Smart Load Management with Energy Storage for Power Quality Enhancement in Wind-Powered Oil and Gas Applications
Abstract
:1. Introduction
- a demonstration of the benefits to power quality that a smart load management together with an ESS can provide in a wind-powered OOGP;
- a control strategy for the ESS that provides inertial and voltage support to the electrical grid during transients and reduces frequency and voltage variations significantly;
- a simplified dynamic model of the HES that can be used to optimally design an ESS and dispatch available power sources.
2. The Case Study
Energy Storage System
- the storage device provides a constant voltage to the DC link of the VSC;
- the EMS can change the setpoint of the GT to charge or discharge the ESS;
- the state of charge (SOC) is ignored, as it is assumed the EMS can maintain a minimum SOC to guarantee inertial support to the electrical grid.
3. Simulation Results
- When analyzing values, one will notice that the proposed controller is not able to substantially improve these indicators. This can be justified by the fact that the ESS has a relatively small power capacity (0.18 pu) when compared to the rated power of the WT (0.36 pu) and GT (1 pu);
- When considering values, the proposed controller reduces frequency deviations considerably (−23.1%). This is visually confirmed in Figure 4 when comparing the amount of time outside the continuous operation range in both cases;
- When analyzing values, one will notice that the proposed controller is not able at all to improve these indicators. This is justified by the fact that the controller bandwidth is not large enough to counteract the voltage spikes induced by abrupt power flow changes;
- Improvements in with the ESS are also reflected in ;
- For the case without ESS, the peak rate of change of the GT mechanical power is very close to the maximum allowed value for a typical GT used in OOGP (e.g., 0.15 pu/s for GE LM2500 [38]). This is greatly alleviated with the ESS (−26.2%), which confirms the controller efficacy in transient inertial support;
- For the case with ESS, the ESS energy throughput is 4.96 kWh during the 10 MW load switch on (with wind variations), and 4.17 kWh during the 10 MW load switch off (no wind variations). It is interesting to notice that wind variability affects considerably (+18.9%) the energy throughput during a disturbance;
- The difference between absolute values of the ESS active (p) and apparent (s) power are minimal (RMSE = 0.0065 pu).
4. Simplified Model
4.1. Model Validation
- For both scenarios, some steady-state errors are observed. This is clearly seen in Figure 8 in the offset of the , and in Figure 9 in the offset of the curves. Those also affect , as they are correlated by Equation (12). This is expected, as the simplified model considers only power flows and ignores ohmic losses;
- The simplified model approximates frequency dynamics really well for both scenarios, as seen by the negligible values of , as well as very close to 1;
- The simplified model gives considerable absolute errors in both scenarios for variables that are influenced by the voltage dynamics, namely . This is seen on the metrics . This is expected, as the simplified model ignores voltage dynamics, hence voltage spikes and power oscillations are not captured. However, are close to 1, which shows that the goodness of fit of the simplified model for those variables is accurate;
- The GT mechanical power and ESS active power are very well captured by the simplified model. Absolute errors present small values, i.e., . On the other hand, while gives an almost perfect goodness of fit, is penalized by the steady-state error;
- For the scenario with ESS, the ESS energy throughput is 4.51 kWh during the 10 MW load switch on, and 4.53 kWh during the 10 MW load switch off. This represents respectively errors of −9.1% and +8.6% when compared to the detailed model. The reason for those large deviations is the steady-state error between in both models;
- The required simulation steps are 20 µs for the detailed model and 1ms for the simplified model. Moreover, the latter has no VSCs and their associated control loops, what reduces dramatically the model complexity and total simulation time. For reference purposes, the detailed and simplified model run respectively in 694 and 1.1 s in a laptop equipped with MATLAB 2018a, an Intel Core i7-8650U CPU at 2.11 GHz, and 16 GB of RAM.
5. Discussion and Further Work
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
EMS | energy management system |
ESS | energy storage system |
GHG | greenhouse gas |
GT | gas turbine |
HES | hybrid energy system |
NMSE | normalized mean square error |
OOGP | offshore oil and gas platform |
PCC | point of common coupling |
RMSE | root mean square error |
SG | synchronous generator |
SOC | state of charge |
VSC | voltage source converter |
WT | wind turbine |
Appendix A
Parameter | Value | Unit | Parameter | Value | Unit |
---|---|---|---|---|---|
Base values | Simulink configurations | ||||
Apparent power | 25 | MVA | Simulink solver | Fixed-step ode4 | - |
Line voltage | 11 | kV | powergui discrete solver | TBE | - |
Frequency | 50 | Hz | |||
SG | Exciter | ||||
Apparent power | 1 | pu | IEEE421.5 [41] Type | AC1C | - |
Number of poles | 2 | pairs | Ka | 20 | pu/pu |
Shunt resistance | 0.002 | pu | Ke | 0.8 | pu/pu |
Shunt reactance | 0.3 | pu | Kf | 0.01 | pu/pu |
Inertia constant | 1.85 | s | Ta | 0.02 | s |
Damping factor | 7.04 | pu/pu | Tb | 0.2 | s |
Tc | 1.2 | s | |||
Te | 0.5 | s | |||
Tf | 0.35 | s | |||
Turbine governor | Fixed loads rated power | ||||
Permanent droop | 0.8222 | pu/pu | Load 1 | (0.4727 + j0.0834) | pu |
Actuator delay | 2.25 | s | Load 2 | (0.3464 + j0.2000) | pu |
Wind speed | Flexible load setpoint | ||||
Low-pass filter time constant | 1.2 | s | Low-pass filter time constant | 1 | s |
WT VSC | ESS and flexible load VSCs | ||||
Apparent power | 0.36 | pu | Apparent power | 0.18 | pu |
Filter inductance | 12.6 | mH | Filter inductance | 25.2 | mH |
Filter shunt resistance | 121 | mΩ | Filter shunt resistance | 242 | mΩ |
Filter capacitance | 4 | µF | |||
Filter parallel resistance | 13.2 | ||||
dq current proportional gain | 0.5556 | pu/pu | dq current proportional gain | 0.5556 | pu/pu |
dq current integral gain | 5.3333 | pu/s | dq current integral gain | 5.3333 | pu/s |
ESS frequency controller | ESS voltage controller | ||||
Transient droop | 50 | pu/pu | Transient droop | 20 | pu/pu |
Transient reset time | 50 | s | Transient reset time | 50 | pu/pu |
Low-pass filter time | 0.001 | s | Low-pass filter time | 0.001 | s |
Integral gain | 0.2 | s | Integral gain | 0.2 | pu/s |
References
- Devold, H. Oil and Gas Production Handbook: An Introduction to Oil and Gas Production; OCLC: 1058182209; Lulu. com.: Morrisville, NC, USA, 2013. [Google Scholar]
- Bothner, T.M.; Høie, H. Utslipp av Klimagasser; Technical Report; Statistik Sentralbyrå: Oslo, Norway, 2018. [Google Scholar]
- Korpås, M.; Warland, L.; He, W.; Tande, J.O.G. A Case-Study on Offshore Wind Power Supply to Oil and Gas Rigs. Energy Procedia 2012, 24, 18–26. [Google Scholar] [CrossRef] [Green Version]
- He, W.; Uhlen, K.; Hadiya, M.; Chen, Z.; Shi, G.; del Rio, E. Case Study of Integrating an Offshore Wind Farm with Offshore Oil and Gas Platforms and with an Onshore Electrical Grid. J. Renew. Energy 2013, 2013, 607165. [Google Scholar] [CrossRef]
- Kolstad, M.L.; Årdal, A.R.; Sharifabadi, K.; Undeland, T.M. Integrating Offshore Wind Power and Multiple Oil and Gas Platforms to the Onshore Power Grid Using VSC-HVDC Technology. Mar. Technol. Soc. J. 2014, 48, 31–44. [Google Scholar] [CrossRef] [Green Version]
- Riboldi, L.; Nord, L.O. Concepts for Lifetime Efficient Supply of Power and Heat to Offshore Installations in the North Sea. Energy Convers. Manag. 2017, 148, 860–875. [Google Scholar] [CrossRef]
- Legorburu, I.; Johnson, K.R.; Kerr, S.A. Multi-Use Maritime Platforms—North Sea Oil and Offshore Wind: Opportunity and Risk. Ocean Coast. Manag. 2018, 160, 75–85. [Google Scholar] [CrossRef]
- Isaksen, E.A. Wind Farm Being Considered at Snorre and Gullfaks. Available online: https://www.equinor.com/en/news/27aug2018-hywind-tampen.html (accessed on 29 July 2019).
- Marvik, J.I.; Øyslebø, E.V.; Korpås, M. Electrification of Offshore Petroleum Installations with Offshore Wind Integration. Renew. Energy 2013, 50, 558–564. [Google Scholar] [CrossRef]
- Sanchez, S.; Tedeschi, E.; Silva, J.; Jafar, M.; Marichalar, A. Smart Load Management of Water Injection Systems in Offshore Oil and Gas Platforms Integrating Wind Power. IET Renew. Power Gener. 2017, 11, 1153–1162. [Google Scholar] [CrossRef]
- IEEE Std 1547.4-2011: IEEE Guide for Design, Operation, and Integration of Distributed Resource Island Systems with Electric Power Systems; OCLC: 762137491; IEEE: New York, NY, USA, 2011.
- IEEE Std 2030.2-2015: IEEE Guide for the Interoperability of Energy Storage Systems Integrated with the Electric Power Infrastructure; OCLC: 923758286; IEEE: New York, NY, USA, 2015.
- Chen, S.X.; Gooi, H.B.; Wang, M.Q. Sizing of Energy Storage for Microgrids. IEEE Trans. Smart Grid 2012, 3, 142–151. [Google Scholar] [CrossRef]
- Bahramirad, S.; Reder, W.; Khodaei, A. Reliability-Constrained Optimal Sizing of Energy Storage System in a Microgrid. IEEE Trans. Smart Grid 2012, 3, 2056–2062. [Google Scholar] [CrossRef]
- Dong, J.; Gao, F.; Guan, X.; Zhai, Q.; Wu, J. Storage-Reserve Sizing With Qualified Reliability for Connected High Renewable Penetration Micro-Grid. IEEE Trans. Sustain. Energy 2016, 7, 732–743. [Google Scholar] [CrossRef]
- Fossati, J.P.; Galarza, A.; Martín-Villate, A.; Fontán, L. A Method for Optimal Sizing Energy Storage Systems for Microgrids. Renew. Energy 2015, 77, 539–549. [Google Scholar] [CrossRef]
- Wong, L.A.; Ramachandaramurthy, V.K.; Taylor, P.; Ekanayake, J.; Walker, S.L.; Padmanaban, S. Review on the Optimal Placement, Sizing and Control of an Energy Storage System in the Distribution Network. J. Energy Storage 2019, 21, 489–504. [Google Scholar] [CrossRef]
- IEEE Std 1547-2018: IEEE Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces; OCLC: 1048178696; IEEE: New York, NY, USA, 2018.
- Aghamohammadi, M.R.; Abdolahinia, H. A New Approach for Optimal Sizing of Battery Energy Storage System for Primary Frequency Control of Islanded Microgrid. Int. J. Electr. Power Energy Syst. 2014, 54, 325–333. [Google Scholar] [CrossRef]
- Ulleberg, Ø. The Importance of Control Strategies in PV–Hydrogen Systems. Sol. Energy 2004, 76, 323–329. [Google Scholar] [CrossRef]
- Dufo-López, R.; Bernal-Agustín, J.L.; Contreras, J. Optimization of Control Strategies for Stand-Alone Renewable Energy Systems with Hydrogen Storage. Renew. Energy 2007, 32, 1102–1126. [Google Scholar] [CrossRef]
- Bernal-Agustín, J.L.; Dufo-López, R. Simulation and Optimization of Stand-Alone Hybrid Renewable Energy Systems. Renew. Sustain. Energy Rev. 2009, 13, 2111–2118. [Google Scholar] [CrossRef]
- MET Norway. Frost API. Available online: https://frost.met.no/index.html (accessed on 29 July 2019).
- Nielsen, F.G. Hywind—From Idea to World’s First Wind Farm Based upon Floaters. Available online: https://www.uib.no/sites/w3.uib.no/files/attachments/hywind_energy_lab.pdf (accessed on 29 July 2019).
- Pappala, V.; Erlich, I.; Rohrig, K.; Dobschinski, J. A Stochastic Model for the Optimal Operation of a Wind-Thermal Power System. IEEE Trans. Power Syst. 2009, 24, 940–950. [Google Scholar] [CrossRef]
- Kuznetsova, E.; Li, Y.F.; Ruiz, C.; Zio, E. An Integrated Framework of Agent-Based Modelling and Robust Optimization for Microgrid Energy Management. Appl. Energy 2014, 129, 70–88. [Google Scholar] [CrossRef]
- Chapaloglou, S.; Nesiadis, A.; Iliadis, P.; Atsonios, K.; Nikolopoulos, N.; Grammelis, P.; Yiakopoulos, C.; Antoniadis, I.; Kakaras, E. Smart Energy Management Algorithm for Load Smoothing and Peak Shaving Based on Load Forecasting of an Island’s Power System. Appl. Energy 2019, 238, 627–642. [Google Scholar] [CrossRef]
- Guerrero, J.M.; Vasquez, J.C.; Matas, J.; de Vicuna, L.G.; Castilla, M. Hierarchical Control of Droop-Controlled AC and DC Microgrids—A General Approach Toward Standardization. IEEE Trans. Ind. Electron. 2011, 58, 158–172. [Google Scholar] [CrossRef]
- Suul, J.A.; Molinas, M.; Norum, L.; Undeland, T. Tuning of Control Loops for Grid Connected Voltage Source Converters. In Proceedings of the 2008 IEEE 2nd International Power and Energy Conference, Johor Bahru, Malaysia, 1–3 December 2008; IEEE: Piscataway, NJ, USA, 2008; pp. 797–802. [Google Scholar] [CrossRef]
- Franklin, G.F.; Powell, J.D.; Emami-Naeini, A. Feedback Control of Dynamic Systems, 6th ed.; OCLC: 845656750; Pearson: Upper Saddle River, NJ, USA, 2010. [Google Scholar]
- Patel, R.; Bhatti, T.S.; Kothari, D.P. Improvement of Power System Transient Stability Using Fast Valving: A Review. Electr. Power Components Syst. 2001, 29, 927–938. [Google Scholar] [CrossRef]
- Lewis-Beck, C.; Lewis-Beck, M. Applied Regression: An Introduction; SAGE Publications, Inc.: Thousand Oaks, CA, USA, 2016. [Google Scholar] [CrossRef]
- NORSOK E-001:2016 Electrical Systems, 6th ed.; NTS: Oslo, Norway, 2016.
- IEC 61892:2015 Mobile and Fixed Offshore Units—Electrical Installations, 3rd ed.; IEC: Geneva, Switzerland, 2015.
- IEEE Std C50.13-2014: IEEE Standard for Cylindrical-Rotor 50 Hz and 60 Hz Synchronous Generators Rated 10 MVA and Above; OCLC: 1096667824; IEEE: Piscataway, NJ, USA, 2014.
- IEEE Std C37.102: IEEE Guide for AC Generator Protection; IEEE: New York, NY, USA, 2006. [CrossRef]
- IEEE Std C37.106: IEEE Guide for Abnormal Frequency Protection for Power Generating Plants; IEEE: New York, NY, USA, 2004. [CrossRef]
- Badeer, G.H. GE Aeroderivative Gas Turbines-Design and Operating Features; GE Power Systems: Evendale, OH, USA, 2000. [Google Scholar]
- Mahmoud, M.; Azher Hussain, S.; Abido, M. Modeling and Control of Microgrid: An Overview. J. Frankl. Inst. 2014, 351, 2822–2859. [Google Scholar] [CrossRef]
- Machowski, J.; Bialek, J.W.; Bumby, J.R. Power System Dynamics: Stability and Control, 2nd ed.; OCLC: Ocn232130756; Wiley: Chichester, UK, 2008. [Google Scholar]
- IEEE Std 421.5-2016: IEEE Recommended Practice for Excitation System Models for Power System Stability Studies, 2016th ed.; IEEE: New York, NY, USA, 2016.
Metric | Without ESS | With ESS | Difference |
---|---|---|---|
1.030 pu | 1.021 pu | −0.9% | |
0.968 pu | 0.979 pu | 1.1% | |
0.009 pu | 0.007 pu | −23.1% | |
1.086 pu | 1.086 pu | 0% | |
0.910 pu | 0.910 pu | 0% | |
0.005 pu | 0.003 pu | −37.6% | |
1.086 pu | 1.086 pu | 0% | |
0.910 pu | 0.910 pu | 0% | |
0.010 pu | 0.007 pu | −26.9% | |
0.071 pu | 0.056 pu | −21.8% | |
0.130 pu/s | 0.096 pu/s | −26.2% |
Metric | Without ESS | With ESS | ||
---|---|---|---|---|
[pu] | [%] | [pu] | [%] | |
0.003 | 0.4 | 0.008 | 1.2 | |
−0.002 | −0.8 | 0.006 | 2.7 | |
0.999 | - | 0.996 | - | |
0.044 | 6.1 | 0.071 | 10.0 | |
0.009 | 6.00 | 0.026 | 16.7 | |
0.997 | - | 0.995 | - | |
−0.017 | −4.4 | −0.017 | −4.4 | |
0.014 | 10.8 | 0.014 | 10.8 | |
0.998 | - | 0.998 | ||
- | - | −0.002 | −1.3 | |
- | - | 0.000 | 0 | |
- | - | 0.955 | - | |
0.001 | 0.1 | 0.000 | 0 | |
0.000 | 0 | 0.001 | 0.1 | |
0.998 | - | 0.993 |
© 2019 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
Alves, E.; Sanchez, S.; Brandao, D.; Tedeschi, E. Smart Load Management with Energy Storage for Power Quality Enhancement in Wind-Powered Oil and Gas Applications. Energies 2019, 12, 2985. https://doi.org/10.3390/en12152985
Alves E, Sanchez S, Brandao D, Tedeschi E. Smart Load Management with Energy Storage for Power Quality Enhancement in Wind-Powered Oil and Gas Applications. Energies. 2019; 12(15):2985. https://doi.org/10.3390/en12152985
Chicago/Turabian StyleAlves, Erick, Santiago Sanchez, Danilo Brandao, and Elisabetta Tedeschi. 2019. "Smart Load Management with Energy Storage for Power Quality Enhancement in Wind-Powered Oil and Gas Applications" Energies 12, no. 15: 2985. https://doi.org/10.3390/en12152985