impact analysis of distributed pv and energy storage
TRANSCRIPT
Template_PowerTech_Final_2015.pdfImpact Analysis of Distributed PV
and Energy Storage Systems in Unbalanced LV Networks
Francesco Lamberti Vito Calderaro Vincenzo Galdi Antonio Piccolo
Department of Industrial Engineering University of Salerno
84084, Fisciano (SA) - Italy [email protected], [email protected]
Giorgio Graditi Research Center of Portici
ENEA 80055, Portici (NA) - Italy
[email protected]
Abstract—The integration of distributed PV units in LV distri-
bution networks with co-located energy storage systems (ESSs)
allows increasing the amount of consumed local electrical energy
while significantly raising the PV penetration. The installation of
ESSs may in addition promote the self-consumption of energy
and reduce the mismatch between the demand and the PV
power generation making such power source dispatchable. A
Monte Carlo simulation is performed by varying residential load
profiles, sizes and locations of PV units and ESSs in order
to assess the impact that a local and independent control of
co-located PV units and ESSs have on the grid. Time series
unbalanced power flow simulations are carried out considering
different scenarios on a typical LV Italian distribution network.
The results are evaluated in terms of benefits on voltage profiles
pointing out a significantly reduction of voltage problems on the
network at each penetration level.
Index Terms—Distributed Generation, Energy storage, Monte
Carlo methods, Photovoltaic systems.
I. INTRODUCTION
Photovoltaic (PV) systems are the most widespread form of distributed generation (DG) in low voltage (LV) Italian distribution networks. The performances of the system can be often improved by PV units when loads are locally supplied. Consequently, the system capacity is released while the feeder losses and the stress of the network are reduced. However, the intermittent and stochastic production obtained from PV systems poses technical and economic challenges for distri- bution system operators (DSOs) such as reverse power flow and voltage problems at LV networks [1]. In particular, if the PV generation is greater than the local power demand, the excess power may produce reverse power flow in the feeder causing voltage rise [2]. Instead, if the power demand is high and PV production is low or absent, voltage drops can be consistent and operation problems can arise. For both cases, violations of the voltage limits are possible [3]. In order to preserve a compliance with the system operation limits, technical solutions are expected.
In the past, active power curtailments and reactive power controls were proposed to solve voltage infringements [4], also considering coordination strategies between independent
power producers and DSOs [5]. Other solutions were based on the coordination of energy storage systems (ESSs) operations with traditional voltage regulators as step voltage regula- tors (SVR) and on-load tap changer (OLTC) [6]. Nowadays, storage systems are not very widespread in the residential sector; however, this situation is bound to change due to the possibilities for customers and small power producers to optimize electricity management. Thus, battery energy storage systems are likely to have a significant impact in small-scale integration of renewable energy sources into commercial and residential sector [7]. Indeed, in [8] the usage of storage systems to reduce the mismatch between the demand and PV production was proposed. Refs. [9], [10] propose a coordinated use of PV and battery storage systems to face voltage rise and voltage dip problems on a LV distribution network. Ref. [11] also evaluates the impact of different local voltage control strategies for PV storage systems in reducing voltage rises on a distribution grid supporting, at the same time, the self- consumption. Although economic analyses show still several barriers to the deployment of storage systems, in light of the fact that recently several economic incentives have been in- troduced, such technical solutions seem to be very promising. For this reason, here we propose a complete analysis, based on Monte Carlo method, to assess the effects of a local integration of PV units with ESSs in order to promote self-consumption of energy on a real unbalanced LV Italian distribution network.
The main contribution of the paper is the evaluation of the co-locate PV and energy storage residential systems impact on voltage profiles, without implementing specific local controls. Thus, the developed analysis allows obtaining a clear vision of the benefits in terms of power quality for DSO and self- consumption for residential customers.
The remainder of this paper is organized as follows: Section II describes the model of the system, the control strategy of ESSs and the Monte Carlo method used for the simulation analysis, Section III presents the case study describing the fundamental components of the LV network test, Section IV analyses and discusses the achieved results. Conclusions are drawn in Section V.
Fig. 1. Layout of the co-located PV generator and ESS.
II. ASSUMPTIONS AND METHOD
The problem of assessing the effects that the integration of PV systems and ESSs has on the voltage profiles of a LV network must take into account the stochastic behaviours of PV generation and load demand. Thus, in order to solve a power flow on a radial distribution systems, it is necessary to known the characteristic parameters of the analysed network for each simulation time step (for instance active and reactive powers). Furthermore, if the goal is to demonstrate that the achieved results can be reached in different scenarios, then the adopted solution needs to be tested in different conditions.
There are several methods able to forecast voltage profiles on LV networks taking into account the generation and demand uncertainties. In this paper, the problem is solved applying a classical Monte Carlo approach that allows also to take into account different sizes and locations of PV units and ESSs. Indeed, Monte Carlo method is often a good solution to simulate and analyse complex systems with many degrees of freedom.
The analysis is performed by varying the positions and the penetration level of PV systems connected to residential customers and the size of the co-located electric storage systems. The study is carried out also by changing the single customer load profile and the PV production minute by minute.
A. Modelling of the system
The main components of the system are feeders, loads, PV units and ESSs. A series impedance and a shunt reactance model each feeder section; the loads are modelled as P-Q constant power bus considering typical load curves.
The profiles of the residential customers are produced using a modified high-resolution model developed by CREST (Centre for Renewable Energy Systems Technology) at Lough- borough University [12]. PV generation profiles are calculated multiplying the rated power of PV units by a typical normal- ized photovoltaic profile.
An ideal current generator, which supplies/stores constant power for each time interval, models ESSs. State of Charge (SoC) can be evaluated according to the following:
SoC(T +T ) = SoC(T )± I(T ) T
3600 C ESS
ESS
[Ah] is the ESS’s capacity, I(T ) represents the ESS’s current, obtained by dividing the charging/discharging power of the ESS to its constant voltage, and T is the time interval of the control.
The idea is to use ESSs co-located with PV units to promote local consumption of energy by residential customers considering also the effects that the described control have on the distribution network. For these reasons, the ESS is charged if the following inequality are verified:
8 <
:
max
c
that identify a limited time period where it is possible to charge the ESS, during the daylight hours characterized by a peak in the PV generation. Instead, T
min
d
max
d
identify the time period where the ESS can be discharged; this interval is set in correspondence of the hours in which no power generation comes from PV and there is a load demand to satisfy (night time). P
PV
i
ESS
i
(T ) are the power generated by the i-th PV unit, the power demand and the SoC of the ESS of the i-th residential customer at the time step T , respectively. Furthermore, SoC
ESS
min
i
ESS
max
i
are the minimum and maximum allowable value of the SoC related to ESS
i
, which depend on the specific battery system technology.
In Fig.1 is depicted the connection scheme of the co-located PV/ESS at the Point of Common Coupling (PCC) of the distribution network.
B. Application of the Monte Carlo method
The final aim of the analysis is to evaluate the possible benefits that the introduction on a residential PV system of battery ESS, installed by the final users to increase their self- consumption of energy, can have on the voltage profiles of the LV network.
An algorithm based on Monte Carlo method is applied in order to explore the solution space of the problem. The step-by-step procedure of the Monte Carlo based algorithm is discussed as follow:
1) set the number of PV units in percentage compared to the number of the total residential customers (penetra- tion level) from a minimum value of 0% to a maximum value of 100% (with steps of 10%);
2) perform a random allocation of PV units on residential buses and associate to each one a load profile chosen randomly from a database;
Fig. 2. Monte Carlo analysis flow chart.
Fig. 3. LV Italian test network.
3) perform a random choose of the sizes of the co-located ESSs from a list of different models (Table I);
4) perform two different daily time series unbalanced power flow simulations. The first one considering only the PV units allocated (Step 2) and the second one taking into account also the co-located ESSs;
5) repeat the Step 2, 3 and 4 up to the maximum number of cases (k
max
); 6) come back to the Step 1 and increase the PV penetration
level up to the upper limit.
The flow chart in Fig.2 depicts, in detail, the time sequence of the Monte Carlo method steps.
TABLE I ESSS CHARACTERISTICS
Rated Power (kW) Maximum Available Energy (kWh) SoC limits
2 2.5 30-90 % 3 3.75 30-90 % 4 5 30-90 % 5 6.25 30-90 %
III. CASE STUDY
The network diagram of the considered LV Italian distribu- tion network is shown in Fig. 3 [13]. It consists of 3 LV feeders (A, B, C) connected to the MV network through a 10/0.4 kV /Y
g
T
cc
= 4%. The transformer tap is fixed to 1.00 p.u. The network is composed by 68 buses connected to 136
mono-phase residential loads, and 9 three-phase industrial and commercial loads. The installed power of the loads is divided as reported in [13]: 43% on the feeder A, 44% on the feeder B and 13% on the feeder C.
Mono-phase loads are modelled independently using a modified version (customized for the Italian case) of the software elaborated by the University of Loughborough [12], which allows to create minute by minute domestic profiles for specific periods of the year, load compositions and number of family members. Furthermore, the three-phase loads are characterized by typical commercial and industrial profiles [5].
Mono-phase PV systems are randomly allocated on the residential load buses at each Monte Carlo iteration. The size is fixed at 3 kW with a power factor equal to 1. Furthermore, we consider 3 three-phase PV units with a rated power of 15 kW and a unitary power factor, connected as shown in Fig. 3, which do not change during the Monte Carlo analysis. For all PV systems, a classical PV profile of a summer day in Italy is used [5].
The simulations are carried out analysing both the case with only PV systems connected to the residential customers and the case with ESSs co-located with PV systems. The rated power of the ESSs, and, consequently, the main parameters of the storage systems, change randomly during the Monte Carlo analysis with sizes between 2 kW and 5 kW with step of 1 kW. The main characteristics of the ESSs considered during the study are reported in Table I. The storage systems operations are set to promote self-consumptions of energy generated by PV units independently for each customer. For this reason, the ESS can be charged only if the power of the pertinent PV system is greater than the power absorbed by the residential customer.
Furthermore, the charging and discharging periods of the ESSs are limited between 10 a.m. - 6 p.m. (higher generation period during the day) and 20 p.m - 00 a.m. (higher demand period during the day), respectively, in order to support also the network during peak generation and demand periods providing to the DSO an ancillary service.
Fig. 4. Mean of voltage drop violations at different PV penetration level on feeders A and B with and without ESSs.
0 20 40 60 80 100
A
B
C
Penetration (%)
A
B
C
Penetration (%)
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10
Storage
Fig. 5. Map of voltage drop violations (mean values) of the whole network at different PV penetration level with and without ESSs.
Fig. 6. Mean of voltage rise violations at different PV penetration level on feeders A and B with and without ESSs.
0 20 40 60 80 100
A
B
C
Penetration (%)
A
B
C
Penetration (%)
0.0 2.0 4.0 6.0 8.0 10 12 14 16 18 20
Storage
Fig. 7. Map of voltage rise violations (mean values) of the whole network at different PV penetration level with and without ESSs.
0 10 20 30 40 50 60 70 80 90 100 -5
0
5
10
15
20
25
No Storage
0 10 20 30 40 50 60 70 80 90 100 -5
0
5
10
15
20
25
Penetration (%)
ps
Storage
Fig. 8. Box chart of voltage drop violations during Monte Carlo analysis with and without ESSs.
0 10 20 30 40 50 60 70 80 90 100 -5 0 5 10 15 20 25 30 35 40
V ol
ta ge
R ise
No Storage
0 10 20 30 40 50 60 70 80 90 100 -5 0 5 10 15 20 25 30 35 40
Penetration (%)
V ol
ta ge
R ise
s Storage
Fig. 9. Box chart of voltage rise violations during Monte Carlo analysis with and without ESSs.
IV. RESULTS AND DISCUSSION
Unbalanced power flows are solved monitoring each phase of the network by using OpenDSS [14]. The simulations are carried out on a summer week day. In order to analyse the impact that ESSs have on voltage profiles of the LV network, 1000 cases are simulated.
The ST. CEI-EN50160 is considered as reference to de- termine whether voltage violations happened on the feeders (10 minutes mean of the supply voltage shall be within the range of 1.00 p.u. ± 10%) [15]. The position of 8 meters to monitor voltage profiles is showed in Fig. 3. The results of the simulations show that feeders A and B are subjected to voltage excursions. The feeder C, instead, does not have any voltage problem because it is short compared to the other two feeders, and its load is only the 13% of the total demand.
Fig. 4 compares the mean of the voltage drops violations
0 100 200 300 400 500 600 700 800 900 1000 0
2
4
6
8
0 100 200 300 400 500 600 700 800 900 1000 0
1
2
N° Iterations
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
No Storage
M ea
n of
V ol
ta ge
R is
N° Iterations
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Storage
Fig. 10. Variation of the mean of voltage rises on Feeder B without and with ESSs, respectively.
0 2 4 6 8 10 12 14 16 18 20 22 24 −200
−150
−100
−50
0
50
100
150
200
Storage No Storage
Fig. 11. Active power at LV busbar connection (50% of PV penetration).
obtained from the Monte Carlo analysis with (in green) and without (in red) the installation of the ESSs. The storage systems are able to reduce substantially voltage drops at each penetration level. It is clear that the benefits increase with the percentage of penetration because the grid has a growing capacity to supply the energy demand locally. The complete map of voltage drop violations occurred on the three feeders is shown in Fig. 5: also in this case, it is possible to observe a substantial reduction of voltage infringements by means of a color map.
If the results were promising analysing voltage drop prob- lems, they are noteworthy in the case of voltage rises. Fig. 6 shows how the average of voltage rise problems on the network is almost zero at each penetration level. This result may be explained by the fact that the integration of the ESS with a photovoltaic system can solve, in most of the cases, voltage rise violations thanks to an extra power available in the network. As in the previous case, the achieved results on the three feeders are represented by means of the scale map depicted in Fig. 7. The reduction of voltage rise infringements allows avoiding the disconnection of PV systems increasing, at the same time, the self-consumption of energy produced by
0 2 4 6 8 10 12 14 16 18 20 22 24
0.9
1
1.1
Time (h)
0 2 4 6 8 10 12 14 16 18 20 22 24
0.9
1
1.1
Time (h)
0 2 4 6 8 10 12 14 16 18 20 22 24
0.9
1
1.1
No Storage Storage
No Storage Storage
No Storage Storage
Fig. 12. Three-phase daily voltage profiles on feeder A (100% of PV penetration).
PV residential systems. In Figs. 8-9 the statistical parameters related to voltage
drops and rises on feeder A are depicted by means of a box chart representation. Upper and under limits of the box correspond to the 25th and 75th percentiles. The whiskers are determined by the 5th and 95th percentiles. The minimum and maximum values obtained after 1000 cases are depicted as triangles. The square and the line in the box represents the mean and the median values at different penetration levels, respectively.
Fig. 10, instead, shows the variation of the mean of voltage rises with increasing cases on feeder B without and with the ESSs. Without ESSs, it is possible to note that the values are almost stable after 500 cases. On the other hand, if ESSs are considered in the Monte Carlo simulation it can be observed an increase of the variability, which requires almost 800 cases before reaching stable values.
Fig. 11 shows the effects that the charging (limited between 10 a.m. - 6 p.m.) and discharging phases (limited between 20 p.m - 00 a.m.) of the distributed ESSs have on the active power flows at LV busbar connection during a whole day at a fixed PV penetration (50%) and for a fixed load configuration. In particular, it is worth noting that the active power profile at MV/LV transformer is shaved compared with the same profile without ESSs. The reduction of generation and peak demand with the consequent derived advantages are reached indirectly without implementing a direct control action to reduce the load demand and/or generation but only conveying charging and discharging phases within specific periods of time.
Thus, the ESSs allow bringing benefits to the customers,
reducing the cost of electricity and increasing the local con- sumption of energy produced by PV systems (sometimes this allows also power losses reduction), and indirectly to DSO, shaving generation and demand peaks (this allows deferring infrastructural costs, reverse power flows and PV disconnec- tions).
Finally, a snapshot of the three-phase daily voltage profiles at 100% of PV penetration on feeder A is depicted in Fig. 12 in order to show how the proposed strategy allows to keep the voltage within the mandatory limits of 0.9 p.u. and 1.1 p.u.
V. CONCLUSIONS
A Monte Carlo analysis has been carried out in order to show the possible benefits that the integration of ESSs with PV units can have in terms of increasing local consumptions of renewable energy.
The proposed strategy has allowed not only to promote self- consumption but, with an appropriate setting of storage sys- tems charging and discharging phases, also to reduce the risk of possible PV disconnections due to voltage infringements.
The results have shown that an indirect voltage support can be reached promoting the ESSs installations (for instance by means of incentives), in order to support both local con- sumptions of energy produced by distributed generation and the DSO with a global balance of demand and generation on the network. Moreover, the co-located systems are monitored locally by the residential customers without adding other meters on the grid. Indeed, the possibility to provide an ancillary service makes storage systems more economically sustainable and more attractive in order to be incentivized.
Furthermore, the achieved results have been encouraging in terms of peak shaving (due to an increase in self-consumption of energy), reduction of voltage problems and improvement of voltage profile, demonstrating that sometimes two different aims can be as two sides of the same coin.
REFERENCES
[1] A. Canova, L. Giaccone, F. Spertino, and M. Tartaglia, “Electrical impact of photovoltaic plant in distributed network,” IEEE Trans. Ind. Appl., vol. 45, pp. 341-347, Jan.-Feb. 2009.
[2] R. Tonkoski, D. Turcotte, and T. H. M. El-Fouly, “Impact of high PV penetration on voltage profiles in residential neighborhoods,” IEEE
Trans. Sustain. Energy, vol. 3, pp. 518-527, July 2012. [3] R. A. Walling, R. Saint, R. C. Dugan, J. Burke, and L. A. Kojovic,
“Summary of distributed resources impact on power delivery systems,” IEEE Trans. Power Del., vol. 23, pp. 1636-1644, July 2008.
[4] V. Calderaro, G. Conio, V. Galdi, G. Massa, and A. Piccolo,“Optimal Decentralized Voltage Control for Distribution Systems With Inverter- Based Distributed Generators,” IEEE Trans. Power Syst., vol. 29, pp. 230-241, Jan. 2014.
[5] V. Calderaro, V. Galdi, F. Lamberti, and A. Piccolo, “A Smart Strategy for Voltage Control Ancillary Service in Distribution Networks,” IEEE
Trans. Power Syst., vol. 30, pp. 494-502, Jan. 2015. [6] L. Xiaohu, A. Aichhorn, L. Liming, and L. Hui, “Coordinated control
of distributed energy storage system with tap changer transformers for voltage rise mitigation under high photovoltaic penetration,” IEEE Trans.
Smart Grid, vol. 3, pp. 297-906, June 2012. [7] N. C. Nair, N. Garimella, “Battery energy storage systems: Assessment
for small-scale renewable energy integration,” Energy and Buildings, vol. 42, pp. 2124-2130, Nov. 2010.
[8] M. J. E. Alam, K. M. Muttaqi, and D. Sutanto, “Mitigation of Rooftop Solar PV Impacts and Evening Peak Support by Managing Available Capacity of Distributed Energy Storage Systems,” IEEE Trans. Power
Syst., vol. 28, pp. 3874-3884, Nov. 2013. [9] F. Marra, G. Yang, C. Traeholt, J. Ostergaard, E. Larsen, “A Decentral-
ized Storage Strategy for Residential Feeders With Photovoltaics” IEEE
Trans. Smart Grid, vol. 5, pp. 974-981, Mar. 2014. [10] M. N. Kabir, Y. Mishra, G. Ledwich, Z. Y. Dong, and K. P. Wong,
“Coordinated Control of Grid-Connected Photovoltaic Reactive Power and Battery Energy Storage Systems to Improve the Voltage Profile of a Residential Distribution Feeder,” IEEE Trans. Industrial Informatics, vol. 10, pp. 966-977, May 2014.
[11] J. v. Appen, T. Stetz, M. Braun, A. Schmiegel, “Local Voltage Control Strategies for PV Storage Systems in Distribution Grids,” IEEE Trans.
Smart Grid, vol. 5, pp. 1002-1009, Mar. 2014. [12] I. Richardson, M. Thomson, D. Infield, and C. Clifford, “Domestic
electricity use: a high-resolution energy demand model,” Energy and
Buildings, vol. 42, pp. 1878-1887, 2010. [13] http://www.progettoatlantide.it/ [14] http://electricdss.sourceforge.net/ [15] Voltage Characteristics in Public Distribution Systems, CEI EN 50160,
May, 2011.
Francesco Lamberti Vito Calderaro Vincenzo Galdi Antonio Piccolo
Department of Industrial Engineering University of Salerno
84084, Fisciano (SA) - Italy [email protected], [email protected]
Giorgio Graditi Research Center of Portici
ENEA 80055, Portici (NA) - Italy
[email protected]
Abstract—The integration of distributed PV units in LV distri-
bution networks with co-located energy storage systems (ESSs)
allows increasing the amount of consumed local electrical energy
while significantly raising the PV penetration. The installation of
ESSs may in addition promote the self-consumption of energy
and reduce the mismatch between the demand and the PV
power generation making such power source dispatchable. A
Monte Carlo simulation is performed by varying residential load
profiles, sizes and locations of PV units and ESSs in order
to assess the impact that a local and independent control of
co-located PV units and ESSs have on the grid. Time series
unbalanced power flow simulations are carried out considering
different scenarios on a typical LV Italian distribution network.
The results are evaluated in terms of benefits on voltage profiles
pointing out a significantly reduction of voltage problems on the
network at each penetration level.
Index Terms—Distributed Generation, Energy storage, Monte
Carlo methods, Photovoltaic systems.
I. INTRODUCTION
Photovoltaic (PV) systems are the most widespread form of distributed generation (DG) in low voltage (LV) Italian distribution networks. The performances of the system can be often improved by PV units when loads are locally supplied. Consequently, the system capacity is released while the feeder losses and the stress of the network are reduced. However, the intermittent and stochastic production obtained from PV systems poses technical and economic challenges for distri- bution system operators (DSOs) such as reverse power flow and voltage problems at LV networks [1]. In particular, if the PV generation is greater than the local power demand, the excess power may produce reverse power flow in the feeder causing voltage rise [2]. Instead, if the power demand is high and PV production is low or absent, voltage drops can be consistent and operation problems can arise. For both cases, violations of the voltage limits are possible [3]. In order to preserve a compliance with the system operation limits, technical solutions are expected.
In the past, active power curtailments and reactive power controls were proposed to solve voltage infringements [4], also considering coordination strategies between independent
power producers and DSOs [5]. Other solutions were based on the coordination of energy storage systems (ESSs) operations with traditional voltage regulators as step voltage regula- tors (SVR) and on-load tap changer (OLTC) [6]. Nowadays, storage systems are not very widespread in the residential sector; however, this situation is bound to change due to the possibilities for customers and small power producers to optimize electricity management. Thus, battery energy storage systems are likely to have a significant impact in small-scale integration of renewable energy sources into commercial and residential sector [7]. Indeed, in [8] the usage of storage systems to reduce the mismatch between the demand and PV production was proposed. Refs. [9], [10] propose a coordinated use of PV and battery storage systems to face voltage rise and voltage dip problems on a LV distribution network. Ref. [11] also evaluates the impact of different local voltage control strategies for PV storage systems in reducing voltage rises on a distribution grid supporting, at the same time, the self- consumption. Although economic analyses show still several barriers to the deployment of storage systems, in light of the fact that recently several economic incentives have been in- troduced, such technical solutions seem to be very promising. For this reason, here we propose a complete analysis, based on Monte Carlo method, to assess the effects of a local integration of PV units with ESSs in order to promote self-consumption of energy on a real unbalanced LV Italian distribution network.
The main contribution of the paper is the evaluation of the co-locate PV and energy storage residential systems impact on voltage profiles, without implementing specific local controls. Thus, the developed analysis allows obtaining a clear vision of the benefits in terms of power quality for DSO and self- consumption for residential customers.
The remainder of this paper is organized as follows: Section II describes the model of the system, the control strategy of ESSs and the Monte Carlo method used for the simulation analysis, Section III presents the case study describing the fundamental components of the LV network test, Section IV analyses and discusses the achieved results. Conclusions are drawn in Section V.
Fig. 1. Layout of the co-located PV generator and ESS.
II. ASSUMPTIONS AND METHOD
The problem of assessing the effects that the integration of PV systems and ESSs has on the voltage profiles of a LV network must take into account the stochastic behaviours of PV generation and load demand. Thus, in order to solve a power flow on a radial distribution systems, it is necessary to known the characteristic parameters of the analysed network for each simulation time step (for instance active and reactive powers). Furthermore, if the goal is to demonstrate that the achieved results can be reached in different scenarios, then the adopted solution needs to be tested in different conditions.
There are several methods able to forecast voltage profiles on LV networks taking into account the generation and demand uncertainties. In this paper, the problem is solved applying a classical Monte Carlo approach that allows also to take into account different sizes and locations of PV units and ESSs. Indeed, Monte Carlo method is often a good solution to simulate and analyse complex systems with many degrees of freedom.
The analysis is performed by varying the positions and the penetration level of PV systems connected to residential customers and the size of the co-located electric storage systems. The study is carried out also by changing the single customer load profile and the PV production minute by minute.
A. Modelling of the system
The main components of the system are feeders, loads, PV units and ESSs. A series impedance and a shunt reactance model each feeder section; the loads are modelled as P-Q constant power bus considering typical load curves.
The profiles of the residential customers are produced using a modified high-resolution model developed by CREST (Centre for Renewable Energy Systems Technology) at Lough- borough University [12]. PV generation profiles are calculated multiplying the rated power of PV units by a typical normal- ized photovoltaic profile.
An ideal current generator, which supplies/stores constant power for each time interval, models ESSs. State of Charge (SoC) can be evaluated according to the following:
SoC(T +T ) = SoC(T )± I(T ) T
3600 C ESS
ESS
[Ah] is the ESS’s capacity, I(T ) represents the ESS’s current, obtained by dividing the charging/discharging power of the ESS to its constant voltage, and T is the time interval of the control.
The idea is to use ESSs co-located with PV units to promote local consumption of energy by residential customers considering also the effects that the described control have on the distribution network. For these reasons, the ESS is charged if the following inequality are verified:
8 <
:
max
c
that identify a limited time period where it is possible to charge the ESS, during the daylight hours characterized by a peak in the PV generation. Instead, T
min
d
max
d
identify the time period where the ESS can be discharged; this interval is set in correspondence of the hours in which no power generation comes from PV and there is a load demand to satisfy (night time). P
PV
i
ESS
i
(T ) are the power generated by the i-th PV unit, the power demand and the SoC of the ESS of the i-th residential customer at the time step T , respectively. Furthermore, SoC
ESS
min
i
ESS
max
i
are the minimum and maximum allowable value of the SoC related to ESS
i
, which depend on the specific battery system technology.
In Fig.1 is depicted the connection scheme of the co-located PV/ESS at the Point of Common Coupling (PCC) of the distribution network.
B. Application of the Monte Carlo method
The final aim of the analysis is to evaluate the possible benefits that the introduction on a residential PV system of battery ESS, installed by the final users to increase their self- consumption of energy, can have on the voltage profiles of the LV network.
An algorithm based on Monte Carlo method is applied in order to explore the solution space of the problem. The step-by-step procedure of the Monte Carlo based algorithm is discussed as follow:
1) set the number of PV units in percentage compared to the number of the total residential customers (penetra- tion level) from a minimum value of 0% to a maximum value of 100% (with steps of 10%);
2) perform a random allocation of PV units on residential buses and associate to each one a load profile chosen randomly from a database;
Fig. 2. Monte Carlo analysis flow chart.
Fig. 3. LV Italian test network.
3) perform a random choose of the sizes of the co-located ESSs from a list of different models (Table I);
4) perform two different daily time series unbalanced power flow simulations. The first one considering only the PV units allocated (Step 2) and the second one taking into account also the co-located ESSs;
5) repeat the Step 2, 3 and 4 up to the maximum number of cases (k
max
); 6) come back to the Step 1 and increase the PV penetration
level up to the upper limit.
The flow chart in Fig.2 depicts, in detail, the time sequence of the Monte Carlo method steps.
TABLE I ESSS CHARACTERISTICS
Rated Power (kW) Maximum Available Energy (kWh) SoC limits
2 2.5 30-90 % 3 3.75 30-90 % 4 5 30-90 % 5 6.25 30-90 %
III. CASE STUDY
The network diagram of the considered LV Italian distribu- tion network is shown in Fig. 3 [13]. It consists of 3 LV feeders (A, B, C) connected to the MV network through a 10/0.4 kV /Y
g
T
cc
= 4%. The transformer tap is fixed to 1.00 p.u. The network is composed by 68 buses connected to 136
mono-phase residential loads, and 9 three-phase industrial and commercial loads. The installed power of the loads is divided as reported in [13]: 43% on the feeder A, 44% on the feeder B and 13% on the feeder C.
Mono-phase loads are modelled independently using a modified version (customized for the Italian case) of the software elaborated by the University of Loughborough [12], which allows to create minute by minute domestic profiles for specific periods of the year, load compositions and number of family members. Furthermore, the three-phase loads are characterized by typical commercial and industrial profiles [5].
Mono-phase PV systems are randomly allocated on the residential load buses at each Monte Carlo iteration. The size is fixed at 3 kW with a power factor equal to 1. Furthermore, we consider 3 three-phase PV units with a rated power of 15 kW and a unitary power factor, connected as shown in Fig. 3, which do not change during the Monte Carlo analysis. For all PV systems, a classical PV profile of a summer day in Italy is used [5].
The simulations are carried out analysing both the case with only PV systems connected to the residential customers and the case with ESSs co-located with PV systems. The rated power of the ESSs, and, consequently, the main parameters of the storage systems, change randomly during the Monte Carlo analysis with sizes between 2 kW and 5 kW with step of 1 kW. The main characteristics of the ESSs considered during the study are reported in Table I. The storage systems operations are set to promote self-consumptions of energy generated by PV units independently for each customer. For this reason, the ESS can be charged only if the power of the pertinent PV system is greater than the power absorbed by the residential customer.
Furthermore, the charging and discharging periods of the ESSs are limited between 10 a.m. - 6 p.m. (higher generation period during the day) and 20 p.m - 00 a.m. (higher demand period during the day), respectively, in order to support also the network during peak generation and demand periods providing to the DSO an ancillary service.
Fig. 4. Mean of voltage drop violations at different PV penetration level on feeders A and B with and without ESSs.
0 20 40 60 80 100
A
B
C
Penetration (%)
A
B
C
Penetration (%)
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10
Storage
Fig. 5. Map of voltage drop violations (mean values) of the whole network at different PV penetration level with and without ESSs.
Fig. 6. Mean of voltage rise violations at different PV penetration level on feeders A and B with and without ESSs.
0 20 40 60 80 100
A
B
C
Penetration (%)
A
B
C
Penetration (%)
0.0 2.0 4.0 6.0 8.0 10 12 14 16 18 20
Storage
Fig. 7. Map of voltage rise violations (mean values) of the whole network at different PV penetration level with and without ESSs.
0 10 20 30 40 50 60 70 80 90 100 -5
0
5
10
15
20
25
No Storage
0 10 20 30 40 50 60 70 80 90 100 -5
0
5
10
15
20
25
Penetration (%)
ps
Storage
Fig. 8. Box chart of voltage drop violations during Monte Carlo analysis with and without ESSs.
0 10 20 30 40 50 60 70 80 90 100 -5 0 5 10 15 20 25 30 35 40
V ol
ta ge
R ise
No Storage
0 10 20 30 40 50 60 70 80 90 100 -5 0 5 10 15 20 25 30 35 40
Penetration (%)
V ol
ta ge
R ise
s Storage
Fig. 9. Box chart of voltage rise violations during Monte Carlo analysis with and without ESSs.
IV. RESULTS AND DISCUSSION
Unbalanced power flows are solved monitoring each phase of the network by using OpenDSS [14]. The simulations are carried out on a summer week day. In order to analyse the impact that ESSs have on voltage profiles of the LV network, 1000 cases are simulated.
The ST. CEI-EN50160 is considered as reference to de- termine whether voltage violations happened on the feeders (10 minutes mean of the supply voltage shall be within the range of 1.00 p.u. ± 10%) [15]. The position of 8 meters to monitor voltage profiles is showed in Fig. 3. The results of the simulations show that feeders A and B are subjected to voltage excursions. The feeder C, instead, does not have any voltage problem because it is short compared to the other two feeders, and its load is only the 13% of the total demand.
Fig. 4 compares the mean of the voltage drops violations
0 100 200 300 400 500 600 700 800 900 1000 0
2
4
6
8
0 100 200 300 400 500 600 700 800 900 1000 0
1
2
N° Iterations
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
No Storage
M ea
n of
V ol
ta ge
R is
N° Iterations
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Storage
Fig. 10. Variation of the mean of voltage rises on Feeder B without and with ESSs, respectively.
0 2 4 6 8 10 12 14 16 18 20 22 24 −200
−150
−100
−50
0
50
100
150
200
Storage No Storage
Fig. 11. Active power at LV busbar connection (50% of PV penetration).
obtained from the Monte Carlo analysis with (in green) and without (in red) the installation of the ESSs. The storage systems are able to reduce substantially voltage drops at each penetration level. It is clear that the benefits increase with the percentage of penetration because the grid has a growing capacity to supply the energy demand locally. The complete map of voltage drop violations occurred on the three feeders is shown in Fig. 5: also in this case, it is possible to observe a substantial reduction of voltage infringements by means of a color map.
If the results were promising analysing voltage drop prob- lems, they are noteworthy in the case of voltage rises. Fig. 6 shows how the average of voltage rise problems on the network is almost zero at each penetration level. This result may be explained by the fact that the integration of the ESS with a photovoltaic system can solve, in most of the cases, voltage rise violations thanks to an extra power available in the network. As in the previous case, the achieved results on the three feeders are represented by means of the scale map depicted in Fig. 7. The reduction of voltage rise infringements allows avoiding the disconnection of PV systems increasing, at the same time, the self-consumption of energy produced by
0 2 4 6 8 10 12 14 16 18 20 22 24
0.9
1
1.1
Time (h)
0 2 4 6 8 10 12 14 16 18 20 22 24
0.9
1
1.1
Time (h)
0 2 4 6 8 10 12 14 16 18 20 22 24
0.9
1
1.1
No Storage Storage
No Storage Storage
No Storage Storage
Fig. 12. Three-phase daily voltage profiles on feeder A (100% of PV penetration).
PV residential systems. In Figs. 8-9 the statistical parameters related to voltage
drops and rises on feeder A are depicted by means of a box chart representation. Upper and under limits of the box correspond to the 25th and 75th percentiles. The whiskers are determined by the 5th and 95th percentiles. The minimum and maximum values obtained after 1000 cases are depicted as triangles. The square and the line in the box represents the mean and the median values at different penetration levels, respectively.
Fig. 10, instead, shows the variation of the mean of voltage rises with increasing cases on feeder B without and with the ESSs. Without ESSs, it is possible to note that the values are almost stable after 500 cases. On the other hand, if ESSs are considered in the Monte Carlo simulation it can be observed an increase of the variability, which requires almost 800 cases before reaching stable values.
Fig. 11 shows the effects that the charging (limited between 10 a.m. - 6 p.m.) and discharging phases (limited between 20 p.m - 00 a.m.) of the distributed ESSs have on the active power flows at LV busbar connection during a whole day at a fixed PV penetration (50%) and for a fixed load configuration. In particular, it is worth noting that the active power profile at MV/LV transformer is shaved compared with the same profile without ESSs. The reduction of generation and peak demand with the consequent derived advantages are reached indirectly without implementing a direct control action to reduce the load demand and/or generation but only conveying charging and discharging phases within specific periods of time.
Thus, the ESSs allow bringing benefits to the customers,
reducing the cost of electricity and increasing the local con- sumption of energy produced by PV systems (sometimes this allows also power losses reduction), and indirectly to DSO, shaving generation and demand peaks (this allows deferring infrastructural costs, reverse power flows and PV disconnec- tions).
Finally, a snapshot of the three-phase daily voltage profiles at 100% of PV penetration on feeder A is depicted in Fig. 12 in order to show how the proposed strategy allows to keep the voltage within the mandatory limits of 0.9 p.u. and 1.1 p.u.
V. CONCLUSIONS
A Monte Carlo analysis has been carried out in order to show the possible benefits that the integration of ESSs with PV units can have in terms of increasing local consumptions of renewable energy.
The proposed strategy has allowed not only to promote self- consumption but, with an appropriate setting of storage sys- tems charging and discharging phases, also to reduce the risk of possible PV disconnections due to voltage infringements.
The results have shown that an indirect voltage support can be reached promoting the ESSs installations (for instance by means of incentives), in order to support both local con- sumptions of energy produced by distributed generation and the DSO with a global balance of demand and generation on the network. Moreover, the co-located systems are monitored locally by the residential customers without adding other meters on the grid. Indeed, the possibility to provide an ancillary service makes storage systems more economically sustainable and more attractive in order to be incentivized.
Furthermore, the achieved results have been encouraging in terms of peak shaving (due to an increase in self-consumption of energy), reduction of voltage problems and improvement of voltage profile, demonstrating that sometimes two different aims can be as two sides of the same coin.
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