Low carbon technologies as providers of operational flexibility in future power systems

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.

[1]  Marko Aunedi,et al.  Benefits of flexibility from smart electrified transportation and heating in the future UK electricity system , 2016 .

[2]  Goran Krajačić,et al.  How to achieve a 100% RES electricity supply for Portugal? , 2011 .

[3]  Zita Vale,et al.  Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints , 2012 .

[4]  Salman Habib,et al.  Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks – A review , 2015 .

[5]  Mark O'Malley,et al.  Quantifying the Long-Term Impact of Electric Vehicles on the Generation Portfolio , 2014, IEEE Transactions on Smart Grid.

[6]  M. Carrion,et al.  A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem , 2006, IEEE Transactions on Power Systems.

[7]  Juha Kiviluoma,et al.  Methodology for modelling plug-in electric vehicles in the power system and cost estimates for a sys , 2011 .

[8]  Daniel S. Kirschen,et al.  Near-Optimal Method for Siting and Sizing of Distributed Storage in a Transmission Network , 2015, IEEE Transactions on Power Systems.

[9]  Zofia Lukszo,et al.  Does controlled electric vehicle charging substitute cross-border transmission capacity? , 2014 .

[10]  Marko Aunedi,et al.  Whole-Systems Assessment of the Value of Energy Storage in Low-Carbon Electricity Systems , 2014, IEEE Transactions on Smart Grid.

[11]  Bryan Palmintier,et al.  Heterogeneous unit clustering for efficient operational flexibility modeling , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[12]  Goran Krajačić,et al.  Planning for a 100% independent energy system based on smart energy storage for integration of renewables and CO2 emissions reduction , 2011 .

[13]  Robert C. Green,et al.  The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook , 2010, IEEE PES General Meeting.

[14]  G. Strbac,et al.  Value of Bulk Energy Storage for Managing Wind Power Fluctuations , 2007, IEEE Transactions on Energy Conversion.

[15]  M. P. Moghaddam,et al.  Optimised performance of a plug-in electric vehicle aggregator in energy and reserve markets , 2015 .

[16]  Murray Thomson,et al.  Going with the wind: temporal characteristics of potential wind curtailment in Ireland in 2020 and opportunities for demand response , 2015 .

[17]  Abbas Khosravi,et al.  A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources , 2015 .

[18]  Bryan Palmintier,et al.  Flexibility in generation planning: Identifying key operating constraints , 2014, 2014 Power Systems Computation Conference.

[19]  Miloš Pantoš,et al.  Impact of electric-drive vehicles on power system reliability , 2015 .

[20]  Daniel S. Kirschen,et al.  Wind generation as a reserve provider , 2015 .

[21]  P. Meibom,et al.  A Stochastic Unit-commitment Model for the Evaluation of the Impacts of Integration of Large Amounts of Intermittent Wind Power , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[22]  Goran Strbac,et al.  Stochastic Scheduling With Inertia-Dependent Fast Frequency Response Requirements , 2016, IEEE Transactions on Power Systems.

[23]  da Silva,et al.  Value of flexibility in systems with large wind penetration , 2010 .

[24]  Zhe Chen,et al.  Electric vehicles and large-scale integration of wind power – The case of Inner Mongolia in China , 2013 .

[25]  Anastasios G. Bakirtzis,et al.  Optimal yearly scheduling of generation and pumping for a price-maker hydro producer , 2010, 2010 7th International Conference on the European Energy Market.

[26]  David B. Richardson,et al.  Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration , 2013 .

[27]  Daniel S. Kirschen,et al.  A Hybrid Stochastic/Interval Approach to Transmission-Constrained Unit Commitment , 2015, IEEE Transactions on Power Systems.

[28]  Paul Denholm,et al.  Grid flexibility and storage required to achieve very high penetration of variable renewable electricity , 2011 .

[29]  Robert Gross,et al.  The costs and impacts of intermittency: An ongoing debate: "East is East, and West is West, and never the twain shall meet." , 2008 .

[30]  Francois Bouffard,et al.  Electric vehicle aggregator/system operator coordination for charging scheduling and services procurement , 2013, 2013 IEEE Power & Energy Society General Meeting.

[31]  Luis F. Ochoa,et al.  Evaluating and planning flexibility in sustainable power systems , 2013, 2013 IEEE Power & Energy Society General Meeting.

[32]  Amirhossein Khazali,et al.  Spinning reserve quantification by a stochastic–probabilistic scheme for smart power systems with high wind penetration , 2015 .

[33]  Jianhui Wang,et al.  Coordinated control for large-scale EV charging facilities and energy storage devices participating in frequency regulation , 2014 .

[34]  E. Karangelos,et al.  Towards Full Integration of Demand-Side Resources in Joint Forward Energy/Reserve Electricity Markets , 2012, IEEE Transactions on Power Systems.

[35]  M. O'Malley,et al.  Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland , 2011, IEEE Transactions on Power Systems.

[36]  R. Baldick,et al.  Transmission Planning Under Uncertainties of Wind and Load: Sequential Approximation Approach , 2013, IEEE Transactions on Power Systems.

[37]  Pavlos S. Georgilakis,et al.  A new memetic algorithm approach for the price based unit commitment problem , 2011 .

[38]  Scott Samuelsen,et al.  The importance of grid integration for achievable greenhouse gas emissions reductions from alternative vehicle technologies , 2015 .

[39]  Jun Yang,et al.  An improved PSO-based charging strategy of electric vehicles in electrical distribution grid , 2014 .

[40]  Graham W. Ault,et al.  Evaluation of Wind Power Curtailment in Active Network Management Schemes , 2015, IEEE Transactions on Power Systems.

[41]  Christoph M. Flath,et al.  Quantifying load flexibility of electric vehicles for renewable energy integration , 2015 .

[42]  Pierluigi Mancarella,et al.  Unified Unit Commitment Formulation and Fast Multi-Service LP Model for Flexibility Evaluation in Sustainable Power Systems , 2016, IEEE Transactions on Sustainable Energy.

[43]  Damian Flynn,et al.  Variable Generation, Reserves, Flexibility and Policy Interactions , 2014, 2014 47th Hawaii International Conference on System Sciences.

[44]  Marko Aunedi Value of flexible demand-side technologies in future low-carbon systems , 2013 .

[45]  Daniel S. Kirschen,et al.  Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration , 2009, IEEE Transactions on Power Systems.

[46]  Regine Belhomme,et al.  Optimizing the flexibility of a portfolio of generating plants to deal with wind generation , 2011, 2011 IEEE Power and Energy Society General Meeting.

[47]  Neven Duić,et al.  A 100% renewable energy system in the year 2050: The case of Macedonia , 2012 .

[48]  Phil Blythe,et al.  A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts , 2015 .

[49]  Igor Kuzle,et al.  Value of Flexible Electric Vehicles in Providing Spinning Reserve Services , 2015 .

[50]  W.L. Kling,et al.  Impacts of Wind Power on Thermal Generation Unit Commitment and Dispatch , 2007, IEEE Transactions on Energy Conversion.

[51]  Luis S. Vargas,et al.  Wind power curtailment and energy storage in transmission congestion management considering power plants ramp rates , 2015, 2015 IEEE Power & Energy Society General Meeting.

[52]  G. Strbac,et al.  Efficient Stochastic Scheduling for Simulation of Wind-Integrated Power Systems , 2012, IEEE Transactions on Power Systems.

[53]  Juan P. Ruiz,et al.  Stochastic unit commitment with sub-hourly dispatch constraints , 2013 .

[54]  Linni Jian,et al.  Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid , 2015 .

[55]  Mort Webster Incorporating Operational Flexibility into Electric Generation Planning , 2013 .

[56]  Bo Qu,et al.  Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system , 2016 .

[57]  Amirhossein Khazali,et al.  A stochastic–probabilistic energy and reserve market clearing scheme for smart power systems with plug-in electrical vehicles , 2015 .

[58]  Mahmud Fotuhi-Firuzabad,et al.  On the Use of Pumped Storage for Wind Energy Maximization in Transmission-Constrained Power Systems , 2015, IEEE Transactions on Power Systems.

[59]  T. Green,et al.  The uk energy research centre review of the costs and impacts of intermittency , 2012 .

[60]  M. Shahidehpour,et al.  Price-based unit commitment: a case of Lagrangian relaxation versus mixed integer programming , 2005, IEEE Transactions on Power Systems.

[61]  Alan O'Connor,et al.  Assessing the Economic Benefits of Compressed Air Energy Storage for Mitigating Wind Curtailment , 2015, IEEE Transactions on Sustainable Energy.

[62]  Igor Kuzle,et al.  Role and impact of coordinated EV charging on flexibility in low carbon power systems , 2014, 2014 IEEE International Electric Vehicle Conference (IEVC).

[63]  François Bouffard,et al.  Scheduling and Pricing of Coupled Energy and Primary, Secondary, and Tertiary Reserves , 2005, Proceedings of the IEEE.

[64]  Janet F. Barlow,et al.  Increasing thermal plant flexibility in a high renewables power system , 2015 .