power Research Institute Knoxville, TN Abstract—This paper presents an approach for developing dynamic load model data records for use in planning studies. The load model structure used is the so-called WECC Composite Load Model (CLM). The approach is based on using information about customer classes, various load components in each customer class, and electrical characteristics of each load component. The process is implemented in a tool that can be customized for an individual utility based on its geographical location within the US. Also, the tool can be refined over time due to changing load mix or as more accurate information becomes available. The output of the tool is a dynamic data record for the CLM which can be readily used in dynamic simulations. The main objective of these efforts is to provide an easy-to-use tool for transmission planners to develop load composition for CLM that can be used in time-domain dynamic simulations. Index Terms—Component-Based Load Modeling, the WECC Composite Load Model, Load Classes, Load Components, Load Characteristics, Load Composition, Static and Dynamic Loads
I. INTRODUCTION The utility industry and power system research community have for many years realized the significance of load modeling in both steady-state and time-domain dynamic studies. At the same time, both the research and utility communities fully understand the complexity of the problem of coming up with a suitable dynamic load model. The new NERC standard TPL-001-04 section 2.4.1 requires system peak and off-peak load studies to represent “the expected behavior of Loads that could impact the study area, considering the behavior of induction motor Loads. An aggregate System Load model which represents the overall dynamic behavior of the Load is acceptable.” The newly approved NERC MOD-33 standard requires that planning coordinators shall have documented processes in place which allow them to routinely compare the performance of their system-wide powerflow and dynamic models against real event data. Having a good and representative dynamic model for system loads is a critical piece of this validation process.
Pouyan Pourbeik, Fellow Grid Operations & Planning Electric Power Research Institute Irving, TX Considerable progress has been made in the area of load modeling for system planning studies in the last few years. Work performed by various entities, including Electric Power Research Institute (EPRI) and the Western Electricity Coordinating Council (WECC), has led to a better understanding of end-use loads, improvements in loadmodeling techniques, and the development of standard composite load models for use in commercial power system software tools [1]-[8]. The WECC efforts culminated in the development of the CLM which is now part of the standard model library in all the main commercial planning software programs used in North America. Details of this model are provided in [2]. Reference [1] provides the latest information on load modeling from across the world. References [3], [5], and [6] provide details on previous research on load modeling conducted at EPRI. Reference [4] provides a comprehensive summary of 1-phase residential air conditioner laboratory testing performed by EPRI, Southern California Edison (SCE), and Bonneville Power Administration (BPA) to characterize their dynamic performance following a voltage sag. Reference [7] summarizes the WECC efforts to model 1phase air conditioner motor using dynamic phasors as opposed to the performance model which is based on algebraic equations. Reference [8] describes the electromagnetic transient model of the 1-phase air conditioner motor that was developed for electromagnetic transient simulations. In spite of these noteworthy efforts, load modeling is still the most difficult aspect of power systems modeling in planning studies. The most challenging aspect is find a suitable way to represent the aggregate behavior of a large number of end use devices with significantly different sizes and characteristics that are dispersed across a service territory. Added to this is the inherent variability and uncertainty associated with loads at any given time on a daily as well as seasonal basis. Furthermore, the load characteristics change (mainly in composition) as a function of geographic location due to the difference in the climate and demographics of various regions. Realizing these challenges, transmission planners have in the past used simplistic load models–
constant power load for power flow (steady state) analysis and constant current real and constant impedance reactive for dynamic studies. Many recent events across the world have shown that such a simplistic representation is not adequate to capture system response for time domain dynamic studies, especially for fault induced delayed voltage recovery (FIDVR)-type events [1]. Therefore, a load model using a combination of static and dynamic components such as the CLM is preferred for time domain simulation studies. There are two basic approaches to determination of the parameters for a given load model structure: 1.
a component-based approach, and
2.
a measurement-based approach
References [1] and [3] provide details along with the pros and cons of using either approach. This paper describes implementation of the component-based approach to develop load composition for the WECC CLM. The approach has been used to develop the WECC CLM for multiple utilities which are part of a research effort in this area. Section II summarizes the component-based load modeling approach. A summary of the implementation of the component-based methodology is given in Section III. Conclusions and ideas for improving the approach are given in Section IV.
Figure 1. Component-Based Load Modeling
II. METHODOLOGY The component-based load modeling approach is a bottom-up methodology that aggregates distribution loads according to standard load classes, and load components within each load class. In this approach, a common load model structure and associated set of parameter values are used throughout the system model. What is changed throughout the system model is the percentage composition (e.g. percentage motor load versus static load) from bus to bus and parameters that may be heavily temperature dependent (e.g. stall voltage for a/c motor load stalling). In many cases, even these percentages may be kept constant throughout a region or zone of the system model due to lack of better information.
The next step is to come up with percentages of load components within each load class (indicated by the blue box). Each load class has typical load components that account for the majority of the power consumed by end users within that load class. The biggest challenge is to come up with a percentage number to assign to each load component. This information will vary from utility-to-utility and from seasonto-season. This information is hard to come by without undertaking customer surveys and could be a time consuming and expensive endeavor. Each load component has associated load characteristics (indicated by the green box). Load characteristics emulate electrical behavior of the load component. Load characteristics are comprised of static (constant impedance, constant current and constant power) and dynamic (one or more induction motors) mathematical models. For example, resistive load components such as dryers, cooking ranges and incandescent lamps are modeled as constant impedance loads. Load characteristics of the devices are obtained from extensive laboratory testing. This information is device specific and independent of temporal and spatial variation.
Figure 1 shows that the load supplied at a bus can be categorized into load classes based on real power consumption (indicated by the red box). Typically, the load classes used are residential, commercial, industrial, agricultural and general lighting (includes public and street lighting). The load at a bus may be divided into one or more of these classes. Metered demand at the load bus can be used to determine load class split. It should be noted that metered demand will be typically available for each hour of the year but for planning studies, only specific times of the year are used. These typically include summer peak system load, winter peak system load, and light or shoulder system load cases.
III. DEVELOPING A SYSTEM-WIDE LOAD MODEL The aim of the project described here is to use the component-based approach to develop load composition for the WECC CLM (Figure 2). The details of the model which has over 100 parameters are given in [2], [9]. Default parameters can be selected for almost 90% of these parameters (e.g. parameters for the on-load tap changing transformer, electrical parameters for the motors based on laboratory tests, etc.); however, it is necessary to provide fractions of motor and static components for the system under study to emulate the load behavior as closely as possible for the given season and region under study.
The authors can be reached at Anish Gaikwad (
[email protected]), Pouyan Pourbeik (
[email protected]), Penn Markham (
[email protected]).
Figure 3. Climate Zones Figure 2. WECC Composite Load Model [7]
The approach was implemented as a Microsoft Access database application. At present, the software generates load model records for summer and winter seasons. The details are as follows: A. Load Class Information At present three load classes – residential, commercial, and industrial are considered. However, other load classes such as agriculture, general lighting can be easily added. Load class information is provided for each season by megawatt usage and not by number of customers. The load class information can be provided by area, zone, owner, or by individual load bus, which can be read into the tool. Most of the utilities in North America have load class information, albeit in varying degree of detail. B. Load Component Information Each load class has typical load components that account for the majority of the power consumed by end users within that load class. The biggest challenge in load modeling is to come up with fractions of each load component. This information will vary from utility-to-utility and from seasonto-season (and from hour to hour, etc.) and is hard to come by without undertaking customer surveys that could be quite a time consuming and expensive endeavor. Even with such surveys it is still difficult to find an exact representation of composition. As such, in this effort an approximate database of load components was used for residential and commercial load classes. This database was developed as part of a research effort on understanding end-use load behavior and is based on using residential and commercial energy consumption survey from the Energy Information Administration (EIA) [10]-[12], along with typical end-use load shapes for different climate zones for each state in the United States. These climate zones are defined by the U.S. Department of Energy Building America Program [13] and are shown in Figure 3. The load component data can be generated for each state in the U.S. for summer and winter seasons for residential and commercial load classes based on its climate zone.
Although approximate, this database gives a first cut at coming up with load components. If more accurate data is available at higher fidelity (for example by individual utility or by zip codes), the tool can be enhanced to use that as well. Examples of load components for Hot-Humid climate zone for a state in the southeastern United States are shown in Table I for residential load class and in Table II for commercial load class respectively. TABLE I. LOAD COMPONENTS FOR RESIDENTIAL LOAD CLASS FOR HOT-H UMID CLIMATE ZONE FOR A STATE IN SOUTHEASTERN U.S. Component Clothes Dryer Cooling Dishwasher Freezer Furnace Fans Heating Hot Tubs & Spas Lighting Other (mostly Electronics & Cooking) Pool Pumps & Filters Refrigeration Television Water Heating
Summer Peak Percentage 2.63 39.85 0.39 5.19 0 0.25 17.81
Winter Peak Percentage 5.45 1.13 1.13 5.89 0 28.43 0.66
2.51 12.21
7.09 22.21
6.88
1.89
3.53 3.93 4.81
4.61 9.14 12.37
TABLE II. LOAD COMPONENTS FOR COMMERCIAL LOAD CLASS FOR HOT-H UMID CLIMATE ZONE FOR A STATE IN SOUTHEASTERN U.S. Component Computer Use Cooking Cooling Heating Lighting Office Equipment Refrigeration Ventilation Water Heating Miscellaneous
Summer Peak Percentage 3.49 0.36 44.85 1.88 21.55 1.21
Winter Peak Percentage 6.17 0.46 5.38 14.49 32.67 2.11
1.64 15.83 0.1 9.09
2.23 20.34 0.15 16.02
C. Load Characteristics Load characteristics emulate electrical behavior of the load components - mainly real and reactive power consumed as a function of voltage. In some cases, frequency dependency of loads is also modeled. Load characteristics comprise of static (constant impedance, constant current and constant power) and dynamic (one or more induction motors) models comprising of algebraic or differential equations. Load characteristics of different load components for residential and commercial load classes are given in Table III and Table IV, respectively, where A, B, C, and D are the four motor types, PE, Z, and I are Power Electronic, Constant Impedance, and Constant Current components, respectively, of the static portion of the composite load model. Motor A is used to represent aggregate response of three-phase motors driving constant torque loads, such as commercial and industrial airconditioning and refrigeration compressors, and is not used for representing residential loads. Motor B represents aggregate response of motors with large inertia, such as fan motors. Motor C represents aggregate response of low-inertia loads, such as water pumps. Motor D represents aggregate response of single-phase motors driving constant torque loads which predominantly consists of residential air conditioners. Note that Motor D is not used for commercial loads. Also, the Motor D model has been implemented using what is referred to as a “performance model” as explained in [2]. For the industrial load class, the following percentages were assumed: Motor A – 15%, Motor B – 20%, Motor C – 20%, Motor D – 0%, PE – 20%, Z – 10%, and I – 15%. These are only rough estimates based on the work performed by the Load Modeling Task Force (LMTF) of WECC. The percentages will vary depending on the actual industrial process. If more accurate information is available, the tool can be modified accordingly. D. Tool Output The output of the tool is dynamic data records for the WECC CLM in either Siemens PTI PSS®E (CMLD) or GE PSLF™ (CMPLDW) format. The records are generated at either area, zone, owner, or individual load bus level, depending on the load class information provided and can be readily used in time domain simulations in these two software platforms. IV. CONCLUSIONS & FUTURE WORK The tool has been used to develop the WECC CLM for three utilities in the Eastern Interconnection so far and will be used for many more utilities in the near future. For two of the three utilities, dynamic data records were developed at area and zone levels. One utility had load class information for each load bus in their planning case and therefore dynamic data records were developed at the bus level. The utilities are in the process of testing these load models. Experience so far has indicated that the overall process works seamlessly and saves significant time and efforts in developing the CLM data records. Future work involves refinements to the tool and testing of the load models developed. Specific activities are summarized as follows:
1.
Add potentially more load classes (for example agricultural) depending on the need of various utilities.
2.
Add load components for shoulder seasons (fall, spring).
3.
Refine information about the industrial load class to account for various industrial processes.
4.
If more accurate, utility-specific data becomes available in the future for load components, then use that data and modify the load composition percentages.
5.
If suitable system disturbance data is available, the CLM model developed by the tool can be verified by performing dynamic simulations. If there is a discrepancy, the model can be tuned to match the observed system response. Continued work on a tool previously developed in [5] may be a means of using disturbance data from the field to improve the dynamic load models.
In conclusion, the tool summarized in this paper provides a systematic way of assimilating load class, load component, and load characteristics information to develop the CLM. Although using climate zone information is an approximation at best, for many utilities in Eastern Interconnection (EI) and Electric Reliability Council of Texas (ERCOT), this is the only systematic approach available at present to come up with baseline percentages of motors and static loads in the CLM. Using this tool will significantly improve the ability of utilities to develop system-wide load models for performing dynamic studies in the future. TABLE III.
LOAD CHARACTERISTICS FOR RESIDENTIAL CLASS (%)
Component
A
B
C
D
PE
Z
I
Cooking
0
15
0
0
0
85
0
Cooling
0
10
0
85
5
0
0
Heating
0
0
0
30
0
70
0
Lighting
0
0
0
0
0
70
30
Refrigeration
0
0
0
100
0
0
0
Water Heating
0
0
0
0
0
100
0
Clothes Dryer
0
20
0
0
0
80
0
Dishwasher
0
50
0
0
0
50
0
Freezer
0
0
0
100
0
0
0
Furnace Fans
0
100
0
0
0
0
0
Other
0
0
30
0
20
50
0
Hot Tubs & Spas
0
0
100
0
0
0
0
Pool Pumps & Filters
0
0
100
0
0
0
0
Personal Computers
0
0
0
0
100
0
0
Television
0
0
0
0
100
0
0
TABLE IV.
LOAD CHARACTERISTICS FOR COMMERCIAL CLASS (%)
Component
A
B
C
D
PE
Z
I
Computer Use
0
0
0
0
100
0
0
Cooking
0
15
0
0
5
80
0
Cooling
80
5
10
0
5
0
0
30
0
0
0
Lighting
0
0
0
Other
0
0
0
Office Equipment
0
0
0
Refrigeration
100
0
0
0
0
0
0
Ventilation
0
70
0
0
30
0
0
Water Heating
0
0
0
0
0
100
0
Heating
0
70
0
0
0
0
100
0
100
0
0
0
100
0
0
[5]
[6] [7] [8] [9] [10]
REFERENCES [1] [2] [3] [4]
CIGRE Technical Brochure 566, “Modeling and Aggregation of Loads in Flexible Power Networks”, CIGRE Working Group C4.605, February 2014. WECC MVWG Load Model Report ver. 1.0 (June 2012). [Online]Available:https://www.wecc.biz/Reliability/WECC%20MVW G%20Load%20Model%20Report%20ver%201%200.pdf Comprehensive Load Modeling for System Planning Studies. EPRI, Palo Alto, CA, 2009, Product ID 1015999. A. M. Gaikwad, R. J. Bravo, D. Kosterev, S. Yang, A. Maitra, P. Pourbeik, B. Agrawal, R. Yinger, and D. Brooks, “Results of Residential Air Conditioner Testing in WECC”, Proceedings of the IEEE PES General Meeting, Pittsburgh, July 2008.
[11] [12] [13]
A. Maitra, A. Gaikwad, P. Pourbeik and D. Brooks, “Load Model Parameter Derivation Using an Automated Algorithm and Measured Data”, Proceedings of the IEEE PES General Meeting, Pittsburgh, July 2008. P. Pourbeik and B. Agrawal, “A Hybrid Model for Representing AirConditioner Compressor Motor Behavior in Power System Studies”, IEEE General Meeting, Pittsburgh, July 2008. B. Lesieutre, D. Kosterev, J. Undrill, “Phasor Modeling Approach for Single Phase A/C Motors”, Proceedings of the IEEE PES General Meeting, Pittsburgh, July 2008. Y. Liu, V. Vittal, J. Undrill, J. Eto, “Transient Model of AirConditioner Compressor Single Phase Induction Motor”, IEEE Transactions on Power Systems, Vol. 28, no. 4, November 2013. GE PSLF User’s Manual Version 19.0. Residential Energy Consumption Survey (RECS 2005 microdata) [Online]. Available: http://www.eia.doe.gov/emeu/recs/ Commercial Building Energy Consumption Survey (CBECS 2003 microdata)[Online].Available: http://www.eia.gov/emeu/cbecs/contents.html Annual Energy Outlook 2011 [Online]. Available: www.eia.gov/forecasts/aeo DOE Energy Efficiency and Renewable Energy (EERE) Guide to Determining Climate Regions by Country [Online]. Available: http://apps1.eere.energy.gov/buildings/publications/pdfs/building_amer ica/climate_region_guide.pdf
