Download Coordination of Active and Reactive Distributed

Paper accepted for presentation at the 2011 IEEE Trondheim PowerTech
1
Coordination of Active and Reactive Distributed
Resources in a Smart Grid
Marco Bronzini, Member, IEEE, Sergio Bruno, Member, IEEE, and Massimo La Scala, Fellow, IEEE,
and Roberto Sbrizzai Member, IEEE,
Abstract—The authors present a methodology for assessing
centralized control of active and reactive distributed resources in
a smart distribution grid. The methodology is based on threephase optimal power flow and is able to deal properly with
unbalanced conditions and both single-phase and three-phase
control resources. Single-phase resources that can be exploited by
means of this approach are domestic loads, photovoltaic and
distributed micro-generation. The methodology is developed on
an open-source simulating environment and tested on the IEEE
123-bus Radial Distribution Feeder test case.
Index Terms-- Smart Grids, Optimal Power Flow, Distribution
Management System, Automatic Meter Reading.
D
I. INTRODUCTION
ESIGNING the future of transmission and distribution
grids is a current issue of the power system community.
According to general opinion, the modernization of power
systems should move around the concept of smart or
intelligent grids. Even though the debate about the definition
of a smart grid is still open, some of the main principles have
already been established: resiliency, flexible and self-healing
infrastructures, enhanced power quality standards, high
penetration of ICT technologies, integration of distributed
generation and power storage, advanced metering
infrastructures, availability of new power customer services
[1]-[4].
The actual impact that this new fashionable concept will
have on power systems can be hardly predicted, but the
necessity of renovating electric power networks is
indisputable. More prudently it can be asserted that, in the
years to come, power systems will be characterized by an
increasing penetration of ICT, sensors and computation that
will allow to implement advanced monitoring and control
functions [5].
In this call for modernization, distribution systems must
surely face the greatest challenge. Distribution systems are
traditionally passive networks, built with a straightforward
radial (or multi-radial) configuration and minimal ability of
monitoring and controlling power flows [3]. Differently from
The present study was funded under the grant PST #44 “Smart-Grids:
Advanced Technologies for utilities and energy”, received by the Regione
Puglia as Strategic Project in the Framework Program Agreement on the
scientific research sector in the Apulian region.
M. Bronzini, S. Bruno, M. La Scala, R. Sbrizzai are with the Electrical and
Electronic Department (DEE) of the Polytechnic School of Bari (Politecnico
di Bari), Bari, 70125, Italy. (e-mail: [email protected],
[email protected]).
978-1-4244-8417-1/11/$26.00 ©2011
transmission systems where the innovation follows an
evolutionary course [6], in distribution systems the
implementation of smart grid principles requires a
revolutionary transition whose major driving forces are given
by increasing penetration of distributed energy resources
(DERs) and request for more sophisticated customer-sided
energy market services.
This revolution has partly begun, also thanks to the
diffusion of advanced digital meters, distribution automation,
building automation, low-cost cabled and wireless
communication systems [1] and to the setting up of specific
plans for the modernization of distribution systems.
In Europe, for example, the European Commission has
promulgated several directives for the development of smart
grids and smart metering systems [7]-[8]. The most recent
Directive 2009/72/EC encourages the modernization of
distribution networks through the introduction of smart grids
and intelligent metering systems, setting targets for the
penetration of advanced metering systems and aiming at
reaching the 80% in 2020 [8]. In Italy, the energy regulator
(Autorità per l’energia elettrica ed il Gas) with the Decision
ARG/elt39/10 introduced incentives for distribution
companies promoting investments and research activity for
smart grid pilot projects through a specific financial support
plan [9]. In North America [1], major research projects are
oriented in the same direction, including results of the
Department of Energy (DOE) and EPRI.
In this scenario, it is foreseeable that in a next future
control centers, and Distribution Network Operators (DNOs)
in the case of distribution systems, will be able to develop
advanced functions for monitoring and control of distribution
systems [10]. In particular, due to the increasing penetration of
dispatchable resources, control equipments, real time
monitoring architectures and two-ways communication
systems, DNOs will be able to dispatch active and reactive
distributed resources in order to respond to technical and
economical requirements.
In a distribution network, centralized control is a function
that can be developed within the extended real-time operative
framework of what has been defined advanced Distribution
Management System (DMS) [11]. Originally, DMS was
developed as an extension of Supervisory Control And Data
Acquisition (SCADA), scaling SCADA methodologies and
technologies from transmission system down to distribution
[11]. In a smart grid, advanced DMS will be responsible for
elaborating all available data (on-line or historical) and
perform several management applications [11] that can be
2
performed in extended real-time for system operation (load
curtailment/shedding, voltage regulation, line switching, etc.)
or in the medium-long term for planning (optimal network
configuration, optimal placement of capacitors, protection
relay coordination, short-circuit analysis, etc.).
The tools that will be adopted by DNOs in advanced DMS
framework can be conceived similarly to the ones adopted for
operation and control of transmission systems (topology
processor, state estimator, load modelling, load flow and
optimal power flow-OPF), but they must be suitably adapted
to the specific characteristics of distribution systems and to the
new operative requirements.
For example load flow and OPF, that are traditionally the
most commonly adopted tools for power system analysis,
operation and planning, must be able to deal properly with
radial networks, three-phase (or multi-phase) circuits and
unbalanced conditions [11]-[13].
In [11], the authors present a methodology for solving a
three-phase distribution OPF, that is based on the conversion
of a mixed-integer non-linear programming (MINLP) problem
into a non-linear programming problem.
In this paper, the authors present a methodology, based on
[13], for solving unbalanced Three-phase Optimal Power Flow
(TOPF). The proposed methodology is based on a quasiNewton method and is implemented in an open-source
simulation environment for distribution systems.
In this paper, the authors propose the use of TOPF in an
extended real-time framework, and explore some possible
coordinated control strategies for active and reactive control of
distribution grids.
Test results are presented on the IEEE 123-bus Radial
Distribution Feeder test case.
II. ACTIVE AND REACTIVE CONTROL IN A DMS FRAMEWORK
The main assumption of this paper is that in a near future
DNOs will have at their disposal suitable pieces of
information about main electrical parameters that can be
collected and elaborated at the distribution control centre for
performing on-line centralized control of the network in
extended real-time framework (approximately every 15-30
minutes).
Together with more conventional functions related to
distribution system operation such as optimal network
reconfiguration, reactive power compensation, operation under
fault conditions, DNOs will be called to perform some
advanced centralized control functions that might involve
voltage regulation, demand side management and control,
demand or generation peak shaving, distributed generation
redispatch, optimization of storage charge and discharge
cycles.
Currently, in distribution systems, voltage regulation and
reactive power compensation are usually performed by means
of on-load tap changers and switching capacitors that are
manually operated or can automatically adapt their settings on
the basis of local measurements [12]. In an advanced DMS
framework, set-points of such devices might be remotely
controlled on the basis of real-time measurements and
according to specific operational requirements or new control
strategies.
For example tap changers, that are usually adopted for
boosting voltage levels up along the radial back-bone of the
distribution system and guaranteeing that every node is above
nominal voltage, can be controlled for performing what has
been defined conservative voltage regulation (CVR) [14].
CVR working principle is far from usual voltage regulation
since it aims at keeping voltage profiles close to functional
bottom limits, reducing overall load demand. In order to be
effective, CVR must be applied to loads that are voltage
responsive and that can be modelled close to a fixed
impedance (residential and tertiary areas are therefore
preferred to industrial ones where motors are the bulk
component of load demand).
Clearly, tap changer control can be integrated by dispatch
of reactive distributed resources for what is defined Volt/VAr
optimization (VVO). This can be aimed to improve voltage
profiles across feeders, sustain voltage, reduce losses,
improving energy efficiency or achieve better performances in
conservative voltage regulation.
Reactive resources can be ensured by remotely controlled
switching capacity [12] [14], static voltage compensators [3],
but also by distributed micro-generation or any power
controllable device at residential level. In [15], the authors
suggest that reactive resources in the grid can be found in any
location where electric machines or converters are installed,
being either part of load or generations equipments (voltage
control is then operated by means of a sensitivity analysis that
detects nodes and devices that have highest potentials in
controlling voltage globally or locally).
The more straightforward idea of employing the inverters
of distributed photovoltaic (PV) generators for centralized
[16]-[18] voltage regulation of smart grids is also a current
issue, even though some issues about control structure (control
schemes for local or generalized control) and ancillary
services remuneration (PV inverters participating at voltage
regulation must be oversized and therefore are more expensive
than the ones operating at a fixed power factor) should still be
overcome. This hypothesis is not unrealistic since certain
system operators (SOs) are already settings the minimal
reactive regulating capacity that any installed DER must
provide to the system. As an example the Grid code of the
Italian TSO recently introduced specific requirements for non
programmable renewable resources in order to guarantee
active and reactive control [19].
In smart distribution grids active power could be a further
control resource. Mostly it is foreseeable that DNOs will be
able to dispatch distributed generation or manage power
storage charge/discharge cycles (as with pluggable hybrid
electric vehicles - PHEVs), not very differently from how a
system operator would dispatch generation on transmission
level [10], [15]. An alternative control architecture can be
based on energy hubs that can manage multiple energy
resources on the basis of market or dispatching signals
provided by the utilities [20]-[21].
More original active power control strategies can be based
3
on demand side control. It is foreseeable, for example, that in
the presence of violation of security limits (for a example a
sudden overload on the HV/MV interface) or as a response to
particular energy market signals, the DNO would be able to
control certain specific loads that, in return of electricity tariff
discounts, are available to load shedding or curtailment. AMR
devices can be easily employed as actuators of load
curtailment control actions (AMR devices already receive a
signal that can limit load capacity, as done for example for
insolvent customers).
Cleary, all above mentioned techniques can be
implemented only if an extensive set of real-time
measurements is available to control centre. For example,
CVR can work successfully if the voltage at each node of the
network is known (or at least at those nodes that,
independently from actual network configuration, are
characterized by lowest voltages).
At this stage of development DNOs have scarce details
about the state of the network. Distribution systems might lack
completely of SCADA infrastructure or have monitored only
few electric quantities and switches. Nevertheless, previous
assumptions are credible having considered that part of the
investments necessary for developing such control architecture
have already been made or scheduled.
In countries like Italy or Sweden the replacement of energy
meters with smart Automatic Metering Reading (AMR)
devices will be close to 100% very soon. First
implementations of AMR showed how basic functions of
energy metering can be easily overcome, transforming AMR
devices into smart terminal units and gateways for many
functions and multiple services, able to guarantee a real-time
bidirectional communication between customers and utilities
(what is usually defined as an Advanced Metering
Infrastructure – AMI).
A bottleneck to the implementation of such devices in
smart grid framework is the time response which is still quite
far from real-time requirements. However, improvements in
the communication technology and computer architectures
will make these concepts implementable in the very near
future.
III. PROPOSED METHODOLOGY
A. Three Phase Optimal Power Flow For Smart Grids
Most of proposed control functions must be performed at
control centre level by means of suitable analysis and
decision-making tools. Optimal Power Flow, that is
undoubtedly the most commonly adopted tool for operation
and planning of power systems, will have for sure a part in this
but, as already remarked, it must be suitably adapted to the
operative requirements of smart distribution systems.
In the past years, many studies have been devoted to
reformulate OPF, or Distribution Optimal Power Flow
(DOPF), equations in order to face the new challenges
introduced by Distributed Generation (DG) in terms of voltage
control, losses reduction and power flow management. Most
of them, being based on multi-object or multiperiod OPF, are
referred to system planning and very few studies are able do
deal properly with unbalanced conditions and three-phase
representation of load flow equations [12], [22].
Distribution systems are unbalanced because of unequal
three-phase loads, untransposed lines, conductor bundlings
[22]. Moreover, in the last few years, the spreading of singlephase DG plants (domestic solar and micro wind generators)
contributes heavily to produce further imbalance, whose
extent is very difficult to forecast due to randomness of
intermittent energy sources.
The proposed TOPF methodology is aimed at optimizing
active and reactive control resources in the presence of
unbalanced conditions and in the extended real-time
framework. Moreover, being based on a full multi-phase
representation of load flow equations, the methodology is able
to treat single-phase components (i.e. load or micro-generator
installed at LV levels) and unbalanced electric variables.
B. Mathematical Formulation
The proposed formulation for TOPF is similar to the
classical single-phase OPF, with main differences limited to
the representation of the steady-state grid equations. Singlephase OPF is commonly based on the use of the sole positive
sequence component model, whereas TOPF can adopt both
sequence and multi-phase models.
In general a TOPF problem can be formulated as a
min Cobj ( x ,u )
(1)
u
subject to
f ( x ,u ) = 0
(2)
g( x , u ) ≤ 0
(3)
and where Cobj is the objective function, x is the n-dimension
state variable vector, u is the m-dimension control variable
vector, f is the set of load flow equations, g is the set of
inequality constraints that keep into account thermal or
capacity limits, acceptable voltage profiles, maximum injected
reactive power and other functional constraints.
According to the proposed approach, the function f
represents load flow equations with a full multi-phase model,
whereas the control variable vector u is given by active and
reactive power at any device whose power reference is
remotely controlled via the ICT layer of the smart grid.
Different formulations are possible for the representation of
load flow equations in both sequence and phase components.
The proposed method is sufficiently general to consider both
full multi-phase and sequence models.
The OPF problem can be expressed in terms of an
unconstrained minimization problem, by applying the penalty
factor methods. This means that inequality constraints (3) are
treated as soft inequality constraints and formulated as penalty
functions, leading to the following formulation:
min C ( x , u)
(4)
u
subject to
f ( x ,u ) = 0
with
(5)
C( x , u ) = Cobj ( x , u ) + ¦ C ip ( x , u )
i
and where C is an objective function, Cpi is the ith penalty
4
function.
In addition, the feasibility domain for control variables can be
defined by the following hard limits:
umin ≤ u ≤ umax
(6)
Through the Implicit Function Theorem [23], [13], whose
conditions are often satisfied for a large set of practical cases,
it is possible to assume that around the solution of the load
flow equations ( x , u ) it exist a unique function Ȗ ( u ) = x that
permits to reformulate the constrained problem (4)-(5) as an
unconstrained problem:
min C( Ȗ ( u ), u )
(7)
u
whose minimum can be obtained by imposing the conditions:
dC( Ȗ ( u ), u )
F( u ) =
=0.
(8)
du
By applying the Newton method, equation (8) can be
solved iteratively considering the rule
u
k +1
d Fk
=u −
du
Fk
(9)
k
dC( Ȗ ( u ), u )
=0.
du k
The evaluation of the second order term in (9) can be
characterized by a heavy computational burden. In order to
speed-up convergence and avoid time consuming calculations
the proposed methodology, based on Quasi-Newton method,
solves equation (8) with the approximate formula:
u k +1 = u k + ȁk F k
(10)
Fk =
k
where k is the iteration number, and ȁ is an m×m matrix
which can assume different structures (scalar, diagonal, full)
as largely discussed in [24].
Among all structures of ȁk matrix, the simplest one is
given by:
ȁk = Ȝ k ⋅ I
(11)
where λk is a scalar suitably chosen on the basis of
computational and convergence properties and I is the m×m
identity matrix.
Different formulations have been proposed for the
evaluation of λk [24]. A suitable choice, according to the
method proposed by Barzilai and Borwein [25]-[26], is given
by:
T
Ȝk =
three-phase load flow (OpenDSS)
evaluate C0 (u0 ) and F 0
set initial guess Ȝ0
u1 = u 0 + Ȝ 0 ⋅ I ⋅ F 0
k= k+ 1
three-phase load flow (OpenDSS)
evaluate Ck(uk)
Numerical evaluation of gradient Fk
for i = 1, … , m
fix a small deviation İik
u'ik = u ik + İ ik
Fik =
C' ik − C k
İ ik
three-phase load flow (OpenDSS)
evaluate Cƍ ik(uƍ ik)
evaluate Ȝ k through eqn. (12)
k
where
k
k=0 u=u0
−1
k
k
evaluation of first order derivatives of F is required.
ΔF k −1 ⋅ Δu k −1
(12)
T
ΔF k −1 ⋅ ΔF k −1
where Δ denotes the forward difference operator defined
usually as Δa i = a i +1 − a i .
C. Solving Algorithm
The solving algorithm, whose structure is presented in Fig.
1, finds a solution through eqns. (10-12). According to the
Implicit Function Theorem, sensitivities Fk are obtained
evaluating numerical derivatives of C on the basis of small
deviations of control variables u around the solution of a
three-phase Distribution Load Flow (DLF). No analytical
control variables update
u k +1 = u k + Ȝ k ⋅ I ⋅ F k
no
Δu k < İ
yes
STOP
Fig. 1 Flow-chart of the proposed algorithm
The advantage of this structure is that, for solving DLF, any
software (research or industrial grade) can be exploited,
provided that is fast, reliable and has an easy data exchange
interface.
In this paper, the algorithm was implemented on a MatlabOpenDSS platform and relies on two-way data exchange
between a Matlab code, that evaluates sensitivities and
assesses control variable variations, and the OpenDSS
simulation engine that performs DLF and implements control
variable variations on the network model. This data exchange
is performed by means of a COM (Component Object Model)
interface.
OpenDSS is a open source software that is developed by
EPRI in an on-going project [27]. This software is specifically
designed for solving distribution circuits (it represents
unbalanced conditions, stochastic processes and load models,
detailed distribution equipment models) and was chosen under
multiple rationales. OpenDSS is specifically optimized and
compiled for solving rapidly DLF of large distribution
networks. OpenDSS is freeware and open-source and, given
that distribution systems lack of software standards, it has
good chances in becoming a standard software framework in
the near future.
The proposed methodology can be implemented through
any other DLF software (research or industrial grade),
provided to be fast and reliable, and have an easy data
exchange interface.
5
IV. TEST RESULTS
The proposed methodology was tested on the IEEE 123-bus
Radial Distribution Feeder case [27]-[28], represented in Fig.
2, suitably adapted for testing the proposed methodology. It
was assumed that active control resources (curtailable loads)
and reactive control resources (micro-generators and
controlled capacitor banks) were available at selected nodes
and circuits.
load reactive power is not a control variable since it was
assumed that loads have a constant power factor at nominal
voltage.
Thermal ratings at the HV/MV interface are taken into
account through the penalty function:
2
2
§I −I ·
+ α pc ¨¨ c max ¸¸
© I max ¹
α pa = 0 if I a < I max
with
Fig. 2 Scheme of the IEEE 123-bus Radial Distribution Feeder
A. Test A
In this test the grid was supposed to be affected by a 35%
uniformly distributed load increase, causing an overcurrent on
the HV/MV interface. The feeder is connected to the
subtransmission network by means of a 115kV/4.16kV
transformer. Considering the rate of the HV/MV transformer
[28], it was supposed that the thermal rate of the MV line at
the supply-side of the feeder (in Fig. 2 the line between buses
#149 and #1) is 650A for each phase.
In this test, loads are modeled, according to the original
base case in [27], with the following models: constant P and
Q, constant impedance, constant P and quadratic Q, linear P
and quadratic Q.
Three test cases were developed in order to assess the
potentiality of the approach. Further strategies can be
implemented for controlling active and reactive resources
(generation redispatch, energy storage management, etc.).
In the three cases the objective function is aimed at
minimizing the control effort:
2
§ ui − u 0 ·
¸
(13)
Cobj = ¦ α o ,i ¨
¨ u0 ¸
i
©
¹
where the index 0 refers to the initial conditions, Įo,i is a
weighting factor, ui is the ith control variable.
The control variable vector u is given by active power at
each load (case A1), or reactive power at generators and
switching capacities (case A2). The case A3 was solved
considering the availability of both active and reactive control
variables adopted in case A1 and case A2. Please note that
i
i
2
§I −I ·
§I −I ·
C p = α pa ¨¨ a max ¸¸ + α pb ¨¨ b max ¸¸ +
© I max ¹
© I max ¹
α pb = 0 if
I b < I max
α pc = 0 if
I c < I max
(14)
and where Ia, Ib and Ic are the phase-currents on the
interconnection line, Imax is the thermal rate (650A).
Please note that due to the three-phase formulation of the
optimization problem and, in particular, of eqn. (14) the
algorithm is able to evaluate sensitivities of any load,
generator or capacitor (being either single phase, two-phase or
three phase) with respect to the actual currents flowing in each
phase of the congested interconnection line.
Results are summarized in Table I. The first row of the
table shows the values of currents at the substation supply-side
in the base case (i.e. system state before optimization). Id , Ia ,
Ib , and Ic , are respectively the positive current component and
the physical currents at phases a, b and c.
TABLE I
TEST RESULTS
Case
Base
A1
A2
A3
Id
[A]
707.1
680.7
649.2
605.8
Ia
[A]
867.7
650.1
806.3
650.5
Ib
[A]
560.2
557.9
522.9
521.7
Ic
[A]
693.9
650.1
650.5
645.4
¨PL
[kW]
0
-545.9
0
-383.6
¨QG
[kVAr]
0
0
352.2
285.0
In the test A1, where the sole control of active resources is
performed, the congestion is removed curtailing loads in such
way that all phase currents are brought below the threshold
limit of maximum current protections (650 A).
The total curtailment is about 546 kW (about 11.6% of the
overall active demand) and is distributed among loads as
showed in Fig. 3. The requested curtailment is averagely 2530% for single phase loads on phase a, null for single phase
loads on phase b, and 3-7% for single phase loads on phase c.
Two- and three-phase loads are out of this average statistic as
showed in Fig. 3 for loads #24, #33-34, #50-52, #61-63.
In case A2, reactive control is not able to remove the
congestion. Reactive control can just mitigate currents by
increasing power factor and hence minimizing the reactive
component of Ia , Ib , and Ic.
The third case A3 is referred to a combined active/reactive
redispatch. Results for case A3 are obtained by solving the
TOPF considering the concurrent availability of active and
reactive resources.
6
variable vector is given by dispatchable reactive power and tap
ratio of transformers at the substation and at bus #160 (see
Fig. 2).
Voltages were constrained by the penalty function:
35
load curtailement [%]
30
25
§ Vi − Vlim
¨¨
¦
σi
i =1 ©
Cv = α v
nbus
nbus
20
15
10
·
¸¸
¹
2
(15)
where
5
Vlim
0
1
6
11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91
load number
Fig. 3 Test case A1, expected load curtailment.
As clearly showed in table I, the combined exploitation of
active and reactive resources allows to remove the congestion,
reducing the overall amount of expected curtailed active
power (384 kW, about 8.1% with respect to the total). The
distribution of curtailment is represented in Fig. 4. Differently
from the previous case, loads which needed small adjustments
on phase c do not experience any active power curtailment
thanks to reactive compensation.
35
­Vi M if Vi > Vi M
°
= ®Vi m if Vi < Vi m
°V otherwise
¯ i
and
­1
¯Vi
m
Vi m < Vi < Vi M
if
σi = ®
otherwise
M
In this test, Vi and Vi were set at each node respectively
at 0.95 and 1.0, assuming that 0.95 represents for all loads the
minimum acceptable voltage.
Figure 5 represents voltage profile before and after
optimization. Tap changers and reactive power outputs were
regulated in such way that all voltages were constrained in the
interval 0.95-1.00 p.u. The voltage regulation resulted in an
overall active power decrease of about 10% (from 3710 kW to
3330 kW). Elapsed time was about 43 seconds.
30
]
25
[%
t
n
e
m20
e
il
a
rt 15
u
c
d
a 10
lo
1,2
5
0
1
6
11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91
load number
voltage magnitude [p.u]
1,1
1
0,9
no CVR
after CVR
Fig. 4 Test case A3, expected load curtailment
Elapsed time on a HP Compaq 8000 Elite CMT PC, with
Intel Core 2 Quad CPU Q 9650 3.00 GHz and 4.00 GB RAM
for Cases A, B and C was 2.3s, 11.2s, 23.1s respectively.
B. Test B
This second test was aimed at implementing the CVR/VVO
strategy described in the previous section. In order to show the
potentials of this approach the base case was modified
representing all loads with a constant impedance model.
The TOPF problem was formulated considering an
objective function aimed at minimizing the exchange of active
power at the HV/MV interconnection:
2
where Psup
§ Psup ·
Cobj = α o ¨ 0 ¸
(14)
¨ Psup ¸
©
¹
is the active power supplied at the feeder at the
substation and the index 0 refers to the base case. The control
0,8
1
10
19
28
37
46
55
64
73
82
91
100 109 118 127
bus number
Fig. 5 Voltage profiles before and after CVR
V. CONCLUSIONS
Centralized control of a large set of control variables is one
of the possible functions to be developed in smart distribution
grids. Having assumed the availability of a centralized
monitoring and control architecture, control can be assessed in
both extended real-time or daily system operation by means of
classical power system analysis and optimization
methodologies such as power flow and optimal power flow.
These methodologies, already well-known and fully
developed, should be re-adapted to specific requirements and
properties of distribution systems and to the availability of
new control resources and innovative smart control strategies.
7
The authors have proposed a three-phase OPF methodology
for assessing centralized control of active and reactive
distributed resources in a smart distribution grid. The
methodology is based on quasi-Newton method and was
implemented on an open source load-flow code for
distribution systems. The proposed methodology is general
enough to solve several optimization problems during
operation of distribution networks and can be easily
implemented on any other distribution load flow software.
The approach was tested for congestion management and
conservative voltage regulation applications, showing good
convergence performances in the presence of different sets of
objective functions and control resources. Computational
performances appear to be compatible with extended real-time
framework.
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
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