Tech-invite3GPPspaceIETF RFCsSIP
Quick21222324252627282931323334353637384‑5x

Content for  TR 22.867  Word version:  18.2.0

Top   Top   Up   Prev   Next
1…   5…   5.2…   5.3…   5.4…   5.5…   5.7…   5.8…   5.10…   5.12…   5.13…   5.15…   5.16…   5.17…   5.18…   5.20…   5.22…   5.23…   6…   8…

 

5.23  Edge cloud driven data acquisition (edgePMU)Word‑p. 82

5.23.1  DescriptionWord‑p. 82

In the power grid, there are devices deployed to measure physical quantities from the grid, such quantities are e.g. electrical quantities such as amplitudes of voltage and current, phase, frequency or rate of change of frequency (ROCOF). One device that can provide such measurements is called a Phasor Measurement Unit (PMU). This particular device provides the above-mentioned values synchronized to a global time clock derived, for example from GPS. Such synchronized values can be used as an input to power distribution grid services, such as state estimation service [61]. Another possible service that relies on measurements of voltage, current and phase is a voltage control service [62].
As PMUs are very expensive devices, they are placed only at key points of interest. However, it is possible to use cheaper field devices that collect sampled measurement data and then process them in the edge cloud hosted PMU software, virtualizing the processing functionality of a traditional PMU, thus enabling the concept of the edgePMU [63]. Due to the lower processing requirements of the field devices, their cost is reduced, and they can also be deployed in the distribution grid providing power system operators with a more precise knowledge of the state of their grids. The edgePMU concept separates the data acquisition from the data processing by exploiting the computational capabilities offered by distributed 5G edge clouds.
The edgePMU, with its modular approach, tackles the growing need for low-cost measurement devices in distribution networks from a new perspective. It utilizes the scalability of cloud infrastructure and decreases the specialization needed in the data acquisition device, by providing flexible cloud solutions for different data use cases. Furthermore, the overall deployment is simplified from a communication point of view since there is no need for special communications cabling and a network infrastructure managed by distribution grid operators. This approach simplifies the adoption of PMUs in distribution networks by helping distribution grid operators gradually deploy their measurement architectures. In fact, the cloud-based approach offers great flexibility in the development of monitoring and automation functionalities, by gradually stacking services and processing modules. It also provides scalability opportunities, thanks to the cloud technology-based computational infrastructure. The increased computational power offered by 5G edge cloud compared to that of low-cost single board computers enables more computationally challenging algorithms to be deployed.
Here are the key features of the edgePMU concept [63]:
  • High rate raw data transmission between the data acquisition unit and the edge cloud using 5G wireless connectivity
  • The ability to deploy services or other applications on edge cloud infrastructure instead of having to deploy them in a traditional PMU with its high processing power
  • A single field device can be used as a data acquisition unit for different services hosted in the edge cloud
  • Services can be deployed, modified and upgraded on demand without the need to physically visit the sites
  • The cloud-based software is not limited to only providing phasor measurements, but could also be used to calculate other metrics for power distribution grid services
Up

5.23.2  Pre-conditionsWord‑p. 83

Coverage by 5G communications networks of regions in which measurement devices and edge cloud are to be located is required. The voltage or current measurement sensors and their associated secure data acquisition units have to be deployed in the power grid.
The secure data acquisition unit is connected wirelessly by the 5G network to the edge cloud hosted services.
The service of phasor calculation is deployed in the edge cloud infrastructure.

5.23.3  Service FlowsWord‑p. 83

Copy of original 3GPP image for 3GPP TS 22.867, Fig. 5.23.3-1: Service flow diagram for edgePMU use case
Up
  • The data acquisition unit is collecting data, such as voltage and current values, from the power grid.
  • This data is then sent via secure 5G communication to the relevant service hosted on the edge cloud.
  • After the data has been collected in the edge cloud, the service processes the data. A small buffer is needed for post-processing with the service hosted on the edge cloud.
  • In the case of the phasor calculation service, the estimated phasors can be forwarded to further applications or actors, either hosted by the edge cloud or external to it.
Up

5.23.4  Post-conditionsWord‑p. 83

The relevant power distribution grid service is successfully deployed in the edge cloud and running smoothly.
The voltage or current measurements are collected by the data acquisition units and transmitted to the service hosted on the edge cloud.
The service deployed in the edge cloud provides upstream services, such as state estimation or voltage control, with the input data they need.
The resulting service output data, hosted on the edge cloud, is stored permanently or forwarded to upstream services.
Up

5.23.5  Existing features partly or fully covering the use case functionalityWord‑p. 84

Existing LTE wireless connectivity, if configured to provide the required latency, could support the transmission of field device measurement data to services hosted on clouds hosted on distribution system operator owned servers.
Latency:
Lower than 2/Fs, where Fs is the output reporting rate of the edge cloud service. Example: 60 phasors per second at 60 Hz means Fs = 60 and 2/60=0.033 means the overall latency budget is less than 33 ms. Typical networks aim for 50 or 60 phasors, depending on the geo location. Higher reporting rates are also allowed by the standard.
Characteristic parameter Influence quantity
Communication service availability: target value Communication service reliability: mean time between failures End-to-end latency: maximum (notes 1, 2) Service bit rate: user experienced data rate (note 1, 2) Message size [byte] Transfer interval: target value Survival time UE speed # of UEs Service area
> 1 year< 33 ms< 20 Mbit/s≤ 1 msStationary
NOTE 1:
Unless otherwise specified, all communication includes 1 wireless link (UE to network node or network node to UE) rather than two wireless links (UE to UE).
NOTE 2:
It applies to UL.
Up

5.23.6  Potential New Requirements needed to support the use caseWord‑p. 84

None.

5.24  Use case of power distribution grid load and generation prediction serviceWord‑p. 84

5.24.1  DescriptionWord‑p. 84

The Load Prediction (LP) and Generation Prediction (GP) service [52] aim at forecasting the future values of power consumption and injection, respectively, in order to give to the Distribution System Operator (DSO) the awareness on how the grid operating conditions are expected to evolve in the future. This service works by processing the historical data on the power consumption/injection of the customer, generator, or substation under analysis, and possibly taking into account other information that is likely to affect the power levels (e.g. like weather conditions, temperature, etc.). The forecast given by the LP and GP can refer to different time horizons and can have a different time resolution, according to the requirements of the DSOs. As an example, day ahead forecasts (for example with a time resolution of 15 minutes) can be generated in order to predict possible contingencies and, in case, to be prepared to take adequate countermeasures. Day ahead forecasts could be refined by shorter-term forecasts, e.g. a forecast referred to the next hour, which in general could be more reliable since it can be based on more recent information on the grid status. This could be, for example, a solution to apply preventive control schemes aimed at minimizing the risk of problems in the grid. On the other side, longer term forecasts (e.g. on a seasonal or yearly basis) are also possible and they can support DSOs in planning, and in supporting strategical decisions on managing and reinforcing the grid.
Up

5.24.2  Pre-conditionsWord‑p. 84

Power meters are available at the customer (or substation) site where the forecast is required. The State Estimation service (see clause 5.20 "Use case of power distribution grid state estimation" above) is deployed and active in the power distribution grid. Output of the State Estimation service can be used by the LP and GP service.

5.24.3  Service FlowsWord‑p. 85

5.24.3.1  IntroductionWord‑p. 85

LP and GP service is based on historical data as the input information. In the case of the LP service, the needed historical data include the power consumption (demand) measured in the past with a time resolution that is equal (or better) than the one required in output for the service. As a consequence, the application of the LP service automatically implies the need to have power meters available at the customer (or substation) site where the forecast is required. In the case of the GP service, similar requirements also apply. Moreover, since the generation can be largely affected by external factors, additional information can be also needed. E.g., in case of a GP service to forecast the power generation from photovoltaic (PV) plants, additional factors such as weather conditions (irradiance, temperature, cloudiness, etc.) can play a relevant role to determine the expected power generation and they need to be duly considered by the service. In summary, the data required as input to the LP/GP service are:
  • Active and reactive power consumption (or generation) measured at the customer (generator) or substation that is object of the analysis; data should possibly cover a quite long period of time (months or years) to lead to accurate results.
  • Weather forecast data (irradiance, temperature, cloudiness, etc.) as input to the GP service.
Generally, the LP/GP service has loose power system requirements. It is worth noting that no data about the grid are needed for this service and that the measurement devices deployed to get the needed power data do not need real-time communication requirements.
Up

5.24.3.2  Power system requirements of LP/GP serviceWord‑p. 85

Needed components in the field:
sensors and meters.
Information about the electrical grid:
no details on the electric grid are needed.
Type of measurements:
power measurements at the monitored load, generator, or substation (or both voltage and current in order to be able to compute the associated power).
Measurement number:
for each point where an accurate forecast is needed, a meter providing power measurements (or information on both voltage and current) is needed.
Measurement accuracy:
no strict requirements on the accuracy of the measurement chain.
Measurement synchronization:
no need for accurate synchronization, but meter data need to have a time tag.
Additional data needed from the field:
no additional data needed from the field.
Real-time communication capability:
not required.
Bi-directional communication:
not required, the communication flow is mono-directional from the meters to the control centre.
Up

5.24.4  Post-conditionsWord‑p. 85

The future values of power consumption and injection are forecasted. DSO is aware of how the grid operating conditions are expected to evolve in the future, e.g., to predict possible contingencies. Longer term forecasts can support DSO in planning, and in supporting strategical decisions on managing and reinforcing the grid.

5.24.5  Existing features partly or fully covering the use case functionalityWord‑p. 85

Data transmission is based on the uplink direction only, when using historic values for load and power generated.
Load and generation prediction service does not take data from the field, but only the data stored in the database. Of course, the data stored in the database arrived at some point from the field.
Connection density
See State Estimation service (clause 5.20 "Use case of power distribution grid state estimation" above) for the number of substations where measurements that will be used for Load and Generation Prediction service can be collected. Additional measurements can arrive from generation plants and in this case the density of the connection end points will become higher comparing to State Estimation service.
In addition, residential smart meters could be also used in for LP/GP service. In this case the number of connection end points would increase drastically.
The connection density is the most critical requirements for LP/GP service. Accordingly, it requires enhanced coverage.
Latency
There are no critical latency requirements, since data are taken a posteriori from the database.
Sampling rate
See State Estimation service (clause 5.20 "Use case of power distribution grid state estimation" above) since the same data are likely to be used for LP/GP service. Note that for LP/GP service, sampling rate of 1 message per minute is enough.
Message size
See State Estimation service (clause 5.20 "Use case of power distribution grid state estimation" above) since the same measurement device can be used also for LP/GP service measurements collection.
Communications service reliability
Important but not critical. Prediction service works also if there are some missing values, since there are procedures to replace them.
Communication service availability
Not critical. 99.9% (3 nines meaning communications downtime per month of 43 minutes) or even more is acceptable.
Security
Important but not critical. If a few data are corrupted, it is not a big problem. Of course, if all the data of a certain period are wrong then also the prediction would be wrong. But in such scenario, there will be already other services that are more critical suffering from the bad data.
Table 5.24.5-1 shows communications requirements for the LP/GP service.
Characteristic parameter Influence quantity
Communication service availability: target value Communication service reliability: mean time between failures End-to-end latency: maximum (notes 1, 2) Service bit rate: user experienced data rate (note 2) Message size [byte] (note 2) Transfer interval: target value (note 2) Survival time (note 2) UE speed # of UEs Service area
99.9%Not critical100 bit/s< 10001 minuteStationary< 30 per km²several km² up to 100,000 km²
NOTE 1:
Unless otherwise specified, all communication includes 1 wireless link (UE to network node) rather than two wireless links (UE to UE).
NOTE 2:
It applies to UL.
Up

5.24.6  Potential New Requirements needed to support the use caseWord‑p. 86

None.

Up   Top   ToC