Lecture #5 Energy Flexibility Strategies

Transcription

1 Lecture #5 Energy Flexibility Strategies PHYS-E0483_ Peter Lund 2017

2 New energy technologies in largescale require systemic bridging Old 100% New 100% Multi-energy networks Flexible demand EV,ICT How does the energy system work with much new renewable energy? Storage P2H,V2G,P2Gas New 0% Old 0%

3 Contents Lecture # 5 energy demand and supply side flexibility power plant characteristics and response time integrated or isolated flexibility solutions simple flexibility strategies: grid extension, curtailment advanced flexibility strategies: Power-to-X (P2X, X=thermal, methane, hydrogen, mobility, etc), vehicle-to-grid (V2G), demand side management (DSM), smart grid (SG), storage TOOL: examples and cases of applying flexibility strategies (Helsinki, Mega-cities energy demand and energy supply side flexibility Reading: See Articles in MyCourses

4 Starting point for energy system flexibility considerations Energy demand and supply matching requirement (balance) 1. different time scales 2. critical for the electric system 3. large inertia in thermal systems Demand is NOT constant Present energy systems contain inherent flexibility capabilities Existing flexibility mostly on supply side, but also some demand flexibility New energy technologies integration Variable renewable electricity (VRE) share up to XYZ% without major new flexibility components; XYZ depends on the (local, regional, national) energy system characteristics and could be from 5 to 50% Ultimate goal of flexibility measures is to increase the VRE share well beyond 50%, closer to %

5 Time variations Mismatch between demand supply versus time Here solar causes socalled duck phenomenon) Source :PwC, October 2013

6 Impacts of solar and wind on elec. markets Average electricity price, Price volatility Flexibility with RE: Renewables ; Avg. elec. price ; Volatility German electricity market (EEX) Traditional Solar Wind Source: Fraunhofer Institute, Germany

7 Type of impacts of variable renewable electricity systems System operation Resource operation Resource planning Resource investments PHYS-E0483_ Peter Lund 2017

8 Time scale and magnitude of demand fluctuations Magnitude of demand and supply (RE) fluctuations increases with time scale PHYS-E0483_ Peter Lund 2016

9 Emplyoing existing reserves Increasing VRE needs more flexibility, which in a traditional thinking would require more reserves, e.g. more flexibility of existing power plants or use of strategic reserves; but so far, no new reserve power has been built for wind specifically Theoretical studies on reserves vs wind penetration PHYS-E0483_ Peter Lund 2017

10 Traditional power supply flexibility Ramping capabilities of different types of power plants on different time scales: traditional thermal power plants slow PHYS-E0483_ Peter Lund 2016

11 Power plant (supply side) flexibility characteristics Peak power capacity cheap, but fuel expensive Base power capacity expensive, but fuel cheap Flexible power capacity cheap, but fuel expensive PHYS-E0483_ Peter Lund 2016

12 New flexibility options PHYS-E0483_ Peter Lund 2017

13 Options for improved energy system flexibility&integration 1. RE in urban context 2. Grid infrastructures 3. Smart Grids 4. Electricity markets 5. Co-generation (CHP) 6. Power-to-Thermal 3 7. Power-to-Gas 8. RE+Gas integration 9. Demand side management 10. Energy storage

14 Spatial smoothing effects Integration of VRE (wind, solar) over a large area provides smoothing of the wind power output Wind forecast errors drop with spatial integration PHYS-E0483_ Peter Lund 2017

15 Grid extension strategy ΔV = Z I = P/V (R+ Xtanφ) - Stronger cable à Z reduced - Parallel cables à I/cable reduced Higher voltage à I reduced Grid strenghtening Grid interconnections Supergrids Long-distance (HVDC) Smart Grids Super-Smart Grids Microgrids q Integrates different loads and distributed generation technologies q Island mode/main grid connection Picogrids (DC) PHYS-E0483_ Peter Lund 2017

16 Cable choice for picogrids Case: 1 kw total load (10 nodes) Voltage= 96 VDC Max V drop 10% Conductivity σ= Ohm/mm2 Cable cross-section influences resistance R= σ * A; A=π (d/2)^2, d=diameter of cable PHYS-E0483_ Peter Lund 2017 Source: Eetu Ahonen, M.Sc. Thesis 2015

17 Curtailment of RE power Curtailment = limiting/cutting the power supply from nominal power level, i.e. operating at partial power Definition: capacity factor =yearly eletricity produced [8760 hours * nominal capacity]; base load = 80-90%, peak power=10%, wind power 25-40%, solar 8-20% Rationale: capacity factor of RE is <<100%, i.e. cutting power does not affect much the produced electricity contrary to basepower plants;

18 Justifying Curtailment of RE power Justification of curtailment : Lost energy when curtailed= curtailed power * time Yearly electricity production from a power plant (MWh) = nominal power (MW)* capacity factor * 8760 (h) If permanent curtailment: Yearly electricity lost (MWh) = curtailment rate (%) * nominal power (MW)* capacity factor * 8760 (h) Example 1: Wind CF=30%, permanent curtailment 10% à lost power is 10%, lost electricity = 10%* 30%= 3% of yearly wind electricity Example 2: Solar CF=10%, permanent curtailment 20% à lost power is 20%, lost electricity = 10%* 20%= 2% of yearly solar electricity

19 Benefits of Curtailment Definition of self-use limit: RE supply=power demand With curtailment RE power could be sized well beyond the self-use limit of power (surplus situation can be easily handled with curtailment) RE power could provide very fast ramp-up and ramp-down capacity (e.g. if RE is always run below nominal value à ramp up; if RE cut à ramp-down) Technically curtailment can be realized through fast electric switches

20 Theory of curtailment of RE power for increased flexibility Power supply levels Curtailed power Power demand level

21 Example: Curtailing PV power in Finland Reduction in solar yield 100% 80% 60% 40% 20% 0% 0% 20% 40% 60% 80% 100% Power cut-off limit (% of nominal PV power installed)

22 What to do with curtailed RE? Simpliest strategy: NONE (some RE will be wasted) P2X; power-to-x strategies, X=thermal, gas, etc P2T/P2H: turn curtailed/surplus RE into thermal energy or heat E2V/P2V; charge electric vehicle batteries P2G/P2H: turn RE into gas (H=hydrogen, methane) Storage: store RE for later use

23 Power-to-thermal strategy (P2T) PHYS-E0483_ Peter Lund 2017

24 Basics of the P2T/P2H strategy Power-to-thermal conversion of RE electricity into thermal energy (often surplus or curtailed RE) Heat (resistance, heat pump) or cool (refrigration cycle) η (resistance) 100% COP (heat pump) 3 Surplus for E2T Self-use of electricty Directly coupled to thermal demand, and/or, to thermal storage A builiding itself could function as thermal storage PHYS-E0483_ Peter Lund 2017 Quick on/off switching

25 Rationale of P2H (power-to-heat) Final energy use forms in buildings: heating, cooling, electricity EU-27household energy: 15% Space heating Appl&Light Hot water Cooking 14% 4% 67% >50% of final energy use in cities is thermal With a high RE share, marginal value of thermal energy >electricity Thermal storage: capacity (~battery), cheap (~$5-25/kWh), long-term and large-size (max. unit size~1-2gwh, MW), centralized or decentralized schemes P2H decreases power price volatility at high RE shares P2H allows more RE to be integrated

26 Example on how P2H affects the upper limit of RE power (wind power in Helsinki) (Feb 6) 1349 MW 2 Wind power 486 MW 1 1. Wind = electricity demand à max. 486 MW wind power = 20-25% % of yearly electricity in Helsinki 2. Wind = power and heat demand à1349 MW wind power = 60% of electricity per year (2% of heat) Ref: Lund, P. : Large-scale urban renewable electricity schemes - integration and interfacing aspects. Energy Conversion and Management, 2012 Ref: R. Niemi, J. Mikkola, P.D. Lund: Urban energy systems with smart multi-carrier energy networks and renewable energy generation. Renewable Energy, /02/17 Peter Lund 2016

27 Application of P2H Low carbon pathways for Helsinki (Finland) Picture: Helsinki Energy Peter Lund 2016 Annual Report 2010

28 Application of big VRE to urban-scale: Case Helsinki Power and heat demand & plants in Helsinki 1GW el ;1.3GW th,chp ; 2.0GW th,peak (coal, gas) IEEES-8 Lund 2016

29 Helsinki Wind+CHP+DH base case (simple integration, P2H=elec.boiler) Technical simulation 1-hour steps over 1 yr; no overflow of power from the city Wind power share: 55% 25% 62% 0% 1% 10% power yearly demand heat Self-use limit

30 Dynamic system effects of large wind power scheme in Helsinki Capacities:1GW el ;1.3GW th,chp ; 2.0GW th,peak System elements: P2H, th. store Special features: Ramping etc Full spot market coupling MILP optimization 10-min steps MW Peter Lund 2016 Location of energy production plants (A G) and yearly electricity consumption in the districts of Helsinki. Plants A C are large CHP plants (A natural gas; B and C coal), D F heat-only boilers (D natural gas; E oil; F coal), and G heat pump station. Jani Mikkola, Peter D. Lund. Modelling flexibility and optimal use of existing power plants with large-scale variable renewable power schemes, Energy 2017

31 How does a wind+p2h strategy affect existing thermal plants? Gas price 56, coal 43 /MWh Wind decreases CHP, but levels out (heat dominates); P2H(1) No P2H P2H(3) P2H (COP=1) shifts slightly to coal from gas P2H (COP=3) shifts from coal to gas (electricity dominates, NGCC more flexible & higher efficiency) P2H (COP=3) makes coal more sensitive to price, +20 /MWh leads to 0% coal Peter Lund 2016

32 Effect of coal price (CP) In reference case coal less sensitive to price, wind affects little power mix P2H+HP makes coal more sensitive to price, +20 /MWh leads to 0% coal Base case P2H(3) (with HP) Base: Gas 56, coal 43 /MWh Peter Lund 2016

33 Conclusions of P2H for energy system dynamics P2H means considering energy system (power+heat) as a whole à VRE share could be increased 2-3 times over the self-use limit of VRE P2H (+ sometimes thermal storage) helps to re-optimize the existing energy system, and provide more flexibility

34 Basics of the P2V/V2G strategy Power-to-vehicle (P2V) and Vehicle-to- Grid (V2G) use surplus RE to charge EV batteries, which can also be used for grid support 1 PHEV or EV= 3-20 kwh battery A car is most of the time idle Where s a Road, there s a Grid BUT: each charging/discharging cycle wears the battery (there is a cost) 1 km EV = kwh electricity km= kwh, or 1-2 kwp of PV PHYS-E0483_ Peter Lund 2017

35 Basics of the P2G strategy In Power-to Gas (P2G) schemes surplus RE power converted to gas, hydrogen or methane; The RE gas either stored or fed into a gas network Key components: Electrolyzer; Methane synthesis (Sabatier process) PHYS-E0483_ Peter Lund 2017 Source wikipedia

36 Basics of the P2G strategy (2) Step efficiences of P2G Method Efficiency Remarks Electricity Gas Hydrogen % 200 bar compression Methane (SNG) % Hydrogen % 80 bar compression (Natural gas pipeline) Methane (SNG) % Hydrogen % Without compression Methane (SNG) % Electricity Gas Electricity Hydrogen % 80 bar compression up to 60% back to electricity Methane (SNG) % Electricity Gas Electricity & heat (cogeneration) Hydrogen % 80 bar compression and electricity/heat for 40/45 % Methane (SNG) % PHYS-E0483_ Peter Lund 2017 Source wikipedia

37 Basics of DSM strategy Demand Side Management (DSM) affects load patterns and magntidue; power and energy Different time scales (< 1 day) Often coupled to ICT 5-20% peak reduction; electric heating provides a major DSM potential, also large industrial units Obligatory, Shiftable, Cut-off loads PHYS-E0483_ Peter Lund 2017

38 Basics of the electrical storage strategy Storage balance dq sto = Q in -Q out Q sto t+1 = Q sto t + dq sto Q stot = [Q sto min, Q sto min ] Sto.demand is condition-dependent Elec. Storage characterization Power density (kw/kg) Storage density (kwh/kg) Response time (s) Various physical ways to store electricity (more Lecture #10) pumped hydro is 99% of all existing electrical storage potential energy = mgh PHYS-E0483_ Peter Lund 2017

39 EXTRA Case examples (using a computer model in the class, here some summary results) PHYS-E0483_ Peter Lund 2017

40 How much wind power could Helsinki utilize through the energy networks? Sizing wind power beyond the self-use limit of power Converting surplus wind into thermal energy and feeding into the DH network, coupling with CHP 60% wind share of annual electricty demand without electrical storage nor out-flow of power Lund, P., Mikkola, J., Ypyä, J., Smart energy system design for large clean power schemes in urban areas, Journal of Cleaner Production 2014, DOI: /j.jclepro Lund, P.D., Clean energy systems as mainstream energy option, Int. J. of Energy Research 2014 Picture: Helsinki Energy Annual Report 2010

41 Strategy 1: Curtailment of RE power Yearly electricity demand in Helsinki is 4.4 TWh Self-use limit of wind power in Helsinki 472 MW (27.8% of yearly demand) 1700 MW wind power Equals to yearly electricity demand (100%) When wind output limited to self-use limit 100% à 54.8% (à surplus 46.2%)

42 Strategy 1: Curtailment of RE power (2)

43 How much more of PV could be employed through P2T/P2H? Helsinki, Finland Dhahran, Saudi-Arabia Shanghai, China PV share of electricity,%/year PV provides 100% of daily cooling demand PV provides 100% of daily heat demand PV matches hourly self-use limit of electricity and heat PV matches hourly self-use limit of electricity

44 Role of electrical storage Self-use limit of variable RE electricity is ca 20 % of power demand PV hours storage increases RE share 2-3 fold in Asian cities RE share, % PV: Delhi, India ( 28.7 N) D A B C Optimal storage capacity Average RE power / Power demand Storage capacity (hours) RE %(+STO) RE %(-STO) STO(Wh)/RE(nom W) P.D. Lund et al. Smart energy system design for large clean power schemes in urban areas. Journal of Cleaner Production, 2014 E No storage RE share, % RE share, % PV: Helsinki, Finland (60 N) PV: Shanghai, China (31 N) E 1000 E C B 1 D A Average RE power / Power demand RE %(+STO) RE %(-STO) STO(Wh)/RE(nom W) D B C 1 A Average RE power / Power demand RE %(+STO) RE %(-STO) STO(Wh)/RE(nom W) Storage capacity (hours) Storage capacity (hours)