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25th International Conference on Electricity Distribution Madrid, 3-6 June 2019

Paper n° 1099

CIRED 2019 1/5

LARGE SCALE AGENT BASED SIMULATION OF DISTRIBUTION GRID LOADING AND

ITS PRACTICAL APPLICATION

Chris KITTL, Johannes HIRY, Christian WAGNER Christoph ENGELS

Christian PFEIFFER, Christian REHTANZ

TU Dortmund University – Germany Univ. of Appl. Sciences Dortmund – Germany

chris.kittl@tu-dortmund.de christoph.engels@fh-dortmund.de

ABSTRACT

Academic studies and long-term planning demand for

highly sophisticated simulation of distribution system’s

usage considering operational actions and repercussions

of market driven measures when applied on a large scale.

This paper presents enhancements to the SIMONA tool

enabling a large-scale distribution system simulation of a

lifelike 50,000 nodes model.

INTRODCUTION AND MOTIVATION

For efficient strategic energy system development, the

long-term planning stage as well as academic studies try to

anticipate the system’s usage for periods of several years.

Within this time horizon, new, yet unknown system usage

patterns may arise. One of the most important steps in

planning is to check, whether a given energy system is able

to serve an assumed energy demand. Especially time-

coupled assets, like storages or electric vehicles, do

challenge conventional methods, such as static power flow

calculations and require a time-series based assessment. A

system is explicitly suitable when system operation staff is

able to operate the foreseen system in real time without the

system reaching a prohibited state. Therefore, the

suitability-check shall be able to simulate operational

actions as well. Moreover, the strategic assessment may

also identify suitable incentive-based measures to reduce

conventional grid reinforcement needs. Those measures

may invoke unwanted and unintended independencies

when applied to a large amount of entities. The agent based

simulation environment SIMONA [1] is capable to fulfil all

these requirements. Its modular bottom-up design allows

for simulating grids of any theoretical size, while

accounting for individual aims and strategies of single

customers at the same time. The downside of this approach

is a high computational effort and a huge amount of data.

Within the present paper, we will introduce relevant key

features of SIMONA being mandatory for a large-scale

simulation and afterwards apply it to a case study made

with a real distribution grid model comprising approx.

50,000 nodes and nearly the same number of branches

spanning five voltage levels.

RELEVANT SIMULATION FEATURES

Assessing the distribution system state of the

aforementioned model for a period of one year in an hourly

resolution means to calculate at least 438 million complex

nodal powers – not accounted for iterative simulation of

control schemes. SIMONA is a bottom-up simulation

framework, determining each single nodal power based on

the individual behaviour of all approx. 20,000 connected

assets. This gives some impression of the computational

effort raised by such a case study. In the following some

insight into simulation features needed, to handle such a

comprehensive distribution grid model and its arising

computational complexity are depicted.

Tap-changing three winding transformers

Higher voltage levels often comprise special and complex

assets. One of those assets is a three winding transformer.

Whilst tap changers may occur on high or low voltage side

of two winding transformers, with three windings, it can

only be apparent on the high voltage side.

Based on [2] the authors model a three winding

transformer as an adopted T-one-line diagram shown in

Figure 1. The virtual node does belong to voltage level A

whilst the admittances ,

and ,

are referred to

voltage level A as denoted by the apostrophe. One main

design aspect of SIMONA is to assign galvanically

separated subnets to distinct NetAgents justified by the

advantage, that the simulation of different subnets can be

dispersed on different computers, as the used JADE

framework allows message delivery along physically

networks. The different voltage levels are connected via

messages sent during a forward backward sweep through

the levels. The lowest levels do a power flow calculation

and do sent their apparent power exchanged via the

interconnection nodes to the higher voltage levels until the

highest level is reached. That subnet then sends the

calculated nodal voltages at the interconnection nodes to

its lower grids, which recalculates the power flow, so that

it also can forward its nodal voltages. This is done back

and forth until exchanged power values do not change

anymore between the iterations.

Figure 1: Circuit symbol and equivalent one-line diagram of

a tap-changing three winding transformer

25th International Conference on Electricity Distribution Madrid, 3-6 June 2019

Paper n° 1099

CIRED 2019 2/5

For two winding transformers this process is quite simple,

as by design we choose to regard for them in the lower

voltage level. With three winding transformers, it becomes

more complicated, as the exchanged power has to be

handled at the virtual node. Therefore, we split up the

equivalent circuit and disperse it to the three concerned

NetAgents as shown in Figure 2. Following the above-

mentioned forward backward sweep, NetAgent B and C

first announce their apparent residual power via

message (1). If there is a voltage measurement assigned to

the given transformer in one of the inferior subnets, the

respective NetAgent also attaches a voltage regulation

request with its favoured in- or decrease in nodal

voltage at the connecting node. NetAgent A does receive

the messages, adds the apparent powers up and assigns

them as node apparent power for later power flow

calculation. Additionally, it balances the received voltage

regulation requests and adjusts the tap changer

accordingly. When the forward backward sweep is on its

backward part, NetAgent A sends the newly calculated

nodal voltage as well as the chosen tap changer position to

its inferior NetAgents B and C via message (2).

Figure 2: Message transfer between different NetAgents

Although the subnets A, B and C are strongly coupled in a

triangular interacting dependency, the above presented

approach allows for easy parallelisation of grid simulation

and thereby enables simulations of large-scale grids.

Multiple slack nodes

In addition to having three winding transformers in high

voltage grids, it is also common to have meshed grid

structure fed at more than one node from extra high voltage

level. Having in mind, that SIMONA divides the total grid

model up into galvanically decoupled grid models,

multiple coupling points to superior levels impose the need

to model multiple slack nodes per grid.

By default, the used Newton Raphson (NR) power flow

algorithm does not allow for having multiple slack nodes.

To overcome this shortcoming, we introduce a

SlackEmulator model. One node is arbitrarily chosen as

the “real” slack node, whereas the others serve as

connecting point for the previously mentioned

SlackEmulators. Those are dummy elements providing or

consuming a fixed nodal apparent power and storing the

target voltage of its connecting node.

We establish an additional loop around the NR calculation:

For the first iteration, the nodal residual powers are

summed up to estimate the total subnets residual power,

that later has to be balanced by the available slack nodes.

We evenly assign this apparent residual power to all slack

nodes – better to say available SlackEmulators – of this

subnet. Given a valid power flow result, the actual residual

power is recalculated and once again dispersed to all

SlackEmulators. The additional loop ends, when the

change in all SlackEmulators is less than a pre-set

threshold. During the backward stage of the power flow

algorithm, the given NetAgent receives the calculated

nodal voltage at each coupling point from its superior

NetAgent. In order to account for this recalculated nodal

voltage, all nodes serving as emulated slack nodes are

modeled as PV nodes having the received voltage

magnitude as target voltage.

In this way, the total subnets’ residual power is evenly

divided up to all coupling points. For future development

an impedance weighted balancing is intended to use to

allow for an even more detailed calculation, when the

coupling points differ much in their impedance-based

distance to the total grid model’s slack node.

Simple continuous power flow calculation

In SIMONA the power flow calculation is realised as a

numeric NR calculation. Based on an initial guess of the

nodal voltages describing the system’s state, non-linear

system equations are solved, until the system state

converges. The grid usage defines different classes of

challenges to the NR algorithm [3]. The proposed

approach addresses performance improvements for well-

conditioned power flow problems as well as leveraging the

risk of not finding a solution based on ill-conditioned

problems with small regions of attraction [3]. Until now

various complex methods have been developed to improve

performance of power flow calculation [4]. Anyhow, the

authors intend to make use of the special application

properties, time series based power flow calculation

proposes to the classic NR algorithm: Time series based

distribution system assessment is to simulate continuous

system usage. Hence, we assume, that a) the system is not

changing drastically over each time step and b) the usage

pattern will also not change too much. Therefore, we use

the last known information about the system to make a

better starting guess for the NR calculation of the next time

step.

The nodal residual apparent powers and Kirchhoff’s

law describe the system state – by the nodal voltages –

as a system of non-linear equations:

=

=[]

(1)

In equation (1) the nodal admittance matrix is denoted as

[] and describes the Hadamard product – the element

wise product of each vector. The NR algorithm is an

iterative approach and its basic principle is to linearize the

quadratic system equations in each th iteration step by

means of a multi-dimensional Taylor transformation and

applying the corrections to the solution of the previous

iteration step (1):

25th International Conference on Electricity Distribution Madrid, 3-6 June 2019

Paper n° 1099

CIRED 2019 3/5

()

()=()

()+()

()

(2)

with

()

()

()

()=

()

()

() (3)

()

()=()()

()

()

(4)

Equations (2)-(4) comprise the current iteration step , the

jacobian matrix () of this iteration step, the node

voltage corrections () resp. () and the vector of

changes in active power (()), reactive power (())

for each PQ node as well as the change in squared voltage

magnitude (()) in each PV node in comparison to the

previous iteration step (1).

Given the aforementioned assumption, that both the grid

structure as well as the grid usage – described by the vector

of nodal apparent powers – are not expected to differ much

from time step (1) to , the last known Jacobian matrix

() may help in making a good estimation for the start

vector in

()

()=

()()

()

(

)

(

)

()

()

(5)

by the help of the known nodal powers in time step (1)

lying satisfactorily close to the final result, reducing the

amount of iterations. Although a single iteration does not

take a long time, each saved iteration highly increases the

scalability of the simulation due to the high number of

power flow calculations.

Wide area voltage regulation

Time series based distribution grid simulation reduces the

disparity of planning process to operation simulation, as

the operative measures or control schemes should

favourably be accounted for in planning as well. One

interesting aspect in this context is the simulation of

transformer tap control schemes.

In general, there are two schemes used in practical

application. Local transformer tap control simply

compares the voltage magnitude at the transformer’s

secondary bus to a pre-set threshold. On the other hand,

wide area control scheme accounts for measurements

submitted by voltage measurements installed at nodes

prone to extremal voltage magnitudes.

To account for this, we introduce measurement system

models to SIMONA. They may be placed at some nodes in

the grid and define a restriction on what simulation values

may be available to a given control scheme. Within the

simulation’s configuration stage, the user is able to define

a trigger model – comprising minimum and maximum

voltage magnitude threshold as well as a list of available

measurement systems – and assign it to the given

transformers, both two and three winding.

By the help of those measurement system, SIMONA is

capable to simulate both local and wide area tap control

schemes, examine the impact of different measurement

placing strategies and may develop further control

schemes based on the availability of measurements in the

grid under testing.

RESULT PRESENTATION

The outcome of such a large-scale simulation is a huge

amount of data, which needs to be presented and analysed

appropriately. In conformance with the Gartner definition,

large-scale simulation is regarded as part of Big Data [5].

Big Data analysis requires an extensive data access when

joining different data sources. Usually this includes full

table scans over the entire data volume, which define

expensive database operations. Here the authors follow a

Deep Data approach, which takes the data gathered and

pairs it with industry experts who have in-depth

knowledge of the area. Deep Data pares down the massive

amount of information into useful sections, excluding

redundancy. Instead of just thinking "big" when it comes

to data, the approach is to start thinking "deep". The Deep

Data framework is based on the premise that a small

number of information-rich data sources, when leveraged

properly, can yield greater value than vast volumes of

data [6],[7]. The approach starts with the definition of

appropriate granularity levels derived from business use

cases of grid planning or asset management using methods

like the Kimball Enterprise-Bus-Matrix [8]. The

identification of coarser granularity allows for pre-

aggregated data representations and smaller data volumes.

The Kimball matrix is used to derive the information-rich

data sources as an efficient foundation of further analysis.

Figure 3: Visualisation of Key Performance Values

25th International Conference on Electricity Distribution Madrid, 3-6 June 2019

Paper n° 1099

CIRED 2019 4/5

The proposed approach includes the preparation of

measures like asset loading, voltage magnitude and angle

in geospatial, schematic and tabular views. These views

can be controlled by rich filter functions restricting the key

performance values to dedicated dimension elements like

scenarios, time intervals, regions or voltage levels on

aggregated and detailed levels. Figure 3 shows the

aggregated asset loading as the selected key performance

indicator in a mid-voltage grid sector.

CASE STUDY

To demonstrate the presented concept, we carry out a case

study. Please notice, that it has not been focus yet to find a

good system state by trimming the model parameters, but

to show the large scale applicability of our agent-based

simulation environment SIMONA.

Simulation model

A lifelike distribution grid model of the project

Agent.GridPlan [9] has been used in this paper. It

comprises two medium voltage (MV) levels and each one

extra high (EHV), high (HV) and low voltage (LV) level

with the key values listed in Table 1.

Table 1: Key values of the simulation model

Volt. lvl.

Subnets

Nodes

Branches

Shunts

5

568

47,661

47,814

20,403

The shunt elements represent both assets for consuming

and producing energy. Loads are modelled as standard

household loads as per German standard load profile [10].

All generating assets are modelled following a bottom-up

approach, calculating the apparent power output based on

(non-)electrical fundamental data [1]. Simulations are

carried out with weather data for one day in July with an

hourly resolution. As the correct geographical siting of

nodes is known, all weather dependent assets are served

with the geographically correct weather data.

Investigation A: Performance increase with

simple continuous power flow

The first investigation targets the potential performance

increase using the aforementioned approach of guessing

improved start vectors for power flow calculation in

comparison to simply using the target voltages. A PC with

Intel Xeon E5-1650 CPU and 128 GB RAM serves as

simulation platform.

The total simulation times shown in Table 2 reveal that the

improved guessing of start vectors can be reduced by

approx. 0.98 % depending on the actual simulation. In

order to determine the final distribution grid state of one

subnet in each time step, a lot of power flow calculations

have to be carried out. Iteration loops are introduced by

• the forward backward sweep to integrate all

voltage levels,

• balancing out the subnets residual power on

different slack nodes and

• control schemes (like () control and traffic

light concept [11]) or negotiations

among others. Therefore, if one of four to six inner

iterations can be saved, this has a major impact on the

overall performance.

Table 2: Total simulation time for both calculation approaches

Simple

Extended

Simulation time

640.79 s

629.68 s

+ Export time

1,140.82 s

1,180.10 s

Moreover, the comparison between simulation with or

without result export highlights the urgency to think about

further usage of simulation results. Persisting everything

costs around 180 % to 190 % of total time. Although using

buffers and parallelisation decouples actual simulation and

Input/Output-processes, further steps could only take

place, when all results are available in database. Therefore,

the following remarks should be considered when

applying time series based simulations on a larger scale:

1) The (needed) output of the simulation shall be

specified properly. Data filtering and information

compression shall be used where possible and

loss-free in terms of information – Deep Data

instead of Big Data as already mentioned.

2) Integration of processes and tools plays a major

role. When data can be kept in memory and

directly handed over to next process steps, major

savings can be made. Therefore, increased

discussion about open interface definitions and

open source tools is appropriate.

How iteratively interacting process modules and

information compression could be realised, was also part

of the Agent.GridPlan project and can be reviewed in [12].

Investigation B: Wide area monitoring system

As a simple realistic application example, we conduct a

comparative assessment of using local vs. wide area tap

control scheme.

The permissible voltage dead band of ± 10 % [13] at each

end customer’s connection point has been assigned with

± 4 % to MV level and ± 6 % LV level comprising also the

voltage drop over the secondary substation transformer as

usually applied in German distribution grid studies [14].

To determine the transformer trigger settings, two initial

simulations are made with the following settings:

Transformers have a fixed tap position that would lead to

a secondary bus voltage close to 1.03 p. u.. The first

simulation A is ran with only load to determine each

subnet ’s maximum voltage drop ,,.

Analogously for high infeed and low load (30 %) to

determine ,, (simulation B). To ensure

compliance with voltage thresholds the triggers are set to:

,,= 0.96 p. u. +,,

(6)

,,= 1.04 p. u. ,,

(7)

25th International Conference on Electricity Distribution Madrid, 3-6 June 2019

Paper n° 1099

CIRED 2019 5/5

Additionally simulation A and B define the candidates for

voltage measurements used in wide area control scheme

(simulation C) as the nodes with the extremal voltages.

The triggers for simulation C are set to:

,,= 0.96 p. u. +

(8)

,,= 1.04 p. u.

(9)

Figure 4: Nodal voltages in both control schemes (green: local

control, orange: wide area control)

Figure 4 shows, that local tap control is not able to

prohibit violations in LV, whereas no violation in MV is

apparent. Moreover, also the wide are control scheme is

not able to relieve the violations in LV. Obviously the

assumed voltage limits per voltage level are not suitable

and need to be revised. This highlights SIMONAs potential

in assisting planning engineers in their decision-making.

CONCLUSION AND OUTLOOK

The present paper gives insight into simulation (model)

complexity arising, when time series based grid

performance assessment shall be used. With the shown

adoptions SIMONA proofs to be a powerful tool to be used

for academic studies, like [12] and for use in long term

planning processes.

Future work will mostly focus on how time series based

grid performance assessment can be incorporated in easy

to use and comprehensive future-ready planning

processes. Main topics of interest are reduction of data,

recognition of repeating usage patterns as well as decision

supportive functionalities.

ACKNOWLEDGEMENTS

The authors gratefully thank Westnetz GmbH for

supporting the presented research by granting access to the

mentioned real-life grid model during their participation in

the research project Agent.GridPlan. The European Fund

for Regional Development has funded the project under

grant agreement number EU-1-1-006.

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Proceedings of the 25th International Conference on

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