Storage and flexibility in future electricity systems relevance and evaluation methodologies

Transcription

1 Storage and flexibility in future electricity systems relevance and evaluation methodologies Prof. Christoph Weber Workshop on Renewable Energy Integration and Regional Interconnection in South America Santiago de Chile September 6, 2018

2 Renewables are a powerful source of energy...

3 ... but occasionally not

4 Increasing interest in storage main drivers Technology development Particularly in Li-Ion batteries But also Redox-flow batteries, Zinc-air batteries, CAES are discussed High-pace transition of generation landscape 20% renewables in overall energy mix by 2020, at least 27 % by % - 45% RE in electricity by 2020, 65 % in 2030 and 80% by 2050 Integration of intermittent renewables being a challenge Wind and PV with very low capacity credit Availability of controllable thermal capacity not evident Electricity storage and Demand Response as a complement?

5 Table of contents Background: Inflexible Demand and Supply 1 Flexibility alternatives 2 Economics of Storage 3 Applications 4

6 Energy Transition: Challenge for the future German Energy System Motivation Target Year 2050 for the Energy Transition Various scenarios developed by different research and consultancy groups Common objective: -80 % in GHG emissions compared to 1990 Here: Annual Electricity Generation mix Studies published

7 Quelle: eigene Berechnungen, s. Droste-Franke et al. (2015) Energy Transition: Challenge for the electricity system and market operation 3 Vorteile konsistenter CO2-Preise RES surplus RES deficit price 0 High price 7

8 Table of contents Background: Inflexible Demand and Supply 1 Flexibility alternatives 2 Economics of Storage 3 Applications 4

9 Multiple sources of flexibility Generation: Conventional thermal generation Notably gas turbines CHP units With heat storage Hydropower with reservoirs Controllable renewables Biomass, (geothermal) Renewable curtailment Grid: Existing and new lines Storage: Pumped Hydro Compressed air storage (CAES) Batteries Power-to-Heat (PtH) Power-to-Gas (PtG) & -Liquid Batteries Large scale Electric vehicles Home / Commercial scale Demand response: Demand shifting Demand shedding 9

10 Existing Hydropower Turbine Capacities without Pumping Function Turbine Capacities with Pumping Function 10

11 Drivers for Storage Decarbonization targets & approach Ambitious objectives: at least -80 % in 2050 compared to 1990 Costs Focus on (intermittent) renewables, i.e. wind and solar Cost decrease for batteries: 1000 /kwh a few years ago, 100 /kwh expected in next decade Markets (short term) price variability: still rather low, average within-day price-spread <10 /MWh Push for electric vehicles: available capacity first & second life, Regulation Incentives for self-consumption rather high for residential customers ~30 ct/kwh retail price, ~13 ct/kwh infeed tariff System services Reserves: high prices for primary reserves so far currently 230 MW of batteries prequalified, prices have decreased by 20 % in 2018 Congestion management: substitution/competition with line extensions 11

12 Table of contents Background: Inflexible Demand and Supply 1 Flexibility alternatives 2 Economics of Storage 3 Applications 4

13 Perspectives on the Economics of Storage 1) Management perspective: Profitability of a (new or existing) storage at current or future prices Prices exogenous to decision maker Yet (partly) uncertain Approaches for optimal operation and valuation: Stochastic dynamic programming, e.g. Felix, Weber (2012) Least-Squares Monte-Carlo simulation, e.g. Carmona, Ludkovski (2010) Approximate dynamic programming, e.g. Löhndorf et al. (2013) 2) General system perspective: Storage control strategies and needs in (future) systems Prices endogenous Approaches for optimal operation: Stochastic dual dynamic programming: e.g. Pereira, Pinto (1991) Approaches for optimal investment Optimizing system models (usually deterministic linear programs), e.g. Böcker Weber (2018)

14 Storage in a system perspective Key elements from Böcker, Weber (2018) Analytical insights on efficient storage investment and operation Charging / Discharging capacity Storage volume Social optimum Theoretical literature Two-period peak-load-pricing models: Jackson (1973), Gravelle (1976) Optimizing individual plant's profit, e.g. Horsley and Wrobel (2002) Efficient storage operation: Crampes and Moreaux (2010) Efficient storage without volume constraint: Steffen & Weber (2012) MCP formulation of storage investment: Lamont (2013) Applied literature Broad range of techno-economic analyses and scenario analyses e.g. Sinage (2009), IEA(2013), EPRI (2013) Improved understanding of fundamental economics of storage needed

15 Lagrangian of the system optimization problem Investment cost Operation cost Demand-Supply balance Capacity constraints Storage filling constraint Stroage volume constraint Karush-Kuhn-Tucker constraints for 15

16 Key findings Even in rather stylized electricity systems Storage operation exhibits rather complicated patterns Storage economics depend on multiple drivers Yet this may be traced back to the interplay of a few basic principles Time-varying position of storage technologies in the supply stack Depending on the marginal storage value ( water value ) Constant marginal storage value over half-cycles Half cycles are limited by storage filling reaching bounds Alignment of marginal storage to generation costs in two MOMs MOM1: alignment of marginal charging cost MOM2: alignment of marginal discharging cost Pay back of investment cost for all (storage) components through capacity shadow prices (operation margins) 16

17 Table of contents Background: Inflexible Demand and Supply 1 Flexibility alternatives 2 Economics of Storage 3 Applications 4

18 Management perspective: Wholesale market values of demand response potentials Cost saving potentials from flexible operation of residential demand-side flexibilities in the year 2015: electric storage heating (ESH) and heat pumps (WP) Determined through deterministic optimization with given prices and technical constraints Annual savings Savings per kwh consumption Optimization in hourly day-ahead spot market Optimization in quarter hourly day-ahead spot market limited cost saving potentials, for future years some improvement expected Cf. Dietrich, Weber (2018) Folie 18

19 Management perspective: joint optimization of battery storage for energy and reserve provision Focus on existing markets for energy and positive secondary reserve Simultaneous spot and reserve price simulation Stochastic fundamental approach: fundamental spot prices and reserve prices (positive secondary res., afrr + ) Hybrid-Model approach, combination of merit-order model and stochastics: spot price Advanced Least-Square Monte Carlo method computation of the value of the battery under uncertainties, considering multiple-value streams (market participation), using simplified aging model, taking both calendar and cyclic aging effects into account 19

20 Management perspective: joint optimization of battery storage - Basic Results Lithium-Ion Battery 2MWh / 1MW 70% End-of-(First)Life 30% End-of-Life double of aging effects Battery Value (new) Only Spot market: 70T <35 /kwh volume target costs Spot & Reserve market (hourly decision) 660T <330 /kwh volume target costs Cf. Böcker, Weber, Kiesel (2018) 20

21 System perspective: Joint use of storage for market and distribution grid purposes physical energy flows financial flows Grid system border Ex-post- Analyse System perspective (grid) Generation Storage Demand Markets Focus on low voltage grid Assumptions for example results: Grid length: Median 0,3 km (1,1 km Q95) Energy trading at spot market Penalties for curtailment 33 ct/kwh (average FIT for PV Investment cost: storage (100 /kw, 200 /kwh), grid (80 T /km) In cooperation mit: 21

22 System perspective: Joint use of storage for market and grid results for LV feeders Results show optimal grid development strategy as a function of PV Feed-in and Feeder length Storage only used for grid support Storage additionally used for energy market In cooperation with:, cf. Böcker, Kippelt, Weber, Rehtanz (2017) 22

23 System perspective: Storage for energy market arbitrage input data Scenario for Germany in 2040 based on hourly supply & demand (2011), greenfield approach, no imports & exports Main input parameters Limitation to realistic sites in Germany for wind offshore 54 GW and for PHS 2 TWh 8,000 full cycles for Li-Ion technology Minimal share of RES in power supply: 65% CO 2 emission Capacity bound: 20% Volume of 1990 level Technical Efficiency Operational costs costs lifetime costs Unit k /MW k /MWh years /MWh Lignite plant 1, % 8.2 (Hard) Coal 1, % 23.9 plant CCGT % 50.5 OCGT % 76.3 Wind offshore 1, % 0 Wind onshore 1, % 0 Photovoltaic % 0 PHS % 0 Li-Ion % 0 Source: based on data by IEA (2013), ISE (2013), RWTH Aachen (2013/2014), own analyses 6/17/

24 System perspective: Storage for energy market arbitrage Results reference case 290 GW generation & storage capacities 64 GW conventional technologies 188 GW RES 28 GW storages Demand coverage 65% by RES and 35% by conventional techn. 91% of RES feed-in is used directly, 5% indirectly and 4% is curtailed or lost during storage process System costs of 43 bn Mean electricity price of 63 /MWh CO 2 emission price 76 /t CO2 RES certificate price 46 /MWh Capacities Power Supply Demand 90 GW 541 TWh Peak-Load Lignite 4 GW 24 TWh Hard coal 0 GW 0 TWh CCGT 45 GW 162 TWh OCGT 15 GW 3 TWh Wind onshore 60 GW 97 TWh Wind offshore 54 GW 172 TWh Photovoltaic 74 GW 63 TWh PHS Li-Ion Curtailment RES Load curtailment 14 GW 16 TWh (29 h) 4 GW 3 TWh (3 h) 61 GW 9 TWh 2 GW 3 GWh 6/17/

25 0,6 System perspective: Storage for energy 0,5 market arbitrage Results market 0,4 equilibrium 0,3 Change in marginal storage content value for PHS over Refinancing with PHS with PHS time Lignite CCGT OCGT LiIon PHS 0,1 0 1 FÜR LEGENDE! 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 Lignite CCGT Lignite OCGT LiIon CCGT PHS OCGT Lignite CCGT LiIon OCGT LiIon PH S PHS 0,7 0,2 0,9 Distribution operation margins by marginal Refinancing with LiIon with and LiIon PHS and PHS technology Lignite CCGT OCGT LiIon PHS RES Lignite CCGT OCGT LiIon_CH LiIon_DC PHS_CH PHS_DC LiIon_PHS Load shed. Cf. Böcker, Weber (2018) 25

26 System perspective: Storage for energy market arbitrage - sensitivities Li-Ion capacity Investment costs variations (x-axis) compared to the reference case Efficient Li-Ion capacity (y-axis) Main results Monotonous dependency on Li-Ion and PHS costs Non-monotonous dependency on PV costs beyond 37.5% variation monotonous relation between ± 37.5% substitution between Li-Ion and PHS in ± 50.0% 6/17/

27 System perspective: Storage for energy market arbitrage - Sensitivity CO 2 emission bound Installed capacities Annual electricity generation 6/17/

28 System perspective: Storage for energy market arbitrage - Sensitivity minimal share of RES Installed capacities Annual electricity generation 6/17/2014 Cf. Böcker, Steffen, Weber (2014) 28

29 Conclusions Technically and economically optimized use of battery storage is driven by: Share of intermittent renewables in generation mix (Un-)Availability of other flexibility options Coherent policies and regulation Elements of coherent policies: Clear price signals for decarbonization Electricity spot prices reflective of marginal costs No excessive incentives for self-consumption 29

30 30 Thank you for your attention Prof. Christoph Weber Chair for Management Sciences and Energy Economics House of Energy Markets and Finance University Duisburg-Essen

31 References Böcker, B., Kippelt, S., Weber, C., Rehtanz, C. (2017): Storage valuation in congested grids. IEEE Transactions on Smart Grids. (in press) Böcker, B., Weber, C. (2018): How will storage affect future energy systems? Fundamentals of storage operation and investment HEMF Working Paper No. 01/2018 Felix, B., Weber, C. (2012): Gas storage valuation applying numerically constructed recombining trees. In: European Journal of Operational Research, 216 (1), Carmona, R., Ludkovski, M. (2010): Valuation of Energy Storage: An Optimal Switching Approach. Quantitative Finance 10, Crampes, C., Moreaux, M. (2010). Pumped storage and cost saving. Energy Economics 32, Gravelle, H., The peak load pricing problem with feasible storage. The Economic Journal 86, Horsley, A., Wrobel, A. J. (2002). Efficiency rents of pumped-storage plants and their uses for operation and investment decisions. Journal of Economic Dynamics & Control 27, Jackson, R. (1973). Peak load pricing model of an electric utility using pumped storage. Water Resources Research 9, Lamont, Alan D. (2013): Assessing the economic value and optimal structure of large-scale electricity storage. In: IEEE Trans. Power Syst. 28 (2), Löhndorf N., Wozabal D., Minner S. (2013): Optimal Bidding of Electricity Storage using Approximate Dynamic Programming. Operations Research 61(4), Pereira, M. V. F., Pinto, L. M. (1991): Multi - stage stochastic optimization applied to energy planning. In: Mathematical programming A 52, Steffen, Bjarne; Weber, Christoph (2013): Efficient storage capacity in power systems with thermal and renewable generation. In: Energy Economics 36,