2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 1 IIESI 102 COURSE, 3-7 AUGUST 2015 FLEXIBILITY IN INTEGRATED ENERGY SYSTEMS AND VIRTUAL STORAGE Pierluigi Mancarella, PhD Reader in Future Energy Networks The University of Manchester, Manchester, UK p.mancarella@manchester.ac.uk Energy Systems Integration Facility, NREL, Golden, USA 06/08/2015
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 2 Contents Context and challenges Basic concepts of flexibility in integrated energy systems Flexibility in distributed multi-energy systems Buildings and virtual storage Integrated electricity and gas flexibility Concluding remarks
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 3 The challenging of system balance with volatile and variable renewable 70.000 60.000 MW EE RES*-infeed D Installiert Installed EE RES*-capacity D German example 50.000 40.000 30.000 min. RES*-infeed: 356MW Wind: 356MW PV: 0MW approx. 0.6% (25.01.2012) max. RES*-infeed: 29.196MW Wind: 14.745MW PV: 14.451MW approx. 48% (09.06.2012) 20.000 10.000 0 1 501 1001 1501 2001 2501 3001 3501 4001 *RES: Wind+PV h RES*-production Courtesy of J. Vanzetta (Amprion) and M. Paolone (EPFL)
It s not only about electricity 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 4
A glance into the future: Electrification 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 5
A Smart Multi-energy Grid vision AGP Additional Generation Block F R fuel / GDS EDS DCN DH CHP Combined Heat and Power DCN District Cooling Network DH District Heating RES EDS Electricity Distribution System storage / HDS GDS Gas Distribution System Q W H R R H Q W H R AGP block W Q CHP block Q R Q Q W H Q local user Q Q HDS Hydrogen Distribution Network MG Multi-Generation W electricity, Q heat, R cooling H hydrogen, F - Fuel W W Q storage / HDS RES DH DCN F W EDS fuel / GDS P.Mancarella, Multi-energy systems: an overview of models and evaluation concepts, Energy, Vol. 65, 2014, 1-17, Invited paper P. Mancarella, G. Chicco, Distributed Multi-Generation Systems: Energy Models and Analyses, Nova, 2009 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 6
Rethinking the energy system: The Smart Multi-energy Grid Vision Integration as opposed to Electrification Heat recovery, alternative means for cooling production at the distributed level Flexibility from energy vector switching Economy of scale if coupled to district energy systems Can we rethink the overall energy system from a multi-energy perspective and enhance environmental and economic performance, and system flexibility and reliability? Distributed multi-energy systems, including Microgrids and District Energy Systems, offer an ideal opportunity for energy infrastructure and ESI at a decentralized level P.Mancarella, Multi-energy systems: an overview of models and evaluation concepts, Energy, Invited paper, Jan 2014 P. Mancarella, G. Chicco, Distributed Multi-Generation Systems: Energy Models and Analyses, Nova, 2009 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 7
What is flexibility? Flexibility describes the system ability to cope with events that may cause imbalances between supply and demand at different time frames while maintaining the system reliability in a cost effective manner Flexibility has always been present in power systems, but its operational requirements will increase in the future with more renewables, CCS and nuclear More flexibility will also be needed for networks in order to maximise asset utilisation in a Smart Grid framework New sources of flexibility are needed 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 8
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 9 What are the flexibility issues? System dispatch with no wind Ideal system dispatch with wind Minimum-load issue Steep down and up ramps
Another example of flexibility issues: the infamous duck curve 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 10 Duck curve elaborating on increasing flexibility requirements due to PV in California by 2020 (source: CAISO)
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 11 Flexibility providers LOW FLEXIBLE AND INFLEXIBLE GENERATION NUCLEAR COAL INFLEXIBLE CONSUMPTION FLEXIBLE GENERATION UNITS GAS HYDRO CCGT CHP MARKET SERVICES FLEXIBLE CONSUMPTION IMPORTED FLEXIBILITY INTECONNECTIONS AC LINES HVDC EV STORAGE DR (courtesy of T Capuder, University of Zagreb)
What is flexibility in Energy Systems Integration (ESI) terms? Can other energy systems/vectors provide flexibility to the electrical power system (= ability to provide supply and demand balance quickly )? Can flexibility in other energy systems constrain the electrical power system? 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 12
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 13 Flexibility providers LOW FLEXIBLE AND INFLEXIBLE GENERATION what constraints? NUCLEAR COAL FLEXIBLE GENERATION UNITS GAS HYDRO CCGT CHP MARKET SERVICES INFLEXIBLE CONSUMPTION Electricity or? to what extent? IMPORTED FLEXIBILITY INTECONNECTIONS AC LINES HVDC FLEXIBLE CONSUMPTION EV STORAGE DR (courtesy of T Capuder, University of Zagreb)
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 14 Demand Response, ESI and Virtual Storage How different is DR from storage? Philosophical question or not? How much DR is actually provided by virtual storage and other (energy) services? Y. Zhou, P. Mancarella and J. Mutale, A framework for capacity credit assessment of electrical energy storage and demand response, submitted to IET Generation, Transmission and Distribution, March 2015, under second review.
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 15 Home appliances and DR Appliance Classifications Deferrable Appliances Curtailable Appliances Non-Flexible (e.g., WM) Flexible (e.g., PHEV) Partially Curtailed (e.g., AC) Fully Curtailed (e.g., Light bulb) Waiting time Based on preference starting time End time, & Consumed energy Consumer Preferences Set points Periorities Starting time Optimization Engine Find optimum set points Power consumption, Curtailed Power starting & end time Status (on/off) Modify consumer profile S. Altaher, P. Mancarella, and J. Mutale, Automated Demand Response from Home Energy Management System under Dynamic Pricing and Power and Comfort Constraints, IEEE Transactions on Smart Grid, accepted for publication, January 2015.
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 16 A possible classification of home appliances Deferrable load may be classified into: Non-flexible load such as Washing Machine (WM), which has a predefined profile that cannot be altered during operation time Flexible load such as Electrical Vehicle (EV) Curtailable load may be classified into: Partially curtailable load such as Air Conditioning (AC), whose power consumption can be controlled according to its temperature set point Fully curtailable, according to the consumer priorities, meaning that these appliances can be switched off without the need of re-turned them on later (for example, light bulbs) S. Altaher, P. Mancarella, and J. Mutale, Automated Demand Response from Home Energy Management System under Dynamic Pricing and Power and Comfort Constraints, IEEE Transactions on Smart Grid, accepted for publication, January 2015.
Multi-energy flexibility from arbitrage between energy vectors and services Hic et nunc Energy shifting Comfort level (reliability) arbitrage Energy to power arbitrage Power to energy arbitrage In time (Virtual) Storage Comfort level (reliability) arbitrage In space Network arbitrage Up- and down-flexibility (generation reference) 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 17
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 18 How much flexibility? E imp E imp EDS EDS ED E EHP ED F aux Boiler η aux H aux C O N S U M E R S ED, HD EHP COP H EHP C O N S U M E R S HD, ED EDS = Electricity Distribution System ED = Electricity Demand HD = Heat Demand T. Capuder and P. Mancarella, Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options, Energy, 2014
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 19 Energy shifting hic et nunc Down-flexibility Multi-energy flexibility from energy vector arbitrage E exp E imp EDS ED F chp CHP ŋ e, ŋ t E chp H chp CONSUMERS HD, ED F aux Boiler ŋ aux H aux T. Capuder and P. Mancarella, Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options, Energy, 2014
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 20 Multi-energy flexibility from energy vector arbitrage Energy shifting Up- and down-flexibility (Virtual) storage E exp E imp EDS ED F chp CHP E chp ŋ e, ŋ t H chp TES H s HD CONSUMERS HD, ED F aux Boiler ŋ aux H aux T. Capuder and P. Mancarella, Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options, Energy, 2014
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 21 Energy to power and power to energy arbitrage Up- and down-flexibility (Virtual) storage E imp EDS E EHP ED EHP H EHP TES COP H s H D CONSUMERS HD, ED EHP = Electric Heat Pump COP = Coefficient of Performance T. Capuder and P. Mancarella, Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options, Energy, 2014
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 22 Fully flexible multi-energy system Operational flexibility Real-time control and response Shifting between different energy vectors Virtual storage, Power-to-heat E exp E imp E exp E imp EDS EDS F chp CHP ŋ e, ŋ t (1-α)E chp αe chp EHP COP E EHP HEHP ED F chp CHP ŋ e, ŋ t (1-α)E chp H chp αe chp EHP COP E EHP H EHP ED H chp CONSUMERS HD, ED HD CONSUMERS HD, ED F aux Boiler ŋ aux H aux F aux Boiler ŋ aux H aux TES H s T. Capuder and P. Mancarella, Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options, Energy, 2014
An example from multi-generation electricity electricity energy networks and fuel CHP electricity Reversible EHP heat cooling heat local user markets Auxiliary boiler Absorption chiller cooling heat DMG plant Generic black box energy hub model -> buildings, districts, cities,... Source: P. Mancarella and G.Chicco, Real time Demand Response from Distributed Multi-Generation, IEEE Trans. on Smart Grid, 2013 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 23
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 24 Energy optimization: baseline EDS Input ( i F, W ) i W i multi-generation black-box FDS F F i CHP W, Q W y Q y EDS W W i 1 EDS W W i y Q Q y CHP 1 W W y CHP W W y y Q CHP Q Q y EHP EHP COP c EHP COP t R e Q e User ( W d, Q d, R d ) and EDS FDS F i 1 FDS F F i AB t y y 1 Q Q (waste heat) Q t y 1 1 y Q AB Q Qt AB Q Q t CHP Q Q WARG WARG COP c R w ( o Output W, Q o, R o ) Source: G.Chicco and P.Mancarella, Matrix modelling of small-scale trigeneration systems and application to operational optimization, Energy, Volume 34, No. 3, March 2009, Pages 261-273.
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 25 Baseline optimization min FDS f s.t. f(x) = 0 g(x) 0 F i EDS i max EDS W,0 min W,0 i (equality constraints) (inequality constraints) o i Decision variables x F T,,,,,,, T i Wi α Wy Qy Qt Re Rw NLP problem, solved with SQP in Matlab Source: G.Chicco and P.Mancarella, Matrix modelling of small-scale trigeneration systems and application to operational optimization, Energy, Volume 34, No. 3, March 2009, Pages 261-273.
Flexibility from energy vector switching Energy-shifting possibility, internal to the plant from one form of energy to another (multi-energy arbitrage - generalisation of the fuel substitution concept) Electricity shifting potential: the time-dependent maximum reduction in the electricity input from the network starting from a given initial operational state: 1. By switching heat/cooling loads from the electric heat pump (EHP) onto the auxiliary boiler (AB)/absorption chiller (WARG) 2. By increasing the CHP generation set point (if there is headroom for that) owing to the twofold effect of: increasing the local electricity production; and increasing the thermal production for heating/cooling purposes (which in case also displaces heat/cooling previously produced through electricity in the EHP operating in heating/cooling mode, respectively) 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 26
electricity shifting potential (kwhel) minimum energy costs (mu) Techno-economic flexibility 16 EHP in cooling mode 14 12 EHP in heating mode 10 8 6 no heating load no heating load Optimal baseline cost solution for the EHP operation in the Summer day 4 2 0 140 120 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 half-hour EHP in heating mode EHP in cooling mode 100 80 60 40 20 Electricity shifting potential in the Summer day - EHP operated in heating mode or in cooling mode 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 half-hour Source: P. Mancarella and G.Chicco, Real time Demand Response from Distributed Multi-Generation, IEEE Trans. on Smart Grid, 2013 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 27
total extra costs (mu) 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 28 Provision of flexibility: Speed and cost 15 10 5 withdraw electricity input from EDS to EHP (switch cooling output to WARG) with CHP off fast services CHP ON CHP startup cost slow services start the CHP at half capacity and increase its output (switch the heat output from AB to CHP and switch electricity from EDS to CHP) until serving the full heat load electricity output from EDS further increase the CHP output and switch WARG heat from AB to CHP 0 58.7 108.7 127.7 138.0 158.7 0 20 40 60 80 100 120 140 160 electrical energy shift (kwhel) Total extra costs for shifting energy outside the optimal point P. Mancarella and G.Chicco, Integrated energy and ancillary services provision in multi-energy systems, Proceedings of the IREP 2013, Rethymnon, Crete, Greece, 25-30 August 2013
total extra costs (mu) 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 29 Provision of flexibility: Speed and cost 15 slow services 10 CHP ON electricity output from EDS 5 0 58.7 69.0 82.7 138.0 158.7 0 20 40 60 80 100 120 140 160 electrical energy shift (kwhel) Total extra costs for shifting energy outside the optimal point P. Mancarella and G.Chicco, Integrated energy and ancillary services provision in multi-energy systems, Proceedings of the IREP 2013, Rethymnon, Crete, Greece, 25-30 August 2013
costs and benefits (mu) Business case for flexibility 8 7 energy costs variation DR benefits electricity shifting potential DR incentive (mu/kwhel) 0.14 6 0.12 5 0.10 4 0.08 3 0.06 2 1 non-profitable region 0.04 0.02 0 0 10 20 30 40 50 reduced electricity input from EDS (kwhel) Source: P. Mancarella and G.Chicco, Real time Demand Response from Distributed Multi-Generation, IEEE Trans. on Smart Grid, 2013 DMG plant 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 30 energy networks and markets electricity fuel CHP Auxiliary boiler electricity Reversible EHP Absorption chiller electricity heat cooling heat cooling heat local user
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 31 Flexibility profitability maps availability fee 0.05 mu/kw/halfhour electricity electricity energy networks and markets fuel CHP Auxiliary boiler DMG plant electricity Reversible EHP Absorption chiller heat cooling heat cooling heat local user availability fee 0.02 mu/kw/halfhour P. Mancarella and G.Chicco, Integrated energy and ancillary services provision in multi-energy systems, Proceedings of the IREP 2013, Rethymnon, Crete, Greece, 25-30 August 2013
Some considerations Optimal capacity offer is a function of: Initial operational point cost Out-of-optimal costs (including start-up costs) Availability fee Exercise fee Service speed and plant constraints It is not possible to generalize the optimal strategy (for instance, always offer the electricity shifting potential) A suitable operational optimization model is needed to take into account all the complexity, including off-design operation models for all equipment The customer s comfort level is not affected! P. Mancarella and G.Chicco, Integrated energy and ancillary services provision in multi-energy systems, Proceedings of the IREP 2013, Rethymnon, Crete, Greece, 25-30 August 2013 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 32
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 33 Comfort-to-power arbitrage and virtual storage in buildings E imp EDS E EHP ED EHP H EHP TES COP H s H D CONSUMERS HD, ED
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 34 Electro-thermal modelling Supply Building Heat emitters N. Good, L. Zhang, A. Navarro Espinosa, and P. Mancarella, High resolution modelling of multi-energy demand profiles, Applied Energy, 2015
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 35 Physical modelling of multi-energy demand in buildings for flexibility studies N. Good, L. Zhang, A. Navarro Espinosa, and P. Mancarella, High resolution modelling of multi-energy demand profiles, Applied Energy, Volume 137, 1 January 2015, Pages 193 210, 2014
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 36 Technical model of virtual storage: Example of up-flexibility Payback peak (kw) BaU baseline (kw) Amount of energy saving (kwh) Amount of energy payback (kwh)
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 37 Virtual storage in buildings: negative losses and comfort level arbitrage Heating units compulsory off from 18 to 21 Dwelling type: 200 modern (medium insulation) semi-detached houses Heating Units: ASHP with electric boiler Heat emitter: Radiator (left) and Underfloor (right) Season: Winter weekday Location: North West of England N. Good, E. Karangelos, A. Navarro- Espinosa, and P. Mancarella, Optimization under uncertainty of thermal storage based flexible demand response with quantification of thermal users discomfort, IEEE Transactions on Smart Grid, 2015
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 38 Example of up-flexibility from building virtual storage and comfort tradeoff: ASHP with immersion heater and underfloor heating Probability of indoor temperature below target temperature, when control starts at 18 PM Control duration 30 min Bau 1h Bau 2h Bau 3h Bau Time 18 18:30 19 20 21 win wd below 78.00% 77.00% 79.50% 77.50% 80.50% 78.00% 82.50% 80.50% 1 degree below 51.00% 47.50% 51.50% 43.50% 68.00% 39.00% 79.00% 34.00% 2 degree below 22.50% 19.00% 23.00% 16.00% 34.00% 10.50% 42.50% 9.50%
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 39 Can we provide down-flexibility too? E imp EDS E EHP ED EHP H EHP TES COP H s H D CONSUMERS HD, ED
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 40 Future Great Britain generation portfolio National Grid s `Gone Green scenario predicts in 2035: 55 GW installed capacity of wind generation Solar Other Nuclear Gas 20 GW installed capacity of solar generation Wind Coal Total installed capacity: 177GW S. Clegg and P. Mancarella, Integrated modelling and assessment of the operational impact of power-to-gas (P2G) on the electrical and gas transmission networks, IEEE Transactions on Sustainable Energy, accepted for publication, April 2015.
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 41 Example of curtailment levels S. Clegg and P. Mancarella, Integrated modelling and assessment of the operational impact of power-to-gas (P2G) on the electrical and gas transmission networks, IEEE Transactions on Sustainable Energy, accepted for publication, April 2015.
Annual levels of unutilised renewables Annually 79.4 TWh of renewable generation unutilised (without powerto-gas) 57% of renewable generation capability is otherwise curtailed S. Clegg and P. Mancarella, Integrated modelling and assessment of the operational impact of power-to-gas (P2G) on the electrical and gas transmission networks, IEEE Transactions on Sustainable Energy, accepted for publication, April 2015. 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 42
Down-flexibility and arbitrage with large scale penetration of renewables: power-to-gas (P2G) storage Excess of renewables, also due to grid stability reasons and constraints Power-to-heat as an effective means of deploying flexibility from heat in an integrated manner with a time scale in the order of one to few days How do we deal with seasonal flexibility? Can we do something with gas? Power-to-gas (P2G) Electrification of gas via electrolysis and H2 formation Hic et nunc power to gas arbitrage Electricity and gas network arbitrage Seasonal storage of electricity in the gas network 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 43
GB gas and electrical network modelling Gas and electrical networks coupled at gas turbines and combined heat-andpower facilities Power-to-gas adds an additional point of coupling between gas and electrical networks Electrical transmission network Gas transmission network S. Clegg and P. Mancarella, Integrated modelling and assessment of the operational impact of power-to-gas (P2G) on the electrical and gas transmission networks, IEEE Transactions on Sustainable Energy, accepted for publication, April 2015. 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 44
Levels of hydrogen content of gas in gas network Wide ranging technical and legislative restrictions limits to H 2 content of the gas in natural gas networks Unknown factors (e.g., age and applicability of home boilers, gas engines, etc.) suggest legislative limits to remain restrictive Current NTS limit 0.1% Limit to hydrogen content of gas in network (% vol.) Proposed relaxation to limit for GB distribution networks 3% German trials 10% Dutch trials 12% Level at which blended hydrogen begins to affect gas boilers ~17% French trials 20% 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 45
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 46 Power-to-gas required to meet hydrogen production potential Limit to hydrogen content of gas in network (% vol.) Average winter gas demand Average summer gas demand 3% 1.7GW e 0.9GW e 10% 5.6GW e 3.2GW e 17% 9.5GW e 5.4GW e (assuming 74% efficiency of hydrogen production and consequent network injection) S. Clegg and P. Mancarella, Storing renewables in the gas network: modelling of power-to-gas (P2G) seasonal storage flexibility in low carbon power systems, submitted to IET Generation, Transmission and Distribution, March 2015, under second review.
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 47 Wind curtailment Total curtailed wind 2.3TWh for December S. Clegg and P. Mancarella, Integrated modelling and assessment of the operational impact of power-to-gas (P2G) on the electrical and gas transmission networks, IEEE Transactions on Sustainable Energy, accepted for publication, April 2015.
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 48 Power-to-gas modelling Generation portfolio Variable generation profile Assess levels of renewable curtailment Power-to-gas operational strategy Levels of gas production Power-to-gas operational strategies consider: Power-to-gas facility installation capacities Levels of hydrogen which may be blended with natural gas (higher throughput allowing greater hydrogen injection) Possibility to store H2 or transform it into methane S. Clegg and P. Mancarella, Integrated modelling and assessment of the operational impact of power-to-gas (P2G) on the electrical and gas transmission networks, IEEE Transactions on Sustainable Energy, accepted for publication, April 2015.
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 49 Application Relief of gas network constraints (network arbitrage) To what extent could power-togas be used as a substitute for gas network reinforcement? If investment is made in gas storage for power-to-gas, could this increase the viability of gas storage above increased linepack via pipeline expansion? S. Clegg and P. Mancarella, Integrated modelling and assessment of the operational impact of power-to-gas (P2G) on the electrical and gas transmission networks, IEEE Transactions on Sustainable Energy, accepted for publication, April 2015. Choakford P2G reinforcement Pipeline reinforcement
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 50 Application Relief of electrical network constraints (network arbitrage) To what extent could power-togas be used to by-pass electrical network constraints? Need for reinforcement S. Clegg and P. Mancarella, Integrated modelling and assessment of the operational impact of power-to-gas (P2G) on the electrical and gas transmission networks, IEEE Transactions on Sustainable Energy, accepted for publication, April 2015.
A glance into the future: Electrification 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 51
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 52 Integrated electricity-heat-gas network modelling OPF Curtailed renewable generation Use as low grade fuel for heating or electrical generation Form hydrogen or synthetic natural gas Use natural gas network as means to store and transport gas Electrical network model 29 buses Gas network model 71 buses
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 53 Application: Power-to-gas seasonal storage S. Clegg and P. Mancarella, Storing renewables in the gas network: modelling of power-to-gas (P2G) seasonal storage flexibility in low carbon power systems, submitted to IET Generation, Transmission and Distribution, March 2015, under second review.
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 54 More multi-energy interactions and flexibility in the future? Steam Turbine From Gas Turbine HRSG CO 2 Heat Power CCS To Gas Turbine CH 4 methanation CO 2 H 2 Solar electricity
Renewables and the gas network: Transfer of variability Gas networks require gas to be transported for many hours from sources to power stations Delay between implementation and effect of reconfiguration of the gas network s operation (e.g., additional supplies, storage withdrawal, compressor station usage, etc.) System linepack is required to allow for unpredicted changes in demand The effect of intermittent generation on CCGTs is becoming dominant source of UK gas network demand uncertainty Can the gas network constrain the electrical network operation? 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 55
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 56 Example of future RES production in the UK National Grid s 2030 Gone Green Scenario: Wind 47.5 GW, PV 15.6 GW
A closer look 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 57
Case Study: Impact of renewables on gas network Effect on CCGT generation 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 58
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 59 Effect on linepack on gas network You ll hopefully see more soon in: S. Clegg and P. Mancarella, Integrated electrical and gas transmission network flexibility assessment in low carbon multi-energy systems, IEEE Transactions on Sustainable Energy, April 2015, under second review.
Back to the future... 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 60
Concluding remarks Rethinking the entire energy system Optimal electrical systems framed within an overall optimal (energy) system framework Operational flexibility in the power system can be effectively provided by other energy vectors and services We ll see new sets of constraints arising in classical Unit Commitment models in a multi-energy context Economic and commercial issues besides technical issues Complex, challenging, but fascinating 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 61
Selected references Multi-energy systems and distributed multi-generation framework P.Mancarella, Multi-energy systems: an overview of models and evaluation concepts, Energy, Vol. 65, 2014, 1-17, Invited paper P.Mancarella and G.Chicco, Distributed multi-generation systems. Energy models and analyses, Nova Science Publishers, Hauppauge, NY, 2009 G.Chicco and P.Mancarella, Distributed multi-generation: A comprehensive view, Renewable and Sustainable Energy Reviews, Volume 13, No. 3, April 2009, Pages 535-551 P.Mancarella, Urban energy supply technologies: multigeneration and district energy systems, Book Chapter in the book Urban energy systems: An integrated approach, J.Keirstead and N.Shah (eds.), Taylor and Francis, in press, 2012 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 62
Selected references Operational flexibility in multi-energy systems P. Mancarella and G.Chicco, Real-time demand response from energy shifting in Distributed Multi-Generation, IEEE Transactions on Smart Grid, vol. 4, no. 4, Dec. 2013, pp. 1928-1938 T. Capuder and P. Mancarella, Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options, Energy, Vol. 71, 2014, pages 516-533 N. Good, E. Karangelos, A. Navarro-Espinosa, and P. Mancarella, Optimization under uncertainty of thermal storage based flexible demand response with quantification of thermal users discomfort, IEEE Transactions on Smart Grid, 2015 Y. Kitapbayev, J. Moriarty and P. Mancarella, Stochastic control and real options valuation of thermal storage-enabled demand response from flexible district energy systems, Applied Energy, 2014 S. Altaher, P. Mancarella, and J. Mutale, Automated Demand Response from Home Energy Management System under Dynamic Pricing and Power and Comfort Constraints, IEEE Transactions on Smart Grid, January 2015. G.Chicco and P.Mancarella, Matrix modelling of small-scale trigeneration systems and application to operational optimization, Energy, Volume 34, No. 3, March 2009, Pages 261-273. 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 63
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 64 Selected references Integrated electricity and gas operation S. Clegg and P. Mancarella, Integrated modelling and assessment of the operational impact of power-to-gas (P2G) on the electrical and gas transmission networks, IEEE Transactions on Sustainable Energy, accepted for publication, April 2015 S. Clegg and P. Mancarella, Integrated electrical and gas transmission network flexibility assessment in low carbon multi-energy systems, invited submission to the IEEE Transactions on Sustainable Energy, April 2015, under second review S. Clegg and P. Mancarella, Storing renewables in the gas network: modelling of power-to-gas (P2G) seasonal storage flexibility in low carbon power systems, submitted to IET Generation, Transmission and Distribution, March 2015, under second review
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 65 One hour compulsory off EHP/underfloor option
Acknowledgments My fantastic research team! European Commission (EC) Framework Programme (FP) 7 COOPERATE EC FP7 DIMMER EC FP6 ADDRESS UK Engineering Physical Science Research Council (EPSRC) HUBNET EPSRC and National Grid for the Electricity and gas icase project 2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 66
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 67 Thank you Any Questions? p.mancarella@manchester.ac.uk
2015 P.Mancarella - The University of Manchester iiesi 102, NREL, 06/08/2015 68 IIESI 102 COURSE, 3-7 AUGUST 2015 FLEXIBILITY IN INTEGRATED ENERGY SYSTEMS AND VIRTUAL STORAGE Pierluigi Mancarella, PhD Reader in Future Energy Networks The University of Manchester, Manchester, UK p.mancarella@manchester.ac.uk Energy Systems Integration Facility, NREL, Golden, USA 06/08/2015