Management of Voltage Control Using Distributed Generation in the
Colombian Power System: a system dynamics approach
Sandra Ximena Carvajal Q.*, PhD. (c), Adriana Arango M., Msc.,"
Santiago Arango A., PhD‘.
a. Department of Electrical, Electronic and Computer Engineering, Universidad
Nacional de Colombia, Campus La Nubia. A.A. 127, Manizales, Colombia, Tel.:+57
68 879400x55725; fax: +57 68 879400x55725.
b. Escuela de Sistemas, Universidad Nacional de Colombia, Facultad de Minas, Cra.
80 N.° 65-223 bl M8. Medellin, Colombia.
E-mail addresses: sxcarvjalq@unal.edu.co, aarangoma@unal.edu.co, saarango@unal.edu.co
Abstract— This paper presents a study on the penetration of Distributed Generation (DG)
under the consideration of a proposal for trading, environmental, and technical incentives,
in the Colombian power system. To quantify the technical incentives, we simulated a part of
the Colombian grid that presents some busbars with low levels of voltage. We connect DG
to the busbars "problem" and we found an increase in voltage that in a controlled manner
can to help to better quality and continuity of electricity supply. Environmental and trade
incentives were quantified using international experiences. In addition, we built a system
dynamics model to evaluation the complete proposal. We found that the current incentives
presented in the Colombian regulation such as tax breaks are insufficient to cover the total
costs. Moreover, environmental incentives can be an efficient way to promote renewable
energy use in Colombia, in order to achieve, more generating capacity with less pollution
indices; and technical incentives in conjunction with environmental incentives can improve
further the growth of DG in Colombia. Thus, the DG diffusion becomes an additional tool
for the operator of the interconnected system to make voltage control, improve the quality,
and security of electrical power systems.
Keywords— Ancillary Services, Distributed Generation, Reactive Power, Voltage Control,
System Dynamics.
INTRODUCTION
Nowadays, ensuring a secure electricity supply is an important policy objective in virtually
all modern economies (IEA, 2002). This objective deals not only with availability of
electricity but also with power quality. The power system operator must maintain the
frequency and the stable voltage profiles within the required ranges (Bacon and Besant,
2001). The system operator counts with technical support services known as ancillary
services in order to satisfy the requirements. Voltage control, frequency control, and black
start services are the most frequently used ancillary services (Bacon and Besant, 2001).
Voltage control is related to reactive power (Q) supply in the systems busbars using
different equipment and technologies (Kirby and Hirst, 1997). This control is known as
local control, since the reactive power can be supplied by the demand and thus reduces the
voltage drop at busbars and improve the indices of power quality (Kirby and Hirst, 1997).
Distributed Generation (DG) has the potential to provide voltage control and a number of
collateral advantages (Viawan and Karlsson, 2008). DG also helps decongesting
transmission grids (Viawan and Karlsson, 2008) because it is located near consumption
centers. It also helps generating or absorbing the reactive power required by the system in
order for the voltage in the nearby busbars to meet regulations (Gomez, 2002). Regarding
environmental issues, DG uses electrical plants with capacities below 20 MW and has the
capacity to use renewable resources thus helping to reduce harmful emissions to the
environment (Diaz, 2007).
This paper presents a system dynamics diffusion model (Sterman, 2000) to study DG
integration in the Colombian power system.The simulation model allows the analysis of
DG growth when trade, environmental and technical incentives are included as an addition
to the tax exemption incentives currently provided by the regulations in Colombia.
The study was conducted in a Colombian sub-region known as the Coffee Region, which
includes Caldas, Quindio and Risaralda (CQR). This is part of the Southwest operative area
of the Colombian power system. We have selected this region for three reasons: i. there are
voltage stability problems due to the connection of highly inductive loads (XM, 2009); ii.
DG seems to be a feasible solution to improve electricity quality; and iii. There is a
potential for DG due to the existence of water resources and the raw material for biofuel
production (Diaz, 2007).
The paper is organized as follows. Section 2 explains the technical aspects of voltage
control when using DG, Section 3 examines the economic analysis for the model, Section 4
shows the implementation of the DG diffusion model in a subregion of the southwest
operational area, Section 5 evaluates alternatives to the studied incentives to implement DG
diffusion, and Section 6 concludes.
TECHNICAL ASPECTS OF DG VOLTAGE CONTROL
This section explains the behavior of loads of high inductive value and its effect on the
waveform and on voltage quality. It also shows the results of the impact of DG on Voltage
control and Q supply to the electrical grid, in an area of the Colombian power system
corresponding to a subregion of the southwest area.
Voltage control is an ancillary service that helps to keep voltage on a node or busbar within
the values required by the regulator (Assili et al., 2008). Nodes of stable voltage profiles
have small losses because of the active and reactive power occurring in the lines
interconnecting such busbars (Kundur, 1994).
Voltage control is important because disturbances in the voltage sinusoidal waveform
might damage the equipment and in turn the industrial loads, as consequence the other
users will operate inadequately (Kundur, 1994). Thus, the voltage control improves the
quality of the local supply and the nearby busbars. DG has been recognized as a solution to
improve voltage control (Hirst, 2000). DG keeps the voltage within the range required by
system, through of the production and availability of Q injection to solve the problems of
low-voltages busbars, and with the capability to absorb Q for high-voltage busbars (Kirby
and Hirst, 1997).
Behavior of industrial loads in the power system
Industrial loads have high inductive value, which means, loads of nonlinear behavior that
need to consume high amounts of reactive power of the electrical grid (CREG, 1995). The
reactive power sharply declines and changes the sinusoidal waveforms of the voltage
busbars and of the busbars near the operational area. According to the regulator, voltage
waveform should be evaluated by an index called PST (Percibility Short Time) (Ramirez
and Cano, 2006), which defines that an electrical power system meets power quality when
registering a PST between 0.9 and 1.
Figure 1 shows the record of a device measuring the PST index in a busbar of the
operational area CQR for a 24 hour period. The chosen busbar is connected to an iron and
steel industry. Unstable behavior is observed in its daily operation, and it gets worse
between 13:00 and 15:00 hours as the furnaces to melt metal and to develop chemical
compounds begin operating. This increase in consumption in high inductive loads causes a
PST index of 0.2, which means a system with a distorted voltage wave that affects not only
their own consumption but also the consumption of the nearby loads.
Figure 1. PST index
The operator of the Colombian power system uses centralized generators to manage Q
supply and Voltage control. However, these generators suffer a decrease of nominal active
power at the time of delivery changes or when absorbing reactive power due to operation
restrictions (Kundur, 1994). This condition reduces the net generation capacity and it might
affect the power system security indices (Assili et al., 2008).
Impact of DG in voltage control in electric power systems
As we previously mentioned, voltage waveform disturbances cause low quality electric
supply. These disturbances, widespread in the interconnected power system, might trigger
voltage collapse and in severe cases can lead to operational decomposition and total or
partial blackout. The operational area in which the study was carried shows a number of
voltage events out of range, which justifies the implementation of solutions to avoid
possible voltage collapse. The results of the technical study based on DG connection in the
busbars where voltage was out of the regulated range are shown below.
Case study
The region CQR includes 22 busbars, two busbars connected to 220 kV, six busbars of 115
kV and 14 busbars connected to 33 kV. The loads connected to the electrical grid had a
high inductive value because of the industrial activity.
This system has voltage problems in busbars 19 and 22 which are connected to 33kV.
There are five cases in the study that take into account the connection of distributed
generators of 10 MW in the busbars where voltage is out of the permitted range. . Table 1
shows the characteristics of each case.
Table 1. Cases of the technical study using DG for Voltage control
Cases Description
1 System without DG
2 DG only in busbar 22
3 DG busbar 22 and
Capacitor Banks
4 DG in busbar 19
5 DG in busbar 22, 19 and
Capacitor Banks
6 Only Capacitor Banks
Figure 2 shows voltage behavior in busbars 19 and 22. Regulation determines that the
minimum voltage in a 33kV busbar is 0.9 p.u. (CREG, 1995). In this system, busbar 19
had a voltage of 0.6pu. The voltage increases to 0.97 when it is connected to DG thus
complying with regulations. When Capacitor Banks (CB) is connected, the result is 0.98
improving the magnitude but with no substantial change. In case five, voltage increases up
to 0.7p.u., which shows the need to implement DG due to the fact that even though CB has
the same magnitude as the distributed generator, does not increase voltage in busbar 19 in
the same proportion.
In the case of busbar 22 (fig. 2), voltages is improved when DG is installed. It is important
to state that for voltage control in this busbar it is possible to use the BC, since it shows a
favorable behavior in the busbar voltage magnitude.
Figure 2. Voltage in busbars 19 and 22
Figure 3 shows how the magnitudes of electrical losses are reduced more than 80% when
DG is installed on nodes with low voltage problems. Losses decrease proportionally to the
increase of voltage in the busbars with problems. (Case three and four). Thus, DG provides
local control with regional implications. The most visible consequences are voltage
increase, decongestion in lines, and loss reduction.
Cases
mw auvar
Figure 3. Losses of Active and Reactive power in the lines between busbars 17 and 19
It is important to note that implement DG in electrical grid needs a technical previous
study, because DG can cause worse damage to power quality in busbars that generate
surges.
It is possible to evaluate the effect of DG in the voltage magnitude regarding the number of
MW installed using the results of the simulations. Figure 4 shows the above mentioned
behavior of the voltage in per unit when we increase the relationship between DG_potential
and IDG.
Installed Distributed Generation (IDG) factor (in x axis) is the factor that allows the
evaluation of the capacity of the DG installed with relation to the DG projected; in other
words, the factor determining the impact of DG installation in the magnitude of the voltage
in the busbars when the DG system is installed.
The IDG factor shows in figure 4, is simulated in an electrical model. The voltage indices
(in y axis) fail to improve in all the system busbars when the relation of the IDG regarding
DG_potential is lower. At At the same way, the power quality decreases when the relation
is very high, impairing the value of the voltage magnitude and its connected loads. This
factor is use in DS model to limit the growth in technical incentives as will be explained
later in section 4.2.
os Lf _\
teed \
> > NY NS wD A
s s a s wv xP
IDG Factor
Figure 4. IDG factor for the simulation model
ECONOMIC ANALYSIS OF DG DIFFUSION
Before modeling DG diffusion, we consider an economic analysis, which provides
information about the operation and investment costs when implementing DG. Of particular
interest, the analysis is made for DG with organic materials to produce biofuels. Thus, we
make the feasibility analysis of biofuel plants. These plants are expected to be beneficial in
terms of pollution indices (IEA, 2007). Furthermore, Colombia has begun to develop
successful projects for biofuel production due to its agricultural potential. Some examples
are the sugar mills and the African palm cultivation.
Operation costs are based on costs of generation associated with fuel prices, administration
and maintenance (IEA, 2007).Investment costs are related to the plant capital
costs. According the International Energy Agency (IEA), capital costs for these types of
technologies will decrease in time because an improvement in the efficiency and
consumption of these technologies is expected (IEA, 2007).
In (IEA, 2007) the levels of investment and the average generation costs for different
technologies are compared. It also shows a projection that in 20 years alternative energy
generation will reduce investment and generation costs because its use will be increased
worldwide.
The current capital costs of DG projects using biomass are elevated with regard to the
incurred costs by centralized conventional generation projects (Huacuz, 2010). For this
reason it is essential to count on economic incentives to increase this type of generation.
We found that the experiences in countries such as Spain, United States, and Germany
show that incentives are crucial (Iberdrola, 2010); especially in the initial construction and
operation period. In the medium term, incentives are important to increase the number of
distributed plants with the thought of using them as a complement for the operation of the
interconnected systems, thus creating an additional tool for voltage control.
Economic incentives for DG can be justified because they can be used to improve power
quality through the provision of ancillary services (Viawan and Karlsson, 2008). In
addition, DG helps to postpone additional labor in transmission and distribution systems
because surges in the system loads are reduced which allows the usage of conductors of the
same caliber (Kundur, 1994), and transformers, protectors and generators of the same
capacities (Hirst, 2000).
DG has also the potential to use a broad portfolio of renewable and nonrenewable
technologies (Rodriguez, 2009). The benefits are not only for the environment (Hammons
and Boyer, 2000), but also to provide greater flexibility and reserve margin to increase
reliability (Kundur, 1994) during periods of drought and also in times of fossil fuel supply
and price volatility (Rothwell and Gomez, 2003).
Although the regulatory framework does not include DG as a generation option, industries
such as oil, cement, sugar mills and others have machinery to carry out self-generation and
co-generation processes (alcongen, 2009).
The main barrier for the diffusion of DG in Colombia are costs and is that incentives are
available only in the investment period and are indirect because they are based on the tax
exemption given to plants under construction (Rodriguez, 2009).
In this sense, we propose to test the effect of environmental incentives for operation as well
as technical incentives related to voltage and reactive control as a diffusion mechanism of
DG in Colombia.
MODEL TO PROMOTE THE USAGE OF DG IN COLOMBIA
The electric industry deregulation introduced decentralization and competition (Hunt and
Shuttleworth, 1996). The decentralization increases the complexity in the system operation
because before, in a regulated system, the utilities were vertically integrated and cooperated
voluntary to operate a reliable system by coordinating their resources with neighboring
utilities (Chao and Huntington, 1998), knowing that regulated tariffs would cover bundled
costs (Hirst and Kirby, 1998). Under deregulation, the system operator is responsible for
system reliability (Gomez, 2002). It buys different ancillary services from generators and
users to maintain a reliable system (Hirst, 2000). However, legal responsibilities of system
operators must be clearly defined by new regulations (Rothwell and Gémez, 2003).
On the other hand, a major objective of electricity deregulation is to achieve a workably
competitive wholesale market (Rothwell and Gomez, 2003). Wholesale electricity markets
have high price volatility due to daily and seasonal variations in supply and demand (Hunt
and Shuttleworth, 1996). This raises two important issues under deregulation: demand
responsiveness to price variations and new investment in generation resources (Hunt and
Shuttleworth, 1996).
Larsen and Dyner, 2001 showed that the uncertainty and the risks increase when you want
long-term studies, making it difficult to create highly accurate predictive models in
deregulated electrical systems.
An alternative is to make models to understanding the dynamic path into the future and
between the tools useful for strategy formulation in utilities which needs to be added after a
deregulation is the business dynamics or the System Dynamics (SD) (Sterman, 2000).
The DS has been used in the deregulated electricity sector for the analysis of investment in
generation process (Bunn and Larsen 1992), (Ford, 1999) and (Arango, 2007). It has also
been used in studies on competition between generation plants that use different primary
energies (Quadrat-Ullah and Davidsen, 2001), (Botterud et al., 2002) and studies on the
inclusion of alternative energy in decentralized markets (Ford et al., 2007), (Zuluaga and
Dyner, 2007).
The model is inspired by the Bass classical diffusion model (Bass, 1969). This model is
adapted to the features of the system, in particular the use of DG integration as a function of
the different incentives in place and proposed for this technology. We first present the
dynamic hypothesis for the model; thereafter, we present the formal model.
Dynamic hypothesis
The feedback loop diagram reported in Figure 5 represents the dynamic hypothesis for the
DG diffusion. It shows the main variables and its relations to analyze DG diffusion in the
Colombian power system taking into account additional incentives. Note that this approach
is thought for a particular region, instead of the whole system.
The main driver for the DG in the investment, such that the increase in invest increase the
installed capacity of DG, after a delay. The Colombian power system has a market oriented
structure, which means that investments are driven by the profitability of the investments.
The profitability is increased by endogenous incentives, such as technical and
environmental incentives; as well as exogenous incentives, which are the regulatory
incentives. The environmental incentive is justified with the fact that there is a CO» and
NOx reduction (Denne and Waikato, 2006) since this generation has lower environmental
impact when compared to large central power systems (IEA, 2007).
The second incentive is aimed to the use of DG as a tool to provide support services to the
power system, specifically to pay distributed generators to help maintain voltage and
reactive levels within the required ranges in order to improve quality and safety indices.
The technical incentive has a limit due to operational constraints of the systems of
transmission. In particular, an inordinate amount of DG can cause electrical imbalances at
any point within an interconnected transmission network can have immediate and severe
repercussions for the quality and deliverability of electricity throughout the whole
interconnected grid (Kundur, 1994).
The model also takes into account existing regulatory incentives. Currently in Colombia
there is tax breaks for sales from alternatives energies (wind and biomass resources) for 15
years. For this exemption, generators are required to hold carbon emissions certificates and
to invest fifty percent of certificates in social infrastructure projects (Law 788, 2002). Once
in full operation, the generator agents do not receive any type of remuneration or additional
bonuses for the use of renewable or low environmental impact resources.
conn sox
ge: reduction
DG Quality Incentives
f \wo ld
g Technical |
‘ WS) iy ion /
® Proftailty
Regubtory
Incentives
Figure 5. The feedback loop diagram - DG diffusion
Formulation of the simulation model
The feedback diagram in Figure 5 is translated into a formal visual diagram at a more
detailed (operational) level, which distinguishes between stock variables (i.e. state
variables) and flow variables (or rates) (Sterman, 2000).
This type of mapping is shown in Figure 6. The visualization used is from one of the
specialist simulation software available for this type of simulation; stocks accumulate
resource flows and characterize the memory of the system. Stock variables (boxes) can only
change when in the associated flows changes. The stock and flow diagram provides the
structure of the actual mathematical formulation underlying the model.
We can observe from the macrostructure of the model sketched in Figure 5, the main stock
or state variables of the model are Distributed Generation Potential (DG_Potential) and
Installed Distributed Generation (IDG). The level of each state variable is defined in terms
of the associated flows.
Figure 6. The formal diagram - DG diffusion
The model formulation takes into account the Bass diffusion model (Bass, 1969). This
model solved the startup problem by assuming that potential adopters become aware of the
innovation through external information sources whose magnitude and persuasiveness are
roughly constant over time. Bass assumed the probability that a potential adopter will adopt
as the result of exposure to a given amount of advertising and the volume of advertising and
other external influences each period are constant (Sterman, 2000).
In our model, potential adopters and adopters are represented in levels DG_potential and
Installed DG. The growth of DG will depend on investment_rate, which is affected by the
perception of potential generating agents about invest in DG and the profitability given by
incentives. The equations associated with the above described process are:
DG _ potential
= sivestment _ rate (1)
or
alDG
ar
Investment (2)
Investment _ rate =DG _ potential * profitability (3)
Profitability can be considered how the difference between the expected income or
purchase price with incentives, and the capital and operational costs. Capital costs are
called Capital Expenditure (CAPEX) and operational costs are called Operating
expenditure (OPEX). CAPEX is the investment cost in plant property and equipment and
OPEX is an ongoing cost for running a product as it is a cost associated with the size of
productivity. Equations associated with this process are:
Proftability =( Expected _ Income ~CAPEX —OPEX) (4)
Investment _cost* DG _ potential (5)
1+Rate
CAPEX =
OPEX =Generation _cost* IDG (6)
Expected Income change for each scenario evaluated since they depend on the number of
MWh of DG sold in the Colombian market and incentives approved by the regulator.
The commercialization of P in DG is based on the commercialization of plants with
installed power lower than 20 MW and it is described in equation (7) using a non-
centralized dispatch. The price paid to each generator is the power Pool price (Rodriguez,
2009).
Commercialization =IDG* Pool _ price (7)
The Pool_ price parameter is taken from the XM record, the organization in charge of
electricity market in Colombia, to calculate the fee they are paid for participating in the
electricity market (XM, 2009).
The environmental incentives are evaluated by the air quality that measures the amount of
greenhouse gases that are emitted into the environment at the time of generation, especially
when fossil fuels are used.
Equation (8) defines the remuneration for the CO2 and NOx reduction and is given by the
reduction bonus and the installed DG capacity.
Enviromental _incentives =IDG *
Redhetion_boms_CO, (3)
Technical incentives are implemented when DG provides voltage and reactive control
services. As demonstrated in the case study, DG allows voltage increase in the connecting
and surrounding busbars, situation favorable while not exceeding the regulatory ranges,
which depend on the voltage level.
A magnitude of DG reference was found for this case. This magnitude corresponds to the
number of MW of installed DG that can be connected to the electrical grid prior to causing
quality problems such as fluctuations in nominal values of busbar voltage, voltage collapse,
among other quality problems associated with the voltage waveform. The IDG factor is
determined from the relationship between DG reference and the IDG (see section 2.2.1).
The quality of the system at a given time might be known using the result of IDG factor. It
is necessary to adjust this quality to the regulatory voltage values, because value is
compared to the quality_reference.
Quality =IDG _ factor * quality _reference (8)
Resolution 025 of 1995 determines the permitted ranges according to the voltage level. In
this case, voltages of 115 kV were used and the permitted range was between 90-110% of
nominal voltage, and even though the quality reference is the nominal value, the regulator
allows controlled variations without losing quality. The technical incentives, then, would
be granted as long as the voltage is maintained within this range. The following equations
mathematically describe this behavior:
Technical _ incentives =IF (103.5 <Quality <126.5,
IDG*Technical_bonus,0) (0)
The parameter of Technical bonus corresponds to payment received by a generation plant
for providing the ancillary service of secondary regulation of frequency or Automatic
Generation Control (AGC). This value was used in the model because the AGC service is
the only ancillary service regulated in the Colombian electricity market.
EVALUATION
This section evaluates each of the proposed incentives. We first develop a base scenario to
compare with. The base scenario considers the existing indirect incentives and the sale of
generated energy at stock market prices. The simulation experiments allow observing the
effect on the Colombian electrical power system.
Base Scenario
The base scenario is the worst scenario because only considers the indirect incentives such
as income tax and interest rate exemption.
The benefit is annual remuneration for MW sales, the model uses the actual price paid per
MW in bilateral contracts because these plants have a capacity lower than 20 MW and
according to the Colombian regulation (CREG 024, 1995), these plants are not obligated to
participate in the daily auction.
Figure 7 shows the IDG and the DG_potential for the base scenario. It shows that and steep
growth in the first half and smooth in the second half, which is a goal seeking behavior
where IDG reaches to the need amount of DG over the time horizon.
~1" IDG: Base "
2- DG Potential: Base
Figure 7. IDG and DG_potential evolution over time in the base scenario.
Figure 8 shows the behavior of profitability, which is defined in this model as benefit — cost
relation. When this relation is greater than | the project is attractive from the financial point
of view. Therefore, the base scenario shows that even though profitability increases in 20
years, it does not overcome the unit threshold, thus the Project is not financially justified.
years
— Profitability: Base
Figure 8. Profitability over time in the base scenario
Scenario 1
The first scenario of analysis is characterized by the incorporation the inclusion of
environmental incentives. The incentives are given due to the reduction in emission of
greenhouse gases such as CO) y el NOx.
Environmental incentives were implemented in the model as a direct subsidy which the
government defines a payment to be handed out during a period of time to producers of
alternative energy.
The bonus system or in tariffs has been successful in Germany, where premiums vary
according to type of primary energy used, premiums range from $ 5 per MWh to $ 15 per
MWh (Huacuz, 2000). In the Unites State these incentives are 1 cent per kwh (Hammons
and Boyer, 2000). In China, rates are set according to the average price of coal in the
relevant province, with a premium of about 3 cents per kWh (Denne and Waikato, 2006).
Figure 9 shows the development of IDG. The IDG reaches the DG reference value faster
with the environmental incentives compared with the base scenario, in only 10 years.
+ IDG: Base eo
2 IDG: Scenario 1
“> DG_Potential: Base
«- DG_Potential: Scenario 1
Figure 9. IDG and DG_Potential evolution over time in the base and scenario].
Figure 10 shows that projects benefit the environment and that receive financial
remuneration have income or profits greater than costs, and a profitability value greater
than one. The profitability growth over a 20 year period has an increasing behavior, the
comparison with the base scenario, indicating that incentives will trigger investors to think
of this type of generation.
1-Profitability: Base
> Profitability: Scenario 1
Figure 10. Profitability over time in the base scenario
Scenario 2
All the characteristics of Scenario | in addition to the modeling of technical incentives are
taken into account in the implementation of scenario 2. This scenario has the most
favorable behavior since the plants of installed DG count on a period of five years to reach
its potential value. Figure 11 show that this scenario provides investors with greater
certainty that the project is economically feasible as it takes into account incentives to
improve the grid technical conditions.
years
+ 1DG: Base
P IDG: Scenario 1
oe
«- GD_Potential: Base
© GD_Potential: Scenario 1
«© GD_Potential: Scenario 2
Figure 11. IDG and DG_Potential over time in the base, scenario! and scenario 2.
3: Scenario 2
The profitability shown in Figure 12 in scenario 2, compare with the profitability of
previous scenarios, since the assumption was that the technical remuneration was based on,
as previously mentioned, AGC ancillary service; a very high price for this reactive control
service.
profitability
° 5 ” 6 Py
1+ Profitability: Base
» Profitability: Scenario 1
> Profitability: Scenario 2
Figure 12. Profitability over time in the base, scenariol and scenario 2.
CONCLUSIONS
Voltage and reactive control is an ancillary service of great importance in the operation,
quality, and safety of a power system. An alternative for these services and worldwide used
is Distributed Generation (DG). DG has proven efficient to increase voltage and to decrease
active and reactive power losses within an interconnected area of influence. International
experiences have shown that DG requires additional economic incentives to promote the
diffusion, particularly in electricity markets where exists economies of scale.
This paper presents a system dynamic model to analyze the diffusion of DG in a Colombian
power system operation zone. The model analyzes the effect of environmental and
technical incentives in installed DG, and improves the voltage profiles and the reactive
power flow in systems busbars.
The evaluation with the model shows that the environmental incentives improve
profitability, but they are not sufficient to achieve a significant DG growth in the
Colombian system. A feasible solution to improve the voltage control in Colombian power
system is to convert DG into an active generation. In this case, to remunerate DG plants is
important to include in the payment the technical incentives that are conditioned by the low
voltages in the operation area and environmental incentives that are essential for plants
with small capacities using renewable sources to achieve important developments to
impact the Colombian generation park.
Biographies
Sandra Ximena Carvajal Quintero: Master’s in Electrical Engineering, Universidad
Nacional de Colombia, Manizales Branch. Student of Doctorate in Engineering,
Universidad Nacional de Colombia, Manizales Branch. Tenured professor, Universidad
Nacional de Colombia, Manizales Branch. Field of interest: Applied Optimization, Power
Systems, and Electricity Markets sxcarvajalq@unal.edu.co.
Adriana Arango Manrique: Master’s in Industrial Automation, Electric Engineer.
Member of Research Group on Power and Distribution Grids (GREDYP) at Universidad
Nacional de Colombia, Manizales Branch. Professional Junior I+D+i in CIDET.
aarangoma@unal.edu.co.
Santiago Arango Aramburo: Associate Professor since 2009, Universidad Nacional de
Colombia, Medellin Branch. Civil Engineer. Master’s in Hydraulic resources. PhD,
Bergen University, Norway. Research Interests: Electricity markets through simulation and
experimental economics. Author of the book “Mercado Eléctrico Colombiano”. Author of
articles published in journals such as Utilities Policy y Socio-Economic Planning.
saarango@unal.edu.co.
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