Evaluating the robustness of an active network management function in an operational environment

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Strathprints Institutional Repository

Venturi, Alberto and Dolan, Michael James and Clarkson, Paul and Wright, Paul and Forbes, Alistair and Yang, Xin-She and Roscoe, Andrew and Ault, Graham and Burt, Graeme (2013) Evaluating the robustness of an active network management function in an operational environment. In: EU EURAMET EMRP Metrology for Smart Grids Workshop, 25-26 June 2013, 25 - 2013-07-26, Noordwijk.

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Evaluating the robustness of an active network

management function in an operational

environment

Alberto Venturi, Michael J. Dolan, Paul Clarkson, Paul Wright, Alistair Forbes, Xin-She Yang, Andrew J. Roscoe, Graham W.Ault, Graeme M. Burt

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Introduction

Incentives to renewable sources of energy are causing an increase of the number of generators connected to distribution networks.

The cost of the network reinforcement are very high and utilities are interested in active network management solutions in order to manage the generator

connection to the net, reducing to the minimum the network reinforcement. So, automatic control systems, based on software tools, are becoming more

desirable in distribution power systems.

Primarily, such schemes are expected to manage system voltage fluctuations, network power flows and fault levels.

Functionalities include also power balancing, system frequency control and management of demand side resources for the primary system constraints.

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Introduction

A critical concern is the robustness of online and automatic active network management (ANM) algorithms/schemes.

The ANM scheme’s functionality depends on convergence to a solution when faced with uncertainty and its reliability can be reduced by data skew and errors.

The work presented evaluates power flow management (PFM) functionality based on the Constraint Satisfaction Problem (CSP) in an operational environment. The objective is to assess performances when subjected to different levels of data

uncertainty and verify the introduction of a state estimator (SE) in the ANM architecture to mitigate the data uncertainty effects on the control action.

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LPC-controlled microgrid #1 LPC-controlled microgrid #2 10kVA Inverter 10kW, 12.5kVA Controllable loadbank 10kW, 12.5kVA Controllable loadbank 2kVA Synchronous generator R jX R jX 40kW, 50kVA Controllable loadbank 4 regenerative MG sets 6 x 3kW single-phase inverters “Windy Boys” 15kW 4-wire back-back inverter

Test Environment

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125A LV board 1 N/C 200A 125A 11/0.4kV 500kVA MCCB 600A 11kV supply R1.34J 1/1 isolating Sync Contactor (key) LV board 2 Switch 200A IM

~

DC MOTOR 80kVA M-G SET D63A D63A D20A 32A 3Ø Sockets D20A DC GEN 13A sockets from red phase C16A D20A 3Ø CTs - 100:1A GSP V GSP E GSP C

Key to instrumentation symbols

3Ø VTs - 400/110V 3Ø VTs and CTs 250A 250A F 125A 125A D 125A 125A A 125A 125A C 125A 125A B GSP V Neutral exists but does not connect to transformer DC Bridge 80A variac 16A 3Ø Sockets 20kW 1kV DC supply

Tx line / primary substation impedance simulator. 0, or 2Ω to 43Ω in 9 discrete steps. May need to be shorted for some MGI experiments Where all loads and no generation, or vice versa, are engaged, due to current capacity. Rated 500A for 5 seconds. Continuous

rating ??X

System neutral is earthed through this path, even when all fuse-switches and contactors are open Neutral is common

throughout the entire system at present (no fuses or contactors/breakers in the path).

Load bank 40kW 27kVAR 50kVA Link box lines 3,4 Link box lines 1,2 63A 63A DG1 switchboard E 32A 63A A 40A 63A D 40A 63A B 6A 63A C DG1 V DG1 E DG1 D DG1 C DG1 B DG1 A 63A 63A DG2 switchboard 40A 63A A 20A 63A D 10A 63A B 20A 63A C DG2 V DG2 D DG2 C DG2 B DG2 A Tx line / transformer impedance

0.76mH per phase, 0.24 Ω @ 50Hz 6% pu @ 40kVA (58A)

125A ring connection on East wall (outside control room)

DG1 Load bank 10kW 7kVAR 12.5kVA DG2 Load bank 10kW 7kVAR 12.5kVA

~

“Small Set” 2kVA synchronous (with controlled rectifier& DC motor drive from

LV board 2) 10kVA Inverter “Small set” power 20kW 1kV DC supply (powered from LV board 2, 63A 3Ø)

Test Environment

09th_{March 2012}

Good ground via LV board 1 and earth strap

Remote trip From control room, and control panel red lights from red phase Generators:- 80kVA (Big Gen) = 115A 3kVA (Small Gen) = 5A 23kVA (PV inverters) = 35A 10kVA (Inverter) = 15A

3+7+10+10=30kVA (Induction MG sets in gen mode) = 44A All gen = 80 + 3 + 23 + 10 +30 = 146kVA = 210A All gen apart from 80kVA = 66kVA = 96A Loads:-

50kVA Loadbank = 72A

2.2kW (3kVA) Induction motor = 6A (16A DOL start) 5.5kW (7kVA) induction motor = 12A (32A DOL start) 2 * 7.5kW (10kVA) Induction motor = 16A (35A DOL start) All induction motors = 23kW => 30kVA, 50A, (118A DOL start) 2 * 12.5kVA Loadbanks = 12.5kVA, 18A

Single phase loads = 10.5kW => 10.5kVA, 15A.

All loads = 50+30+12.5+12.5+10.5 = 115.5kVA, 166A (241A DOL start) All loads apart from 50kVA loadbank = 66kVA = 95A (169A DOL start) Other:-

DG3 F

DG3 D DG3 C DG3 B DG3 A

116kVA load max (167A, 241A DOL start) 146kVA gen max (210A)

66kVA load max (95A) (169A DOL start) 36kVA gen max (52A)

66kVA gen max (96A) if infuction MG sets used also 33kVA load max (48A) (81A DOL start) 26kVA gen max (38A)

36kVA gen max (52A) if induction MG sets used also

2.2kW 3kVA Dynamic load (induction) IM IG Siemens drive 5.5kW 7kVA Dynamic load (induction) IM IG Siemens drive

10kVA load max (15A) (48A DOL start)

7.5kW (A) 10kVA Dynamic load (induction) IM IG Siemens drive 7.5kW (B) 10kVA Dynamic load (induction) IM IG Siemens drive 20kVA load max (32A) (70A DOL start)

PV PV PV PV PV PV 16AR 16AR 16AY 16AY 16AB 16AB

DC DC DC DC DC DC

10A 16A 20A 20A

6x3kW PV inverters, 224-600VDC in, Single or Three-phase out? 18kW @ 80%efficiency =23kW (33A)

32.5kVA load max (47A) (88A DOL start) 10kVA gen max (15A)

30kVA gen max (44A) if induction MG sets used also

22.7kW @ 80% efficiency = 29kW load max (42A) 30kVA gen max (44A)

Spare D40A Siemens Motor Drives -Induction load/regen bus To PV Bus (40A) To Induction load/regen Bus (40A)

125A type C 125A type C 63A type C 63A RCD DG2 to DG1 DG2 to EastWall 80kVA output 125A 125A E DG3 E

~

80kVA (60kW) Synchronous generator

Tx line / transformer impedance 0.76mH per phase, 0.24 Ω @ 50Hz

6% pu @ 40kVA (58A)

125A ring connection on North wall 63A 20A 3 kettles 3 microwaves Break out to 13A single phase

ring mains

10.5kVA load max (15A) 31kVA load max (45A)

40A RCD Spare connection DG3 Switchboard DG1 Switchbox DG2 Switchbox C63A Windy Boys Spare 200A N o .6 C u rre n tly F ro n iu s 15kW Triphase Inverter 32A 3Ø +N+E Plug E -St o p C o n ta c to rs 1 5 0 A in Pa ra lle l E -S to p C o n ta c to rs 2 2 5 A E -St o p C o n ta c to rs 1 5 0 A in Pa ra lle l 125A C10A E DG2 E 125A C10A 0.34 Ω, 100W Resistors switchable in series with Tx line / transformer impedance

0.34 Ω, 100W Resistors switchable in series with Tx line / transformer impedance

125A B16A 0.5 Ω, 300W Resistors in series with 0.4mH Inductor (24A Max) 125A B1 0 A 0.3 4 Ω , 1 00 W R e s is to rs in s e ri e s w ith 0 .6 m H In d u c to r (2 4 A M a x) 125A type C 125A type C 63A type C 63A RCD DG1 to DG3 DG1 to DG2 Optional Line Impedance Optional Line Impedance

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Test Environment

The effect of the addition of extra impedances has been evaluated and new impedances have been added to the branches of the grid.

0.12 0.125 0.13 0.135 0.14 0.145 0.15 0.155 0 50 100 150 200 250 300 350 R e ac ta n ce , m O h m /m Conductor diameter, mm^2 0 0.25 0.5 0.75 1 1.25 1.5 1.75 0 50 100 150 200 250 300 350 R e si st anc e , m O hm /m Conductor diameter, mm^2 0 5 10 15 20 25 30 35 40 0 48 70 90 V ol ta ge d rop , V

Load power factor angle

Busbar 2 Busbar 3 Busbar 4 Busbar 5

The impedance of the cables has been estimated using values available in literature.

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Test Environment

Part of the microgrid available at Strathclyde University has been

configured to allow the integration and testing process of an ANM function.

Busbar 1 includes a variable load bank. Load banks are also connected to two other busbars (4 and 5). Induction machines, which can also act as

generators, are connected to buses 4, 5, 6 and 7. These units have a maximum real power output of 2.2kW, 5.5kW, 7.5kW and 7.5 kW.

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PFM using a CSP approach

Modelling the PFM problem as a constraint satisfaction problem entails expressing the problem as a set of variables with finite discrete domains and a set of

constraints.

For PFM, the problem to be solved is concerned with deciding what control actions to take, on the Distributed Generation (DG) units, in order to maintain the

network within the thermal limits (i.e. power constraints) and maximize DG access.

The variables of the CSP are the controllable generating plant power output set-points and the domains are the discrete values that the generators’ set-set-points can assume.

V:={gen₁, gen₂,…,gen_n}

D_gen1:={control₁, …, control_n}

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PFM using a CSP approach

These values are the maximum values that a generator can output. However, the intermittent nature of most renewable generating plants means that DG output is such that its output is continuous up to this discrete set-point value.

In addition to variables and their domains we have to set the constraints on the solution:

• Power flow constraints: no thermal overloads

• Contractual constraints: generators access rights • Preference constraints.

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PFM using a CSP approach

(V

gens

, D

Control Signal

, C)

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Where:

V

gens

= {Gen

1,

Gen

2....

Gen

n

}

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D

Control Signal

is:

D

Gen1

={1,…,0}, D

Gen2

={1,…,0}, D

Genn

={v

1

,…,v

n

}

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C is the constraint applied to the sets of variables:

C

Power Flow

= {

| S_ij |  S_ijmax

}

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C

Contractual

= {k, l, m}

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

max



1 MaxDG Gi N _P C   _n

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PFM using a CSP approach

Modelling PFM, in this way, relies upon a load flow engine to evaluate the power flows within the network to determine any control actions that are required. The generators have been ordered in a last-in, first off (LIFO) manner to replicate

the current connection regime used in the UK.

Load Flow Engine CSP Solver Contractual Load Flow Preference Constraints Network Model

N Ranked Control Solution Variables & Domains DG Output Loads PFM Algorithm

M. J. Dolan, E.M.Davidson., G. W. Ault, K.R.W. Bell, S. D. J. McArthur, Distribution Power Flow

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The PFM algorithm, presented above, was chosen as the ANM control approach and installed initially on a COM600 (Windows XP embedded industrial PC).

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The PFM algorithm, presented above, was chosen as the ANM control approach and installed initially on a COM600 (Windows XP embedded industrial PC). The communication system among the PCs of the Microgrid created some problems and different solutions has been tested. The solution chosen at the end is based on the OPC server/client architecture, with the integration of the OpenOPC functionalities.

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The PFM algorithm, presented above, was chosen as the ANM control approach and installed initially on a COM600 (Windows XP embedded industrial PC). The communication system among the PCs of the Microgrid created some problems and different

solutions has been tested. The solution chosen at the end is based on the OPC server/client architecture, with the integration of the OpenOPC functionalities. In order to send the data to the RTS analogue inputs, a Beckhoff CX5010 Embedded PC with Intel® Atom™ processor has been used. The Beckhoff software presents OPC functionalities and the data can be sent using the OPC standard. The Beckhoff unit can be set and controlled also via a standard pc.

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The sensor measurements coming from the microgrid are collected via a real time station (RTS) computer developed by ADI. The RTS has analogue and digital input/output (I/O) interfaces, can execute programs written with Matlab and Simulink, process directly the data collected, manage the electrical machines of the grid and guarantee their safe operation.

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After a first elaboration, the measurements are then made available, mapped on the OPC server variables and sent from the PC, connected to the RTS, to the OPC server.

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After a first elaboration, the measurements are then made available, mapped on the OPC server variables and sent from the PC, connected to the RTS, to the OPC server.

The control software reads them, through the OPC server, and sends the control signals back through the RTS.

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Experimental work and results

The sensors installed on the microgrid and the data acquisition system guarantee a precision of 2.14% in the measurement of voltage and current magnitude, and consequently a precision of 4.5% in measuring the power flow. This level of precision is considered in literature enough to simulate the real operating

conditions of an energy management system on a low voltage network.

In order to show the effect of the uncertainty of the data on the performance of the ANM software a series of tests were executed on the microgrid.

The induction machines located at buses 4, 5, 6 and 7 were set to compensate the load requested on the buses 4 and 5. Thermal constraints were set in the

branches 1 and 3 by reducing the limits within the PFM software and microgrid network model.

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Experimental work and results

The generator access priorities were assigned to represent a LIFO connection arrangement. Gen 1 was set to 1

Gen 3 was set to 2 Gen 2 was set to 3 Gen 4 was set to 4

(i.e. this unit would be the

first to be curtailed if a thermal breach was detected).

Then, progressively, the loads were reduced to zero starting with the load on busbar 5.

This caused a rising power flow through the branches 1 and 3, and a consequent thermal constraint violation.

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Experimental work and results

The response of the ANM function, for this scenario, was evaluated against the following data sets:

• An initial clean set of input data without any uncertainty

• A set of data as collected from the grid (with an uncertainty of 4.5%)

• A set of data in which the uncertainty of the loads and the machines power flow was artificially increased to 6%

• A set of data as calculated by a state estimator that reduces the uncertainty to 2%

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Experimental work and results

The different control signals sent by the ANM to curtail the power output of the generator on busbar 7, Gen 4, in

presence of different levels of uncertainty

The differences between the control signals (relative to the base case with no uncertainty) sent in presence of uncertainty

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Experimental work and results

The analysis found that no divergence of the load flow engine was encountered when erroneous measurements, up to 6.5%, were presented to the ANM software.

However, with data uncertainty it can be seen that the error, in some situations, is large enough to either move the curtailment to a deeper set point (next domain value for the variable) or not curtail sufficient levels of DG.

The studies have also highlighted the importance of the reduction in uncertainty, for example through the use of a SE.

The uncertainty of the input data is reflected in the uncertainty of the final power flow calculation, so the operators taking in account of the uncertainty reduction introduced by a SE can adopt less conservative thermal detection limit to

compensate for expected errors.