Transmission Bus Load Model for Smart Distribution and Transmission Grids

The advances of Active Distribution Networks (ADN) with high penetration of Distributed Energy Resources (DER), Electric Storage (ES), Demand Response (DR), and Electric Vehicles (EV) will significantly change the relationships between the transmission and distribution operations. The operational capabilities of ADN will become comparable with the operations of the bulk generation and transmission systems. Coordination of the transmission and distribution operations will open new opportunities for better security and efficiency of the power system operations, based on the following:

Additional generation in proximity to the load
Additional sources of continuous reactive power capable of supplying and absorbing [1], [2]
Ability to provide continuity of service to critical loads during system disturbances due to microgrids

These resources will provide additional dimensions for EMS applications, such as:

Wide Area Situational Awareness
Unit commitment
Economic dispatch
Optimal power flow/Security constrained dispatch
Contingency analysis
Emergency supply of power/ black start
Load management
Volt/var management
AGC
Development of the Sequence of Event Log

The Active Distribution Networks will also present a number challenges, such as the following:

Vulnerability of distributed generation to disturbances in operating conditions in transmission and distribution [3], [4]
Greater uncertainty of forecast of the generation and load (due to embedded DERs)
Intermittency of distributed generation
Operational constraints of distributed generation [5] imposed by the distribution system
More complex load models in distribution [6]
Reduced observability of generation due to non-monitored and autonomous DER operations
More complex operational functions, remedial action schemes, e.g.,

Voltage/var control due to intermittency, reverse power flow, excessive voltage rise, multiple smart inverters in different modes, etc. [7]
Under-Frequency/Voltage Load shedding due to generation on the feeders, microgrids, etc. [8]

More complex EMS and DMS applications to accommodate the more complex operations
More complex contractual and regulatory conditions between parties
More complex field test methodologies

In order to meet the challenges and utilize the opportunities, adequate information exchanges between different entities in distribution and transmission domains should be provided.

The information exchange between advanced microgrids and the Electric Power System (EPS) was addressed in [9]-[11].

This paper addresses the information exchange between the Distribution Management System (DMS) of ADN and the Transmission EMS. It was suggested that the information to be exchanged between the Distribution and Transmission domains is included and timely updated in a Transmission Bus Load Model (TBLM) [12] – [15].

The aggregated model components of distribution operations submitted to the TBLM should provide the Transmission System Operator (TSO) and /or the transmission EMS with timely and sufficient information to assess and control the transmission operations taking into account and utilizing the dynamic attributes of distribution operations. The Distribution System Operator (DSO) and/or the DMS should get information from the TBLM to be able to assess and coordinate the distribution operations with the operating conditions of the transmission system.

The paper discusses some of the possible components of the TBLM and the infrastructure for the development and support of the TBLM.

The concept of Transmission Bus Load Model (TBLM)

The operations of the Active Distribution Networks are monitored and controlled by the DSO/DMS, Aggregators, CEMS and μEMS. Aggregators will monitor and control their DER and DR installations, while CEMS and μEMS will manage their local electric power systems and interact with DSO/DMS [9].

It will be necessary to include the data about the responses and the capabilities of the Active Distribution Network in the operational models of the transmission domain that are used by the TSO/EMS applications. However, it is unrealistic to expect that the monitoring and control of transmission operations will reach out to every device, CEMS, and μEMS /controller in the distribution and customer domains. In reality, the end buses of the near-real time model of transmission operations are the demarcation points between transmission and distribution systems.

In order to provide the TSO/EMS with the needed information for the secure and efficient management of the transmission system, relevant near real time and short-term look-ahead operation properties of the distribution system should be timely aggregated at the transmission buses representing the dynamic model of the Active Distribution Network.

The new TBLM to be used in the Smart Grid environment should represent the aggregated components of the real and reactive load and generation, including all normal and emergency dependencies of these components on various factors, such as voltage, frequency, demand response controls, price, weather, etc. These components include the near real time and the short-term look-ahead values.

With such a dynamic model supported by new advanced distribution automation (ADA) application, which we call here “TBLM Developer”, advanced TSO/EMS applications will be able to use adequate generation and load models as additional aggregated controllable variables in distribution.

Other components of TBLM include the following:

Aggregated DER capability curves, covering DER Watt, var and volt relationships
Aggregated frequency support capabilities of DERs and ES
Aggregated real and reactive load-to-voltage dependencies
Aggregated real and reactive load-to-frequency dependencies
Aggregated real and reactive generation-to-voltage dependencies
Aggregated real and reactive generation-to-frequency dependencies
Aggregated real and reactive load dependencies on
- Demand response signals,
- Dynamic (marginal) prices,
- External conditions, etc.
Aggregated dispatchable real and reactive loads
Load model forecast
Overlaps of different load management functions, which use the same load under different conditions.
Load shifting capabilities
Degrees of uncertainty for the model components
Post-factum distribution event logs
Other

The components of the TBLM should reflect the normal and emergency conditions in the distribution and bulk power system domains.

There are components of the TBLM that should be contributed by the transmission-side EMS, such as the bus phase angles and some near-real time operational constraints and requirements. The two-way information exchange between the distribution and transmission domains will serve the needed, as well as the optimum coordination of the transmission and distribution operations.

Conceptual diagrams illustrating the information exchange between distribution and transmission domains are presented in Figure 1 and Error! Reference source not found. [15].

Figure 1 depicts the power system domains and their components involved in the creation and support of the TBLM. As seen in the figure, there are three levels of integration of information: a) at the PCCs of composite customers (e.g. Microgrids [10]) b) at the aggregators combining multiple PCCs (market based aggregation), and c) at the demarcation bus between the distribution and transmission systems (TBLM). The TBLM combines the lower hierarchical aggregations and other not aggregated components of the distribution system.

Figure 2 depicts the conceptual information infrastructure for the development of the TBLM and for information exchange between the distribution and transmission domains. As seen in the figure, practically all components taking part in the advanced DMS and EMS are involved in the development of the TBLM. A new DMS application – the TBLM Developer is introduced in this infrastructure.

Figure 1. Power system domains involved in the support of TBLM

Figure 2. Infrastructure for the development of the TBLM and for information exchange between the distribution and transmission domains

Of the primary information sources depicted in Figure 2, the following two will need the most innovations in the Smart Grid environment:

The External Systems that are information systems outside the utility providing the utility and other relevant actors with information on weather and major event relevant to utility operations. More detailed information on weather attributes will be needed for the development of the models of weather- dependent DERs. The weather data for this purpose may include the following near-real time and short-term forecast attributes [16]-[18]:
- Temperature
- Wind direction and wind speed at given height
- Solar irradiance
- Cloudiness cover
- Cloud velocity
- Cloud direction
- Cloud height
- Cloud shadow patterns
The Microgrid EMS (μEMS) is a system that monitors and controls the operations of the components of advanced microgrid[1], analyzes the operational alternatives in accordance with the EPS and contractual requirements, develops near real time and short-term look-ahead aggregated operational models of the microgrid, and interchanges information with the EPS DMS. The μEMS executes different functions of the microgrid in both connected and island modes of operations, such Watts, vars, voltages, and frequency controls according to either locally or remotely installed settings. The aggregated models of the microgrid should contain measurements of current generation, generation schedules, DER capability curves, load-to-voltage and frequency dependencies and generation-to-voltage and frequency dependences for normal and emergency conditions, dispatchable load, setups of protection and remedial action schemes, relationships between load management means, mode of operations and settings of voltage/var and frequency control functions, dependencies of model components on external signals, degree of uncertainty and validity of the model components and other data needed for current and predictive model of microgrid operations [19]-[21], [10].

There are several new “back-office” applications in the Data Management System section [15] depicted in Figure 2:

The DMS Scheduler
The Topology processor
The Load Model processor
The Secondary Equivalent processor
The DER Model processor
The Microgrid Model processor

The DMS Scheduler is a DMS sub-system consisting of Graphic User Interface, and an advanced scheduling application that accepts, checks, and organizes information obtained from DSCADA, DSO and other authorized personnel and triggers DMS applications according to given setups. It accepts output information from DMS applications and initiates execution of their instructions.

The objective of the Topology Processor is conversion and validation of information about incremental changes of circuit connectivity and facility parameters. The Topology Processor should use information from GIS, CIS, and other data management system to construct the circuit connectivity including the distribution transformers and to assess a reasonable level of loading of the subject distribution transformers. . This information should be validated on two levels: 1) validation of connectivity, and 2) integrated validation on operational reasonability. The first level of validation can be performed by analyzing the consistency of connectivity (de-energized elements, loops, wrong phasing, etc.) and by analyzing the consistency of customer association with the distribution transformers and of its loading. The second level of validation is based on the consistency of the power flow and contingency analysis results with the utility expectations (e.g., if power flow application shows that the voltage drop along a feeder significantly exceeds the expectations, or a segment is significantly overloaded, then it is likely, with a certain degree of certainty, that the input data is wrong and need a double-checking).

The Load Model Processor should develop adaptive load models based on information available from CIS, GIS, AMI Data Management system, DER and μGrid Data Management systems and model processors, and external information sources. The load models should also represent the load dependencies on multiple factors affecting the load.

The Smart Grid Load Model Processor should develop adaptive individual and aggregated load models of consumers and prosumers taking into account the variety of possible load components and their dependences on a number of factors, such as embedded distributed generators, electric storage devices, and plug-in electric vehicles, as well as demand response means. The real and reactive load models, individual or aggregated, should reflect the behavior of these composite loads depending on the known weather, prices, voltage, time of day, and other factors [22]- [24] It should normalize the models to the nominal conditions and derive the dependencies of the load on the changes of these conditions.

The secondary equivalent processor provides DMS with equivalents of the voltage drops and power loses in the secondary circuits fed from distribution transformers. It derives the voltage drop and the power loss equivalents in the secondaries as functions of the available near-real time data, based on the historic AMI data and modeled or measured voltages at the LV bus of the distribution transformers [6]

The DER model processor should be able of creating adaptive near-real-time and short-term look-ahead operational models of DERs. It should derive the DER models from the data obtainable from the DER controller, if monitored, from DMS Scheduler, the DER Data Management System, GIS, AMI Data Management system, historic measurements and external data.

The Microgrid Model processor should develop adaptive operational models of μGrids based on data obtained from the snapshots of the DMS scheduler and attributes from the Data Management System and from μEMS [22]-[29]

The Developer of the Transmission Bus Load Model is an application that should collects primary data from different sources. Almost all existing DMS functions and some modified DMS functions will be involved in this development This information is to be used to develop a number of components of the overall TBLM. A set of components of the TBLM derived from the distribution side has been suggested in [15]. Some of these components were updated in later publications. These updates are focused more on the high penetration of DER in the distribution system

It is expected that the components of the TBLM to be supplied from transmission side will be contributed by the relevant modified EMS functions.

This paper suggests an outline for the components of the TBLM.

In most of the cases considered, the data required from the field devices do not need to be transmitted as a real-time stream. In some cases, the data can be submitted to the DMS as an accumulated set once in a given comparatively long time interval. In other cases, the data can be collected only from representative bellwether data sources. Updates of composite data from intelligent controllers and EMS can be transmitted only when they exceed a given threshold.

However, the minimization of the flows of real-time data may require a more intensive processing of the available data in the “back-office” applications of the DMS. These applications should collect, combine, and correlate relevant data from different sources and develop models that can be applied in near real and short-term look-ahead times based in limited external input.

For example, if data from a bellwether PV DER or a small group of bellwether PV DERs selected for a local area are collected for different weather conditions, patterns of the DER behavior for different weather conditions for this area can be created and can be applied to all PV DERs of this area. In addition to the sample data, the DERs’ rated parameters, which should be available in the DER Data Management system, should be used. For instance, the statistics from the bellwether sources may show that under clear sky, the PV DER generates on average 90% of its maximum capacity, when the sun is in its highest point for this particular time interval and follows the change of the sun irradiance during the day. Under light overcast, the generation is, say, on average 50% of the PV DER performance recorded under clear skies, while under fast moving clouds, the standard deviation is about 20%. Knowing such patterns, the forecast of the generation performance can be predicted by the DER model processor based on the available weather forecast. If the real power generation is assessed, and the mode and settings of the volt/var control are available in the DER Data Management system, the DMS applications can apply them to the DER models. As seen in this example, limited field data should be collected from selected points and from a weather system for representative time intervals while the rest of data processing is performed by the back- office applications of the DMS.

Suggested Components of the TBLM.

Some of the suggested components of the TBLM are briefly addressed below.

1Reactive power capability curves

The aggregated reactive power capability at the T/D demarcation bus is the net value of the available reactive power (inductive or capacitive). Hence, it is the available reactive power from all sources of reactive power minus the reactive load and losses. All these components are not independent of each other. They are mostly affected through the voltages along the circuits. In addition, the available reactive power of DERs depends on their mode of Volt/var control and on their real power generation.

Figure 3 and Figure 4 illustrate the difference in the DER’s var capabilities under different modes of DER operations. In this illustration, the feeding substation’s LTC position was changed by 10%. The actual feeding bus voltage changed within a range of about 6%, due to the reactions of all sources of reactive power in the corresponding circuits. The illustration below presents the operations of one of the DERs connected to the distribution circuit. The voltage at the terminals of this DER corresponds to the entire range of the feeding bus voltage.

Figure 3 illustrates two modes of DER Volt/var control function:

VVC with pre-defined settings of the dead-zone and var/volt droop (Open loop control) [30]
VVC with the objective of constant voltage control, when the reactive power of the DER is changed within the DER kvar limits to keep the voltage inside the dead-band (close loop control).

The were also other DER in this circuit operating in the open loop VVC mode with the same dead-band.

In this illustration, the dead-band is between 0.97 and 0.99 of the nominal voltage. The DER kvar limits are voltage and kW dependent.

As seen in the figure, in the close loop mode, the DER generates and absorbs greater reactive power than in the open loop mode and keeps the voltage within the dead-band until the kvars are limited by the DER’s maximum capability, which is around 0.5 pu in this illustration.

In the open loop mode, the DER generates or absorbs smaller reactive power, and the voltage remains beyond the dead-band, even though the DER’s kvar capabilities are not reached.

Figure 3. Reactive power of DER dependencies on voltage under different modes of VVC

Figure 4 illustrates the combination of the VVC and Watt-Volt functions with the objective of constant voltage (both functions are in the close loop modes). As seen in the figure, the kWs are reduced when the reactive power of the DER is insufficient to keep the voltage within the dead-band. The reduction of the DER’s kW lowers the voltage due to the increased voltage drop and increases the DER’s var capability that can be additionally used to keep the voltage in the dead-band [31]. As seen in the figure, in this case, the voltage is kept in the dead-band within the entire range of the bus voltage, and the maximum absorbing DER’s kvar limit is around 0.7 pu.

Figure 4. Reactive and real powers of DER dependencies on voltage under close loop VVC and Watt-Volt functions. Other DERs in open loop VVC.

The reactive power capabilities of each DER also depend on the modes of operations of other sources of reactive power. Figure 5 illustrates the operation of the same DER, when other DERs are in Constant Var mode with voltage override beyond the voltage range of 0.96 – 1.04 pu.

As seen in the figure, the real power is reduced to 0.3 pu, and the var absorption limit is increased to 1.05 pu.

Figure 5. Reactive and real powers of DER dependencies on voltage under close loop VVC and Watt-Volt functions. Other DERs in constant var mode with voltage override

The reactive power capabilities of some sources of reactive power may be also limited by voltage and/or current limits of other circuit facilities and by the modes of operations of the sources[2].

As follows from the above illustrations, the var capabilities of an active distribution network may significantly change in near real time.

The actions for determining this component of the TBLM are envisioned as follows:

DMS (periodically or upon event) calculates the dynamically changing curves of nominal and operational maximum and minimum var capabilities vs the voltage at the transmission-to-distribution demarcation bus. DMS uses threshold criteria to determine whether updated curves are needed.
The TSO may request a different var capability dependency than the one submitted by the DMS.
DSO/DMS submits to TSO/EMS the impact of the requested change on the distribution grid performance
The TSO and the DSO come to a solution on the TSO request based on merit and/or on existing agreement between the parties.

The contents of the information exchange between the DSO/DMS and TSO/EMS may also include the following attributes:

Curves of operational maximum and minimum var capabilities vs the voltage at the transmission-to-distribution (T/D) demarcation bus
Curves of nominal maximum and minimum var capabilities vs the voltage at the T/D demarcation bus
The impact and/or conditions of using the nominal (or requested by the TSO) capabilities
The timeframes of different capabilities
The degree of uncertainty of the corresponding curves
The requested by the TSO capability
The impact of the requested capability on the ADN performance

More details on this component of the TBLM are presented in [32].

2 Dispatchable generation and load.

The dispatchable generation and load aggregated at the T/D demarcation bus consists of the available changes of the real and reactive load and generation. .

The actions for determining this component of the TBLM are envisioned as follows.

DMS (periodically or upon event) calculates and submits to the TBLM the dynamically changing real and reactive dispatchable generation and loads vs bus voltage and other factors (e.g., dynamic prices). Different levels of the dispatchable generation and load may come with different costs for the distribution system. The association of the cost with the level of dispatchable generation and load should be included in this model component. DMS uses predefined threshold criteria to determine if updated dependencies are needed
The TSO/EMS performs their situational awareness, unit commitment, economic dispatch, optimal power flow, security constrained dispatch functions, and load management based on the known model of the dispatchable generation and loads of relevant distribution grids and their attributes for the given time intervals.
Based on the results of the EMS application, TSO/EMS issues a request to the TSO/DMS for dispatchable generation and/or load.
The compensation for meeting the TSO/EMS request is processed according to existing agreements between the parties. .

The contents of the information exchange between the DSO/DMS and TSO/EMS may also include the following attributes:

The means of control (e.g., voltage reduction, ES discharge, Demand Response)
The amount (Watt, var)
The duration of use
Time of activation
Change during commitment (± %)
Price-level tolerance ($/kWh)
Probability of implementation (degree of uncertainty)

More details on this component of the TBLM are presented in [32].

3Real and reactive load-to-voltage and frequency dependencies.

The load of a distribution system connected to the T/D demarcation bus can be determined in the following two ways:

Natural load
Net load

The natural load-to-voltage dependencies are defined by the composition of the different load categories and their individual LTVs. The LTVs of the aggregated natural load may significantly change in time due to the changes in the load composition, which, in turn, may depend on technological, commercial, and life cycles, as well as on ambient conditions [33]-[35].

In addition, the LTV should be considered in the following three timespans:

Immediate (right after the change in voltage and before the reaction of voltage controlling means). The timespans for the immediate LTV are in the range of seconds to single-digit minutes.
Short-term steady-state (after the voltage controlling means operated and before the customer adjustment to the changed voltage. The timespans for the short-term steady-state LTVs are in the range of minutes to several hours)
Long-term, (after the adjustment of the customers to the sustained voltage). The timespans for the long-term LTVs are in the range of several hours to several days. These LTVs are also called CVR-factors.

With low penetration of the DERs, the LTVs of the natural load could be defined by field test of aggregated load sensitivities to voltage for particular time intervals [33], [34], [36]. However, such tests should be repeated when the composition of load categories or other relevant conditions significantly change. For instance, if there is a significant portion of electric heating load, the LTVs of the aggregated load will be considerably different under different ambient temperatures. In addition, in these field tests, the ranges of voltage change cannot be too wide, and the test cannot be performed too often, to minimize the impact on the customer appliances.

The high penetration of the DERs significantly complicates the field tests of the aggregated LTVs due to the volatility of the generation component.

Therefore, it is worth considering a methodology that does not involve intrusive field test. It is worth do consider a methodology that is based on specific LTVs of a number of typical appliances determined in laboratories and on a sophisticated analytical model of current load composition for the subject distribution network. Such model, presumably, can be developed based on a large volume of data obtained from different databases, like CIS, GIS, AMI, Weather Systems, etc., and on an AI-like analytical procedure. A simplified illustration of such approach is presented in [37].

The sensitivities of the net load of a T/D bus to the bus voltage depend on the current amount of contribution by the DERs, on their reactions to the change of voltages, on the current amount of the natural load, and on the current LTV of the aggregated natural load. Hence, the LTVs of the net load are highly volatile even during a short time interval.

Therefore, the LTV of the net load should be derived in near-real times from the combination of the LTVs of the currently aggregated natural load and near-real time models of the reaction of DERs to the changes of voltage. For these purposes, the current model of the natural load should also be determined. This can be done by subtraction of the current model of the DER real and reactive power contributions from the corresponding net load.

In addition, the voltage dependencies of the net load cannot be expressed in the conventional manner as the percent of load over the percent of voltage. For instance, any percent change of load from a net load equal zero is infinity.

Figure 6 to Figure 8 present an illustrative example of the differences between the natural and net LTVs. The natural and net load profiles of a residential feeder are presented in Figure 6. The load of the feeder contains a significant portion of electric heating and cooking. The penetration of solar DERs is 30%.

Figure 6. Load shapes of feeder load

Figure 7 illustrates the natural and net immediate LTVs (in the conventional form), when the DERs do not change their reactive power contribution. As seen in the figure, the LTV of the net load frequently fluctuates within a very wide range from large positive to large negative numbers. With such fluctuations, the net LTV cannot be used even for a 15-min prediction time, which is often the time span of EMS and DMS applications.

Figure 7. Immediate LTVs of real natural and net loads. DER PF=1

Figure 8 illustrates the natural and net immediate LTVs, when the DERs are also controlling the reactive power in different modes. As seen in the figure, when the DER’s volt/var control keeps constant voltage, changes of the T/D voltage, practically, do not result in voltage change along the feeder. This means that the natural load LTVs are close to zero, as long as the DERs are capable to compensate for the changes of the bus voltage. When the DERs generate more real power and the reactive power capabilities are reduced, the full compensation of the bus voltage change may become unavailable, and the LTVs deviate from zero.

Figure 8. Immediate LTVs of real natural load under different DER VVC modes. DER PF=0.9

The relationships between the load/generation and bus voltage become much more complex in the emergency ranges of the voltage.

Figure 9 through Figure 12 illustrate relationships between load, DER generation, voltage, and frequency based on a simple model of lump load, DER generation, energy storage, Under-Frequency and Under-Voltage load shedding, and Under-Frequency and Under-Voltage Ride-Through. In these illustrations, the frequency change is close to the NERC’s UFLS performance requirements [38], and the ride-through settings are consistent with the NERC standard [39]. The voltage changes correlate with the frequency changes.

As seen in the illustrations, the relationships between the load/generation, voltage and frequency are non-monotonic and may be significantly different under different dynamics of the contingencies. It was also assumed here that the DERs disconnected after the ride-through time expired. If the DERs were ceasing to energize, the relationships would be different.

Figure 9. Relationships between load and DER generation in emergency ranges of voltage (Constant frequency)

Figure 10. Relationships between load and DER generation in emergency ranges of frequency (Constant voltage)

Figure 11. Relationships between real load and DER generation in emergency ranges of voltage and frequency

Figure 12. Relationships between reactive load and DER generation in emergency ranges of voltage and frequency

The actions for determining these relationships for the TBLM are envisioned as follows:

DMS (periodically or upon event) analytically estimates the composition of the natural load categories based on data collected from different databases and on current ambient conditions.
DMS (periodically or upon event) calculates the dynamically changing immediate, short-term steady-state and long-term natural Watt/var dependencies on the demarcation bus voltage and frequency based on the estimated composition of the load categories and category-specific LTVs and LTFs. The dependencies should cover the normal and the emergency voltage and frequency ranges. In the emergency ranges, the dependencies also depend on the RAS settings
DMS (periodically or upon event) assesses the current states of the DERs based on the data from the DER/microgrid databases and ambient conditions. The state of the DERs should also include the modes of operations of the DERs.
DMS (periodically or upon event) calculates the dynamically changing immediate, short-term steady-state and long-term reactions of the DERs to the voltage and frequency changes. In the emergency ranges, the dependencies also depend on the DERs’ ride-through settings.
DMS (periodically or upon event) combines, in a weighted manner, the LTVs and LTFs of the natural load and the reactions of the DERs to derive the LTVs and LTVs of the net bus load and submits them to the TBLM. DMS will use a threshold to determine if updated dependencies are needed.
The TSO may request different dependencies than the ones submitted by the DMS. Meeting the TSO’s request may mean changing the modes of operations of the DERs.
The DSO/DMS informs the TSO/EMS about the impact of the change on the distribution grid performance.
The compensation for meeting the TSO/EMS request is processed according to existing agreements between the parties.

4Real and reactive load dependencies on Demand Response control signals

With high penetration of Demand Response (DR), it becomes a significant component in the operations of the transmission and generation domains. The “negawatts” in distribution can be used instead of expensive bulk generation megawatts. They can assist in mitigating congestions and bulk power system emergencies. However, the available load reduction aggregated at the transmission bus cannot be considered as one block of resource.

There is a variety of Demand Response programs in different utilities (see e.g., [40]-[42). Each of these programs has different triggers, different incentives (cost), different time of engagement, durations of the response, different changes of the load reduction during the response time, etc.

When the Demand Response is applied in different locations of the distribution system, it affects the power flow, voltages, losses, and behavior of other DMS applications. Hence, the aggregated at the transmission bus load reduction is not just a sum of all local Demand Responses. It is the resultant change of the total load (including losses) caused by the demand response, which should be determined by adequate simulations of all significant changes in distribution operations caused by the applied demand response.

Different DR arrangements may have different degrees of uncertainty. Hence, the aggregated models of the DR should include properties, which can be used by the TSO/EMS applications for the risk analysis of the utilization of the DR.

The actions for determining this component of the TBLM are envisioned as follows:

DMS (periodically or upon event) calculates and submits to the TBLM the dynamically changing near-real-time and short-term look-ahead real and reactive Demand Response capabilities and associated attribute dependencies on a number of triggers of DR. The demand response characteristics should cover the normal and the emergency situations and should include the impacts of the customer-side demand response on the operational characteristics of the distribution system connected to the subject bus.
The TSO/EMS selects the amount and the duration of the DR that it is going to request for different EMS applications.
The compensation for meeting the TSO/EMS request is processed according to existing agreements between the parties.

The contents of the information exchange between the DSO/DMS and TSO/EMS may include the following:

Amount of available load reduction as a function of the DR signal, e.g., based on the marginal cost of DR
Time needed for activation of the demand response
Available duration of the load reduction
Steadiness of the load reduction during the time of engagement
Probability of execution.
Dynamically changing conditions for DR submitted to the TBLM by the TSO/EMS, e.g., real-time prices
TSO/EMS signals for activation of Demand response

5Real and reactive load dependencies on dynamic prices.

In addition to demand response based on dynamic prices, characteristics of other components of the distribution system may respond to dynamic prices. These may include the characteristics of DER operations, such as generation of kW and/or kvar, volt/var control setups, charge/discharge of Energy Storage Systems, etc.

The charging/discharging times and the values of the ES may be dependent either on the energy price or on other power system operation factors, such as ancillary services. These dependencies may be complicated by an ES optimization procedure. For instance, if it is expected that the energy price exceeds a threshold set by the ES owner two times during the day: at the morning peak and at the evening peak time, and the evening peak time price is higher, the owner of the ES will skip the morning time and will discharge the ES at the evening peak time.

Some DMS applications, such as IVVWO [43], [44], Feeder Reconfiguration, may change their performance based on the dynamic prices.

All responses should cover the normal and the emergency situations.

The actions for determining this component of the TBLM are envisioned as follows:

The DSO/DMS assesses and submits to the TBLM expected in near-real-time and in short-term look-ahead time intervals responses of the aggregated net real and reactive load/generation characteristic to the dynamic prices for the normal and emergency EMS applications.
The TSO/EMS takes into account the distribution-side load/generation dependencies on the dynamic energy prices for its load forecast and corresponding setups of the EMS applications

The contents of the information exchange between the DSO/DMS and TSO/EMS may include the following:

Amount of real and reactive natural load as functions of the dynamic prices at the corresponding bus (the dynamic prices can be different for different buses due to different Locational Marginal Prices – LMP)
Amount of real and reactive generation as functions of the dynamic prices at the corresponding bus
The expected time of the reaction of the load to the change of dynamic prices
The expected time of the reaction of the generation to the change of dynamic prices
Probability (uncertainty) of the assessment of load reaction to the prices
Probability (uncertainty) of the assessment of generation reaction to the prices.

6Dependencies of real and reactive load on ambient conditions and time

Most of the components of the operations of Active Distribution Grids depend on the ambient conditions and time. Natural load of different load categories are changing in the times of day, week, season, and are dependent on the ambient weather conditions

The load-to-voltage dependencies change with the change of load composition and with the change of the ambient conditions

The reciprocating and fuel cell DERs can be time dependent based on schedules, or technological cycles.

The renewable DERs are strongly dependent on the weather, including the sunlight cycles and the intermittency of the weather. The representation of the DER injections of real power under intermittent weather conditions implies probabilistic models. In addition to statistic values of the aggregated load, such models introduce the degree of uncertainty of the model that can be used in the risk management.

The injections/absorption of the DER reactive load is dependent on the performance of the real load and on the modes of volt/var control.

The times, duration, and the values of the charging/discharging of ES are dependent on the previous performance of the ES. For instance, if the ES was not discharged the previous day, it will not charge the following night. However, it is possible that for some ES installations the charging/discharging times can be set by time-schedules. Some ES can be set to compensate the fluctuations of the injections by other DERs.

The dispatchable load, including Demand response (DR), and the load connected to the Remedial Action schemes (RAS) are also dependent on the ambient conditions and time. The demand response capabilities are different at different times and under different weather conditions. Here again, the relationships between the DR capabilities and the weather conditions may be not straightforward. For instance, under hot weather conditions, there is a greater air-conditioning load and a greater potential for DR. However, if the hot weather continues for a longer period, the DR potential may reduce.

The actions for determining these components of the TBLM are the same as for other components of the TBLM with additional correlation to the current and the short-term look-ahead ambient conditions and times.

The contents of the information exchange between the DSO/DMS and TSO/EMS may include the following dependencies on ambient conditions and time:

Natural real and reactive load of the distribution grid
Real and reactive load-to-voltage sensitivities
Real and reactive load to frequency sensitivities
DER/ES real power generation/absorption
Distribution grid reactive power generation/absorption
Distribution grid reactive power nominal and operational capabilities
Distribution grid dispatchable real and reactive power
Setups of the RAS and ride-through functions

7Models of relationship between different load management functions, which use the same load under different conditions

Load management can be executed through several programs, such as:

Dynamic pricing
Volt/var control in distribution
Demand response/direct load control
Interruptible load/Load curtailment
Remedial Actions–

Under-frequency load shedding–
Under-voltage load shedding–
Predictive/special load shedding

The same load can be included in different programs. When the EPS/ EMS and DMS implement load management by using more than one load management means, they need to know what load is left in the consecutive means after the previous means is executed.

In Active Distribution Networks, the allocation and the composition of different load management means may significantly change based on the time of day, ambient conditions, real time pricing, etc. [45], [46]. These changes should be monitored and processed by the DMS and reported to the TBLM. The attributes of the aggregated model of the load management means should include the values and settings of the same load included in different load management functions. Depending on the condition, under which the load management functions operate, the utilization of the common load may be different. Hence, the results of the contingency analyses and, consequently, the development of the emergencies may be significantly different.

An illustration of overlapping loads is presented in Table 1 and Figure 13.

Table 1. Effect of load overlapping among different load management means on the composition of the Under Freaquency Load Shedding.

Figure 13 presents the dynamics of the frequency and voltage under an emergency with a generation deficit of 35% in a region of the power system after the emergency voltage reduction and all three groups of Demand Response were applied before the contingency. This deficit caused an overload of a portion of the transmission system supplying the region. The following versions of the emergency are considered in the illustration:

The overload of the transmission supply does not cause a significant voltage drop. The transmission supply trips by the overload protections. The UFLS operates, the UVLS is not involved.
The overload of the transmission supply causes a significant voltage drop. The UVLS and the UFLS are involved.
- The transmission supply trips after 3 sec
- The transmission supply trips after 5 sec
- The transmission supply trips after 8 sec

Figure 13. Emergency frequency changes with and without overlapping loads

As seen in the figure, it is more likely that the frequency may remain on a below normal level for a longer time if the UVLS operates before the UFLS. Such delay of the rise of the frequency may lead to additional cascading failures.

The actions for determining this component of the TBLM are envisioned as follows:

DMS (periodically or upon event) calculates and submits to the TBLM the following dynamically changing aggregated near-real-time and short-term look-ahead values and attributes of:
- The immediate load-to-voltage dependencies (LTV). The LTVs may significantly change with the change of load compositions.
- The real and reactive Demand Response
- The UVLS and UFLS groups
- The common load in different groups of the load management means
- The DER ride-through setups.
The TSO/EMS uses the relevant information from the TBLM for the contingency analyses and requests changes if needed. .
The compensation for meeting the TSO/EMS request is processed according to existing agreements between the parties.

8Degree of uncertainty of TBLM component models

The Transmission Bus Load Model (TBLM) is to be used predominantly for short-term look-ahead EMS and DMS applications. Therefore, it is based on the forecasts (estimates) of a number of component models of the TBLM. These components are used by the advanced applications for providing operational solution based on some optimization studies. Significant inaccuracies in the input components and in the execution of the solutions may lead to loss of benefits and even to harmful results. Therefore, a risk assessment procedure should be a part of critical applications. Such risk assessment is based on the degrees of uncertainties of the input data, on the sensitivities of other components to these uncertainties, and on uncertainty of execution actions.

The uncertainties of the TBLM components are combinations of uncertainties of measurements, models of loads, models of distributed generation, models of controlling equipment, weather forecast, circuit topology, etc. [47- [49]. The accuracy of execution may depend on multiple factors, including communication aspects and human factors

DMS should define the component uncertainties based on rated accuracy of measurements, statistics of previous modelling and controlling and combine them statistically in a weighted manner.

The information exchange between the DSO/DMS and TSO/EMS should include the following:

Uncertainty of the initial models of the nodal loads in distribution
Uncertainty of the forecast of external factors (weather, price, etc.)
Uncertainty of the load dependencies on external factors (weather, price, etc.)
Uncertainty of the load-to-voltage dependencies
Uncertainty of the load-to-frequency dependencies
Uncertainty of the aggregated DER models, based on the following uncertainties:

For not monitored DER
- Uncertainty of the DER participation
- Uncertainty of the DER modes of operations and settings
- Uncertainty of DER nominal model dependency on ambient conditions [50]
- Uncertainty of DER current and look ahead performance based on current and look-ahead ambient conditions [50]

It is assumed here that the models of non-monitored DERs are developed based on conditionally constant DER data stored in the DER/microgrid Data Management Systems, and the current and look–ahead models are developed in the DER/microgrid Model Processor based on the combination of the data from DER/microgrid databases and near-real time or look-ahead weather data and operational conditions.

For monitored DER
- Uncertainty of the effective capability curves
- Uncertainty of DER nominal model dependency on ambient conditions [50]
- Uncertainty of the assessment of look-ahead performance based on expected external input and power flow model
Uncertainties of the microgrid operational models that are combinations of the uncertainties of the load and DER models

Uncertainty of the variability of operations
Uncertainty of secondary equivalent models
Uncertainties of Demand Response models
Uncertainties of execution of commands and requests
Uncertainties of control actions (e.g., based on the bandwidth of voltage controllers)
Uncertainties of utilization of dispatchable load/generation

Errors of measurements (e.g., the measurements of reference voltage and other measurements used for state estimation)
Errors of modeling (e.g., power flow models)
Other

The uncertainties can be expressed in probabilities and/or other statistical attributes.

9Possible shifting of load from/to the transmission bus

For the most of the transmission buses, a portion of the load fed from the bus can be transferred to other busses without violations of the operational limits. Such transfer may change the economics of both transmission and distribution operations (changes of LMPs, losses, of volt/var control benefits, etc.). The efficiencies of these changes may be in conflicts with each other, e.g., the LMPs may reduce, while the losses in distribution can increase. The transmission operational objectives for feeder reconfiguration are preventive and even corrective measures in case of a contingency [51] and/or LMP reduction [52].

There may be several alternatives for the shifts of load from one bus to other buses. The alternatives may differ by the amount of load that can be shifted and by the economic and reliability effects.

DSO/DMS should assess the least intrusive alternatives of reconfiguration and submit them to the TBLM as defaults.

TSO/EMS uses the default alternatives, if needed. If the default alternatives are insufficient for the TSO/EMS objectives, the TSO/EMS submits a request for other possibilities.

DSO/DMS considers other existing possibilities and submits them to the TBLM with the associated impacts on distribution operations.

The information exchange between the DSO/DMS and TSO/EMS should include the following:

Alternatives of shifting load from/to the subject bus

Amount of kW and kvar changed due to reconfiguration (can be a schedule)
Changes of affected components of the TBLM
Time needed for reconfiguration

Impact of the reconfiguration on

Losses
Voltages
Reliability

TSO/EMS request for load shift

Amount of load to be shifted
Between which buses (may be multiple buses)
Time and duration of requested shift of load

The alternatives of load shifting change with the change of the customer loads, DER operations, and current DR statuses. In some cases, the reconfiguration can be accompanied with some load management actions and/or changes of DER operations in the reconfigured circuits. Hence, the available load shifts should be updated in the near-real time fashion.

10Components of the TBLM before, during and after bulk power system emergencies

The “Before Emergency” model.

This scenario is based on the transmission EMS contingency analyses. The transmission EMS through TBLM submits to the DMS the results of critical EMS’ Contingency Analysis in forms of frequency and voltage vs time curves at the demarcation bus. These curves are based on the last submitted to the TBLM load-to frequency and load –to-voltage dependencies aggregated at the transmission buses (these curves may be two-dimensional or three-dimensional - when the contingency analysis results in simultaneous changes of frequency and voltage). In the latter case, these curves should also take into account the overlaps of the load and generation in different load/generation management schemes.

The DMS should use these curves in its contingency model to verify that the distribution system would responds to the contingency according to the EMS contingency analysis. If it does not respond as assumed in the EMS contingency analyses, correction to the TBLM should be made, and a second iteration of the contingency analysis should be performed.

Based on the final results of the contingency analysis, TSO may suggest preventive measures to the DSO.

The “During Emergency” model

During the emergencies, the load, generation, and the connectivity in the distribution system change in a very fast fashion due to the actions of emergency corrective measures. Hence, the pre-emergency components of the TBLM become inadequate in the time of these transitions. It is unlikely that the TBLM can be timely updated during this time interval. That means that the TBLM cannot be used for adaptive emergency control. The adaptive control of the emergency should be left to the correspondingly designed automatic mitigation means, see e.g., [53], [54].

The “After Emergency” model.

The TBLM components should be updated when the power system comes to the quasi-steady state. In addition, the updated TBLM should contain information about the desired priorities of return to normal in distribution and about the transmission-side constraints for prioritization of distribution restoration.

11Virtual sequence of event log in the TBLM

With the high penetration of DER, microgrids, DR, and ES in distribution, the impact of a number of events in distribution may significantly affect the development of the emergency in the bulk power system. Therefore, an aggregated Virtual Event Log (VEL) should be created in the TBLM to be included in the post-factum analyses of the emergency in the bulk power system [55]. The collection of data for the VEL from the distribution system may be different from the cases of transmission events. For instance, not all events in distribution are captured by SCADA and may not have definite time stamps, e.g., enabling of demand response, etc.

In addition, a number of “software or virtual” events should be considered by the DSO/DMS, such as:

Inaccurate or incomplete information provided by different actors, e.g., incomplete or inaccurate aggregated operational models of distribution sub-systems, of advanced microgrids and other large composite customers
Defining setup parameters of an EMS or DMS application (objective, operational tolerances, coordination of different RAS, etc.)
Inadequate performance of EMS/DMS applications (inaccurate results, inconsistent timing, etc.)
Miss-coordination of actions by major actors
Other

The information exchange between the DSO/DMS and TSO/EMS should include the following:

FLISR (or Data Management Systems) logs with the records of the following:

Load/DER/Microgrid forecast during the time of repair and of overloaded sections
Records of the needed switching operations that could not be executed and why
Pre-defined list and parameters of resources involved in the restoration of service (e.g., number of levels of backup feeders, DER, microgrids, DR, emergency loading limits, ride-trough conditions for DER, etc.)

SCADA logs on switching operations
Logs of Transmission State Estimation application, which calculates voltage angles, or PMU measurements at the involved transmission buses.
Facts of miss-coordination of actors
Distribution SCADA logs on measurements
DOMA logs on power flow/state estimation during the time of restoration
Logs of load-shedding scheme
Matrix of preconditions and remedial actions
IVVWO logs, including the objective and the setup parameters of the application, e.g. whether the transmission constraints, like SVC loading, were included in the objective function, and if yes, what are the constraints.
Excerpts from the DER/microgrid Data Management Systems and Model Processors to support the analysis of the DER/microgrid operations before and during the disturbance.
Other

Conclusions

The EMS and DMS applications for Smart Transmission and Distribution grids are tightly interrelated, and they should be functionally integrated to provide the needed security and efficiency of power system operations.
To make the dynamic coordination of the transmission and distribution operations manageable, an exchange of aggregated relevant information between the domains should be provided.
The concept of the aggregated Distribution Operation Models at the demarcation buses between the transmission and distribution systems (TBLM) has been suggested to meet these requirements.
The sophistication of the TBLM and of the Smart Grid applications should match the complexity of the processes in power systems to achieve maximum benefits.
Eleven components of the TBLM have been summarized in the paper.
The EMS and DMS applications should be modified to utilize the information included in the TBLM.

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[1] An advanced microgrid is comprising distributed generation/storage, and load. It may use internal controlling devices, Remedial Action Schemes, and elements of Information Communications Technology (ICT). Such microgrid may provide a number of ancillary services
[2] The following definitions of the reactive power capabilities of DERs are used in this paper:
Nameplate capability of a DER – is a capability curve in Watt, var, and voltage coordinates based on the nameplate peak W, Power Factor, and Volt. This capability curve is a constant characteristic of the DER.
Nominal capability of DER – is the maximum/minimum var capability derived from the nameplate capability curves under actual W and Volt at the terminal of the DER regardless of the constraints and/or set operating conditions of the relevant electric circuits. This capability changes with the change of the actual W and Volt at the DER terminals. It can be considered as a “theoretical” capability.
Operational capability – is the available reactive power capability within the constraints along the relevant circuits and/or modes of operation of the resources previously set to meet particular objectives. This capability can coincide with the nominal capability, if utilizing the nominal capability does not violate the limits and set modes of operations.