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WIREs Energy Environ.

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Handling renewable energy variability and uncertainty in power systems operation

Advanced Review

Ricardo Bessa, Carlos Moreira, Bernardo Silva, Manuel Matos

Published Online: Feb 21 2013

DOI: 10.1002/wene.76

Full article on Wiley Online Library: HTML PDF

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The concerns about global warming (greenhouse‐gas emissions), scarcity of fossil fuels reserves, and primary energy independence of regions or countries have led to a dramatic increase of renewable energy sources (RES) penetration in electric power systems, mainly wind and solar power. This created new challenges associated with the variability and uncertainty of these sources. Handling these two characteristics is a key issue that includes technological, regulatory, and computational aspects. Advanced tools for handling RES maximize the resultant benefits and keep the reliability indices at the required level. Recent advances in forecasting and management algorithms provided means to manage RES. Forecasts of renewable generation for the next hours/days play a crucial role in the management tools and protocols of the system operator. These forecasts are used as input for setting reserve requirements and performing the unit commitment (UC) and economic dispatch (ED) processes. Probabilistic forecasts are being included in the management tools, enabling a move from deterministic to stochastic methods, which conduct to robust solutions. On the technological side, advances to increase mid‐merit and base‐load generation flexibility should be a priority. The use of storage devices to mitigate uncertainty and variability is particularly valuable for isolated power system, whereas in interconnected systems, economic criteria might be a barrier to invest in new storage facilities. The possibility of sending active and reactive control set points to RES power plants offers more flexibility. Furthermore, the emergence of the smart grid concept and the increasing share of controllable loads contribute with flexibility to increase the RES penetration levels. This article is categorized under: Wind Power > Economics and Policy Energy Infrastructure > Systems and Infrastructure Energy Systems Analysis > Systems and Infrastructure

Generic FRT voltage versus time characteristic curve.

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Architecture of a wind power dispatch center.

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Wind power reduction in Portugal due to extreme wind speed conditions originated by the cyclone Klaus in January 2009 (the wind power generation values are only from telemetered wind farms, available in real time, and not the total wind power generation in Portugal). (Reproduced with permission from Ref 131. Copyright 2009, REN.)

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Example of the expected energy not supplied as a function of the reserve level for a look‐ahead time and taking into account different sources of uncertainty: conventional and load uncertainty; conventional, load and wind power uncertainty.

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Generation variability of solar power due to clouds: (a) hourly average; (b) 15 min average.

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(a) Forecast intervals, centered in the median, and limited by its lower and upper bounds, which are forecasted quantiles. (b) Twenty statistical‐based scenarios with temporal dependency of errors and that respect the marginal distribution of the probabilistic forecasts [i.e., plot in (a)].

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References

Holttinen, H. Wind integration: experience, issues, and challenges. WIREs Energy Environ 2012, 1:243–255.
Abbad, JR. Electricity market participation of wind farms: the success story of the Spanish pragmatism. Energy Policy 2010, 38:3174–3179.
Monteiro, C, Bessa, R, Miranda, V, Botterud, A, Wang, J, Conzelmann, G. Wind power forecasting: state‐of‐the‐art 2009. Argonne National Laboratory Report ANL/DIS‐10‐1, 2009. Available at: http://www.dis.anl.gov/pubs/65613.pdf. (Accessed January 2013).
Ela, E, O`Malley, M. Studying the variability and uncertainty impacts of variable generation at multiple timescales. IEEE Trans Power Syst 2012, 27:1324–1333.
Lalor, G, Mullane, A, O`Malley, M. Frequency control and wind turbine technologies. IEEE Trans Power Syst 2005, 20:1905–1913.
Mohseni, M, Islam, SM. Review of international grid codes for wind power integration: diversity, technology and a case for global standard. Renew Sustain Energy Rev 2012, 16:3876–3890.
Carrasco, JM, Franquelo, LG, Bialasiewicz, JT, Galván, E, Guisado, RCP, Prats, MÁM, León, JI, Moreno‐Alfonso, N. Power‐electronic systems for the grid integration of renewable energy sources: a survey. IEEE Trans Ind Electron 2006, 53:1002–1016.
Almeida, RGd, Lopes, JAP. Participation of doubly fed induction wind generators in system frequency regulation. IEEE Trans Power Syst 2007, 22:944–950.
Tsili, M, Papathanassiou, S. A review of grid code technical requirements for wind farms. IET Renew Power Gen 2009, 3:308–332.
Alatrash, H, Mensah, A, Mark, E, Amarin, R, Enslin, J. Generator emulation controls for photovoltaic inverters. In IEEE 8th International Conference on Power Electronics and ECCE Asia (ICPE %26 ECCE). Korea: The Shilla Jeju; August 30‐September 1, 2011.
Ramtharan, G, Ekanayake, JB, Jenkins, N. Frequency support from doubly fed induction generator wind turbines. IET Renew Power Gen 2007, 1:3–9.
Silva, B, Moreira, CL, Seca, L, Phulpin, Y, Lopes, JAP. Provision of inertial and primary frequency control services using offshore multiterminal HVDC networks. IEEE Trans Sustain Energy 2012, 3:800–808.
Kottick, D, Blau, M, Edelstein, D. Battery energy storage for frequency regulation in an island power system. IEEE Trans Energy Convers 1993, 8:455–459.
Aditya, SK, Das, D. Battery energy storage for load frequency control of an interconnected power system. Electric Power Syst Res 2001, 58:179–185.
Botterud, A, Wang, J, Miranda, V, Bessa, R. Wind power forecasting in U.S. electricity markets. Electric J 2010, 23:1040–6190.
Reikard, G, Pinson, P, Bidlot, J‐R. Forecasting ocean wave energy: the ECMWF wave model and time series methods. Ocean Eng 2011, 38:1089–1099.
Lorenz, E, Scheidsteger, T, Hurka, J, Heinemann, D, Kurz, C. Regional PV power prediction for improved grid integration. Prog Photovolt: Res Appl 2011, 19:757–771.
Taylor JW, McSharry PE. Short‐term load forecasting methods: an evaluation based on European data. IEEE Trans Power Syst 2007, 22:2213–2219.
Contaxis, GC, Kabouris, J. Short term scheduling in a wind/diesel autonomous energy system. IEEE Trans Power Syst 1991, 6:1161–1167.
Pinson, P, Madsen, H. Adaptive modelling and forecasting of offshore wind power fluctuations with Markov‐switching autoregressive models. J Forecasting 2012, 31:281–313.
Giebel, G, Badger, J, Louka, P, Kallos, G, Lac, C, Descombes, G, Palomares, A‐M, Perez, IM. Description of NWP, mesoscale and CFD models. Deliverable 4.1, EU Project ANEMOS; 2006. Available at: http://130.226.56.153/zephyr/publ/ANEMOS_D4.1_ModelDescription.pdf. (Accessed January 2013).
Larson, KA, Westrick, K. Short‐term wind forecasting using off‐site observations. Wind Energy 2006, 9:55–62.
Bessa, RJ, Miranda, V, Gama, J. Entropy and correntropy against minimum square error in offline and online three‐day ahead wind power forecasting. IEEE Trans Power Syst 2009, 24:1657–1666.
Thordarson, FÖ, Madsen, H, Nielsen, HA, Pinson, P. Conditional weighted combination of wind power forecasts. Wind Energy 2010, 13:751–763.
Siebert, N. Development of methods for regional wind power forecasting. PhD Thesis. Centre Energétique et Procédés, Mines ParisTech; 2008.
Focken, U, Lange, M, Mönnich, K, Waldl, H‐P, Beyer, HG, Luig, A. Short‐term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects. J Wind Eng Ind Aerodyn 2002, 90:231–246.
Bremnes, JB. Probabilistic wind power forecasts using local quantile regression. Wind Energy 2004, 7:47–54.
Bessa, RJ, Miranda, V, Botterud, A, Zhou, Z, Wang, J. Time‐adaptive quantile‐copula for wind power probabilistic forecasting. Renew Energy 2012, 40:29–39.
Castronuovo, ED, Lopes, JAP. On the optimization of the daily operation of a wind‐hydro power plant. IEEE Trans Power Syst 2004, 19:1599–1606.
Wang, J, Botterud, A, Bessa, R, Keko, H, Carvalho, L, Issicaba, D, Sumaili, J, Miranda, V. Wind power forecasting uncertainty and unit commitment. Appl Energy 2011, 88:4014–4023.
Pinson, P, Papaefthymiou, G, Klockl, B, Nielsen, HA, Madsen, H. From probabilistic forecasts to statistical scenarios of short‐term wind power production. Wind Energy 2009, 12:51–62.
Papaefthymiou, G, Pinson, P. Modeling of spatial dependence in wind power forecast uncertainty. In PMAPS 2008: 10th International Conference on Probabilistic Methods Applied to Power Systems. IEEE: Puerto Rico; May 25–29, 2008.
Leutbecher, M, Palmer, TN. Ensemble forecasting. J Comput Phys 2008, 227:3515–3539.
Ziehmann, C. Comparison of a single‐model EPS with a multi‐model ensemble consisting of a few operational models. Tellus A 2000, 52:280–299.
Nielsen, HA, Madsen, H, Nielsen, TS, Badger, J, Giebel, G, Landberg, L, Sattler, K, Feddersen, H. Wind power ensemble forecasting using wind speed and direction ensembles from ECMWF or NCEP. Denmark Project PSO, Technical Report, 2005. Available at: http://www.risoe.dtu.dk/rispubl/NEI/nei‐dk‐4552.pdf. (Accessed January 2013).
Cutler, NJ, Outhred, HR, MacGill, IF, Kay, MJ, Kepert, JD. Characterizing future large, rapid changes in aggregated wind power using numerical weather prediction spatial fields. Wind Energy 2008, 12:542–555.
Cutler, NJ, Outhred, HR, MacGill, IF, Kepert, JD. Predicting and presenting plausible future scenarios of wind power production from numerical weather prediction systems: a qualitative ex ante evaluation for decision making. Wind Energy 2012, 15:473–488.
Bossavy, A, Girard, R, Kariniotakis, G. Forecasting ramps of wind power production with numerical weather prediction ensembles. Wind Energy, 2013, 16:51–63.
Ferreira, C, Gama, J, Miranda, V, Botterud, A. Probabilistic ramp detection and forecasting for wind power prediction. In PMAPS 2012—International Conference on Probabilistic Methods Applied to Power Systems. IEEE: Istanbul, Turkey; June 10–14, 2012.
Mellit, A, Pavan, AM. A 24‐h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid‐connected PV plant at Trieste, Italy. Solar Energy 2010, 84:807–821.
Ahlstrom, M, Kankiewicz, A. Solar power forecasting. In Solar Power: Making High Penetration Possible Workshop. Iowa: Cedar Rapids; October 15, 2010.
Heinemann, D, Lorenz, E, Girodo, M. Forecasting of solar radiation. In: Dunlop, ED, Wald, L, Suri, M, eds. Solar Energy Resource Management for Electricity Generation from Local Level to Global Scale. New York: Nova Science Pub Inc.; 2006, 83–94.
Bannehr, L, Rohn, M, Warnecke, G. A functional analytic method to derive displacement vector fields from satellite image sequences. Int J Remote Sens 1996, 17:383–392.
Wittmann, M, Breitkreuz, H, Schroedter‐Homscheidt, M, Eck, M. Case studies on the use of solar irradiance forecast for optimized operation strategies of solar thermal power plants. IEEE J Select Top Appl Earth Observ Remote Sens 2008, 1:18–27.
Lorenz, E, Hurka, J, Heinemann, D, Beyer, HG. Irradiance forecasting for the power prediction of grid‐connected photovoltaic systems. IEEE J Special Top Earth Observ Remote Sens 2009, 2:2–10.
Lannoye, E, Flynn, D, O`Malley, M. Evaluation of power system flexibility. IEEE Trans Power Syst 2012, 27:922–931.
Harnessing Variable Renewables – A Guide to the Balancing Challenge. Paris, France: OECD/IEA; 2011.
Lokhov, A. Technical and economic aspects of load following with nuclear power plants. Nuclear Energy Agency; 2011. Available at: http://www.oecd‐nea.org/ndd/reports/2011/load‐following‐npp.pdf. (Accessed January 2013).
Auer, J, Keil, J. State‐of‐the‐art electricity storage systems. Deutsche Bank Research, 2012. Available at: http://www.dbresearch.com/PROD/DBR_INTERNET_EN‐PROD/PROD0000000000286166/State‐of‐the‐art+electricity+storage+systems%3A+Indispensable+elements+of+the+energy+revolution.PDF. (Accessed January 2013).
Nicolosi, M. Wind power integration and power system flexibility—an empirical analysis of extreme events in Germany under the new negative price regime. Energy Policy 2010, 38:7257–7268.
Soder, L, Abildgaard, H, Estanqueiro, A, Hamon, C, Holttinen, H, Lannoye, E, Gomez‐Lazaro, E, O`Malley, M, Zimmermann, U. Experience and challenges with short‐term balancing in European systems with large share of wind power. IEEE Trans Sustain Energy 2012, 3:853–861.
Gül, T, Stenzel, T. Variability of wind power and other renewables—management options strategies. International Energy Agency Report, 2005. Available at: http://www.uwig.org/IEA_Report_on_variability.pdf. (Accessed January 2013).
European wind integration study (EWIS) towards a successful integration of wind power into European electricity grids. Phase I Final Report; 2007. Available at: http://www.wind‐integration.eu/downloads/library/EWIS_Final_Report.pdf. (Accessed January 2013).
ENTSO‐E. Operating Handbook—Policies P1: Load‐Frequency Control and Performance; 2009. Available at: https://www.entsoe.eu/publications/system‐operations‐reports/operation‐handbook/. (Accessed January 2013).
Banakar, H, Luo, C, Ooi, BT. Impacts of wind power minute‐to‐minute variations on power system operation. IEEE Trans Power Syst 2008, 23:150–160.
Holttinen, H, Milligan, M, Ela, E, Menemenlis, N, Dobschinski, J, Rawn, B, Bessa, RJ, Flynn, D, Lazaro, EG, Detlefsen, N. Methodologies to determine operating reserves due to increased wind power. IEEE Trans Sustain Energy 2012, 3:713–723.
Rebours, Y. A comprehensive assessment of markets for frequency and voltage control ancillary services. PhD Thesis. University of Manchester, Faculty of Engineering and Physical Sciences; 2008.
Anstine, LT, Burke, RE, Casey, JE, Holgate, R, John, RS, Stewart, HG. Application of probability methods to the determination of spinning reserve requirements for the Pennsylvania–New Jersey–Maryland interconnection. IEEE Trans Power Apparatus Syst 1963, 82:720–735.
ERCOT. ERCOT methodologies for determining ancillary service requirements; 2011.
Secretaría General de Energía. REE P.O. 1.5—Establecimiento de la reserva para la regulación frecuencia‐potencia. BOE núm. 2006, 173:27473–27474.
Gouveia, E, Matos, MA. Evaluating operational risk in a power system with a large amount of wind power. Electric Power Syst Res 2009, 79:734–739.
Soder, L. Reserve margin planning in a wind‐hydro‐thermal power system. IEEE Trans Power Syst 1993, 8:564–571.
Holttinen, H. Impact of hourly wind power variations on the system operation in the Nordic countries. Wind Energy 2005, 8:197–218.
Doherty, R, O`Malley, M. New approach to quantify reserve demand in systems with significant installed wind capacity. IEEE Trans Power Syst 2005, 20:587–595.
Ortega‐Vazquez, MA, Kirschen, DS. Estimating the spinning reserve requirements in systems with significant wind power generation penetration. IEEE Trans Power Syst 2009, 24:114–124.
Lange, M. On the uncertainty of wind power predictions—analysis of the forecast accuracy and statistical distribution of errors. J Solar Energy Eng 2005, 127:177–194.
Bludszuweit, H, Dominguez‐Navarro, JA, Llombart, A. Statistical analysis of wind power forecast error. IEEE Trans Power Syst 2008, 23:983–991.
Bessa, RJ, Matos, MA. Comparison of probabilistic and deterministic approaches for setting operating reserve in systems with high penetration of wind power. In 7th Mediterranean Conference and Exhibition on Power Generation, Transmission, Distribution and Energy Conversion (Medpower). Agia Napa: Cyprus; November 7–10, 2010.
Matos, MA, Bessa, RJ. Setting the operating reserve using probabilistic wind power forecasts. IEEE Trans Power Syst 2011, 26:594–603.
Bessa, RJ, Matos, MA, Costa, IC, Bremermann, L, Franchin, IG, Pestana, R, Machado, N, Waldl, H‐P, Wichmann, C. Reserve setting and steady‐state security assessment using wind power uncertainty forecast: a case study. IEEE Trans Sustain Energy 2012, 3:827–836.
Menemenlis, N, Huneault, M, Robitaille, A. Computation of dynamic operating balancing reserve for wind power integration for the time‐horizon 1–48 hours. IEEE Trans Sustain Energy 2012, 3:692–702.
Maurer, C, Krahl, S, Weber, H. Dimensioning of secondary and tertiary control reserve by probabilistic methods. Eur Trans Electric Power 2009, 19:544–552.
Pahlow, M, Möhrlen, C, Jørgensen, JU. Application of cost functions for large scale integration of wind power using a multi‐scheme ensemble prediction technique. In: Castronuovo, ED, ed. Optimization Advances in Electric Power Systems. New York: Nova Science Publishers Inc.; 2009.
Lang, S, Möhrlen, J, Jørgensen, J, Gallachóir, BO, McKeogh, E. Application of a multi‐scheme ensemble prediction system for wind power forecasting in Ireland and comparison with validation results from Denmark and Germany. In European Wind Energy Conference EWEC’06. Athens, Greece; February 27‐March 2, 2006.
Otterson, S, Pinson, P. Are ramp forecasts really useful? In EWEA Annual Event. Copenhagen, Denmark; April 16–19, 2012.
Lin, J, Sun, Y‐Z, Cheng, L, Gao, W‐Z. Assessment of the power reduction of wind farms under extreme wind condition by a high resolution simulation model. Appl Energy 2012, 96:21–32.
Sioshansi, R, Hurlbut, D. Market protocols in ERCOT and their effect on wind generation. Energy Policy 2010, 38:3192–3197.
Ruiz, PA, Philbrick, CR, Zak, E, Cheung, KW, Sauer, PW. Uncertainty management in the unit commitment problem. IEEE Trans Power Syst 2009, 24:642–651.
Tuohy, A, Meibom, P, Denny, E, O`Malley, M. Unit commitment for systems with significant wind penetration. IEEE Trans Power Syst 2009, 24:592–601.
Wang, J, Shahidehpour, M, Li, Z. Security‐constrained unit commitment with volatile wind power generation. IEEE Trans Power Syst 2008, 23:1319–1327.
Bouffard, F, Galiana, FD. Stochastic security for operations planning with significant wind power generation. IEEE Trans Power Syst 2008, 23:306–316.
Pappala, VS, Erlich, I, Rohrig, K, Dobschinski, J. Stochastic model for the optimal operation of a wind‐thermal power system. IEEE Trans Power Syst 2009, 24:940–950.
Xiao, J, Hodge, B‐MS, Pekny, JF, Reklaitis, GV. Operating reserve policies with high wind power penetration. Comput Chem Eng 2011, 35:1876–1885.
Constantinescu, EM, Zavala, VM, Rocklin, M, Lee, S, Anitescu, M. A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation. IEEE Trans Power Syst 2011, 26:431–441.
Zhou, Z, Botterud, A, Wang, J, Bessa, RJ, Keko, H, Sumaili, J, Miranda, V. Application of probabilistic wind power forecasting in electricity markets. Wind Energy, in press.
Sturt, A, Strbac, G. Efficient stochastic scheduling for simulation of wind‐integrated power systems. IEEE Trans Power Syst 2012, 27:323–334.
Growe‐Kuska, N, Heitsch, H, Romisch, W. Scenario reduction and scenario tree construction for power management problems. In IEEE Bologna PowerTech Conference. Bologna, Italy; June 23–26, 2003.
Sumaili, J, Keko, H, Miranda, V, Zhou, Z, Botterud, A, Wang, J. Finding representative wind power scenarios and their probabilities for stochastic models. In ISAP 2011 – 16th International Conference on Intelligent System Applications to Power Systems. Hersonissos, Greece; September 25–28, 2011.
Morales, JM, Conejo, AJ, Pérez‐Ruiz, J. Economic valuation of reserves in power systems with high penetration of wind power. IEEE Trans Power Syst 2009, 24:900–910.
Restrepo, JF, Galiana, FD. Assessing the yearly impact of wind power through a new hybrid deterministic/stochastic unit commitment. IEEE Trans Power Syst 2011, 26:401–410.
Papavasiliou, A, Oren, SS, O`Neill, RP. Reserve requirements for wind power integration: a scenario‐based stochastic programming framework. IEEE Trans Power Syst 2011, 26:2197–2206.
Lowery, C, O`Malley, M. Impact of wind forecast error statistics upon unit commitment. IEEE Trans Sustain Energy 2012, 3:760–768.
Wu, L, Shahidehpour, M, Li, T. Stochastic security‐constrained unit commitment. IEEE Trans Power Syst 2007, 22:800–811.
Villanueva, D, Pazos, JL, Feijóo, A. Probabilistic load flow including wind power generation. IEEE Trans Power Syst 2011, 26:1659–1667.
Hatziargyriou, ND, Karakatsanis, TS, Papadopoulos, M. Probabilistic load flow in distribution systems containing dispersed wind power generation. IEEE Trans Power Syst 1993, 8:159–165.
Morales, JM, Baringo, L, Conejo, AJ, Mínguez, R. Probabilistic power flow with correlated wind sources. IET Gen Transm Distrib 2010, 4:641–651.
Usaola, J. Probabilistic load flow in systems with wind generation. IET Gen Transm Distrib 2009, 3:1031–1041.
ENTSO‐E. Operating Handbook – Policy 3: Operational Security; 2009. Available at: https://www.entsoe.eu/publications/system‐operations‐reports/operation‐handbook/. (Accessed January 2013).
Vlachogiannis, JG. Probabilistic constrained load flow considering integration of wind power generation and electric vehicles. IEEE Trans Power Syst 2009, 24:1808–1817.
Jabr, RA, Pal, BC. Intermittent wind generation in optimal power flow dispatching. IET Gen Transm Distrib 2009, 3:66–74.
Capitanescu, F, Fliscounakis, S, Panciatici, P, Wehenkel, L. Cautious operation planning under uncertainties. IEEE Trans Power Syst 2012, 27:1859–1869.
Díaz‐González, F, Sumper, A, Gomis‐Bellmunt, O, Villafáfila‐Robles, R. A review of energy storage technologies for wind power applications. Renew Sustain Energy Rev 2012, 16:2154–2171.
Hedegaard, K, Meibom, P. Wind power impacts and electricity storage—a time scale perspective. Renew Energy 2012, 37:318–324.
Loisel, R, Mercier, A, Gatzen, C, Elms, N. Market evaluation of hybrid wind‐storage power systems in case of balancing responsibilities. Renew Sustain Energy Rev 2011, 15:5003–5012.
Nyamdash, B, Denny, E, O`Malley, M. The viability of balancing wind generation with large scale energy storage. Energy Policy 2010, 38:7200–7208.
Meibom, P, Barth, R, Hasche, B, Brand, H, Weber, C, O`Malley, M. Stochastic optimization model to study the operational impacts of high wind penetrations in Ireland. IEEE Trans Power Syst 2011, 26:1367–1379.
Tuohy, A, O`Malley, M. Pumped storage in systems with very high wind penetration. Energy Policy 2011, 39:1965–1974.
Connolly, D, Lund, H, Mathiesen, BV, Pican, E, Leahy, M. The technical and economic implications of integrating fluctuating renewable energy using energy storage. Renew Energy 2012, 43:47–60.
Qu, L, Qiao, W. Constant power control of DFIG wind turbines with supercapacitor energy storage. IEEE Trans Ind Appl 2011, 47:359–367.
Cimuca, GO, Saudemont, C, Robyns, B, Radulescu, MM. Control and performance evaluation of a flywheel energy‐storage system associated to a variable‐speed wind generator. IEEE Trans Ind Electron 2006, 53:1074–1085.
Ray, PK, Mohanty, SR, Kishor, N. Proportional–integral controller based small‐signal analysis of hybrid distributed generation systems. Energy Convers Manage 2011, 52:1943–1954.
Nomura, S, Ohata, Y, Hagita, T, Tsutsui, H, Tsuji‐Iio, S, Shimada, R. Wind farms linked by SMES systems. IEEE Trans Appl Supercond 2005, 15:1951–1954.
Oudalov, A, Chartouni, D, Ohler, C. Optimizing a battery energy storage system for primary frequency control. IEEE Trans Power Syst 2007, 22:1259–1266.
Lee, D‐J, Wang, L. Small‐signal stability analysis of an autonomous hybrid renewable energy power generation/energy storage system part I: time‐domain simulations. IEEE Trans Energy Convers 2008, 23:311–320.
Rodríguez‐Amenedo, JL, Arnalte, S, Burgos, JC. Automatic generation control of a wind farm with variable speed wind turbines. IEEE Trans Energy Convers 2002, 17:279–284.
Moursi, ME, Joos, G, Abbey, C. A secondary voltage control strategy for transmission level interconnection of wind generation. IEEE Trans Power Electron 2008, 23:1178–1190.
Almeida, RGd, Castronuovo, ED, Lopes, JAP. Optimum generation control in wind parks when carrying out system operator requests. IEEE Trans Power Syst 2006, 21:718–725.
Vergnol, A, Sprooten, J, Robyns, B, Rious, V, Deuse, J. Line overload alleviation through corrective control in presence of wind energy. Electric Power Syst Res 2011, 81:1583–1591.
Ummels, BC, Gibescu, M, Kling, WL, Paap, GC. Integration of wind power in the liberalized Dutch electricity market. Wind Energy 2006, 9:579–590.
Aparicio, N, MacGill, I, Rivier Abbad, J, Beltran, H. Comparison of wind energy support policy and electricity market design in Europe, the United States, and Australia. IEEE Trans Sustain Energy 2012, 3:809–818.
Holttinen, H. Optimal electricity market for wind power. Energy Policy 2005, 33:2052–2063.
Weber, C. Adequate intraday market design to enable the integration of wind energy into the European power systems. Energy Policy 2010, 38:3155–3163.
Vandezande, L, Meeus, L, Belmans, R, Saguan, M, Glachant, J‐M. Well‐functioning balancing markets: a prerequisite for wind power integration. Energy Policy 2010, 38:3146–3154.
Bessa, RJ, Miranda, V, Botterud, A, Wang, J. ‘Good’ or ‘bad’ wind power forecasts: a relative concept. Wind Energy 2011, 14:625–636.
Weijde, AH, Hobbs, BF. Locational‐based coupling of electricity markets: benefits from coordinating unit commitment and balancing markets. J Regul Econ 2011, 39:223–251.
Farhangi, H. The path of the smart grid. IEEE Power Energy Mag 2010, 8:18–28.
Brooks, A, Ed, Lu, Reicher, D, Spirakis, C, Weihl, B. Demand dispatch. IEEE Power Energy Mag 2010, 8:20–29.
Papaefthymiou, G, Hasche, B, Nabe, C. Potential of heat pumps for demand side management and wind power integration in the German electricity market. IEEE Trans Sustain Energy 2012, 3:636–642.
Lopes, JAP, Soares, FJ, Almeida, PR. Integration of electric vehicles in the electric power system. Proc IEEE 2011, 99:168–183.
Fangxing, L, Wei, Q, Hongbin, S, Hui, W, Jianhui, W, Yan, X, Zhao, X, Pei, Z. Smart transmission grid: vision and framework. IEEE Trans Smart Grid 2010, 1:168–177.
Redes Energéticas Nacionais website. Availabel at: http://www.centrodeinformacao.ren.pt/EN/InformacaoExploracao/Pages/DiagramadeProdu%C3%A7%C3%A3oE%C3%B3lica.aspx. (Accessed January 2013).

Further Reading

Ela, E, Milligan, M, Kirby, B. Operating reserves and variable generation. Technical Report, 2011, NREL/TP‐5500–51978. Availabel at: http://www.nrel.gov/docs/fy11osti/51978.pdf.
Holttinen, H, Meibom, P, Orths, A, Hulle, F, Lange, B, O`Malley, M, Pierik, J, Ummels, B, Tande, JO, Estanqueiro, A, Matos, MA, Gomez, E, Söder, L, Strbac, G, Shakoor, A, Ricardo, J, Smith, C, Milligan, M, Ela, E. Design and operation of power systems with large amounts of wind power. Final Report. Phase one 2006–2008, IEA Wind Task 25. Availabel at: http://www.vtt.fi/inf/pdf/tiedotteet/2009/T2493.pdf.
Birge, JR, Louveaux, F. Introduction to Stochastic Programming. 2nd ed. New York: Springer; 2011.
Matos, MA. Decision under risk as a multicriteria problem. Eur J Oper Res 2007, 138:1516–1529.

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