This tool is used for the Reflexe Project (smartgrid) in order to determine the impacts of PV integration into the PACA (Côte d’Azur) Area in France (Fig. 5) and to evaluate smart solu[r]
Trang 1INTEGRATION OF SOLAR PV SYSTEMS INTO GRID: IMPACT
ASSESSMENT AND SOLUTIONS
Prof Tran Quoc Tuan
CEA-INES (French National Institute for Solar Energy) and INSTN (Paris Saclay University)
50 avenue du Lac Léman, 73377 Le Bourget-du-lac, France
e-mail: TranQTuan@yahoo.com
Abstract The integration of Renewable Energy Resources (RES) or PV systems into
grid, with the intermittent characteristics can have several impacts on the network operation such as stability, protection and challenges for managing… Theses impacts are more complicated for an islanded grid or weak grid To facilitate the integration of renewable energies into the grid, a concept of smart-grid is used The smart grid uses digital technology to improve reliability, flexibility, and efficiency (both economically and energetically) of the electric system This paper presents impacts provided by PV systems integration into grid: voltage variations, frequency variation, voltage unbalance… Several solutions in order to reduce these impacts, to maximize the ancillary services contributed by PV systems are proposed via different projects Intelligent control and energy management are developed in order to minimize operation cost and to maximize the RES penetration rate into grid
Index Terms—Smart grid, microgrid, simulation, impact, stability, forecasting, control,
energy management, protection
I INTRODUCTION
Solar photovoltaic is a sustainable energy source Worldwide growth of photovoltaics is extremely dynamic and varies strongly by country By the end of 2016, cumulative photovoltaic capacity increased by more than 75 gigawatt (GW) and reached
at least 303 GW, sufficient to supply 1.8 percent of the world's total electricity consumption [1] The forecast has shown that from year 2100 solar energy will produce about 50% of total energy in the world Table I shows the solar PV energy development
in 2016 in the world
Trang 2TABLE I: Solar PV energy development in 2013
The connection of solar PV system to the grid, with intermittent characteristic, can raise several technical problems or can have significant impacts on power systems
such as:
Varying the power production
Changing the voltage profile
Increasing the voltage unbalance between phases
Increasing harmonics on the network
The stability, the protection problem and the system management: with great
number of inverters connected to grid, the inertia of network is low, the short-circuit currents are small…
II SOLAR PV POWER FORECASTING AND MONITORING
The integration of variable PV systems into electrical grids is limited because of their intermittences, fast power variations, high dependence on meteorology and low
inertia The variability has to be characterized along a spatial and time dimension For
spatial dimension, PV generation covering a large spatial extent can have an hourly
temporal resolution, while individual PV panel plants will have highly variable PV
power outputs in a short time When power systems are operating with variable PV
systems, the operators have different major issues in different time scales
Since the variability and uncertainty in PV generation create new challenges in the planning and operation of electric power grids, they should be properly accounted to
balance demand and supply Generally, electrical system operators and planners use
mechanisms including forecasting, scheduling, economic dispatch, and power reserves
Trang 3to ensure power grid performances that satisfy reliability standards within an acceptable cost The forecasting of the power generation has been considered as a major solution to handle efficiently PV system integration into grids However, the uncertainty associated with forecast errors cannot be eliminated even with the best models and methods In addition, the combination of generation and consumption variability with forecast uncertainty makes the situation more difficult for power system operators to schedule and to set an appropriate power reserve level
Therefore, forecast information is essential for an efficient use, the management
of the electricity grid and for solar energy trading At CEA-INES, three models for forecasting the PV production have been developed based on stochastic learning method, local and remote sensing method and hybrid method (Fig 1):
Solar PV forecasting model for 6 to 48 h: this model uses the weather forecasting
Solar PV forecasting model for 30 min to 6 h: this model uses the satellites images
Solar PV forecasting model for 5 to 30 min: this model uses the local camera
Fig 1: Three models developed at the CEA-INES for forecasting of PV production
Fig 2: Solar PV monitoring at a ski station “Le Pas du Lac”
Trang 4Fig 3: Solar PV monitoring in France
Fig 2 shows a PV solar monitoring at a ski station “Le Pas du Lac” Solar PV monitoring stations in France is presented in Fig 3 From the information obtained by monitoring during one year (ex in 2013 for this case), we can estimate the variability of
PV production from power plan (central) to country in France as shown in Fig 4
Fig 4: Variability of PV production from power plan to country in France III IMPACT ASSESSMENT OF PV INTEGRATION INTO GRIDS
From random variables of PV production and loads, a probabilistic three phase Load Flow (PLF) is developed by using Monte Carlo techniques Two modes of simulation can be realized by using this tool:
Deterministic simulation: all parameters are fixed
Monte-Carlo simulation: set of simulations are performed, some parameters are defined as random variables such as loads, PV production…
Trang 5In particularly, the neutral currents and losses in neutral conductors are also calculated The program shows also:
Max or min values of these quantities and their occurrence
Distribution of over-voltage, under-voltage or overcurrent
Critical instants and locations (buses) in the network
The developed tool based on the Monte Carlo simulation has the following advantages:
A three-phase load flow program with a fast calculation
A simulation which takes into account the unbalance between phases (single or three-phase loads)
An ability to determine the voltage unbalance and losses in neutral conductors
The identification of critical time, locations (buses) and occurrence probability of load or PV production
An easy analysis of results with the help of proposed indicators
The proposed program allows an assessment of the impacts of PV integration on distribution and the determination of the penetration rate of PV systems After identifying the critical cases by using the developed tool, solutions can be developed and re-evaluated in particular to avoid the congestion, to maintain voltage within limits…
This tool is used for the Reflexe Project (smartgrid) in order to determine the impacts of PV integration into the PACA (Côte d’Azur) Area in France (Fig 5) and to evaluate smart solutions such as PV integration, energy storage and load shedding There are voltage violations in this area (PACA) when a 400 kV line is outraged between Realtor and Necules
Fig 5: PACA (Côte d’Azur) network in France
Trang 6Fig 6: Voltage variation with N-1
Fig 7: Voltage variation with N-1 with solutions: PV+load shedding and PV+Storage
In order to maintain the continuation of operation, several solutions are carried out such as: PV installations, energy storage and load shedding Fig 7a shows the voltage variation with 180 MW of PV and load shedding about 234 MW Fig 7b shows the voltage variation with 180 MW of PV and 100 MW-200 MWh of energy storage With these solutions, voltages are maintained within limits
This tool is also used to determine the maximal PV insertion capacity connected
to grid (Fig 8) The maximal PV inversion capacity is determined by the constraints of voltages and power flows Fig 9 show the voltage variation and power variation without PV installations With a PV system installed at bus 53, the maximal capacity of
PV system is 6.85 MW For this case, they can have overloads on certain lines (Critical lines:10-47, 47-48, 48-49, 49-50, 50-51, 51-52) and no voltage variation (Fig 10a, and 10b) With PV systems installed at bus 53 and 61, the maximal capacity of PV system is 13.09 MW For this case, they can have voltage violation at buses: 52, 53, 54, 14, 15, 61 and no overloads (Fig 11a) With a PV system installed at bus 53, 36, 58, the maximal capacity of PV system is 14.67 MW (P_PV_36 = 6.51 MW, P_PV_53 = 1.31 MW,
0.94 0.96 0.98 1 1.02 1.04 1.06
Time (H)
0.94 0.96 0.98 1 1.02 1.04 1.06
Time (H)
0.94 0.96 0.98 1 1.02 1.04 1.06
Time (H)
Trang 7P_PV_58 = 6.85 MW) There are overloads on lines 9-33, 33-34, 34-36, 12-55, 55-57, 57-58
Fig 8: Distribution network with PV installations
Fig 9: Voltage variation and power flow in lines
Fig 10a: Congestion; Fig 10b: no voltage violation (P_PVmax = 6.85 MW)
0.94 0.96 0.98 1 1.02 1.04 1.06
Time (H)
0 5 10 15 20 25 0
0.5 1 1.5 2 2.5 3
Time (H)
0 50 100 150 200
Time (H)
0.94 0.96 0.98 1 1.02 1.04 1.06
Time (H)
Trang 8Fig 11a: Voltage violation (P_PVmax = 13.09 MW); Fig 10b: Over load (P_PVmax = 14.67)
IV CONTROL CAPABILITIES OF DISTRIBUTED ENERGY RESOURCE TO PARTICIPATE IN DISTRIBUTION SYSTEM OPERATION
This part presents a case study based on a real distribution network with a high share of distributed generation We built the simulation on the present network topology and generated a scenario for the expected future with a high penetration of DER (Distributed Energy Resources) and an increase of the consumption Even with a load growth exceeding the substations capacity the simulated network can be operated with a high security of supply This degree of power quality is guaranteed by controllable DER units which are capable of operating in an islanded mode and of providing voltage control and congestion management as ancillary services Simplified models of common DER units are described They allow a simulation of a thousand-node network
Fig 12: Distribution network in Valencia (Spain)
The connection of DER (Distributed Energy Resource), in particular PV systems
to networks can raise a certain number of technical challenges Important impacts are the influence on the network’s voltage, the network’s stability and the security of supply
0.94 0.96 0.98 1 1.02 1.04 1.06
Time (H)
0 50 100 150 200
Time (H)
Atomix Anillo Industrias
Norte UI-6 Sur Atomizados Euro Pueblos Ratils Arcillas Industrias Sur Onda Riegos Bechi Colomer Sur 9 Miralcamp Pedrizas Regios Onda
0.96 MW
Cristal Ceramica 702
CEE Gaya Fores 691
0.995 MW
0.96 MW
Hispania Ceramica 282
H fco gaya fores 2 644
0.854 MW
12.522 MW
Peronda 708
Atomix SA 712
4.5MW
Arcillas Atomizadas 704
0.960 MW
L-02 L-03 L-04 L-10 L-08 L-09 L-11 L-15 L-16 L-17 L-18 L-21 L-22 L-23 L-24
Azunlindus 706
L-55
HIJOS CIPR CASTELLO
Euroatomizado 624
9.981 MW
Atomizadora SA 705
0.627 MW
0.828 MW
0.855 MW
Cristal Ceramica 716
9.0MW
63kV network
8 MW
4 MW
1 MW
S_L03
521 522 523 525 526 527 528
10.8 MW 1.7 MVAR
9.1 MW 0.9 MVAR
Atomix Anillo Industrias
Norte UI-6 Sur Atomizados Euro Pueblos Ratils Arcillas Industrias Sur Onda Riegos Bechi Colomer Sur 9 Miralcamp Pedrizas Regios Onda
0.96 MW
Cristal Ceramica 702
CEE Gaya Fores 691
0.995 MW
0.96 MW
Hispania Ceramica 282
H fco gaya fores 2 644
0.854 MW
12.522 MW
Peronda 708
Atomix SA 712
4.5MW
Arcillas Atomizadas 704
0.960 MW
L-02 L-03 L-04 L-10 L-08 L-09 L-11 L-15 L-16 L-17 L-18 L-21 L-22 L-23 L-24
Azunlindus 706
L-55
HIJOS CIPR CASTELLO
Euroatomizado 624
9.981 MW
Atomizadora SA 705
0.627 MW
0.828 MW
0.855 MW
Cristal Ceramica 716
9.0MW
63kV network
8 MW
4 MW
1 MW
8 MW
4 MW
1 MW
S_L03
521 522 523 525 526 527 528
10.8 MW 1.7 MVAR
9.1 MW 0.9 MVAR
Synchronous generators Circuit breaker
Feeder
Atomix Anillo Industrias
Norte UI-6 Sur Atomizados Euro Pueblos Ratils Arcillas Industrias Sur Onda Riegos Bechi Colomer Sur 9 Miralcamp Pedrizas Regios Onda
0.96 MW
Cristal Ceramica 702
CEE Gaya Fores 691
0.995 MW
0.96 MW
Hispania Ceramica 282
H fco gaya fores 2 644
0.854 MW
12.522 MW
Peronda 708
Atomix SA 712
4.5MW
Arcillas Atomizadas 704
0.960 MW
L-02 L-03 L-04 L-10 L-08 L-09 L-11 L-15 L-16 L-17 L-18 L-21 L-22 L-23 L-24
Azunlindus 706
L-55
HIJOS CIPR CASTELLO
Euroatomizado 624
9.981 MW
Atomizadora SA 705
0.627 MW
0.828 MW
0.855 MW
Cristal Ceramica 716
9.0MW
63kV network
8 MW
4 MW
1 MW
S_L03
521 522 523 525 526 527 528
10.8 MW 1.7 MVAR
9.1 MW 0.9 MVAR
Atomix Anillo Industrias
Norte UI-6 Sur Atomizados Euro Pueblos Ratils Arcillas Industrias Sur Onda Riegos Bechi Colomer Sur 9 Miralcamp Pedrizas Regios Onda
0.96 MW
Cristal Ceramica 702
CEE Gaya Fores 691
0.995 MW
0.96 MW
Hispania Ceramica 282
H fco gaya fores 2 644
0.854 MW
12.522 MW
Peronda 708
Atomix SA 712
4.5MW
Arcillas Atomizadas 704
0.960 MW
L-02 L-03 L-04 L-10 L-08 L-09 L-11 L-15 L-16 L-17 L-18 L-21 L-22 L-23 L-24
Azunlindus 706
L-55
HIJOS CIPR CASTELLO
Euroatomizado 624
9.981 MW
Atomizadora SA 705
0.627 MW
0.828 MW
0.855 MW
Cristal Ceramica 716
9.0MW
63kV network
8 MW
4 MW
1 MW
8 MW
4 MW
1 MW
S_L03
521 522 523 525 526 527 528
10.8 MW 1.7 MVAR
9.1 MW 0.9 MVAR
Synchronous generators Circuit breaker
Feeder
Trang 9In all cases, DER must take over the responsibilities from large conventional power plants aiming at substituting them considerably They have to provide flexibility and controllability necessary to support economic and secure system operation This represents a shift from traditional central control philosophy presently used to control typically hundreds of generators to a new distributed control paradigm applicable for operation of hundreds of thousands of controllable generators and loads
This case study is based on a real distribution network (Fig 12) A real network topology in Valencia (Spain) of 1540 nodes is used A scenario for the future (say year
2020 - 2030) is defined, it is based on an increase of consumption and distributed generation, in particular PV systems and wind powers
Fig 13: Active power exchange of transformer TF1 with HV network
Congestion management is one of the key issues for secure and reliable network operation If local generators cannot change their power outputs congestions occur as illustrated in Fig 13 in the time span between 17:00 and 22:00 Then, the loading reaches 29.2 MVA for transformer TF1 and 23.5 MVA for transformer TF2 Both transformers with a rated power of 20 MVA are overloaded
In order to avoid congestion, power outputs of CHP plants and BESS (Battery Energy Storage System) are re-dispatched as shown in Fig 13 By those changes, the power exchanges are reduced and power exchanges with HV power system are limited
in the admissible limits of the two transformers (20 MVA) In this case, the generation reserve is sufficient to contribute for congestion management In case the total power generation is not sufficient, a load shedding could be applied
In order to avoid congestion, new active power outputs of CHP plants and BESS, generation shift distribution factors method can be used
V INTELLIGENT VOLTAGE CONTROL
The connection of PV systems to the network can provide voltage variation of the network With P/Q classic control (reactive powers equal to zero) there are overvoltages
Trang 10superior to 1.1 pu in case of strong irradiation and light load and undervoltages inferior
to 0.9 pu in case of heavy load and no sun PV systems can be disconnected in these cases by protections
1 Principe of Auto-Adaptive Voltage Control
The developed auto-adaptive voltage control answers partly to questions with not only technical but also economic advantages: local decisions based only on local measures This avoids investments on communication systems for DNOs
Fig 14 describes the working principle of auto-adaptive voltage control
Fig 14: Principe of auto-adaptive control
2 Simulations
a LV network
To study the voltage problem caused by photovoltaic systems in order to find innovative solutions, a LV distribution network presented in Fig 15 is studied The network consists of nine single-phase residential loads and a three-phase commercial load There are also 9 PV single phase systems of 1, 2 or 3 kW and three-phase system
of 75 kW
Fig 15: LV distributed network with PV systems
Pfixed
Q fixed
Classical
Q adapted
Adaptive module (fuzzy logic) P/Q control or P/V control ?
(V_desired varied adaptively)
+ +
Ré s ea u HTA 20 k V
PV- 2kW
PV- 2kW
PV- 3kW PV- 3kW
PV- 1kW PV- 1kW
PV3P- 75kW
PV- 1kW
30
30
1 R1
LF
LF1
Slack: 20 5kVRM SLL/ _0 Phase: 0
5nF
C1
30
30
30
30
30
R10
30
R11
30
R12
30
R13
30
R14
30
R15
30
R16
p1
ALM 70_130m
PI
p1
ALM 70_185m
PI
p1
ALM 70_1000m
PI
p1
ALM 70_346m
PI
p1
ALM 70_216
PI
p1
ALM 70_130m
PI
p1
ALM 70_251m
PI
p1
ALM 35_45m
PI
p1
ALM 35_57m
PI
p1
ALM 35_21m
PI
p1
ALM 35_30m
PI
p1
ALM 35_27m
PI
p1
AL95_50S_470m
PI
1 DY_1
20/ 0 42
+
S_HTA
20 5kVRM SLL / _0 Slack: LF1
p
V_pu V4
p
V_pu V5
p
V_pu V3
p
V_pu V14
p
V_pu V11
p
V_pu V2
p
V6
V_pu
p
V7 V_pu
p
V13
V12
V_pu
p
V10
V_pu
PV7c_3kW
PV11a_3kW
PV4b_2kW
PV14c_2kW
PV10b_3kW
PV6a_2kW
PV12a_1kW
PV13b_1kW
P
ic 50Hz
50Hz
p3
scope
Pt ot al
scope
Q t ot al
scope
Et ot al
I nt 1
La Lb Lc
L_Dyn
L_Dyn
L5c
L_Dyn
L6a
L_Dyn
L7c
L_Dyn
L_Dyn
L11a
L_Dyn
L14c
L_Dyn
L13b
L_Dyn
L12a
I n
N5_V2sV1
I n
N14_V2sV1
I n
N11_V2sV1
I n
N13_V2sV1
I n
N12_V2sV1
I n
N10_i2si1
I n
N4_V2sV1
PV3P3_75kW
I n
N3_V2sV1
I n
N6_V2sV1
I n
N7_V2sV1
I n
N2_V2sV1
M PLO T
PV5c_1kW
PV13 b
LV9
LV10
b LV8
LV4
b
c LV14
c
a LV11
LV12 a HTA
LV3
b c
LV6 a
LV7
c