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Some Considerations on IoT for Power System Operations

Some Considerations on IoT  for Power System Operations

Nokhum Markushevich

The Internet of Things (IoT) concept (see, e.g., [1] – [3]) that includes cloud analytics and data storages is considered to be a promising approach for the future Information support of operations of power systems (see e.g., {4]-[7]). Under “things” we consider both physical and cyber objects [3].

 The list of data needed for the analytics of the distribution and transmission operations is very extensive [8] – [13].  If any necessary items are omitted from the list, the analysis may be faulty, or even impossible.

As suggested in [7], the “thing” should

  1. Announce itself
  2. Describe itself
  3. Provision itself
  4. Commission itself

As a result, the user of this information (e.g., the utility) “knows what it is, where it is, and what capabilities it has”[7].  This information is supposed to be used for the ”descriptive, predictive, and prescripting” analytics [7].

In order to support the predictive and prescripting analytics, the “describe itself” component should include information needed for “what-if” studies. Some of this information may be provided in near real time by  the “thing” itself, e.g., the settings of the Remedial Action Schemes (RAS) and other controllers and local Energy Management Systems (EMS), assuming communication capabilities. Or this information can be obtained from engineering databases as conditionally constant data, as long as they do not change frequently [9]. 

However, in the Active Distribution Networks, for instance, the settings of some RAS may need to become adaptive to the changing power balances due to near real time changes of the operating condition of Distributed Energy resources (DER) and microgrids [8] – [10].  In these cases, the adapted setups of these objects should be obtained from a number of “what-if” studies. Then, when a particular situation from  the “what-if” conditions appear, the relevant EMS/DMS applications use the appropriate settings adapted to this situation.

There is a significant amount of other data that  is not conditionally constant and is not an attribute of the things that can be provided by sensors (or their equivalents in the case of cyber objects). Many of the attributes needed for “what-if” analyses should be derived by analytic tools in different layers of the information support environment, which also can be different layers of cloud computing , for different levels of the power systems [11]-[16].

The following are examples of such attributes:

  • Load-to-Voltage dependencies (LTV-factors)
  • Energy-to-Voltage dependencies (CVR-factors)
  • Load-to-frequency dependencies
  • Generation-to-frequency dependencies
  • For Active Distributing Networks, the LTV and CVR factors cannot be determined  by performing selective field tests at the heads of distribution systems, as it was done for passive distribution networks [17]-[19], due to the changing contribution of the Distributed Energy Resources (DER) [20]-[21]. To determine the load/generation-to-frequency dependencies for the TBLM,  some near real time and conditionally constant data from individual loads, generators. Microgrids, etc. should be available. [8] – [10], [16]
  • DER Watt/var operational capability curves that depend on the real power contributions, local voltages, activated functionality, and local and remote power grid constraints.
  • Near real time dispatchable load/generation in distribution
  • This information can be derived from sets of power-flow based studies that become cyber things, near real time data and statistics collected by smart building IoT systems, DER controllers, and customer EMS. [13]-[15], [22]-[30].
  • Dependencies of attributes of individual and aggregated loads and DER models on external factors, such as
    • The environment
    • The real-time energy prices
    • The Demand Response signals
    • The requests of Transmission and/or Distribution System Operators (TSO/DSO)
      • This information can be derived from sets of power-flow based studies,  inputs from external systems that become “cyber things”, near real time data and statistics collected by smart building IoT  systems, DER controllers, and customer EMS [13]-[14], [31]-32].

Almost all this information should be provided for the following power system levels:

    • House (apartment)
    • Building
    • Distribution transformer
    • Composite customer’s Point of Common Coupling (PCC)
    • Microgrid PCC [16], [32]
    • Transmission-to-Distribution demarcation bus [13]-[14]

Ultimately, the above components should be aggregated at the transmission-to-distribution demarcation bus, if it is considered to be the end node for the transmission analytics and also an information source for distribution analytics. This aggregated model is a part of the more inclusive Transmission Bus Load Model (TBLM) [13]-[14]. The model components should include the near real time and  the short-term look-ahead values.

The TBLM also includes the following components:

  • Load model forecast
  • Load shifting capabilities from one bus to another
  • Degrees of uncertainty for the model components [33]-[34].
  • Some attributes of the transmission, generation, and market systems relevant to the distribution analytics
  • Post-factum distribution event logs [13].


Envisioning the buildup of the TBLM through cloud infrastructure, the development and updates of the TBLM starts from the primary information sources at the edge of the internet: IoT sensors, local system gateways, engineering and field personnel, and external system interfaces. Each item of this primary information is then transmitted to the appropriate level in the cloud infrastructure [35] for clustering by corresponding Data Management Systems (MS) based on relevant associations (e.g., smart homes connected to the same distribution transformer (DT), DTs and other power equipment connected to the same feeders, feeders to buses, etc.). Based on the conditionally constant and near real time data extracted from relevant Data MS, active models of different composite components of the Active Distribution Networks are developed by the corresponding model processors [13] (e.g., the near real time attributes of DT real and reactive loads including their dependencies on different factors, or  the near real time attributes of microgrids including the current setup of their RAS [16]). This computing can be performed either in a common cloud of the distribution system, or in the edges of the internet (e.g., in a microgrid EMS, in a smart home gateway). In the latter case, the results of the computing, the models,  are transmitted to the common distribution system cloud.  These models are used by distribution cloud computing to run the advanced DMS applications in near real time. The same models that include dependencies of the attributes on external factors are used in a series of distribution modeling  what-if studies under alternative conditions  to develop aggregated models at the Transmission/Distribution demarcation buses (TBLMs). These TBLMs are then transmitted to the next level cloud that includes the attributes of the transmission system and bulk generation and serves the bulk power system by  running EMS applications

Each level in the cloud infrastructure must be 100% populated with data needed to run the analytics of the relevant level. Missing information should be replaced by adequate equivalents (e.g., the secondary circuit attributes [33], distribution transformer loading, etc.).

Figure 1 illustrates a sample of “bottom-up” information supports for the creation and updating of TBLMs, which starts from the internet of physical and cyber things and ascends through a number of expanding clouds. As seen in the illustration, there may be multiple “narrow” clouds on lower levels submitting information to wider clouds of higher levels, up to the most general clouds of the bulk power system and interconnections of systems.

  Figure 1. Sample structure of information  support for TBLMs based on IoT and cloud analytics

Let’s consider for example the development and updating of two of the TBLM attributes: the load-to-voltage (LTV) and energy-to-voltage (CVR) dependencies. As was mentioned above, the field tests of the LTV and CVR factors performed for the passive distribution networks do not represent these load model attributes for active distribution networks. Therefore, a bottom-up methodology was introduced in [33]-[34]. It starts from the smart home systems with the IoT capabilities. The methodology for determining the composite LTV-factors is based on LTV-factors which were determined earlier for different types of equipment/appliances and on the knowledge of the near real time composition and demand of the equipment for the relevant premises. The LTV-factors for different types of equipment were determined by a number of laboratory tests (see e.g., [36]). More such studies are needed to have a comprehensive list of individual LTV-factors for the old and new types of equipment. If these factors are known and the real time composition and loads of equipment is determined by the smart home system (see e.g. [37]-[38], the composite LTV-factor for the home can be determined as a weighted sum of these factors:

LTVj,t = (ΣLTVi x Li,t)/ ΣLi,t                      (1)


LTVj,t – composite LTV-factor for premises j at time t

LTVindividual LTV-factor for the i type of equipment

Li,t – Load of equipment i at time t.

The composition of home appliances and their loading change over time. The changes are different for different homes. That is why the composite LTV-factors are different for different times of day and for different homes and should be determined in a near real time manner.

The following is an illustrative example of an assumed method of determination of the LTV-factors and CVR-factors based on IoT and cloud analytics.

Figure 2 illustrates the difference in load shapes of “similar” customers chosen for our example. The customers are considered “similar” because all of them are residential customers with the same set of appliances (electric heating and cooking) and are connected to the same DT. The difference is in the life style of residents: some of them are working professionals, others are retirees. Therefore, the times of heating and cooking and of other activities are different  (see Figure 3).


 Figure 2. Example load shapes of five “similar” customers

Figure 3. Compositions of appliances for two “similar” customers

It is assumed here that the composition and loading of the appliances are determined by a smart home IoT system, and the LTV-factors for the individual appliances are known and are stored either in the analytical device of the smart home system, or in some kind of low level cloud. Then, the composite LTV-factors for the home can be calculated  based on (1) [33], [34].

When the total load and LTV-factor of every home connected to the same DT are available for the corresponding cloud analytics, the current LTV-factor for the DT can be calculated similarly to (1) as follows:

LTVDT,t = (ΣLTVj,t x Lj,t)/ ΣLj,t          (2)

The LTV-factors for individual appliances used in our example are presented in Table 1. Some of these LTV-factors were derived from [36] by linearizing the ZIP models, others were just assumed for this illustrative example.

Table 1. Example LTV-factors of appliances


LTV-factors, %kW/%Volt

Refrigerators and air conditioners

0.51 [36]

Incandescent lights

1.57 [36]

Resistive load

1.87 [36]

Clothes washers


Clothes dryers





0.05 [36]




Figure 4 presents the LTV-factors for the five homes of our example and the LTV-factors for their feeding DT. As seen in the figure, the DT LTV-factors of the composite residential load are significantly different from the LTV-factors of individual residences and are also significantly different at different times of day.

It is safe to assume that the LTV-factors of other “similar” DT loads are also different from each other.


Figure 4. LTV-factors for loads of five  customers and for the load of the DT

Figure 5 illustrates the difference in daily DT LTV-factors for summer and winter and for loads with air conditioners.

Figure 5. LTV-factors for DT load at winter and summer (with and without air conditioners)

As follows from the above, the LTV-factors are changing in near real time and may be significantly  different for different DTs and different times. The IoT of smart homes and buildings linked to cloud analytics can provide the needed information at the needed times.

Further, when the DT loads and their  LTV-factors are known for the corresponding distribution feeders together with the models of other relevant components of the distribution circuits connected to the same substation bus the LTV-factors for the TBLM can be determined in a higher level of the cloud by running the distribution operation model  under different bus voltages [13] - [15], [21].

It must be noted that when there is a large contribution from electric resistive (especially heating) load,  the LTV-factor determined in the above way can be used only for very short-term analytics, like transient studies. In the case of peak load reduction for a period of several hours or the case of energy conservation (CVR-factors), the increased ON time of the resistive load may result in increased energy consumption under lower voltages and an increased coincidence factor of the DT load, as well as for the  upstream circuits.

Such kind of specific information about the CVR-factors and about the long-term LTV-factors can also be obtained by using near real time and historic information stored by the IoT systems.

The methodology for determining the individual and composite CVR-factors is different from the methodology for determining the LTV-factors, especially when there is a large component of thermostat-controlled load [39]. There are no constant CVR-factors for many types of equipment due to the adjustment to the voltage over time.

If the smart house system can determine and store the times when the heater is ON and its demand during these times,  and the ambient temperature and voltages for these times can be made available either from the same system, or from another source, then  the energy consumption  by the heater under different  ambient temperatures and voltages can be determined under a given duration of observation time. Based on such information the kWh dependencies on voltage for different temperatures can be assessed. 

An illustration of such dependencies for a hypothetical electric heater is presented in Figure 6. The CVR-factors here are the proportion regression coefficients of the linearized dependency (-2.55 in this example). The negative value of the CVR-factor means that the energy consumption increases with the reduction of voltage. As follows from the figure, the kWh dependencies on voltage expressed in p.u. do not differ significantly for different ambient temperatures (at least, for normally sized heaters).

In order to derive such dependencies, the smart home system should capture the measurement for several different voltages and different ambient temperatures. The more history is collected, the more points for interpolation/extrapolation become available. However, acceptable accuracy of the dependencies can be obtained even with a small number of points.  Figure 7 illustrates the comparison of the sample dependencies derived from three different sets of three voltages selected from  different areas of the ±5% range of voltages. As seen in the figure, the CVR-factors in all three-point cases are close to the CVR-factor derived from ten points.

Figure 6. Heater’s kWh dependencies on voltage


Figure 7. Comparison of kWh dependencies on voltage determined based on different sets of three voltages and on  ten voltages

The CVR-factors for individual appliances selected for our hypothetical example are presented in Table 2


Table 2. Example CVR-factors of appliances


CVR-factors, %kWh/%Volt

Refrigerators and air conditioners


Incandescent lights


Resistive load


Clothes washers


Clothes dryers









Based on these CVR-factors, the DT factors were derived (see Figure 8). As seen in the figure, at winter, the CVR-factors are negative most of the day. Even in summer, negative CVR-factors are possible at times of significant electric  cooking  loads.

When electric loads are ON longer, the probability of several being ON at the same time increases.

The smart home systems can determine the probabilities of operation of individual appliances in a given time interval. Based on these probabilities, the probabilities of overlapping of different number of appliances can be determined. Figure 9 illustrates a simplified case of probabilities of overlapping of different number of heaters out of ten heaters for different voltages. It also shows that undersized heaters have higher probabilities of overlapping.

Figure 8.  CVR-factors for DT load at winter and summer (with and without air conditioners)

Figure 9. Probability of the number of heaters ON at the same time (out of ten)

As seen in Figure 9, the average number of the sample heaters that are ON at the same time are 5 for the nominal voltage, but are 6.5 for -5% of voltage deviation and 4 for +5% of voltage deviation from the nominal.

Applying 0.95% of voltage to 6.5 heaters reduces the load of these heaters by ~10% (an equivalent of 0.65 heaters). However, 1.5 heaters more under this voltage increases the load by 0.85 equivalent heaters.

This means that after some time interval of reduced voltage both the energy consumption and the demand of the electric load increase, and the LTV-factors become negative too (lower voltage – higher load). 

The types and composition of loads at different locations and times change in near real time. Such a distinctive  information can be only obtained from the customer sites. If this information is transmitted to higher layers of the information support system, it can be integrated in higher level models, such as the TBLM.  Based on such models,  both the distribution and transmission management systems can provide time-consistent situational assessments and controls, informing the model, for example, that at some times and/or locations voltage reduction is not saving energy and may not be reducing the demand.

The challenge for such an information support system is that for the foreseeable future there will not be enough smart homes to provide sufficient data for the energy management systems.

Several approaches can be suggested during the transitional times, such as:

  • Development of representative groups of customers with similar load attributes based on existing smart home systems and other common characteristics of customers available from the Customer Information and AMI systems.
  • Applying AI methodology to the information obtainable from smart meters of the conventional homes and buildings to estimate probable customer load characteristics. Such algorithms can reside in the Customer Information and/or AMI systems.   


  1. In Active Distribution Networks, many characteristics of distribution operations cannot be determined based on top-down approach (when high-level information is considered to represent local information).
  2. A smart home system with IoT abilities can provide the basic information for a bottom-up approach for determining a number of critical attributes of distribution system operations.
  3. Supplementing the basic information collected by the IoT sensors with conditionally constant data specific for individual components of the customer load  provides the possibility of determining attributes of the load that cannot be determined by the primary sensors. 
  4. Submitting the attributes of individual customer loads to a higher level of cloud analytics provides the ability to integrate these attributes with attributes of other customer loads and other components of the distribution up to the Transmission Bus Load Model.
  5. Until enough smart homes are integrated with the Internet, approximate methodologies for determining the required attributes of distribution operations can be suggested. This methodologies should emulate the methodology of the bottom-up approach to make it easier to accommodate the increasing penetration of the smart home systems



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  9. Nokhum Markushevich, Automatic Load Shedding in Active Distribution Networks. Available:
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  14. Nokhum Markushevich, Transmission Bus Load Model for Smart Distribution and Transmission Grids. Available:
  15. What will the Microgrids and EPS Talk about? Part 2. Available:
  16. Use cases for Microgrid. Available:
  17. Alf Dwyer, Ron Nielsen, Joerg Stangl, Nokhum S. Markushevich, Load to Voltage Dependency Tests at B.C. Hydro,; IEEE/PES 1994 Summer Meeting, July 1994
  18. Nokhum S. Markushevich, R.E. Nielsen, A.K. Nakamura, J.M. Hall, R.L. Nuelk, Impact Of Automated Voltage/Var Control In Distribution On Power System Operations,; DA/DSM Conference January 1996, Tampa, Florida
  19. Nokhum Markushevich; Aleksandr Berman and Ron Nielsen, Methodologies for Assessment of Actual Field Results of Distribution Voltage and Var Optimization, presented at IEEE PES 2012 T and D
  20. Nokhum Markushevich, New Aspects of IVVO in Active Distribution Networks, Presented at IEEE PES 2012 T and D
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  22. Nokhum Markushevich, Operations of Smart Inverters in Active Distribution Networks. Available:
  23. Updates of capability curves of the microgrid’s DERs, Use Case Description. Available:
  24. Nokhum Markushevich, Reflections on Volt/Var Control by Multiple Smart Inverters of DERs. Available:      
  25. Nokhum Markushevich, Reflections on the Topic of Volt/Var Control by Smart Inverters of DERs. Available:
  26. Nokhum Markushevich, Comparison of the effectiveness of var priority mode for reducing and increasing voltage. Available:  
  27. Nokhum Markushevich, Var-priority Mode of DER Volt/var Control Function. Available:
  28. Vars versus Watts from Distributed Energy Resources. Available:  
  29. Nokhum Markushevich, Dispatchable Reactive Load in Active Distribution Networks. Available:
  30.  Updating information on microgrid dispatchable load, Use Case Description. Available:
  31. Nokhum Markushevich, Information Exchange between Advanced Microgrids and Electric Power Systems. Available:
  32. Updating dependencies of the microgrid operational model on external condition. Available:
  33. Nokhum Markushevich. Uncertainty of Voltage Control in Active Distribution Networks. Available:
  34. Nokhum Markushevich, Accuracy of Voltage Optimization in Active Distribution Networks. Available:
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  38. Smart Homes Curb Energy Use. Available:
  39. Nokhum Markushevich,  Analysis of electric heating load dependency on voltage. Available
Dr. Nokhum Markushevich's picture

Thank Dr. Nokhum for the Post!

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Matt Chester's picture
Matt Chester on Oct 3, 2019 3:14 pm GMT

I'm impressed by this well-researched and patiently explained piece, thanks for submitting it Dr. Nokhum.

Supplementing the basic information collected by the IoT sensors with conditionally constant data specific for individual components of the customer load  provides the possibility of determining attributes of the load that cannot be determined by the primary sensors. 

This conclusion is of particular interest to me. How on top of this possibility are utilities and other relevant players? Or should this be read as a more prescriptive conclusion of where the direction should be going?

Dr. Nokhum Markushevich's picture
Dr. Nokhum Markushevich on Oct 5, 2019 10:01 pm GMT

Matt, thank you  very much for your comment. To my knowledge, so far, it is just my suggestion of where the direction should be going.

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