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]
(1)INTEGRATION 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
(2)TABLE I: Solar PV energy development in 2013
Cum Capa Production Cum Capa Production
2015 (GW) 2015 (TWh) 2016 (GW) 2016 (TWh)
1 China 43.53 78.07 66 (1.07%)
2 Japan 34.4 30 (3.4%) 42.75 43 (4.9%)
3 Germany 39.7 38.7 (6.5%) 41.22 41.7 (7%)
4 USA 27.32 26.5 40.3 59.8 (1.4%)
5 Italy 18.9 24.68 (7.8%) 19.28 25.1 (7.5%)
6 UK 8.8 7.56 (2.5%) 11.63 10.3 (3.4%)
7 India 5.1 9.01 7.45
8 France 6.55 7.42 (1.4%) 7.13 8.3 (1.6%)
9 Australia 5.1 5.9 2.4%
10 Spain 5.4 5.49 (3.2%)
World 242 303 1.8%
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
(3)to 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 to 48 h: this model uses the weather forecasting
Solar PV forecasting model for 30 to h: this model uses the satellites images
Solar PV forecasting model for to 30 min: this model uses the local camera
Fig 1: Three models developed at the CEA-INES for forecasting of PV production
(4)Fig 3: Solar PV monitoring in France
Fig shows a PV solar monitoring at a ski station “Le Pas du Lac” Solar PV monitoring stations in France is presented in Fig 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
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
(5)In particularly, the neutral currents and losses in neutral conductors are also calculated The program shows also:
Max or 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
(6)Fig 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 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 10 15 20 25
0.94 0.96 0.98 1.02 1.04 1.06
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tage (
pu)
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0.94 0.96 0.98 1.02 1.04 1.06
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)
0 10 15 20 25
0.94 0.96 0.98 1.02 1.04 1.06
Time (H)
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ge (
pu
(7)P_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) 10 15 20 25
0.94 0.96 0.98 1.02 1.04 1.06
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u)
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0 0.5 1.5 2.5
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0 50 100 150 200
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0 10 15 20 25 0.94
0.96 0.98 1.02 1.04 1.06
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(8)
Fig 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 10 15 20 25 0.94 0.96 0.98 1.02 1.04 1.06 Time (H) V ol tag e (pu )
0 10 15 20 25
0 50 100 150 200 Time (H) B nch cu rr en t( A )
Atomix Anillo Industrias
Norte UI-6Sur AtomizadosEuro Pueblos Ratils Arcillas IndustriasSur Onda RiegosBechi Colomer Sur 9 MiralcampPedrizas RegiosOnda
5 MVAR 5 MVAR
20MVA 20MVA 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
1150 1539 TF1 TF2 Azunlindus 706 566 567 L-55 HIJOS CIPR CASTELLO 709 CEE 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
S_L02 S_L04 S_L10 S_L08 S_L09 S_L11 S_L15 S_L16 S_L17 S_L18 S_L21 S_L22 S_L23 S_L24 S_L55
521 522 523 525 526 527 528
10.8 MW 1.7 MVAR
9.1 MW 0.9 MVAR
Atomix Anillo Industrias
Norte UI-6Sur AtomizadosEuro Pueblos Ratils Arcillas IndustriasSur Onda RiegosBechi Colomer Sur 9 MiralcampPedrizas RegiosOnda
5 MVAR 5 MVAR
20MVA 20MVA 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
1150 1539 TF1 TF2 Azunlindus 706 566 567 L-55 HIJOS CIPR CASTELLO 709 CEE 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
S_L02 S_L04 S_L10 S_L08 S_L09 S_L11 S_L15 S_L16 S_L17 S_L18 S_L21 S_L22 S_L23 S_L24 S_L55
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-6Sur AtomizadosEuro Pueblos Ratils Arcillas IndustriasSur Onda RiegosBechi Colomer Sur 9 MiralcampPedrizas RegiosOnda
5 MVAR 5 MVAR
20MVA 20MVA 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
1150 1539 TF1 TF2 Azunlindus 706 566 567 L-55 HIJOS CIPR CASTELLO 709 CEE 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
S_L02 S_L04 S_L10 S_L08 S_L09 S_L11 S_L15 S_L16 S_L17 S_L18 S_L21 S_L22 S_L23 S_L24 S_L55
521 522 523 525 526 527 528
10.8 MW 1.7 MVAR
9.1 MW 0.9 MVAR
Atomix Anillo Industrias
Norte UI-6Sur AtomizadosEuro Pueblos Ratils Arcillas IndustriasSur Onda RiegosBechi Colomer Sur 9 MiralcampPedrizas RegiosOnda
5 MVAR 5 MVAR
20MVA 20MVA 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
1150 1539 TF1 TF2 Azunlindus 706 566 567 L-55 HIJOS CIPR CASTELLO 709 CEE 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
S_L02 S_L04 S_L10 S_L08 S_L09 S_L11 S_L15 S_L16 S_L17 S_L18 S_L21 S_L22 S_L23 S_L24 S_L55
521 522 523 525 526 527 528
10.8 MW 1.7 MVAR
9.1 MW 0.9 MVAR
Synchronous
generators Circuit breaker
(9)In 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
(10)superior 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 PV single phase systems of 1, or kW and three-phase system of 75 kW
Fig 15: LV distributed network with PV systems
Pfixed
Qfixed
Vmesured
Classical
regulation Distributedgeneration
Qadapted
Ia,b,c
Adaptive module (fuzzy logic) P/Q control or P/V control ? Vmax_desired or min_desired = ?
(V_desired varied adaptively) +
+
Ré s ea u HTA 20 k V
PV- 2kW
PV- 2kW
PV- 3kW PV- 2kW
PV- 3kW PV- 3kW PV- 1kW PV- 1kW PV3P- 75kW PV- 1kW + 30 R3 + 30 R4 + 1R1 LF LF1
Slack: 20 5kVRM SLL/ _0 Phase: + 5nF C1 + 30 R6 + 30 R7 + 30 R8 + 30 R9 + 30 R10 + 30 R11 + 30 R12 + 30 R13 + 30 R14 + 30 R15 + 30 R16 p1p2 N1N2 ALM 70_130m PI p1p2 N1N2 ALM 70_185m PI p1p2 N1N2 ALM 70_1000m PI p1p2 N1N2 ALM 70_346m PI p1p2 N1N2 ALM 70_216 PI p1p2 N1N2 ALM 70_130m PI p1p2 N1N2 ALM 70_251m PI p1p2 N1N2 ALM 35_45m PI p1p2 N1N2 ALM 35_57m PI p1p2 N1N2 ALM 35_21m PI p1p2 N1N2 ALM 35_30m PI p1p2 N1N2 ALM 35_27m PI p1p2 N1N2 AL95_50S_470m PI
1DY_12
20/ 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 V_pu p V12 V_pu p V10 V_pu PV N PV a PV c N PV b PV7c_3kW PV N PV a PV c N PV b PV11a_3kW PV N PVa PVc N PVb PV4b_2kW PV N PV a PV c N PV b PV14c_2kW PV N PV a PV c N PV b PV10b_3kW PV N PV a PV c N PV b PV6a_2kW PV N PV a PV c N PV b PV12a_1kW PV N PV a PV c N PV b PV13b_1kW P ic 50Hz
p1icQ
50Hz
p3
scope
Pt ot al
scope
Q t ot al
scope
Et ot al I nt
LaLb Lc
N L3abc L N L_Dyn L4b L N L_Dyn L5c L N L_Dyn L6a L N L_Dyn L7c L N L_Dyn L10b L N L_Dyn L11a L N L_Dyn L14c L N L_Dyn L13b L N 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 PV a PV c N PV b PV3P3_75kW I n N3_V2sV1 I n N6_V2sV1 I n N7_V2sV1 I n N2_V2sV1
M PLO T
PV
N
PV
a PVc