Solutions to Increase PV Hosting Capacity and Provision of Services from Flexible Energy Resources
Abstract
:1. Introduction
2. Simulation Model and Study Cases
2.1. Simulated Study System
2.2. Droop Control of the DER Units
- ω = angular frequency,
- C = cable capacitance,
- U = cable voltage,
- L = inductance/phase,
- I = cable current.
2.3. Study Cases and Simulation Sequence
3. Simulation Results and New Frequency Adaptive PU-Droop
- Effect of BESSs utilization (Cases 4a–5b), two or three BESSs (with default PU-droops on BESSs).
- Potential DER (PV and BESS) unit Pf- and PU-control interactions during future scenarios with very high PV penetration and very low load in (i) steady-state, (ii) over-frequency, and (iii) under-frequency situations to study the effect of:
- ○
- additional PU-control blocking logic (Cases 2a–4b and 5b);
- ○
- different Pf- and QU-droop settings (Cases 3a–3b).
- Effect of remote LV voltage measurement from PV (further in LV network) connection point to BESS at closest MV/LV secondary substation in order to maximize PV production during very low loads (Case 4b).
- Effect of adapting the OLTC setting during very low load situations (Cases 1b, 2b, 3b, and 4a–5b).
3.1. Simulations to Study Different Potential Solutions for Increasing LV Network PV Hosting Capacity and Provision of Flexibility Services from DER
Simulations with Real-Life Measurement Data
3.2. Frequency Adaptive PU-Droops for PVs and BESSs During Larger Under- and Over-Frequency Situations
- Use of PU-blocking is needed from system-wide perspective in order to enable feasible operation of PV 2 (without BESS 3) in Cases 2a and 2b during 50.07 Hz over-frequency situation.
- With BESS 3 in Case 5b during 49.8 Hz under-frequency situation however, PV 2 output needs to be curtailed more with PU-blocking due to BESS 3 unit’s more rapid and extensive participation in system frequency support.
- ○
- Therefore, use of PU-blocking is not recommended from PV curtailment viewpoint when there are both PV and BESS at the same network connection point. On the other hand, simultaneous BESS frequency support with PU-blocking logic is higher.
- ○
- One potential option to avoid PV 2 curtailment could be disabling Pf-control of BESS 3 during under-frequency events. On the other hand, that is not optimal from whole-system perspective especially if frequency deviation is large and frequency support is needed.
4. New Real-Time Adaptive Management Schemes
4.1. Real-Time Frequency Adaptive PU-Droop
- f = measured frequency (Hz)
- fn = nominal frequency (Hz)
- Umin_ini = chosen initial minimum voltage set-point value when f = fn (pu)
- Umax_ini = chosen initial maximum voltage set-point value when f = fn (pu)
- Pn = nominal active power (MW).
4.2. Real-Time HV/MV Substation PQ-Flow Dependent OLTC Setting Value
4.3. Real-Time Adaptive QU-Droop Based on OLTC Setting Value
5. Conclusions
- PV hosting capacity in LV network can be increased with lower OLTC setting value.
- Feasible and coordinated QU-, PU-, and Pf-droop settings (with dead-zones) of PVs and BESSs are essential in maximizing the provision of their flexibility services.
- During an over-frequency event without BESS, the best frequency support can be achieved by using PU-control blocking logic on PVs.
- PV hosting capacity can be increased to some extent by remote LV voltage measurement from PV connection point to MV/LV substation connected BESS.
- With BESS at the PV connection point however, the PV hosting capacity during steady-state operation can be maximized by simultaneously charging BESS in order to avoid local over-voltages.
- During larger frequency deviations frequency adaptive PU-droops can enable larger PV and BESS power system frequency support as well as maximize smaller LV network connected demand (loads) participation in frequency control.
- Actual implementation logic (e.g., PU-blocking or change of active PU-droop with frequency adaptive PU-droops) affects the PV and BESS frequency support contribution and the extent of PV curtailment (Table 10).
Author Contributions
Funding
Conflicts of Interest
Nomenclature
AC | Alternating Current |
ANM | Active Network Management |
BESS | Battery Energy Storage System |
CB | Circuit Breaker |
DC | Direct Current |
DG | Distributed Generation |
DER | Distributed Energy Resources |
DSO | Distribution System Operator |
ENTSO-E | European Network of Transmission System Operators for Electricity |
ES | Energy Storage |
EU | European Union |
EV | Electric Vehicle |
HV | High Voltage |
J06, J07, J08 | MV feeders in the study case |
LV | Low Voltage |
MV | Medium Voltage |
NC | Network Code |
OLTC | On-Load Tap Changer |
PSCAD | Power System Simulation Software |
P-controller | Proportional controller |
PI-controller | Proportional-Integral controller |
PV | Photovoltaic |
RES | Renewable Energy Sources |
RfG | Requirements for Generators |
RMS | Root Mean Square |
RPW | Reactive Power Window |
SOC | State-of-Charge |
SSG | Sundom Smart Grid |
TSO | Transmission System Operator |
WT | Wind Turbine |
ω | Angular frequency |
C | Capacitance |
cosφ | Power factor |
cosφ(P) | Active Power dependent power factor |
f | Frequency |
fn | Nominal frequency (Hz) |
I | Current |
Imeas | Measured Current |
Id | Current direct (d) component |
Id_ref | Current direct (d) component reference value |
Iq | Current quadrature (q) component |
Iq_ref | Current quadrature (q) component reference value |
L | Inductance |
P | Active Power |
P_MV_flow | Active Power flow between HV and MV networks |
Pf or P(f) | Active Power—Frequency |
PU or P(U) | Active Power—Voltage |
ΔPBESS_3 | Active Power change of BESS unit 3 |
ΔPPV_2 | Active Power change of PV unit 2 |
ΔPTOTAL_75s | Total Active Power change during simulation at t = 75 s |
ΔPTOTAL_95s | Total Active Power change during simulation at t = 95 s |
PBESS_1 | Active Power of BESS unit 1 |
PBESS_3 | Active Power of BESS unit 3 |
Pn | Nominal active power |
PPV | Active Power of PV unit |
PPV_2 | Active Power of PV unit 2 |
PWT | Active Power of Wind Turbine (WT) |
P_ref | Active Power reference value |
PQ | Active Power—Reactive Power |
Q | Reactive Power |
Q_MV_flow | Reactive Power flow between HV and MV networks |
Qcable | Total Reactive Power of a cable |
Qconsumption | Reactive Power consumed by a cable |
Qproduction | Reactive Power produced by a cable |
Qunb | Reactive Power unbalance |
Q(P) | Reactive Power—Active Power |
QU or Q(U) | Reactive Power—Voltage |
QBESS_1 | Reactive Power of BESS unit 1 |
QBESS_3 | Reactive Power of BESS unit 3 |
QPV_2 | Reactive Power of PV unit 2 |
Q_ref | Reactive Power reference value |
t | Time |
U | Voltage |
Un | Nominal Voltage |
Umin_ini | Chosen initial minimum voltage set-point value |
Umax_ini | Chosen initial maximum voltage set-point value |
UPV_2 | Connection point voltage of PV unit 2 |
Vd | Voltage direct (d) component |
Vq | Voltage quadrature (q) component |
VPCC | Voltage at the point of common coupling |
V_ref | Voltage reference value |
References
- Mohandes, B.; El Moursi, M.S.; Hatziargyriou, N.; El Khatib, S. A review of power system flexibility with high penetration of renewables. IEEE Trans. Power Syst. 2019, 34, 3140–3155. [Google Scholar] [CrossRef]
- Laaksonen, H.; Hovila, P. FlexZone concept to enable resilient distribution grids-possibilities in sundom smart grid. In Proceedings of the CIRED Workshop 2016, Helsinki, Finland, 14–15 June 2016. [Google Scholar]
- Astapov, V.; Divshali, P.H.; Söder, L. The potential of distribution grid as an alternative source for reactive power control in transmission grid. In Proceedings of the 20th European Conference on Power Electronics and Applications-EPE 2018, Riga, Latvia, 17–21 September 2018. [Google Scholar]
- Hafezi, H.; Laaksonen, H. Autonomous soft open point control for active distribution network voltage level management. In Proceedings of the 13th IEEE PowerTech 2019, Milan, Italy, 23–27 June 2019. [Google Scholar]
- Hes, S.; Kula, J.; Svec, J. Technical solutions for increasing der hosting capacity in distribution grids in the Czech Republic in terms of European project INTERFLEX. In Proceedings of the 25th International Conference on Electricity Distribution-CIRED 2019, Madrid, Spain, 3–6 June 2019. [Google Scholar]
- Ulasenka, A.; Del Rio Etayo, L.; Cirujano, P.; Ortiz, A.; Brandl, R.; Montoya, J. Holistic coordination of smart technologies for efficient lv operation, increasing hosting capacity and reducing grid losses. In Proceedings of the 25th International Conference on Electricity Distribution-CIRED 2019, Madrid, Spain, 3–6 June 2019. [Google Scholar]
- Wang, Y.; Xu, Y.; Tang, Y.; Syed, M.H.; Guillo-Sansano, E.; Burt, G.M. Decentralized-Distributed Hybrid Voltage Regulation of Power Distribution Networks Based on Power Inverters. IET Gener. Transm. Distrib. 2019, 13, 444–451. [Google Scholar] [CrossRef]
- Divshali, P.H.; Söder, L. Improving hosting capacity of rooftop PVS by quadratic control of an LV-Central BSS. IEEE Trans. Smart Grid 2017, 10, 919–927. [Google Scholar] [CrossRef]
- Parthasarathy, C.; Hafezi, H.; Laaksonen, H.; Kauhaniemi, K. Modelling and simulation of hybrid PV & BES systems as flexible resources in Smartgrids–Sundom smart grid case. In Proceedings of the 13th IEEE PowerTech 2019, Milan, Italy, 23–27 June 2019. [Google Scholar]
- Divshali, P.H.; Alimardani, A.; Hosseinian, S.H.; Abedi, M. Decentralized cooperative control strategy of microsources for stabilizing autonomous VSC-Based Microgrids. IEEE Trans. Power Syst. 2012, 27, 1949–1959. [Google Scholar] [CrossRef]
- Worthmann, K.; Kellett, C.M.; Braun, P.; Grüne, L.; Weller, S.R. Distributed and decentralized control of residential energy systems incorporating battery storage. IEEE Trans. Smart Grid 2015, 6, 1914–1923. [Google Scholar] [CrossRef] [Green Version]
- Nieto, A.; Efstratiadi, M.-A.; Currie, A.; Coughlan, K.; Do, S. Coordinated operation of a grid scale energy storage system with tap changer for voltage control on primary substations. In Proceedings of the 25th International Conference on Electricity Distribution-CIRED 2019, Madrid, Spain, 3–6 June 2019. [Google Scholar]
- Weckx, S.; D’Hulst, R.; Driesen, J. Primary and secondary frequency support by a multi-agent demand control system. IEEE Trans. Power Syst. 2015, 30, 1394–1404. [Google Scholar] [CrossRef]
- Mokhtari, G.; Ghosh, A.; Nourbakhsh, G.; Ledwich, G. Smart robust resource control in LV network to deal with voltage rise issue. IEEE Trans. Sustain. Energy 2013, 4, 1043–1050. [Google Scholar] [CrossRef]
- Lee, S.-J.; Kim, J.-H.; Kim, C.-H.; Kim, S.-K.; Kim, E.-S.; Kim, D.-U.; Mehmood, K.K.; Khan, S.U. Coordinated control algorithm for distributed battery energy storage systems for mitigating voltage and frequency deviations. IEEE Trans. Smart Grid 2016, 7, 1713–1722. [Google Scholar] [CrossRef]
- Miao, L.; Wen, J.; Xie, H.; Yue, C.; Lee, W.-J. Coordinated control strategy of wind turbine generator and energy storage equipment for frequency support. IEEE Trans. Ind. Appl. 2013, 51, 2732–2742. [Google Scholar] [CrossRef]
- Alam, M.J.E.; Muttaqi, K.M.; Sutanto, D. Mitigation of rooftop solar pv impacts and evening peak support by managing available capacity of distributed energy storage systems. IEEE Trans. Power Syst. 2013, 28, 3874–3884. [Google Scholar] [CrossRef] [Green Version]
- Nazariet, M.H.; Costello, Z.; Feizollahi, M.J.; Grijalva, S.; Egerstedt, M. Distributed frequency control of prosumer-based electric energy systems. IEEE Trans. Power Syst. 2014, 29, 2934–2942. [Google Scholar]
- Moghadam, M.R.V.; Ma, R.T.B.; Zhang, R. Distributed frequency control in smart grids via randomized demand response. IEEE Trans. Smart Grid 2014, 5, 2798–2809. [Google Scholar] [CrossRef]
- Xing, L.; Mishra, Y.; Tian, Y.; Ledwich, G.; Su, H.; Peng, C.; Fei, M. Dual consensus based distributed frequency control for multiple energy storage systems. IEEE Trans. Smart Grid 2019, 10, 6396–6403. [Google Scholar] [CrossRef]
- Yu, L.; Chu, C. Consensus-based secondary frequency and voltage droop control of virtual synchronous generators for Isolated AC Microgrids. IEEE J. Emerg. Sel. Top. Circuits Syst. 2015, 5, 443–455. [Google Scholar]
- Baros, S.; Llic, M.D. A Consensus approach to real-time distributed control of energy storage systems in wind farms. IEEE Trans. Smart Grid 2019, 10, 613–625. [Google Scholar] [CrossRef]
- Nguyen, D.H.; Khazaei, J. Multi-agent time-delayed fast consensus design for distributed battery energy storage systems. IEEE Trans. Sustain. Energy 2018, 9, 1397–1406. [Google Scholar] [CrossRef]
- Pilloni, A.; Pisano, A.; Usai, E. Robust finite time frequency and voltage restoration of inverter-based microgrids via sliding mode cooperative control. IEEE Trans. Ind. Electron. 2018, 65, 907–917. [Google Scholar] [CrossRef]
- Hu, J.; Lanzon, A. Distributed finite time consensus control for heterogeneous battery energy storage systems in droop-controlled microgrids. IEEE Trans. Smart Grid 2019, 10, 4751–4761. [Google Scholar] [CrossRef] [Green Version]
- Sugihara, H.; Yokoyama, K.; Saeki, O.; Tsuji, K.; Funaki, T. Economic and efficient voltage management using customer-owned energy storage systems in a distribution network with high penetration of photovoltaic systems. IEEE Trans. Power Syst. 2013, 28, 102–111. [Google Scholar] [CrossRef]
- Giannitrapani, A.; Paoletti, S.; Vicino, A.; Zarrilli, D. Optimal allocation of energy storage systems for voltage control in LV distribution networks. IEEE Trans. Smart Grid 2017, 8, 2859–2870. [Google Scholar] [CrossRef]
- Zarrilli, D.; Giannitrapani, A.; Paoletti, S.; Vicino, A. Energy storage operation for voltage control in distribution networks: A receding horizon approach. IEEE Trans. Control Syst. Technol. 2018, 26, 599–609. [Google Scholar] [CrossRef]
- Sami, S.S.; Cheng, M.; Wu, J.; Jenkins, N. A Virtual energy storage system for voltage control of distribution networks. CSEE J. Power Energy Syst. 2018, 4, 146–154. [Google Scholar] [CrossRef]
- Wang, L.; Liang, D.H.; Crossland, A.F.; Taylor, P.C.; Jones, D.; Wade, N.S. Coordination of multiple energy storage units in a low-voltage distribution network. IEEE Trans. Smart Grid 2015, 6, 2906–2918. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.; Tan, K.T.; Peng, X.Y.; So, P.L. Coordinated control of distributed energy-storage systems for voltage regulation in distribution networks. IEEE Trans. Power Deliv. 2016, 31, 1132–1141. [Google Scholar] [CrossRef]
- Jayasekara, N.; Masoum, M.A.; Wolfs, P.J. Optimal operation of distributed energy storage systems to improve distribution network load and generation hosting capability. IEEE Trans. Sustain. Energy 2016, 7, 250–261. [Google Scholar] [CrossRef]
- Rigas, A.; Messinis, G.; Hatziargyriou, N. Application of topology identification on optimal bess sizing in distribution systems. In Proceedings of the 8th IEEE PES Innovative Smart Grid Technologies Conference Europe-ISGT Europe 2018, Sarajevo, Bosnia and Herzegovina, 21–25 October 2018. [Google Scholar]
- Jahromi, A.A.; Majzoobi, A.; Khodaei, A.; Bahramirad, S.; Zhang, L.; Paaso, A.; Lelic, M.; Flinn, D. Battery Energy storage requirements for mitigating PV output fluctuations. In Proceedings of the 8th IEEE PES Innovative Smart Grid Technologies Conference Europe-ISGT Europe 2018, Sarajevo, Bosnia and Herzegovina, 21–25 October 2018. [Google Scholar]
- Zhang, Y.; Ren, S.; Dong, Z.Y.; Xu, Y.; Meng, K.; Zheng, Y. Optimal placement of battery energy storage in distribution networks considering conservation voltage reduction and stochastic load composition. IET Gener. Transm. Distrib. 2017, 11, 3862–3870. [Google Scholar] [CrossRef]
- Yue, M.; Wang, X. Grid Inertial response-based probabilistic determination of Energy Storage System Capacity under high solar penetration. IEEE Trans. Sustain. Energy 2015, 6, 1039–1049. [Google Scholar] [CrossRef]
- Wang, Y.; Xu, Y.; Tang, Y.; Liao, K.; Syed, M.H.; Guillo-Sansano, E.; Burt, G. Aggregated energy storage for power system frequency control: A finite-time consensus approach. IEEE Trans. Smart Grid 2019, 10, 3675–3686. [Google Scholar] [CrossRef]
- Vasilj, J.; Gros, S.; Jakus, D.; Sarajcev, P. Multi-market scheduling of battery storages within renewable portfolios. In Proceedings of the 8th IEEE PES Innovative Smart Grid Technologies Conference Europe-ISGT Europe 2018, Sarajevo, Bosnia and Herzegovina, 21–25 October 2018. [Google Scholar]
- Laaksonen, H.; Sirviö, K.; Aflecht, S.; Hovila, P. Multi-objective active network management scheme studied in sundom smart grid with MV and LV network connected DER units. In Proceedings of the 25th International Conference on Electricity Distribution-CIRED 2019, Madrid, Spain, 3–6 June 2019. [Google Scholar]
- Laaksonen, H.; Hovila, P.; Kauhaniemi, K.; Sirviö, K. Advanced islanding detection in grid interactive microgrids. In Proceedings of the CIRED 2018 Workshop, Ljubljana, Slovenia, 7–8 June 2018. [Google Scholar]
- Laaksonen, H.; Hovila, P.; Kauhaniemi, K. Combined islanding detection scheme utilizing active network management for future resilient distribution networks. In Proceedings of the IET 14th International Conference on Developments in Power System Protection-DPSP 2018, Belfast, UK, 12–15 March 2018. [Google Scholar]
- Laaksonen, H. Reliable islanding detection with active MV network management. In Proceedings of the CIRED 2014 Workshop, Rome, Italy, 11–12 June 2014. [Google Scholar]
- EU Commission. Commission Regulation (EU) 2016/631 of 14 April 2016 Establishing A Network Code on Requirements for Grid Connection of Generators. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:JOL_2016_112_R_0001#d1e8030-1-1 (accessed on 12 May 2020).
- Faranda, R.S.; Hafezi, H.; Leva, S.; Mussetta, M.; Ogliari, E. The optimum PV plant for a given solar DC/AC converter. Energies 2015, 8, 4853–4870. [Google Scholar] [CrossRef]
- Standard EN 50160. Voltage Characteristics of Electricity Supplied by Public Electricity Networks; European Committee for Electrotechnical Standardization (CENELEC): Brussels, Belgium, 2010. [Google Scholar]
Previous Studies [39] | Research Work in the Paper |
---|---|
− | 0. Initial choices (based on [39] findings)
|
1. Type of DER units
| 1. Type of DER units
|
2. Droops and control functions of DER units
| 2. Droops and control functions of DER units
|
3. Focus on
| 3. Focus on
|
– | 4. In addition, this paper presents new real-time adaptive management schemes (Section 4) for
|
Case | WTs **) | PVs **) | BESSs **) | QU-Droop of PVs/BESSs | PU-Droop of PVs/BESSs | Pf-Droop of PVs/BESSs | PU-Blocking with PVs/BESSs | OLTC Set Value (kV)/(pu) |
---|---|---|---|---|---|---|---|---|
1a | 1 | 5 | 0 | 1/− | 1/− | 1/− | No | 20.7/1.035 |
1b | 1 | 5 | 0 | 1/− | 1/− | 1/− | No | 20.0/1.0 |
2a | 1 | 5 | 0 | 1/− | 1/− | 1/− | Yes | 20.7/1.035 |
2b | 1 | 5 | 0 | 1/− | 1/− | 1/− | Yes | 20.0/1.0 |
3a | 1 | 5 | 0 | 2/− | 1/− | 2/− | Yes | 20.7/1.035 |
3b | 1 | 5 | 0 | 2/− | 1/− | 2/− | Yes | 20.0/1.0 |
4a | 1 | 5 | 2 | 1/1 | 1/1 | 1/1 | Yes | 20.0/1.0 |
4b *) | 1 | 5 | 2 | 1/1 | 1/1 | 1/1 | Yes | 20.0/1.0 |
5a | 1 | 5 | 3 | 1/1 | 1/1 | 1/1 | No | 20.0/1.0 |
5b | 1 | 5 | 3 | 1/1 | 1/1 | 1/1 | Yes | 20.0/1.0 |
Time (s) | Initial Condition/Action |
---|---|
t = 0–110 | PV unit active power (PPV) is at nominal *) i.e., 300 or 450 kW |
t = 10.5 | WT active power (PWT) increases from initial 0.5 to 1.5 MW *) |
t = 15.0 | WT active power (PWT) increases from 1.5 to 2.5 MW *) |
t = 19.0 | WT active power (PWT) increases from 2.5 to 3.6 MW *) |
t = 30–40 | Over-frequency situation (50.07 Hz) **) |
t = 50–60 | Over-frequency situation (50.25 Hz) **) |
t = 70–80 | Under-frequency situation (49.9 or 49.4 Hz) **) |
t = 90–100 | Under-frequency situation (49.8 or 49.4 Hz) **) Part of the load participates in demand response and disconnects from the LV network |
Other issues | In all study cases of Table 2 PVs QU-droop is primary and PU-droop secondary local voltage control method |
Case | Load Level | PPV_2, QPV_2, UPV_2 Steady-State, t = 25 s (kW, kVAr, pu) | PPV_2, QPV_2, UPV_2 50.07 Hz, t = 35 s (kW, kVAr, pu) | PPV_2, QPV_2, UPV_2 50.25 Hz, t = 55 s (kW, kVAr, pu) | PPV_2, QPV_2, UPV_2 49.8 Hz, t = 95 s *) (kW, kVAr, pu) |
---|---|---|---|---|---|
1a | Low | 243, −150, 1.056 | 236, −150, 1.054 | 0, 0, 1.006 | 213, −150, 1.057 |
1b | Low | 316, −150, 1.053 | 310, −150, 1.051 | 0, 0, 0.976 | 286, −150, 1.054 |
2a | Low | 243, −150, 1.056 | 190, −150, 1.038 | 0, 0, 1.006 | 213, −150, 1.057 |
2b | Low | 316, −150, 1.053 | 264, −150, 1.035 | 0, 0, 0.976 | 286, −150, 1.054 |
3a | Low | 241, −150, 1.056 | 242, −150, 1.056 | 0, 0, 1.006 | 210, −150, 1.057 |
3b | Low | 316, −150, 1.053 | 317, −150, 1.053 | 0, 10, 0.979 | 286, −150, 1.054 |
4a | Low | 316, −150, 1.053 | 264, −150, 1.035 | 0, 0, 0.974 | 281, −150, 1.055 |
4b | Low | 334, −150, 1.052 | 282, −149, 1.035 | 0, 0, 0.974 | 298, −150, 1.054 |
5a | Low | 450, −150, 1.046 | 390, −150, 1.039 | 0, 140, 0.950 | 363, −150, 1.051 |
5b | Low | 450, −150, 1.046 | 390, −139, 1.033 | 0, 150, 0.935 | 245, −150, 1.056 |
Case | Load Level | PBESS_1, QBESS_1 Steady-State, t = 25 s (kW, kVAr) | PBESS_1, QBESS_1 50.07 Hz, t = 35 s (kW, kVAr) | PBESS_1, QBESS_1 50.25 Hz, t = 55 s (kW, kVAr) | PBESS_1, QBESS_1 49.8 Hz, t = 95 s (kW, kVAr) |
---|---|---|---|---|---|
4a | Low | 0, 0 | −27, 0 | −200, 0 | 200, 0 |
4b | Low | −200, −67 | −200, 0 | −200, −67 | 0, −67 |
5a | Low | 0, 0 | −27, 0 | −200, 0 | 200, 0 |
5b | Low | 0, 0 | −27, 0 | −200, 0 | 200, 0 |
Case | Load Level | PBESS_3, QBESS_3 Steady-State, t = 25 s (kW, kVAr) | PBESS_3, QBESS_3 50.07 Hz, t = 35 s (kW, kVAr) | PBESS_3, QBESS_3 50.25 Hz, t = 55 s (kW, kVAr) | PBESS_3, QBESS_3 49.8 Hz, t = 95 s (kW, kVAr) |
---|---|---|---|---|---|
5a | Low | −93, −67 | −52, −67 | −165, 62 | −25, −67 |
5b | Low | −93, −67 | −84, −62 | −200, 67 | 108, −67 |
Case | PV Droop | Load Level | PPV_2, QPV_2, UPV_2 Steady-State, t = 25 s (kW, kVAr, pu) | PPV_2, QPV_2, UPV_2 49.4 Hz, t = 75 s (kW, kVAr, pu) | PPV_2, QPV_2, UPV_2 49.4 Hz, t = 95 s *) (kW, kVAr, pu) |
---|---|---|---|---|---|
2b | 1 | Low | 316, −150, 1.053 | 319, −150, 1.053 | 285, −150, 1.054 |
7a | 2 | Low | 316, −150, 1.053 | 450, −150, 1.095 | 426, −150, 1.100 |
7a | 3 | Low | 316, −150, 1.053 | 450, −150, 1.095 | 416, −150, 1.098 |
5a | 1 | Low | 450, −150, 1.046 | 395, −150, 1.050 | 362, −150, 1.051 |
7b | 2 | Low | 450, −150, 1.046 | 400, −150, 1.103 | 376, −150, 1.106 |
7b | 3 | Low | 450, −150, 1.046 | 448, −150, 1.097 | 406, −150, 1.098 |
Case | BESS Droop | Load Level | PBESS_3, QBESS_3 Steady-state, t = 25 s (kW, kVAr) | PBESS_3, QBESS_3 49.4 Hz, t = 75 s (kW, kVAr) | PBESS_3, QBESS_3 49.4 Hz, t = 95 s (kW, kVAr) |
---|---|---|---|---|---|
5a | 1 | Low | −93, −67 | −25, −67 | −25, −67 |
7b | 2 | Low | −93, −67 | 144, −67 | 135, −67 |
7b | 3 | Low | −93, −67 | 76, −67 | 76, −67 |
Case | ΔPPV_2 49.4 Hz, t = 75 s (kW) | ΔPPV_2 49.4 Hz, t = 95 s *) (kW) | ΔPBESS_3 49.4 Hz, t = 75 s (kW) | ΔPBESS_3 49.4 Hz, t = 95 s *) (kW) | ΔPTOTAL_75s **) 49.4 Hz, t = 75 s (kW) | ΔPTOTAL_95s ***) 49.4 Hz, t = 95 s *) (kW) |
---|---|---|---|---|---|---|
2b | +3 | −31 | − | − | +3 | −31 |
7a | +134 | +110 | − | − | +134 | +110 |
7a | +134 | +100 | − | − | +134 | +100 |
5a | −55 | −88 | +68 | +68 | +13 | −20 |
7b | −50 | −74 | +237 | +228 | +187 | +154 |
7b | −2 | −44 | +169 | +169 | +167 | +125 |
DER Unit(s) | Without PU-Blocking | With PU-Blocking | Frequency Adaptive PU-Droops (Without PU-Blocking) |
---|---|---|---|
PV (only) | Over-frequency support can be limited | Better over-frequency support | No difference depending on the PU-droop settings and over-voltage limits in the logic (during under-frequencies) |
PV + BESS (at the same connection point) | During under-frequency events small PV curtailment + smaller BESS contribution | During under-frequencies PV curtailment larger + BESS contribution larger | Smaller PV curtailment + smaller BESS contribution with lower over-voltage limit on the PU-droop and implementation logic (during under-frequencies) |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Laaksonen, H.; Parthasarathy, C.; Hafezi, H.; Shafie-khah, M.; Khajeh, H.; Hatziargyriou, N. Solutions to Increase PV Hosting Capacity and Provision of Services from Flexible Energy Resources. Appl. Sci. 2020, 10, 5146. https://doi.org/10.3390/app10155146
Laaksonen H, Parthasarathy C, Hafezi H, Shafie-khah M, Khajeh H, Hatziargyriou N. Solutions to Increase PV Hosting Capacity and Provision of Services from Flexible Energy Resources. Applied Sciences. 2020; 10(15):5146. https://doi.org/10.3390/app10155146
Chicago/Turabian StyleLaaksonen, Hannu, Chethan Parthasarathy, Hossein Hafezi, Miadreza Shafie-khah, Hosna Khajeh, and Nikos Hatziargyriou. 2020. "Solutions to Increase PV Hosting Capacity and Provision of Services from Flexible Energy Resources" Applied Sciences 10, no. 15: 5146. https://doi.org/10.3390/app10155146
APA StyleLaaksonen, H., Parthasarathy, C., Hafezi, H., Shafie-khah, M., Khajeh, H., & Hatziargyriou, N. (2020). Solutions to Increase PV Hosting Capacity and Provision of Services from Flexible Energy Resources. Applied Sciences, 10(15), 5146. https://doi.org/10.3390/app10155146