Ancillary services; Electric vehicles; Flexibility; Power plant decommissioning; Reserve services; Wind curtailment; Ancillary service; Low-carbon technologies; Mixed integer linear programming; Operational flexibility; Renewable energy source; Building and Construction; Energy (all); Mechanical Engineering; Management, Monitoring, Policy and Law; General Energy
Résumé :
[en] The paper presents a unit commitment model, based on mixed integer linear programming, capable of assessing the impact of electric vehicles (EV) on provision of ancillary services in power systems with high share of renewable energy sources (RES). The analyses show how role of different conventional units changes with integration of variable and uncertain RES and how introducing a flexible sources on the demand side, in this case EV, impact the traditional provision of spinning/contingency reserve services. In addition, technical constraints of conventional units, such as nuclear, gas or coal, limit the inherit flexibility of the system which results in curtailing clean renewable sources and inefficient operation. Following on that, sensitivity analyses of operational cost and wind curtailment shows which techno-economic constraints impact the flexibility of the high RES systems the most and how integration of more flexible units or decommission of conventional nuclear, coal and gas driven power plants would impact the system's operation. Finally, two different wind generation polices (wind penalization and wind turbines as reserve providers) have been analysed in terms of operational flexibility through different stages of conventional unit's decommission and compared with the same analyses when EV were used as reserve providers.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations
Disciplines :
Gestion des systèmes d’information Sciences informatiques Ingénierie électrique & électronique
Auteur, co-auteur :
PAVIĆ, Ivan ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
Capuder, Tomislav; University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia
Kuzle, Igor ; University of Zagreb Faculty of Electrical Engineering and Computing, Zagreb, Croatia
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Low carbon technologies as providers of operational flexibility in future power systems
Titre original :
[en] Low carbon technologies as providers of operational flexibility in future power systems
Smart Grids ERA-Net Croatian Environmental Protection and Energy Efficiency Fund
Subventionnement (détails) :
The work of the authors is a part of the Flex-ChEV – Flexible Electric Vehicle Charging Infrastructure project funded by Smart Grids ERA-Net under project grant No. 13 and SNOVI funded by the Croatian Environmental Protection and Energy Efficiency Fund through EnU-16/2015 program.
Ortega-Vazquez M.A., Kirschen D.S. Estimating the spinning reserve requirements in systems with significant wind power generation penetration. IEEE Trans Power Syst 2009, 24:114-124. 10.1109/TPWRS.2008.2004745.
Ortega-Vazquez M.A., Bouffard F., Silva V. Electric vehicle aggregator/system operator coordination for charging scheduling and services procurement. IEEE Trans Power Syst 2013, 28:1806-1815. 10.1109/TPWRS.2012.2221750.
Ryan J., Ela E., Flynn D., O'Malley M. Variable generation, reserves, flexibility and policy interactions. 2014 47th Hawaii int conf syst sci 2014, 2426-2434. IEEE. 10.1109/HICSS.2014.304.
Black M., Strbac G. Value of bulk energy storage for managing wind power fluctuations. IEEE Trans Energy Convers 2007, 22:197-205. 10.1109/TEC.2006.889619.
Pudjianto D., Aunedi M., Djapic P., Strbac G. Whole-systems assessment of the value of energy storage in low-carbon electricity systems. IEEE Trans Smart Grid 2014, 5:1098-1109. 10.1109/TSG.2013.2282039.
Pandzic H., Wang Y., Qiu T., Dvorkin Y., Kirschen D.S. Near-optimal method for siting and sizing of distributed storage in a transmission network. IEEE Trans Power Syst 2014, 1-13. 10.1109/TPWRS.2014.2364257.
Karangelos E., Bouffard F. Towards full integration of demand-side resources in joint forward energy/reserve electricity markets. IEEE Trans Power Syst 2012, 27:280-289. 10.1109/TPWRS.2011.2163949.
Gross G. Key issues and challenges in the deepening penetration of demand response resources; 2014. < [accessed November 26, 2015]. https://meetings.vtools.ieee.org/m/32397.
Yang J., He L., Fu S. An improved PSO-based charging strategy of electric vehicles in electrical distribution grid. Appl Energy 2014, 128:82-92. 10.1016/j.apenergy.2014.04.047.
Neaimeh M., Wardle R., Jenkins A.M., Yi J., Hill G., Lyons P.F., et al. A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts. Appl Energy 2015, 157:688-698. 10.1016/j.apenergy.2015.01.144.
Jian L., Zheng Y., Xiao X., Chan C.C. Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid. Appl Energy 2015, 146:150-161. 10.1016/j.apenergy.2015.02.030.
Sousa T., Morais H., Soares J., Vale Z. Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints. Appl Energy 2012, 96:183-193. 10.1016/j.apenergy.2012.01.053.
Liu W., Hu W., Lund H., Chen Z. Electric vehicles and large-scale integration of wind power - the case of Inner Mongolia in China. Appl Energy 2013, 104:445-456. 10.1016/j.apenergy.2012.11.003.
Schuller A., Flath C.M., Gottwalt S. Quantifying load flexibility of electric vehicles for renewable energy integration. Appl Energy 2015, 151:335-344. 10.1016/j.apenergy.2015.04.004.
Meng J., Mu Y., Jia H., Wu J., Yu X., Qu B. Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system 2016, 162:966-979. 10.1016/j.apenergy.2015.10.159.
Zhong J., He L., Li C., Cao Y., Wang J., Fang B., et al. Coordinated control for large-scale EV charging facilities and energy storage devices participating in frequency regulation. Appl Energy 2014, 123:253-262. 10.1016/j.apenergy.2014.02.074.
Khazali A., Kalantar M. A stochastic-probabilistic energy and reserve market clearing scheme for smart power systems with plug-in electrical vehicles. Energy Convers Manage 2015, 96:242-257. 10.1016/j.enconman.2015.02.070.
Shafie-khah M., Moghaddam M.P., Sheikh-El-Eslami M.K., Catalão J.P.S. Optimised performance of a plug-in electric vehicle aggregator in energy and reserve markets. Energy Convers Manage 2015, 97:393-408. 10.1016/j.enconman.2015.03.074.
Teng F., Aunedi M., Strbac G. Benefits of flexibility from smart electrified transportation and heating in the future UK electricity system. Appl Energy 2015, 10.1016/j.apenergy.2015.10.028.
Verzijlbergh R., Brancucci Martínez-Anido C., Lukszo Z., de Vries L. Does controlled electric vehicle charging substitute cross-border transmission capacity?. Appl Energy 2014, 120:169-180. 10.1016/j.apenergy.2013.08.020.
Habib S., Kamran M., Rashid U. Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks - a review. J Power Sources 2015, 277:205-214. 10.1016/j.jpowsour.2014.12.020.
Green R.C., Wang L., Alam M. The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook. Renew Sustain Energy Rev 2011, 15:544-553. 10.1016/j.rser.2010.08.015.
Richardson D.B. Electric vehicles and the electric grid: a review of modeling approaches, impacts, and renewable energy integration. Renew Sustain Energy Rev 2013, 19:247-254. 10.1016/j.rser.2012.11.042.
Li T., Shahidehpour M. Price-based unit commitment: a case of lagrangian relaxation versus mixed integer programming. IEEE Trans Power Syst 2005, 20:2015-2025. 10.1109/TPWRS.2005.857391.
Carrion M., Arroyo J.M. A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Trans Power Syst 2006, 21:1371-1378. 10.1109/TPWRS.2006.876672.
Dvorkin Y., Pandzic H., Ortega-Vazquez M.A., Kirschen D.S. A hybrid stochastic/interval approach to transmission-constrained unit commitment. IEEE Trans Power Syst 2015, 30:621-631. 10.1109/TPWRS.2014.2331279.
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. 10.1109/TPWRS.2010.2070848.
Sturt A., Strbac G. Efficient stochastic scheduling for simulation of wind-integrated power systems. IEEE Trans Power Syst 2012, 27:323-334. 10.1109/TPWRS.2011.2164558.
Palmintier B.S. Incorporating operational flexibility into electric generation planning 2013, Massachusetts Institute of Technology.
Palmintier B.S., Webster M.D. Heterogeneous unit clustering for efficient operational flexibility modeling. IEEE Trans Power Syst 2014, 29:1089-1098. 10.1109/TPWRS.2013.2293127.
Zhang L, Member S, Capuder T. Unified unit commitment formulation and fast multi-service lp model for flexibility evaluation in sustainable power systems; 2015. p. 1-14.
Kiviluoma J., Meibom P. Methodology for modelling plug-in electric vehicles in the power system and cost estimates for a system with either smart or dumb electric vehicles. Energy 2011, 36:1758-1767. 10.1016/j.energy.2010.12.053.
Barth R., Brand H., Meibom P., Weber C. A stochastic unit-commitment model for the evaluation of the impacts of integration of large amounts of intermittent wind power. 2006 Int Conf Probabilistic Methods Appl to Power Syst 2006, 1-8. IEEE. 10.1109/PMAPS.2006.360195.
Pavić I., Capuder T., Kuzle I. Value of flexible electric vehicles in providing spinning reserve services. Appl Energy 2015, 157:60-74. 10.1016/j.apenergy.2015.07.070.
Ummels B.C., Gibescu M., Pelgrum E., Kling W.L., Brand A.J. Impacts of wind power on thermal generation unit commitment and dispatch. IEEE Trans Energy Convers 2007, 22:44-51. 10.1109/TEC.2006.889616.
Kubik M.L., Coker P.J., Barlow J.F. Increasing thermal plant flexibility in a high renewables power system. Appl Energy 2015, 154:102-111. 10.1016/j.apenergy.2015.04.063.
Denholm P., Hand M. Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. Energy Policy 2011, 39:1817-1830. 10.1016/j.enpol.2011.01.019.
Krajačić G., Duić N., Carvalho M. How to acheive a 100% RES electricity supply for Portugal?. Appl Energy 2011, 88:508-517.
Krajačić G., Duić N., Zmijarević Z., Mathiesen B.V., Vučinić A.A., da Graça Carvalho M. Planning for a 100% independent energy system based on smart energy storage for integration of renewables and CO2 emissions reduction. Appl Therm Eng 2011, 31:2073-2083. 10.1016/j.applthermaleng.2011.03.014.
Ćosić B., Krajačić G., Duić N. A 100% renewable energy system in the year 2050: the case of Macedonia. Energy 2012, 48:80-87. 10.1016/j.energy.2012.06.078.
Tarroja B., Shaffer B., Samuelsen S. The importance of grid integration for achievable greenhouse gas emissions reductions from alternative vehicle technologies. Energy 2015, 87:504-519. 10.1016/j.energy.2015.05.012.
Božič D., Pantoš M. Impact of electric-drive vehicles on power system reliability. Energy 2015, 83:511-520. 10.1016/j.energy.2015.02.055.
Kirschen D.S., Ma J., Silva V., Belhomme R. Optimizing the flexibility of a portfolio of generating plants to deal with wind generation. 2011 IEEE power energy soc gen meet 2011, 1-7. IEEE. 10.1109/PES.2011.6039157.
Ma J., Silva V., Belhomme R., Kirschen D.S., Ochoa L.F. Evaluating and planning flexibility in sustainable power systems. 2013 IEEE Power Energy Soc Gen Meet 2013, 1-11. IEEE. 10.1109/PESMG.2013.6672221.
Palmintier B. Flexibility in generation planning: identifying key operating constraints. 2014 Power syst comput conf 2014, 1-7. IEEE. 10.1109/PSCC.2014.7038323.
Shortt A., O'Malley M. Quantifying the long-term impact of electric vehicles on the generation portfolio. IEEE Trans Smart Grid 2014, 5:71-83. 10.1109/TSG.2013.2286353.
ENTSO-E WG Ancillary Services, Ancillary Services in Europe Contractual aspects; 2011. <. http://www.entsoe.eu/fileadmin/user_upload/_library/position_papers/ENTSO_BalancingMaps_Final.pdf.
Galiana F.D., Bouffard F., Arroyo J.M., Restrepo J.F. Scheduling and pricing of coupled energy and primary, secondary, and tertiary reserves. Proc IEEE 2005, 93:1970-1983. 10.1109/JPROC.2005.857492.
Aunedi M. Value of flexible demand-side technologies in future low-carbon systems 2013, Imperial College London.
Silva V. Value of flexibility in systems with large wind penetration 2010, University of London.
Gross R, Green T, Leach M, Skea J, Heptonstall P, Anderson D. The costs and impacts of intermittency; 2006. http://dx.doi.org/10.1016/j.enpol.2008.06.013. doi:10.1016/j.enpol.2008.06.013.
National Grid, Winter Outlook 2013/14; 2013.
Teng F., Trovato V., Strbac G. Stochastic scheduling with inertia-dependent fast frequency response requirements. IEEE Trans Power Syst 2015, 1-10. 10.1109/TPWRS.2015.2434837.
Quan H., Srinivasan D., Khambadkone A.M., Khosravi A. A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources. Appl Energy 2015, 152:71-82. 10.1016/j.apenergy.2015.04.103.
Dimitroulas D.K., Georgilakis P.S. A new memetic algorithm approach for the price based unit commitment problem. Appl Energy 2011, 88:4687-4699. 10.1016/j.apenergy.2011.06.009.
Wang J., Wang J., Liu C., Ruiz J.P. Stochastic unit commitment with sub-hourly dispatch constraints. Appl Energy 2013, 105:418-422. 10.1016/j.apenergy.2013.01.008.
Pavic I., Capuder T., Holjevac N., Kuzle I. Role and impact of coordinated EV charging on flexibility in low carbon power systems. 2014 IEEE int electr veh conf 2014, 1-8. IEEE. 10.1109/IEVC.2014.7056172.
Baslis C.G., Bakirtzis A.G. Optimal yearly scheduling of generation and pumping for a price-maker hydro producer. 2010 7th Int conf Eur energy mark 2010, 1-6. IEEE. 10.1109/EEM.2010.5558685.
Cleary B., Duffy a., O'Connor a., Conlon M., Fthenakis V. Assessing the Economic benefits of compressed air energy storage for mitigating wind curtailment. Sustain Energy, IEEE Trans 2015, 6:1021-1028. 10.1109/TSTE.2014.2376698.
Kane L., Ault G. Evaluation of wind power curtailment in active network management schemes. IEEE Trans Smart Grids 2015, 30:8. 10.1109/TPWRS.2014.2336862.
Grünewald P., McKenna E., Thomson M. Going with the wind: temporal characteristics of potential wind curtailment in Ireland in 2020 and opportunities for demand response. IET Renew Power Gener 2015, 9:66-77. 10.1049/iet-rpg.2013.0320.
Hozouri M.A., Abbaspour A., Fotuhi-Firuzabad M., Moeini-Aghtaie M. On the use of pumped storage for wind energy maximization in transmission-constrained power systems. IEEE Trans Power Syst 2015, 30:1017-1025. 10.1109/TPWRS.2014.2364313.
Vargas L.S., Bustos-Turu G., Larrain F. Wind power curtailment and energy storage in transmission congestion management considering power plants ramp rates. IEEE Trans Power Syst 2014, 1-9. 10.1109/TPWRS.2014.2362922.
Park H., Baldick R. Transmission planning under uncertainties of wind and load: sequential approximation approach. IEEE Trans Power Syst 2013, 28:2395-2402. 10.1109/TPWRS.2013.2251481.
Ortega-Vazuqez M.A., Kirschen D.S., Dvorkin Y. Wind generation as a reserve provider. IET Gener Transm Distrib 2015, 9:779-787. 10.1049/iet-gtd.2014.0614.
