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Distribution grid planning and operational principles for EV mass roll-out while enabling DER integration
Deliverable (D) No: 4.5
Prototype tool manual Author: Frederik Geth / Tractebel Stefan Böcker (ICT Annex) / TU Dortmund Date: 28.01.2016 Version: 1.0
www.PlanGridEV.eu
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement
No. 608957.
Confidential (Y / N): Y
D4.5 Prototype tool manual
PGEV D4 5 Manual Public Page 3 of 5
Title of the Deliverable
Prototype tool manual
WP number WP title WP leader WP4 Task title
Main Author Frederik Geth, Tractebel Stefan Böcker (Annex) / TU Dortmund
Project partners involved
Stephane Rapoport, Tractebel
Editors Eduardo Zabala / Tecnalia; Eoghan O'Callaghan / ESB Stefan Greve / RWE Armin Gaul / RWE
Type (Distribution level)
PU, Public PP, Restricted to other program participants (including the Commission Services) RE, Restricted to other a group specified by the consortium (including the Commission Services) CO, Confidential, only for members of the consortium (including the Commission Services)
Status In Process In Revision Approved
Further information www.PlanGridEV.eu
D4.5 Prototype Tool Manual
PGEV D4 5 Manual Public Page 5 of 5
Executive Summary
The overall objective of the PlanGridEV project is to develop new network planning tools and methods for European DSOs to support an optimized large-scale roll-out of electromobility whilst at the same time maximizing the potential of DER integration. The project aims to update tools and methods to address local load and congestion issues, based on the management of electric vehicle (EV) charging processes. A prototype tool is developed as part of WP4. The developed prototype builds upon the analysis, models and methods developed and published in D4.1 and D4.2. Next, this tool is used in WP6 to develop case studies and to explore solutions to grid problems. An AC optimal power flow (OPF) methodology is developed as part of the prototype tool to optimize power system related operational choices. Next to unit characteristics, OPF problems take into account the physical behaviour of the grid. Technical information is required to be able solve such OPF problems. For instance, information on how the grid is structured, which technologies are used, what the operational limits are, etc. This manual to D4.5 (the prototype tool itself) therefore discusses which models are included. Next, it discusses which parameters are required to define a valid case study. Finally, the parameters, which are needed capitalize on the tool in a context to support grid planning processes, are discussed. The aim is to be able to support and validate grid investment decisions in controllability. The OPF simulation core tackles both low voltage and medium voltage distribution grid case studies. This means that mathematical methods used are valid, robust, and have sufficient numerical performance for simulation of radial networks of varying voltage levels, with varying combinations of underground cables and overhead lines, and for varying reactance-to-resistance ratios. The calculation core performs balanced power flow analysis. Furthermore, the OPF methodology includes support of multiperiod simulation (simulation of multiple time steps at once). In the unit models (e.g. EVs) a number of dynamics are taken into account, e.g. storage processes over time. A library of unit models is provided with the tool, including curtailable generators, sheddable loads, PV system models and EVs (including vehicle-to-grid). For DER and EV, the tool includes methods to generate representative behaviour, to simplify the setup of case studies by the user. Due to the large-scale and multiperiod nature of the simulation, the tool returns a significant amount of numerical results. It is rather time-consuming to explore such results using conventional spreadsheet software. Therefore, to streamline the interpretation and analysis of the results, next to the numerical results, the tool returns a number of figures, all adhering to a common visualization approach.
Distribution grid planning and operational principles for EV mass roll-out while
enabling DER integration
Deliverable (D) No: 4.5 Annex
Functional specification of ICT model and methods Author: Böcker, Stefan Date: 17.11.2015 Version: 1.5
www.PlanGridEV.eu
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement
No. 608957.
Confidential (Y / N): N
D4.2 Report on new methods to maximize integration of EV and DER in distribution grids
PlanGridEV_ICT_Model_Specification_D4_5_Annex_v15_final.docx
Title of the Deliverable
Functional specification of ICT model and methods
WP number WP title WP leader
4 Development of Grid Methods and Tools TRACTEBEL
Task title
Main Author Stefan Böcker / TUDo
Project partners involved
F. Geth / Tractebel
Editors Eoghan O’Callaghan / ESB Eduardo Zabala / Tecnalia Raúl Rodríguez / Tecnalia Armin Gaul / RWE Stefan Greve / RWE
Type (Distribution level)
PU, Public
PP, Restricted to other program participants (including the Commission Services)
RE, Restricted to other a group specified by the consortium (including the Commission Services)
CO, Confidential, only for members of the consortium (including the Commission Services)
Status
In Process
In Revision
Approved
Further information www.PlanGridEV.eu
Functional Specification of the ICT model and methods
Page 5 of 37
Table of contents
Abbreviations and Acronyms ..................................................................................................... 6 1. Introduction ......................................................................................................................... 7
1.1. ICT Model and Methods overview ............................................................................... 7 2. Functional Specification of ICT Model ................................................................................ 9
2.1. ICT Model Input ......................................................................................................... 10 2.1.1. Use Case Groups ............................................................................................... 10 2.1.2. ICT Requirements .............................................................................................. 12 2.1.3. Energy Network .................................................................................................. 14
2.2. Smart Grid Traffic Modeling ...................................................................................... 16 2.3. Network Dimensioning ............................................................................................... 17 2.4. Cost Modelling ........................................................................................................... 19
2.4.1. CAPEX Estimation ............................................................................................. 20 2.4.2. OPEX Estimation ................................................................................................ 20
2.5. ICT Model Output ...................................................................................................... 21 2.6. ICT Model Summary .................................................................................................. 22
3. Test Case Study ............................................................................................................... 24 3.1. Sensitivity Analysis .................................................................................................... 26
4. References ........................................................................................................................ 29 4.1. External documents ................................................................................................... 29 4.2. Project documents ..................................................................................................... 30
5. Revisions ........................................................................................................................... 31 5.1. Track changes ........................................................................................................... 31
6. Annex ................................................................................................................................ 32 6.1. ICT Model Input Overview ......................................................................................... 32 6.2. Detailed Use Case Element Overview ...................................................................... 34
Functional Specification of the ICT model and methods
Page 6 of 37
Abbreviations and Acronyms
Table 1 – Acronyms
AMR Automated Meter Reading
CAPEX Capital Expenditures
CO Central Office
CP Connection Point
CS Central System
DA Distribution Automation
DG/DS Distributed Generation/Distributed
Storage
DSM Demand Side Management
EV Electric Vehicle
EVSE Electric Vehicle Supply Equipment
FTTB Fibre-To-The-Building
GPON Gigabit Optical Network
HL High-Level
ICT Information and Communication
Technology
LAN Local Area Network
LC Local Controller
LTE Long Term Evolution
M2M Machine-to-Machine
NPV Net Present Value
OPEX Operating Expenditures
PLC Power Line Communication
QoS Quality of Service
TCO Total Cost of Ownership
UCE Use Case Element
WAN Wide Area Network
Functional Specification of the ICT model and methods
Page 7 of 37
1. Introduction
The scope of the document is to describe the functional specification of the ICT model and methods in
relation with the planning tool developed in PGEV. Therefore, the description given in deliverable D4.2
[12] is detailed and extended to a detailed insight of a cost-benefit analysis illustrated in [1]. Major
functionalities of the ICT model and methods with special focus on economic aspects are published in
[2].
After an introduction to an overview of our tool, different functionalities in particular, Smart Grid Traffic
Modelling, Statistical Network Dimensioning and Cost Modelling, are illustrated in detail. The description
ends with a case study using the previously-introduced tooling.
1.1. ICT Model and Methods overview
The ICT model for distribution network planning is divided into two main parts according to their
functionalities, as presented in Figure 1. First, ICT Pre-Selection provides a general set of applicable
ICT technologies and high-level protocols without consideration of network and infrastructure data.
Secondly, the Infrastructure Planning considers a concrete energy network, in order to propose several
ICT infrastructure and technologies. The main part of the ICT model depends on external input defined
by the planning tool user, such as use case definition and energy network data. Finally, the outputs of
both parts are evaluated in order to provide ICT recommendations for given external input. In the
following sections, each part is described individually.
Figure 1: Overview of ICT Model for Distribution Network Planning [1]
In the following figures, subparts of this overview are mapped to their main internal functionalities, which
will be described in detail in section 2. The scenario based ICT Pre-Selection is mainly based on a use
Use Case
Groups
ICT
Requirements
High Level Protocol
Pre-Selection
Transmission Technology
Pre-Selection
Energy
NetworkControl Flow
(Communication Effort)
Evalu
ation
(Scoring)
ICT
Reco
mm
en
dati
on
s
External Input ICT Analysis Model
Infrastructure
Aggregation Topology
Infrastructure Planning (II)
Scenario based ICT Pre-Selection (I)
Functional Specification of the ICT model and methods
Page 8 of 37
case elements selection, which offers necessary input for smart grid traffic modelling (Section 2.1)
functionality.
Figure 2: Overview of Scenario based ICT Pre-Selection (I)
The infrastructure planning part is mainly based on energy network data and provides opportunities for
statistical network dimension (Section 2.3) functionality.
Figure 3: Overview of Infrastructure Planning (II)
As a conclusion of both ICT-Model subparts, evaluation is implemented within Cost Modelling (Section
2.4) functionality and directly leads to ICT recommendations. In the following section, necessary external
input is explained in advance.
Use Case
Groups
ICT
Requirements
High Level Protocol
Pre-Selection
Transmission Technology
Pre-Selection
External Input
Scenario based ICT Pre-Selection (I)
Smart GridTraffic Modelling
Transmission Technology
Pre-Selection
Energy
NetworkControl Flow
(Communication Effort)
External Input
Infrastructure
Aggregation Topology
Infrastructure Planning (II)
Statistical Network Dimensioning
Functional Specification of the ICT model and methods
Page 9 of 37
2. Functional Specification of ICT Model
As depicted before the ICT Model consists of three different functionalities displayed in Figure 4, Smart
Grid Traffic Modeling consisting of traffic and a device/connection point modelling, Network
Dimensioning and Cost Modeling based on an economic analysis. The following introduced
functionalities have been successfully published on the SmartGridComm Conference, Miami, USA [2].
Figure 4: Structure Overview of Smart Grid Communication [2]
Detailed descriptions for each functionality are given in the following subsections. Hereby, the ICT Model
considers the most appealing candidates of transmission technology solutions with regard to their
Packet Size
Frequency of
Occurence
Maximum Latency
Traffic Class (Priority-Random, Regular,
Scheduled)
Traffic Model
Monthly Data
Volume per UC
Minimum Data
Rate Requirement
per UC
Period under Study
Use Cases (UC)
(AMR, DG/DS, DA, DSM)
Connection Points
(Private, Public, Grid)
Area Expansion, Type (Urban, Suburban, Rural)
Device /
Connection Point
Model
Total Data Rate
Requirement
Total Data
Volume
Communication
Technology
Properties
Cost Factors
Network Planning
Cost Model
Total Costs of
Ownership (TCO)Net Present Value
Sm
art
Gri
d T
raff
ic
Mo
del
ing
(Sec
tio
n I
II)
Net
wo
rk
Dim
ensi
on
ing
(Sec
tio
n I
V)
Co
st M
od
elin
g
(Sec
tio
n V
)
Costs /
Device / Month
Tec
hn
olo
gy
Dep
end
ent
Ap
pli
cati
on
Dep
end
ent
Functional Specification of the ICT model and methods
Page 10 of 37
suitability for future grid applications1:
Dedicated Long-Term-Evolution (LTE) network
Combination of dedicated LTE and wired Broadband Power Line Communication (PLC) networks
Dedicated Fibre networks
Tariff network – using Tariff networks includes the use of public network structures (e.g., GPRS, UMTS, LTE). In contrast to dedicated networks, public networks should result in lower infrastructure costs.
Mixed Region – using mixed region networks considers capabilities to design different transmission technology solutions for different energy network areas (e.g., urban fibre and suburban, rural LTE network).
When necessary, technical characteristics of individual transmission technologies will be discussed
within the following sections.
2.1. ICT Model Input
The ICT model specification is dependent on external input of the planning tool user. It is divided into
three different parts, which are each explained in the following subsections. Additionally, Annex 6.1
illustrates an overview of all parameters that are needed at a minimum. In accordance with following
subsections, it is divided into traffic related parameters (detailed information given in subsection 2.1.1),
communication technology related parameters (see subsection 2.1.2), energy network data (see
subsection 2.1.3), as well as commercial and basic dimensioning parameters (see section 2.4).
2.1.1. Use Case Groups
The choice of use cases which are desired by the planning tool user is based on a predefined set of
Use Case Groups.
Automated Meter Reading (AMR)
Distributed Generation/Distributed Storage (DG/DS)
Distribution Automation (DA)
Demand Side Management (DSM)
The planning tool user is able to activate or deactivate these use case groups. If a use case group is
selected, it will be considered during ICT planning. Each use case group consists of a set of a predefined
Use Case Elements (UCE), which are explained according to their functionalities and application area
in the following subsection 2.1.1.1. The mapping of use case groups to UCE is defined in Annex 6.2. In
addition, Annex 6.2 provides values for UCE frequencies, durations, priority and data volume. These
parameters provide capabilities to calculate data rates and data volumes, which are mandatory for smart
grid traffic modelling (see section 2.2).
1 Selected technologies are an example for most suitable transmission technologies with regard to Smart Grids
and fulfill the requirements of all introduced use cases within this documentation. Nevertheless, the tool can be
extended by additional technologies.
Functional Specification of the ICT model and methods
Page 11 of 37
Remark: Additionally, a use case pre-selection by the planning tool user provides options to estimate reliable High-Level Protocols in order to fulfil requirements and demands of a use case element. This HL-preselection is not integrated in the ICT model, but several HL protocols and their application areas are discussed in deliverable D3.1 [13], task T3.2 and T3.3. 2.1.1.1 Use Case Elements
Use Case Elements and their application areas are defined as follows:
Administration and Configuration (UCE_ADMIN)
Administration and Configuration consolidates UCE options for several management and maintenance activities, such as: Device, Client and Certificate Management, Firmware Update / Upgrade, Wake-up Configuration, Monitoring (System logs) and Time Synchronization.
Alarming and Notification (UCE_ALARM)
Alarming and Notification provides UCE options for: Event/Error Reports and Alive Notification.
Controllable Local Systems (UCE_CLS)
Communication with Controllable Local System (CLS) intended for Demand-Side-Management of e.g., EVs, heat pumps or storage heater systems. These entities are either part of a smart home system or belong to small industrial companies.
Load Optimization (UCE_LO)
Load Optimization provides local load optimization capabilities by means of substation automation (distribution network level).
Distributed Energy Resources (UCE_DER)
Distributed Energy Resources (DER) includes measurement and control related communication with smaller DERs (< 1MW) as part of the distribution grid (such as wind and photovoltaic systems).
Smart Metering (UCE_SM)
Smart Metering incorporates automated Meter Readings and Communication to central systems by means of intelligent metering systems.
Electric Vehicles Supply Equipment Management (UCE_EVSE_MGMT)
Electric Vehicles Supply Equipment Management provides operations for basic charging process management of EVSEs which are not part of smart home systems, but located at public or semi-public area. This use case element provide following options: Charge Authentication, Billing, Remote Customer Support, Charge Spot Reservation and Asset Management.
Electric Vehicles Charge Management (UCE_EV_CM)
Electric Vehicles Charge Management includes charge control of EVs, which are not part of a smart home system, but located at public or semi-public Electric Vehicle Supply Equipment (EVSE). Different types of charge management are covered by appropriate UCE options: Soft / fleet focused charge management based on Time of Use tariffs, Massive charge management based on daily signals and Massive Local Charge Management based on Charge Modulation.
Functional Specification of the ICT model and methods
Page 12 of 37
2.1.2. ICT Requirements
For choosing relevant ICT components, the planning tool user defines requirements and boundaries for
ICT infrastructure. This is realized by defining technical parameters which should be met at minimum by
the eligible communication technologies and are mapped to the above introduced use case elements.
Parameters are divided into optional and mandatory parameters as listed in Table 2.
Remark: The ICT Model implementation focusses on a few communication technologies that are suitable candidates for ICT rollout for future grids. These technologies are implemented and highlighted within network dimensioning functionality of the infrastructure planning part (see section 2.3). Other technologies are listed and described within deliverable D3.1 [13].
Table 2: ICT Requirement Parameters
Ma
nd
ato
ry
Min. Availability (temporal) %
Max. Response time s
Bidirectionality y/n
IT Security Low/High
Remote Maintenance y/n
Op
tio
na
l
Min. Data Rate per unit kbit/s
Min. Availability (spatial) %
Traffic classes and QoS y/n
Maximum data rate per unit kbit/s
Technology life cycle years
Black start capability y/n
The input of mandatory parameters is necessary to filter relevant ICT technologies, and these must be
met by technologies to be considered. Optional parameters do not need to be given, and they do not
define hard exclusion criteria for transmission technologies, although a technology will be considered
less suitable if optional parameters do not meet the requirements given. In this context, the ICT model
is based on the following simple scoring procedure, which guarantees that a communication technology
which does not meet the given input will be excluded from ICT network planning.
+1 ICT requirement parameter is better than the given input
0 ICT requirement parameter meets the given input
Functional Specification of the ICT model and methods
Page 13 of 37
-1 ICT requirement parameter does not meet the given input, ICT
requirement parameter is optional
-20 ICT requirement parameter does not meet the given input, ICT
requirement parameter is mandatory
A definition of the above listed parameters follows subsequently.
Min. Availability (temporal) [%]:
The temporal availability is calculated from the ratio of the average availability of a technology
per connection based on the considered overall period, e.g. 525420 min / 525600 min =
99.9657%
Max. Response Time [s]:
The response time is the delay between sending a message and receiving an acknowledgement
(receipt of a confirmation message transmitted by the receiving station), e.g. 0.2 s.
Bidirectionality [y/n]:
This parameter defines whether a reverse channel is required or not.
IT Security [Low/High]:
This is an indicator for the fulfilment of security requirements in general.
Remote Maintenance [y/n]:
The remote maintenance parameter describes whether a technology has the opportunity of
remote access in order to resolve errors or to add communication functionality (e.g. in case of
the detection of security gaps).
Min. Availability (spatial) [%]:
The spatial availability is calculated from the ratio of a technology coverage in relation to the
desired total area, e.g. 525 km2 / 560 km2 = 93.75%.
Min. Data Rate per Unit [kbit/s]:
The min. data rate defines the data rate that is required per device to fulfil requirements of a
desired smart grid application, e.g. 10 kbit/s in GPRS.
Traffic Classes and QoS [y/n]:
Traffic classes describe the opportunity to define priority classes transmission techniques or
processes.
Maximum Data Rate per Unit [kbit/s]:
Functional Specification of the ICT model and methods
Page 14 of 37
The max. data rate defines the data rate that needs to be supported per device/application in
maximum. It is an indicator for the sustainability of a system.
Technology Life Cycle [years]:
This is an indicator for a period in years after that it is expected that a technology will be replaced
by another.
Black Start Capability [y/n]:
This parameter describes the capability of a communication technology to restart safely after a
failure without operator interaction.
2.1.3. Energy Network
In order to model the ICT infrastructure, detailed knowledge of the underlying energy network is needed.
Therefore, necessary energy network data is based on the desired energy network area, in order to
calculate e.g., coverage areas for mobile cellular networks (compare section 4.2). On the other hand
detailed information about the number of connection points (CP), located within the energy network is
needed in order to calculate the total traffic load within the traffic model (compare section 2.1).
In case of specific transmission technologies (e.g., Power line Communication, PLC), further information
about connection point positions is needed. Thus, special aggregation algorithms, in order to find
suitable number for gateways (local controller, LC) or repeaters, are introduced in subsections 2.1.3.1.
The format of the input table is shown in Table 3.
Table 3: Input for aggregation tool
ID_CP x_CP y_CP Type
… … … …
The input should consist of x- and y-coordinates, as well as an ID for every CP in the energy network.
In addition, a CP should be mapped to one of the following client types:
Private: e.g., home facilities
Public: e.g., work, universities, shop or other facilities
Grid: e.g., substations
2.1.3.1 Pre-Processing of External Network Input
The pre-processing tooling provides an aggregation algorithm to provide possible locations for local
controllers. It is used to aggregate data of several connection points to reduce traffic load in wide area
networks (WAN, compare section 4.2).
The aggregation algorithm is spatially restricted, that means only given connection point locations are
considered as possible positions for local controllers. Different parts of such aggregation algorithm are
Functional Specification of the ICT model and methods
Page 15 of 37
shortly introduced in the following subsections.
Remark: It is possible to consider not only given connection point locations as suitable location for local
controllers, but the overall energy network area. This is not recommended, due to two reasons. First,
due to a significantly higher computational effort. Second, while considering the overall energy network,
it is not guaranteed that a detected location could be used in concrete infrastructure installation, due to
environmental reasons.
2.1.3.2 NeighborFind
NeighborFind searches neighboring connection points in a given radius. Input for NeighborFind are
coordinates of every connection point and the radius for the aggregation. As an output the algorithm
returns the indices of the neighbors for each connection point.
Simple Pseudo-Code NeighborFind
INPUT: connection points = {cp1, cp2, …, cpn}, radius
OUTPUT: neighbors
for all connection points do
(1) find all connection points in radius of current point cpi
(2) add found connection points to neighbors of cpi
end for
2.1.3.3 Spatially-Restricted Aggregation
This aggregation algorithm searches suitable numbers and positions for local controllers under the
constraint that possible positions are limited to connection point locations. The output of NeighborFind
serves as the only input for this algorithm. As an output the algorithm returns the indices of connection
point locations which are suitable for local controller locations as well. This aggregation algorithm is a
greedy algorithm, thus it provides good results in a manageable amount of time, but the result may not
be the optimum.
Simple Pseudo-Code restricted Aggregation
INPUT: neighbors
OUTPUT localController
remainingNeighbors = neighbors
While remainingNeighbors not empty do
(1) find point cpi with most remainingNeighbors
(2) add cpi to localController
(3) remove neighbors of cpi from the list of remainingNeighbors
end while
Functional Specification of the ICT model and methods
Page 16 of 37
2.2. Smart Grid Traffic Modeling
Basically, the ICT Model differentiates between Use Case Groups (see 2.1.1) and Connection Points
(CP). As introduced in section 2.1, CPs are physical communication devices divided into three
categories, private, public and grid. Households are classified as private, public CPs are e.g. wide
parking areas with EV charge spots or wind / solar generation parks. Finally, grid CPs represent entities
of the energy network, such as substations. These devices are then categorized in four use case groups,
Automated Meter Reading (AMR), Distributed Generation and Storage (DG/DS), Distribution
Automation (DA) and Demand Response / Demand Side Management (DSM). According to Annex 6.2
these use cases are linked to a definition of traffic requirements, which are packet size, arrival rate,
required latency and service priority.
These requirements are used for a worst case modelling delivering minimum data rate requirement and
monthly data volume per CP. Traffic classes are not only priority and non-priority services, but a more
detailed differentiation into three categories:
priority-random (P),
regular (R),
scheduled messages (S).
Priority-random messages are high priority messages that could not be foreseen, e.g. fault alarms or
switching commands. Regular messages are periodically transmitted and are characterized by a
comparably low inter arrival time (IAT) and a maximum latency to IAT ratio of approximately 1, whereby
the IAT characterizes the message frequency. Assuming an UCE with a message frequency of 15
minutes (IAT) and maximum latency (allowed duration) of 10 minutes, the maximum latency to IAT ratio
is smaller than 1 in each case. The idea behind this requirement is that information regular messages
with a higher latency than IAT is out of date and thus not of interest. Scheduled messages are non-
priority and can either be transmitted on a regular basis or randomly. These services provide a long IAT
and small maximum latency to IAT ratio, thus enabling scheduling of messages. Detailed examples are
illustrated in the Annex.
As regular and priority random messages cannot be scheduled, these service types build a base load
regarding data rate. So, the minimum data rate requirement for every CP is composed of the sum of
minimum data rate requirements of every regular service, the sum of minimum data rate requirements
of all priority-random services and the highest minimum data rate of scheduled services, as these
services can be scheduled and thus be transmitted when general load is relatively low. Whereby we
assume a worst case in which all priority-random services are triggered simultaneously in case of
priority-random services. This results in the following equation for minimum data rate requirements:
Functional Specification of the ICT model and methods
Page 17 of 37
𝑅𝑐𝑝𝑗 = ∑ 𝑅𝑖 + max𝑛𝑠𝑗
𝑅𝑖 + ∑ 𝑅𝑖𝑛𝑝𝑗
𝑖=0
𝑛𝑟𝑗
𝑖=0 ,
where j is the index of the use case, 𝑅𝑖 marks the minimum data rate requirement of service i and r,s,p
indicate the three service groups regular, scheduled and priority-random. 𝑅𝑖 values are derived from the
traffic model, taking packet size, maximum latency and traffic class into account. The monthly data
volume per CP is based on packet size and arrival frequency of message occurrence of different use
cases.
Additionally, a Device / Connection Point Model is implemented according to the development of the
use case groups over the period under study and the Connection Points of the three types (private,
public, grid) in different areas (urban, suburban, rural). With this model, the minimum data rate
requirement and monthly data volume per CP can be used to calculate the total data rate requirement
and total data volume of the corresponding scenario. Total data rate requirement is defined by the
following equation with 𝑁𝑗 describing the number of devices in use case j:
𝑅𝑡𝑜𝑡𝑎𝑙 = ∑ 𝑁𝑗 ∙ 𝑅𝑐𝑝𝑗
𝐽
𝑗=0
2.3. Network Dimensioning
The Network Dimensioning part uses requirements calculated in the Smart Grid Traffic Model and
combines them with a database of communication technologies and their respective properties
regarding e.g. data rate and latency. Considered technologies are basically dedicated LTE
infrastructure, dedicated fibre infrastructure, PLC distribution network with LTE wide area network and
mixed network solutions (e.g., urban fibre + suburban and rural LTE network).
Functional Specification of the ICT model and methods
Page 18 of 37
Figure 5: Coverage based vs. capacity based network planning
As shown in Figure 5, network dimensioning needs to consider two major aspects:
Coverage, i.e. the spatial availability of network connectivity.
Traffic capacity, which means how much traffic load can be handled in a certain time frame.
As fibre networks innately provide a very high data rate, only coverage needs to be taken into account
for this technology. For LTE and PLC technologies, both aspects need to be taken into account.
Coverage based network planning of LTE infrastructure is premised on the Okumura-Hata propagation
model, which was extended by approaches of studying basement and indoor penetration [3]. Using this
data, it is possible to specify the maximum radii for different frequencies in Table 4. Due to different
propagation characteristics in urban, suburban and rural areas, radii are listed separately.
Table 4: LTE network dimensioning: base station radii [km] for different frequencies and area types (indoor coverage). Based on [3].
Frequency 450 MHz 800 MHz 1800 MHz 2600 MHz
Urban 2.1 1.3 0.6 0.4
Suburban 3.7 2.4 1.4 1.0
Rural 12.2 8.4 5.3 4.2
Table 4 is an example for the obtained radii in indoor coverage. Outdoor coverage can be achieved with
larger base station radii, while basement penetration requires smaller radii. Based on these data, the
required number of base stations can be derived statistically.
Regarding capacity based network planning, the number of required base stations is determined by the
Functional Specification of the ICT model and methods
Page 19 of 37
following equation:
𝑚 = max
(𝑅𝑢𝑝
𝑟𝑐𝑒𝑙𝑙𝑢𝑝
,𝑅𝑑𝑜𝑤𝑛
𝑟𝑐𝑒𝑙𝑙𝑑𝑜𝑤𝑛
)
Hereby, the number of base stations needed is determined by dividing the overall minimum data rate
requirements, individually for up and downlink, by the corresponding maximum cell data rates and
calculating the maximum among uplink and downlink.
Concerning fibre infrastructures, only coverage based dimensioning is necessary as aforementioned. A
Fibre-to-the-Building (FTTB) approach is considered here, disregarding in-house communication. The
framework provides the specification of an area to be covered by one central office (CO), so that the
area is divided into zones handled by different CO providing the required backhaul connectivity. CPs
are then attached to the CO, either in a point-to-point approach, or in a Gigabit Passive Optical Network
(GPON) architecture via intermediate splitters, using a ring topology. This approach locates splitters on
a number of rings around the CO interconnected by two additional fibre links. The splitters then
aggregate fibres from several CPs and establish the communication with the CO.
PLC network planning uses a similar approach where Local Controllers (LC) aggregate data
transmissions of nearby CPs. These LCs compromise a PLC head-end and provide backhaul
connectivity similar to the CO for fibre networks. For PLC networks there are two approaches, complete
PLC infrastructure and a combination of PLC and LTE to reduce wide area traffic load of pure mobile
cellular networks, as shown in Figure 6. When using only PLC, LCs are interconnected via repeaters
that provide distances longer than 250m and attached to the core network using one or two selected
LC. In combination with LTE, an LTE modem is located at each LC, allowing connection to the core
network. With regard to the area covered by one LC, PLC range (150 – 200m) and traffic capacities
need to be taken into account.
Figure 6: Combination of LTE and PLC networks
2.4. Cost Modelling
The results of the technological dimensioning serve as input for the cost model, examining the Total
Base Station
CP
CP
CP CP
CPCP
CP
Traffic Load
CP
CP
CPCP
CP
CP
CP
CP Connection Point
Mobile Link (e.g., LTE)
Wired Link (e.g., PLC)
WANLAN
Functional Specification of the ICT model and methods
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Cost of Ownership (TCO), the net present value and the cost per device per month for the different
network approaches made beforehand. Cost components are exposed and divided into capital
expenditures (CAPEX) and operating expenditures (OPEX), in the first tier, and network respectively
task context, in the second tier. The following subsections provide a short description for each
component and the integration into the overall model.
2.4.1. CAPEX Estimation
Access network
Costs for access network infrastructure are mainly dependent on the used technology. In case of LTE
networks this includes expenditures for base stations (eNodeB) and their installation (100-200 k€). Fibre
networks require optical splitters and central offices, but the majority of expenditures for fibre networks
are trenching (21 k€/km) and cabling (100 €/km) [4]–[6] of fibre. PLC networks are sufficiently
established with head-end units (0.5 k€) and repeaters (0.1 k€). Additionally, purchasing and installation
of user equipment is considered.
Backhaul infrastructure
All network types need a connection between access network and core network. These backhaul links
are commonly realized as fibre or directed radio connections. Both of the realizations are sufficient for
LTE and PLC, while directed radio seems inadequate for fibre connections because of lower traffic
capacity.
Technology-specific investment
In addition to the abovementioned factors, LTE networks produce costs for Evolved Packet Core
components and bandwidth-dependent license fees. The latter have been scaled down in the test
scenario in accordance with the area under study, thus preventing an inappropriate distortion of results
in comparison to other technologies. License fees have been assumed as 500 k€ for 20 MHz.
Location, buildings
Locations and buildings for network components need to be either bought or rented. The framework
allows setting up a ratio between these options.
Network planning
As a matter of course, expenditures for planning and dimensioning a new communication network need
to be considered as well.
2.4.2. OPEX Estimation
Network operation
A majority of operating expenditures are costs for actually operating the network, thus monitoring and
controlling its functioning. These costs are based on an estimated number of required employees and
Functional Specification of the ICT model and methods
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country-specific employee-level costs (60-120 k€) [5]
Network maintenance
Maintenance costs are also based on employee estimations similar to the before-mentioned factor.
Hardware replacement
In addition to employee costs for maintenance, regular costs for hardware replacement (1 %
CAPEX/year) need to be taken into consideration.
Energy costs
Network components have a certain power consumption, which has to be settled up. Costs (0.25 €/kWh)
[7] are calculated using a simplified energy model using approaches in [8], [9].
Location, building rent
See above.
Backbone network rent
It is assumed that core network capacities by existing telecommunication network operators are used
instead of setting up a completely new core network. This induces rental costs for these backbone
networks.
Management functions
Another cost factor is based on organization management, which includes costs for e.g. reporting, legal
services, accounting and management itself. These expenses are also based on estimated employee
numbers.
Communication Service Fee
The framework provides the possibility to compare setting up a new ICT solution to using existing ICT
infrastructures based on public M2M tariffs. However, using existing infrastructure produces costs in
terms of service fees. The model allows a choice between two different tariffs deduced from common
public M2M tariffs. This is the non-QoS tariff (Tariff nQoS), which employs per MB princing (0.1 €/MB)
and the Tariff QoS consisting of different data volume packages and an additional package for QoS
guarantees (5 €/device).
2.5. ICT Model Output
Based on all the aforementioned elements, the model provides the following output values for each
network deployment: TCO, which is the total expenditures for the respective network design and can be
split into CAPEX and OPEX, Net Present Value (NPV), which discounts TCO to an equivalent present
value, and costs per device and month derived from the NPV, which allow a transparent comparison
with common communication tariffs.
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CAPEX
OPEX
Net Present Value (NPV)
Costs / Month /User Equipment (UE Costs)
2.6. ICT Model Summary
Figure 7 serves to illustrate an overview of relevant ICT Model Input, Operation, as well as Output
parameter. As depicted and introduced in section 2.1, the ICT Model is mainly based on energy network
and traffic model input definitions. A detailed description of each parameter is given in Annex 6.1.
Optionally, the ICT Model offers capabilities to change configurations of below presented parameters or
traffic conditions (see section 2.2 and 2.3 for detailed parameter information). These alternative
parameterizations should only be executed by ICT professional and thus are excluded from input
parameters. Nevertheless, the test case study provides a short example of the economic impact of
varying technological, as well as traffic parameters (section 3.1).
ICT Model output parameters are introduced and defined in section 2.5.
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Figure 7: ICT Model Input/Output Overview
Inp
ut
Energy Network Data
• Connection Points [n]
• Energy Network Area
[km²]
Traffic Model Data
• Use Case Groups
• Quantity Structure
• ICT Requirements
Common Data
• Discount rate [%]
• Own Core Network
[yes/no]
• CAPEX
• OPEX
• Net Present Value (NPV)
• Costs / Month / User Equipment (UE Costs)
Ou
tpu
tIC
T M
od
el O
pe
ration
Technological Parameters, e.g.
• Frequency [MHz]
• Bandwidth [MHz]
• Network Topology
• Coverage Area
Traffic Conditions, e.g.
• Data Rate Requirement
• Data Volume
• Quantity Structure
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3. Test Case Study
The following test case study illustrates the above introduced Smart Grid ICT Modelling approach, which
is published in [2] as well.
Figure 8 presents our medium-voltage (MV) test grid with a total area of 49.4 km2 divided into Urban,
Suburban and Rural area types. CPs are randomly distributed among these area types considering
various standard-deviations. In this context, CPs are summarized into three types Private (Home
facilities), Public (Work, Shop and Other facilities) and Grid (e.g. substations).
Figure 8: Medium and Low Voltage Test Grid Structure [2]
Table 5 summarizes case study data and depicts the evolution of our considered quantity structure for
above introduced traffic use case groups, which is the multiplication factor applied per CP to obtain the
total communication traffic (zero equals, use case is not implemented). Using the example of DG/DS at
private CPs, it implies that the traffic load generated by these CPs faces a 300% increase between 2015
and 2030.
Total Area
Urban
Suburban
Rural
Suburban:
31,1 km²
Rural:
14,2 km²
Urban:
4,1 km²
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Table 5: Overview of Grid related Scenario Parameters [2]
Area [km2] Urban Suburban Rural Total
4.1 31.1 14.2 49.4
Type of Connection Point Private Public Grid LC Total
Connection
Points [n]
Total 616 223 45 217 884
Urban 217 127 14 32 358
Suburban 321 28 24 125 383
Rural 78 58 7 60 143
Traffic Use Case Groups Year Private Public Grid
AMR 2015 1 0 0
2030 1 0 0
DG /DS 2015 1 5 0
2013 3 20 0
DA 2015 0 0 1
2030 0 0 1
DSM 2015 0 1 0
2030 1 15 0
The above introduced input data leads to results presented in Figure 9, which compares several
technological variants of ICT infrastructure roll-out solutions with help of following parameters:
CAPEX
OPEX
Net Present Value (NPV)
Costs / Month /User Equipment (UE Costs)
Costs per device and month provide a direct and simple comparison between all considered
communication technologies. Thus, UE Costs values differ in a range of 48 % from 14 € (Tariff nQoS)
up to 27 € (Fibre).
In terms of NPV, results show comparatively small differences, yet a slight advance of the homogeneous
LTE- 450 MHz (5 MHz bandwidth) solution (LTE) compared to Tariff nQoS solution. Both technology
combinations - PLC aggregation network with LTE-450 MHz (5 MHz bandwidth) outdoor network for
backhaul connectivity (PLC+LTE) and urban fibre network with suburban/rural LTE infrastructure
(Fibre+LTE) – suffer the drawbacks of extra investments for different network components and
additional staff for operating two infrastructures. Also, fibre networks in both heterogeneous and
homogeneous deployments incur increased TCO due to high expenses for trenching.
It has to be taken into account that costs per device and month are comparably high due to the small
scenario. This also manifests in a relatively low CAPEX to OPEX ratio, caused by a high degree of fixed
OPEX. Large scenarios will experience better scaling, enabling further cost reductions per device. But
in general Figure 9 illustrates which capabilities the implemented ICT planning approach provides in
terms of grid planning decision making.
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Figure 9: Scenario-Specific Comparison of TCO, NPV and Costs per Device and Month for Different Communication Solutions [2]
3.1. Sensitivity Analysis
In a next step, the economic impact of different technology specific parameters (Figure 10) as well as
varying traffic load conditions (Figure 11) is analysed. Figure 10 illustrates the deviation of NPV with
regard to three different ICT reference solutions – LTE 450 MHZ frequency, 5 MHZ bandwidth; Fibre
GPON architecture, 41 % existing ducts and heterogeneous LTE and PLC network with 150 m local
controller (LC) radius. With regard to the LTE example, it is shown that the NPV rises by 119 % in case
of only 1.4 MHz bandwidth, which is caused by the considerable rise of the number of base stations
from 7 to 28, while employing 10 MHz bandwidth might even optimize the NPV slightly. The impact of
different network architectures or existing ducts for cabling is depicted within examples of Fibre as well
as PLC+LTE.
In contrast to different technical parameters, it is also possible to vary the traffic conditions, which is
implemented by, e.g., varying data rate or data volume requirements of former introduced Use Case
Groups with reference to values listed in Annex 6.2. In this context, Figure 11 illustrates risen NPV as
well as costs per device and month for both the LTE and the PLC+LTE approach, due to more strict
minimum data rate requirements (+1.3 kbps per device for use case groups DG/DS, DA, DSM). This is
a result of reduced latency for switching commands, impacting traffic related network planning. In
Functional Specification of the ICT model and methods
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comparison, this variation does not influence the costs of public telecommunication tariffs, since data
rate requirements are not part of the pricing structure. Instead of a higher minimum data rate, an
increased data volume (+6.2 MB per device per month for use case groups AMR, DG/DS) only affects
Tariff QoS, while LTE as well as LTE+PLC networks are planned for dedicated use and do not depend
on any contract resulting in higher fees for more transferred data volume.
In summary, it was shown that the presented ICT planning approach provides capabilities to compare
several communication technology solutions with regard to economic parameters. Achieved results
depend on technical parameters as well as traffic conditions and provide options to vary initial results in
order to find sufficient solutions for specific scenarios.
Figure 10: Sensitivity Analysis of Net Present Value (NPV) with Regard to Technical Parameters [2]
Functional Specification of the ICT model and methods
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Figure 11: Sensitivity Analysis of Net Present Value (NPV) with Regard to Traffic Conditions [2]
Functional Specification of the ICT model and methods
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4. References
4.1. External documents
List of reference documents used but not produced in the project:
[1] S. Böcker, F. Geth, P. Almeida, S. Rapoport, and C. Wietfeld, “Choice of ICT Infrastructures and Technologies in Smart Grid Planning,” in 23rd International Conference on Electricity Distribution, 2015.
[2] C. Dorsch, N., Böcker, S., Hägerling, C., Wietfeld, “Holistic Modelling Approach for Techno-Economic Evaluation of ICT Infrastructures for Smart Grids,” in accepted for presentation at IEEE SmartGridComm, 2015.
[3] C. Hägerling, C. Ide, and C. Wietfeld, “Coverage and Capacity Analysis of Wireless M2M Technologies for Smart Distribution Grid Services,” in Smart Grid Communications (SmartGridComm), 2014 IEEE International Conference on, 2014, pp. 368–373.
[4] C. Jiajia, L. Wosinska, C. M. Machuca, and M. Jaeger, “Cost vs. Reliability Performance Study of Fiber Access Network Architectures,” Commun. Mag. IEEE, vol. 48, no. 2, pp. 56–65, 2010.
[5] M. Mahloo, P. Monti, J. Chen, and L. Wosinska, “Cost Modeling of Backhaul for Mobile Networks,” in IEEE International Conference on Communications (ICC) Workshops, 2014, pp. 397–402.
[6] S. Kulkarni and M. El-Sayed, “FTTH-based broadband access technologies: Key parameters for cost optimized network planning,” Bell Labs Tech. J., vol. 14, no. 4, pp. 297–309, 2010.
[7] Eurostat, “Energy Price Statistics.” 2015.
[8] A. Hoikkanen, “Economics of 3G Long-Term Evolution: the Business Case for the Mobile Operator,” in Wireless and Optical Communications Networks, 2007. WOCN ’07. IFIP International Conference on, 2007, pp. 1–5.
[9] A. A. W. Ahmed, J. Markendahl, and C. Cavdar, “Interplay Between Cost, Capacity and Power Consumption in Heterogeneous Mobile Networks,” in Telecommunications (ICT), 2014 21st International Conference on, 2014, pp. 98–102.
[10] Forum Network Technology / Network Operation in the VDE (FNN), “ICT Requirements for the Operation of AMR Systems.” (in German), Berlin, 2014.
[11] S. Böcker, C. Lewandowski, C. Wietfeld, T. Schluter, and C. Rehtanz, “ICT based performance evaluation of control reserve provision using electric vehicles,” in IEEE PES Innovative Smart Grid Technologies, Europe, 2014, pp. 1–6.
Functional Specification of the ICT model and methods
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4.2. Project documents
List of reference documents produced in the project or part of the grant agreement and available on
http://www.plangridev.eu/:
[12] PlanGridEV Deliverable 4.2: Report on new methods to maximize integration of EV and DER in
distribution grids (methods for optimization under uncertainty, for storage modelling and for statistical behaviour of EV and DER), DOI: 10.13140/RG.2.1.2047.1526, (30 November 2014, Milestone 3 to 31 December 2014)
[13] PlanGridEV Deliverable 3.1: Joint network architecture model, DOI: 10.13140/RG.2.1.2050.2242, (30 June 2014)
Functional Specification of the ICT model and methods
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5. Revisions
5.1. Track changes
Name Date
(dd.mm.jjjj) Version
Changes Subject of change pages
S. Böcker 14.08.2015 1.0 Pre-Final Version Overall
F. Geth 20.08.2015 1.1 Review WP Leader Various
E. O’Callaghan 02.10.2015 1.2 Review 1st Phase (ESB), Final Version
Various
S. Böcker 21.10.2015 1.3 Consideration of 1st review Various
E. Zabala R. Rodríguez
27.10.2015 1.4 Review Technical Coordinator Various
A. Gaul 12.11.2015 1.4 Review Project Manager Various
S. Böcker 17.11.2015 1.5 Final Version Various
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6. Annex
6.1. ICT Model Input Overview
The following table illustrates an overview of all parameters that need to be defined by the planning tool user.
Energy Network Data
# Connection Points [n] The planning tool user has to define the total
number of connection points, divided into the
three connection point types per area type:
Connection Point Types:
Private: e.g., home facilities
Public: e.g., work, universities, shop or other facilities
Grid: e.g., substations
In case of PLC, number of local controller (see section 2.1.3.1)
Area Types:
Urban
Suburban
Rural
Traffic Data
Use Case Groups The planning tool user has to define which use
case groups should be considered during ICT
planning procedure. This definition is divided into
Downlink (DL) and Uplink (UL) and results in
minimum data rate requirements per use case
group [kbps] and monthly data volume per use
case [MB].
These parameters are internally calculated on the
basis of ICT requirements (see section 2.1.2) per
UCE. Related ICT requirements per UCE and
related UCE per use case group are listed in
Annex 6.2.
Quantity Structure This input characterizes the multiplication factor
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applied per CP to obtain the total communication
traffic. It is divided into connection point type for
each use case group and the evolution between
2015 and 2030.
Assuming the example of DG/DS use case group
and private connection point type: A quantity
structure of 1 in 2015 and a quantity structure of
3 in 2030 implies that the traffic load generated by
these CPs faces a 300% increase within the
desired 15 years.
Common Parameters
Discount rate [%] The interest rate at which an eligible financial
institution may borrow funds directly from a
Federal Reserve bank.
Own Core Network [yes/no] Planning of own core network (backbone) or re-
use public ones. (Refers to all dedicated
communication technologies)
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6.2. Detailed Use Case Element Overview
The following table illustrates technical information for each use case element (UCE), which is considered within the ICT planning tool. UCEs are listed per row,
identifiable via unique ID and assigned to relevant UC groups. Technical information is given for Frequency, communication direction, priority, data volume and
date rate. Remark: Use Case Elements definition and corresponding parameter values are derived from literature [10], [11]
Use Case Element (UCE)
ID Description UC Groups
Fre
qu
ency
Main
Directio
n
Dura
tion [
Min
]
Priority
Bru
tto D
ata
Volu
me [B
yte
]
DL D
ata
Vol.
[Byte
]
UL D
ata
Vol. [B
yte
] Data rates
AMR DG/DS DA DSM min. DL-Data rate [bps]
min. UL-Data rate [bps]
UCE_ADMIN 1.1 Device management x x x x Once in 5 years
DL 1 S 5000 5000 50 666.7 6.7
UCE_ADMIN 1.2 Client management x Once in 3 years
DL 1 S 10000 10000 100 133 13.3
UCE_ADMIN 1.3 Profile management (Meter) x Once in 5 years
DL 1 S 4000 4000 40 533.3 5.3
UCE_ADMIN 1.4 Profile management (Comm.)
x x x x Once per year
DL 1 S 4000 4000 40 533.3 5.3
UCE_ADMIN 1.5 Profile management (Profile) x x x x Monthly DL 1 S 4500 4500 45 600 6
UCE_ADMIN 1.6 Key-/Certificate -Mgmt. x x x x Once in 2 years
DL, UL
1 S 4000 2000 2000 266.7 266.7
UCE_ADMIN 1.7 Firmware upgrade (replacement)
x x x x Once per year
DL 2880 S 10800000
10800000
108000
500 5
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UCE_ADMIN 1.8 Firmware update/patch x x x x Twice per year
DL 1440 S 540000 540000 5400 50 0.5
UCE_ADMIN 1.9 Wake-up configuration x Once in 3 years
DL 1 S 4200 4200 42 560 5.6
UCE_ADMIN 1.10 Monitoring (Condition Log) x x x x Once per year
UL 5 S 4000 40 4000 1.07 106.7
UCE_ADMIN 1.11 Monitoring (Calibration Log) x Once per year
UL 5 S 12000 120 12000
3.20 320
UCE_ADMIN 1.12 Monitoring (System Log) x x x x Once per year
UL 5 S 120000 100 120000
32 3200
UCE_ADMIN 1.13 Time Synchronization x x x x Once in 48 h
DL, UL
0,3 R 217 108.5 108.5 48.22 48.22
UCE_ADMIN 1.14 Firmware Download (Call) x x x x Once in 48 h
UL R 2000 20.0 2000 2.67 266.7
UCE_ADMIN 1.15 Dist. of tariff. measurement values (infrequently) - CALL ONLY
Once per year
UL 1440 S 4000 40.0 4000 0.004 0.37
UCE_ADMIN 1.16 Dist. of tariff. measurement values (frequently) - CALL ONLY
x Monthly UL 15 S 4000 40.0 4000 0.356 35.56
UCE_ADMIN 1.17 Dist. of network conditions- CALL ONLY
x x x x Once per week
UL 1 S 4000 40.0 4000 5.33 533.3
UCE_ADMIN 1.18 Wake-up x x x x Once in 48 h
DL 0,5 P 212 212.0 2.1 56.54 0.57
UCE_ALARM 2.1 Alarming - Event/Error Reports
x x x x Once in 5 years
UL 1 P 2145 21.5 2145 2.86 286.0
UCE_ALARM 2.2 Alarming - Alive Notifications x x x x Daily UL 1440 R 2000 20 2000 0.002 0.19
UCE_SM 3.1 Periodic transmission to third party (infrequently)
x Monthly UL 1440 S 2145 21.5 2145 0.002 0.2
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UCE_SM 3.2 Periodic transmission to third party (frequently)
x Every 15 Min.
UL 15 R 2145 21.5 2145 0.19 19.07
UCE_SM 3.3 Periodic transmission to third party (daily profile)
x Once per year
UL 1 S 4000 40 4000 5.33 533.3
UCE_SM 3.4 Periodic transmission to third party (network conditions)
x Twice per day
UL 1 P 4000 40 4000 5.33 533.3
UCE_SM 3.5 Spontaneous measurement value reading
x x x Twice per year
UL 0 P 12205 1.2 122.0 1.63 162.7
UCE_SM 3.6 Central tariffing x Monthly DL 1440 S 4000 4000 40 0.37 0.003
UCE_CLS 4.1 Communication third party with CLS (fast)
x x Monthly DL, UL
1 P 4000 2000 2000 266.7 266.7
UCE_CLS 4.2 Communication third party with CLS (slow)
x x Hourly DL, UL
15 P 4000 2000 2000 17.78 17.78
UCE_DER 5.1 Spontaneous measurement value reading (DER)
x Monthly UL 1 ? 4000 40 4000 5.33 533.4
UCE_DER 5.2 Communication DSO with DER-low-voltage (control)
x x Monthly DL, UL
1 P 4000 2000 2000 266.7 266.7
UCE_LO 6.1 Communication DSO with Smart Operator (control)
x Weekly DL, UL
1 P 4000 2000 2000 266.7 266.7
UCE_EVSE_MGMT
7.1 Charge Authentication x x Daily (*) DL 0.5 P 1600 1600 16 426.7 4.27
UCE_EVSE_MGMT
7.2 Billing x x Daily UL 15 S 200 2 200 0.02 1.78
UCE_EVSE_MGMT
7.3 Remote Customer Support x x x Monthly UL 30 S 1700 17 1700 0.08 7.56
UCE_EVSE_MGMT
7.4 Asset Management x x x Quarterly
UL 720 S 1700 17 1700 0.01 0.31
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UCE_EV_CM 8.1 Soft / fleet focused charge management based on Time of Use tariffs
x x Quarterly
DL, UL
15 P 1300 650 650 5.78 5.78
UCE_EV_CM 8.2 Massive charge management based on daily signals
x x Daily DL, UL
1 P 1300 650 650 86.67 86.67
UCE_EV_CM 8.3 Massive Local Charge Management based on Charge Modulation
x x Two times daily
DL, UL
0.1 P 1300 650 650 1083 1083